Strategy to Empower Nontargeted Metabolomics by Triple-Dimensional Combinatorial Derivatization with MS-TDF Software.

Chemical derivatization is a widely employed strategy in metabolomics to enhance metabolite coverage by improving chromatographic behavior and increasing the ionization rates in mass spectroscopy (MS). However, derivatization might complicate MS data, posing challenges for data mining due to the lack of a corresponding benchmark database. To address this issue, we developed a triple-dimensional combinatorial derivatization strategy for nontargeted metabolomics. This strategy utilizes three structurally similar derivatization reagents and is supported by MS-TDF software for accelerated data processing. Notably, simultaneous derivatization of specific metabolite functional groups in biological samples produced compounds with stable but distinct chromatographic retention times and mass numbers, facilitating discrimination by MS-TDF, an in-house MS data processing software. In this study, carbonyl analogues in human plasma were derivatized using a combination of three hydrazide-based derivatization reagents: 2-hydrazinopyridine, 2-hydrazino-5-methylpyridine, and 2-hydrazino-5-cyanopyridine (6-hydrazinonicotinonitrile). This approach was applied to identify potential carbonyl biomarkers in lung cancer. Analysis and validation of human plasma samples demonstrated that our strategy improved the recognition accuracy of metabolites and reduced the risk of false positives, providing a useful method for nontargeted metabolomics studies. The MATLAB code for MS-TDF is available on GitHub at https://github.com/CaixiaYuan/MS-TDF.

eLIMS: Ensemble Learning-Based Spatial Segmentation of Mass Spectrometry Imaging to Explore Metabolic Heterogeneity.

Spatial segmentation is an essential processing method for image analysis aiming to identify the characteristic suborgans or microregions from mass spectrometry imaging (MSI) data, which is critical for understanding the spatial heterogeneity of biological information and function and the underlying molecular signatures. Due to the intrinsic characteristics of MSI data including spectral nonlinearity, high-dimensionality, and large data size, the common segmentation methods lack the capability for capturing the accurate microregions associated with biological functions. Here we proposed an ensemble learning-based spatial segmentation strategy, named eLIMS, that combines a randomized unified manifold approximation and projection (r-UMAP) dimensionality reduction module for extracting significant features and an ensemble pixel clustering module for aggregating the clustering maps from r-UMAP. Three MSI datasets are used to evaluate the performance of eLIMS, including mouse fetus, human adenocarcinoma, and mouse brain. Experimental results demonstrate that the proposed method has potential in partitioning the heterogeneous tissues into several subregions associated with anatomical structure, i.e., the suborgans of the brain region in mouse fetus data are identified as dorsal pallium, midbrain, and brainstem. Furthermore, it effectively discovers critical microregions related to physiological and pathological variations offering new insight into metabolic heterogeneity.

Comprehensive Profiling of Amine-Containing Metabolite Isomers with Chiral Phosphorus Reagents.

Metabolite isomers play diverse and crucial roles in various metabolic processes. However, in untargeted metabolomics analysis, it remains a great challenge to distinguish between the constitutional isomers and enantiomers of amine-containing metabolites due to their similar chemical structures and physicochemical properties. In this work, the triplex stable isotope N-phosphoryl amino acids labeling (SIPAL) is developed to identify and relatively quantify the amine-containing metabolites and their isomers by using chiral phosphorus reagents coupled with high-resolution tandem mass spectroscopy. The constitutional isomers could be effectively distinguished with stereo isomers by using the diagnosis ions in MS/MS spectra. The in-house software MS-Isomerism has been parallelly developed for high-throughput screening and quantification. The proposed strategy enables the unbiased detection and relative quantification of isomers of amine-containing metabolites. Based on the characteristic triplet peaks with SIPAL tags, a total of 854 feature peaks with 154 isomer groups are successfully recognized as amine-containing metabolites in liver cells, in which 37 amine-containing metabolites, including amino acids, polyamines, and small peptides, are found to be significantly different between liver cancer cells and normal cells. Notably, it is the first time to identify S-acetyl-glutathione as an endogenous metabolite in liver cells. The SIPAL strategy could provide spectacular insight into the chemical structures and biological functions of the fascinating amine-containing metabolite isomers. The feasibility of SIPAL in isomeric metabolomics analysis may reach a deeper understanding of the mirror-chemistry in life and further advance the discovery of novel biomarkers for disease diagnosis.

Comparative study of bilateral putamen for patients with severe Parkinson's disease detected by 1H magnetic resonance spectroscopy.

Background: The diagnosis of Parkinson's disease (PD) is challenging because the clinical symptoms overlap with other neurodegenerative diseases. The discovery of reliable biomarkers is highly expected to facilitate clinical diagnosis. Through the analysis of the 1H magnetic resonance spectroscopy (1H-MRS) in the putamen, the purpose of the study was to discuss the possibility of the difference in metabolite concentrations between the left and right putamen as biomarkers for patients with severe PD. Methods: We collected 1H-MRS of unilateral or bilateral putamen from 41 patients and used the independent sample t-test and paired t-test to analyze 4 metabolite concentrations, including choline (Cho), total N-acetyl aspartate (tNAA), total creatine (tCr), and combined glutamate and glutamine; Bonferroni correction was used to correct P values for multiple comparisons. We designed 4 controlled experiments as follows: (I) PD patients versus healthy controls (HCs) in the left putamen; (II) PD patients versus HCs in the right putamen; (III) the left putamen versus the right putamen for PD patients; and (IV) the left putamen versus the right putamen for HCs. Results: No statistically significant differences (P>0.05) were detected among 4 metabolites in the ipsilateral and bilateral putamen for the PD and HCs groups, except for tCr in the left putamen (PD 6.426±0.557, HCs 6.026±0.460, P=0.046) for ipsilateral comparisons. Conclusions: In the bilateral putamen of severe PD patients, there was no statistically significant difference in the 4 metabolites. The difference (P<0.05) in tCr in the left putamen might be a potential biomarker to distinguish HCs from severe patients in clinic. This might provide a reference for the clinical diagnosis and acquisition strategy of 1H-MRS in severe PD.

Differential Correlations Informed Metabolite Set Enrichment Analysis to Decipher Metabolic Heterogeneity of Disease.

Metabolic pathways are regarded as functional and basic components of the biological system. In metabolomics, metabolite set enrichment analysis (MSEA) is often used to identify the altered metabolic pathways (metabolite sets) associated with phenotypes of interest (POI), e.g., disease. However, in most studies, MSEA suffers from the limitation of low metabolite coverage. Random walk (RW)-based algorithms can be used to propagate the perturbation of detected metabolites to the undetected metabolites through a metabolite network model prior to MSEA. Nevertheless, most of the existing RW-based algorithms run on a general metabolite network constructed based on public databases, such as KEGG, without taking into consideration the potential influence of POI on the metabolite network, which may reduce the phenotypic specificities of the MSEA results. To solve this problem, a novel pathway analysis strategy, namely, differential correlation-informed MSEA (dci-MSEA), is proposed in this paper. Statistically, differential correlations between metabolites are used to evaluate the influence of POI on the metabolite network, so that a phenotype-specific metabolite network is constructed for RW-based propagation. The experimental results show that dci-MSEA outperforms the conventional RW-based MSEA in identifying the altered metabolic pathways associated with colorectal cancer. In addition, by incorporating the individual-specific metabolite network, the dci-MSEA strategy is easily extended to disease heterogeneity analysis. Here, dci-MSEA was used to decipher the heterogeneity of colorectal cancer. The present results highlight the clustering of colorectal cancer samples with their cluster-specific selection of differential pathways and demonstrate the feasibility of dci-MSEA in heterogeneity analysis. Taken together, the proposed dci-MSEA may provide insights into disease mechanisms and determination of disease heterogeneity.

Multimodal Image Fusion Offers Better Spatial Resolution for Mass Spectrometry Imaging.

High-resolution reconstruction has attracted increasing research interest in mass spectrometry imaging (MSI), but it remains a challenging ill-posed problem. In the present study, we proposed a deep learning model to fuse multimodal images to enhance the spatial resolution of MSI data, namely, DeepFERE. Hematoxylin and eosin (H&E) stain microscopy imaging was used to pose constraints in the process of high-resolution reconstruction to alleviate the ill-posedness. A novel model architecture was designed to achieve multi-task optimization by incorporating multi-modal image registration and fusion in a mutually reinforced framework. Experimental results demonstrated that the proposed DeepFERE model is able to produce high-resolution reconstruction images with rich chemical information and a detailed structure on both visual inspection and quantitative evaluation. In addition, our method was found to be able to improve the delimitation of the boundary between cancerous and para-cancerous regions in the MSI image. Furthermore, the reconstruction of low-resolution spatial transcriptomics data demonstrated that the developed DeepFERE model may find wider applications in biomedical fields.

Isobaric Stable Isotope N-Phosphorylation Labeling (iSIPL) for Ultrasensitive Proteome Quantification.

Stable isotope chemical labeling methods have been widely used for high-throughput mass spectrometry (MS)-based quantitative proteomics in biological and clinical applications. However, the existing methods are far from meeting the requirements for high sensitivity detection. In the present study, a novel isobaric stable isotope N-phosphorylation labeling (iSIPL) strategy was developed for quantitative proteome analysis. The tryptic peptides were selectively labeled with iSIPL tag to generate the novel reporter ions containing phosphoramidate P-N bond with high intensities under lower collision energies. iSIPL strategy are suitable for peptide sequencing and quantitative analysis with high sensitivity and accuracy even for samples of limited quantity. Furthermore, iSIPL coupled with affinity purification and mass spectrometry was applied to measure the dynamics of cyclin dependent kinase 9 (CDK9) interactomes during transactivation of the HIV-1 provirus. The interaction of CDK9 with PARP13 was found to significantly decrease during Tat-induced activation of HIV-1 gene transcription, suggesting the effectiveness of iSIPL strategy in dynamic analysis of protein-protein interaction in vivo. More than that, the proposed iSIPL strategy would facilitate large-scale accurate quantitative proteomics by increasing multiplexing capability.

iMSEA: A Novel Metabolite Set Enrichment Analysis Strategy to Decipher Drug Interactions.

Drug combinations are commonly used to treat various diseases to achieve synergistic therapeutic effects or to alleviate drug resistance. Nevertheless, some drug combinations might lead to adverse effects, and thus, it is crucial to explore the mechanisms of drug interactions before clinical treatment. Generally, drug interactions have been studied using nonclinical pharmacokinetics, toxicology, and pharmacology. Here, we propose a complementary strategy based on metabolomics, which we call interaction metabolite set enrichment analysis, or iMSEA, to decipher drug interactions. First, a digraph-based heterogeneous network model was constructed to model the biological metabolic network based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Second, treatment-specific influences on all detected metabolites were calculated and propagated across the whole network model. Third, pathway activity was defined and enriched to quantify the influence of each treatment on the predefined functional metabolite sets, i.e., metabolic pathways. Finally, drug interactions were identified by comparing the pathway activity enriched by the drug combination treatments and the single drug treatments. A data set consisting of hepatocellular carcinoma (HCC) cells that were treated with oxaliplatin (OXA) and/or vitamin C (VC) was used to illustrate the effectiveness of the iMSEA strategy for evaluation of drug interactions. Performance evaluation using synthetic noise data was also performed to evaluate sensitivities and parameter settings for the iMSEA strategy. The iMSEA strategy highlighted synergistic effects of combined OXA and VC treatments including the alterations in the glycerophospholipid metabolism pathway and glycine, serine, and threonine metabolism pathway. This work provides an alternative method to reveal the mechanisms of drug combinations from the viewpoint of metabolomics.

Monitoring live mitochondrial metabolism in real-time using NMR spectroscopy.

Investigation of mitochondrial metabolism is gaining increased interest owing to the growing recognition of the role of mitochondria in health and numerous diseases. Studies of isolated mitochondria promise novel insights into the metabolism devoid of confounding effects from other cellular organelles such as cytoplasm. This study describes the isolation of mitochondria from mouse skeletal myoblast cells (C2C12) and the investigation of live mitochondrial metabolism in real-time using isotope tracer-based NMR spectroscopy. [3-13 C1 ]pyruvate was used as the substrate to monitor the dynamic changes of the downstream metabolites in mitochondria. The results demonstrate an intriguing phenomenon, in which lactate is produced from pyruvate inside the mitochondria and the results were confirmed by treating mitochondria with an inhibitor of mitochondrial pyruvate carrier (UK5099). Lactate is associated with health and numerous diseases including cancer and, to date, it is known to occur only in the cytoplasm. The insight that lactate is also produced inside mitochondria opens avenues for exploring new pathways of lactate metabolism. Further, experiments performed using inhibitors of the mitochondrial respiratory chain, FCCP and rotenone, show that [2-13 C1 ]acetyl coenzyme A, which is produced from [3-13 C1 ]pyruvate and acts as a primary substrate for the tricarboxylic acid cycle in mitochondria, exhibits a remarkable sensitivity to the inhibitors. These results offer a direct approach to visualize mitochondrial respiration through altered levels of the associated metabolites.

Divide and Conquer: A Flexible Deep Learning Strategy for Exploring Metabolic Heterogeneity from Mass Spectrometry Imaging Data.

Research on metabolic heterogeneity provides an important basis for the study of the molecular mechanism of a disease and personalized treatment. The screening of metabolism-related sub-regions that affect disease development is essential for the more focused exploration on disease progress aberrant phenotypes, even carcinogenesis and metastasis. The mass spectrometry imaging (MSI) technique has distinct advantages to reveal the heterogeneity of an organism based on in situ molecular profiles. The challenge of heterogeneous analysis has been to perform an objective identification among biological tissues with different characteristics. By introducing the divide-and-conquer strategy to architecture design and application, we establish here a flexible unsupervised deep learning model, called divide-and-conquer (dc)-DeepMSI, for metabolic heterogeneity analysis from MSI data without prior knowledge of histology. dc-DeepMSI can be used to identify either spatially contiguous regions of interest (ROIs) or spatially sporadic ROIs by designing two specific modes, spat-contig and spat-spor. Comparison results on fetus mouse data demonstrate that the dc-DeepMSI outperforms state-of-the-art MSI segmentation methods. We demonstrate that the novel learning strategy successfully obtained sub-regions that are statistically linked to the invasion status and molecular phenotypes of breast cancer as well as organizing principles during developmental phase.

Exosomal EphA2 promotes tumor metastasis of triple-negative breast cancer by damaging endothelial barrier.

Many evidences show that exosomes play an important role in cancer development, invasion and metastasis. This study is based on the need to explore exosomal protein that promote breast cancer metastasis. We found that tyrosine kinase EphA2 was enriched in Triple-negative breast cancer -derived exosomes and it could disrupt the endothelial monolayer barrier through downregulating tight junction proteins of endothelial cells. These mechanisms were confirmed by in vivo experiments. After periodical injection of exosomal EphA2 into mice caudal vein, we found increased vascular permeability and breast cancer metastases in distant organs, and this phenomenon decreased dramatically after exosomal EphA2 knockdown. This study provides a new mechanism of exosome promoting breast cancer metastasis and suggests a new therapeutic target for the prevention and treatment of breast cancer metastasis.

iSegMSI: An Interactive Strategy to Improve Spatial Segmentation of Mass Spectrometry Imaging Data.

Spatial segmentation is a critical procedure in mass spectrometry imaging (MSI)-based biochemical analysis. However, the commonly used unsupervised MSI segmentation methods may lead to inappropriate segmentation results as the MSI data is characterized by high dimensionality and low signal-to-noise ratio. This process can be improved by the incorporation of precise prior knowledge, which is hard to obtain in most cases. In this study, we show that the incorporation of partial or coarse prior knowledge from different sources such as reference images or biological knowledge may also help to improve MSI segmentation results. Here, we propose a novel interactive segmentation strategy for MSI data called iSegMSI, which incorporates prior information in the form of scribble-regularization of the unsupervised model to fine-tune the segmentation results. By using two typical MSI data sets (including a whole-body mouse fetus and human thyroid cancer), the present results demonstrate the effectiveness of the iSegMSI strategy in improving the MSI segmentations. Specifically, the method can be used to subdivide a region into several subregions specified by the user-defined scribbles or to merge several subregions into a single region. Additionally, these fine-tuned results are highly tolerant to the imprecision of the scribbles. Our results suggest that the proposed iSegMSI method may be an effective preprocessing strategy to facilitate the analysis of MSI data.

MS-IDF: A Software Tool for Nontargeted Identification of Endogenous Metabolites after Chemical Isotope Labeling Based on a Narrow Mass Defect Filter.

Chemical isotope labeling liquid chromatography mass spectrometry (LC-MS) is an emerging metabolomic strategy for the quantification and characterization of small molecular compounds in biological samples. However, its subsequent data analysis is not straightforward due to a large amount of data produced and interference of biological matrices. In order to improve the efficiency of searching and identification of target endogenous metabolites, a new software tool for nontargeted metabolomics data processing called MS-IDF was developed based on the principle of a narrow mass defect filter. The developed tool provided two function modules, including IsoFinder and MDFinder. The IsoFinder function module applied a conventional peak extraction method by using a fixed mass differences between the heavy and light labels and by the alignment of chromatographic retention time (RT). On the other hand, MDFinder was designed to incorporate the accurate mass defect differences between or among stable isotopes in the peak extraction process. By setting an appropriate filter interval, the target metabolites can be efficiently screened out while eliminating interference. Notably, the present results showed that the efficiency in compound identification using the new MDFinder module was nearly doubled as compared to the conventional IsoFinder method (an increase from 259 to 423 compounds). The Matlab codes of the developed MS-IDF software are available from github at https://github.com/jydong2018/MS_IDF. Based on the MS-IDF software tool, a novel and effective approach from nontargeted to targeted metabolomics research was developed and applied to the exploration of potential primary amine biomarkers in patients with schizophrenia. With this approach, potential biomarkers, including N,N-dimethylglycine, S-adenosine-l-methionine, dl-homocysteine, and spermidine, were discovered.

Spatiotemporal Flexible Sparse Reconstruction for Rapid Dynamic Contrast-Enhanced MRI.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a tissue perfusion imaging technique. Some versatile free-breathing DCE-MRI techniques combining compressed sensing (CS) and parallel imaging with golden-angle radial sampling have been developed to improve motion robustness with high spatial and temporal resolution. These methods have demonstrated good diagnostic performance in clinical setting, but the reconstruction quality will degrade at high acceleration rates and overall reconstruction time remains long. In this paper, we proposed a new parallel CS reconstruction model for DCE-MRI that enforces flexible weighted sparse constraint along both spatial and temporal dimensions. Weights were introduced to flexibly adjust the importance of time and space sparsity, and we derived a fast-thresholding algorithm which was proven to be simple and efficient for solving the proposed reconstruction model. Results on both the brain tumor DCE and liver DCE show that, at relatively high acceleration factor of fast sampling, lowest reconstruction error and highest image structural similarity are obtained by the proposed method. Besides, the proposed method achieves faster reconstruction for liver datasets and better physiological measures are also obtained on tumor images.

Brain metabolic differences between temporal lobe epileptic seizures and organic non-epileptic seizures in postictal phase: a retrospective study with magnetic resonance spectroscopy.

Magnetic resonance spectroscopy (MRS) is employed to investigate the brain metabolites differences between patients with temporal lobe epileptic seizures (TLES) and organic non-epileptic seizures (ONES) that appear to be epileptic seizures. Twenty-three patients with TLES and nine patients with ONES in postictal phase underwent MRS examinations on a clinical 1.5T system, with 15 healthy controls in comparison. Statistical analyses on the ratios of brain metabolites were performed using the Mann-Whitney U test with age as a covariate. The results showed that N-acetyl-aspartate/Creatine (NAA/Cr) ratio of patients with TLES was statistically different from that of patients with ONES in postictal phase, i.e., TLES 1.422±0.037, ONES 1.640±0.061, P=0.012 in left temporal pole, while TLES 1.470±0.052, ONES 1.687±0.084, P=0.023 in the right temporal pole. Besides, compared with healthy controls, patients with TLES in postictal phase present significant differences in ratios of NAA/Cr, N-acetyl-aspartate/Choline (NAA/Cho) and NAA/(Cho + Cr). Experimental results demonstrate that NAA/Cr can be used to discriminate TLES from ONES, which has not been found in the references to the best of our knowledge. Although a prospective controlled validation is needed in the future, this retrospective study reveals that MRS may provide useful metabolites information to facilitate the epilepsy diagnosis.

Polyphenols from Securidaca inappendiculata alleviated acute lung injury in rats by inhibiting oxidative stress sensitive pathways.

Objective: Securidaca inappendiculata is a medicinal plant frequently used in the treatment of inflammatory diseases in south China. In this study, we aimed to explore its bioactive constituent which contributes to the anti-inflammatory activity. Methods: Polyphenol-enriched and polyphenol-deprived fractions (PRF and PDF, respectively) were separated from the ethanolic extract by HPD300 macroporous resin-based method, and their anti-inflammatory activities were investigated on a lipopolysaccharide (LPS)-induced acute lung injury (ALI) model in rats. The possible mechanism of action in alleviating acute inflammation was studied using RAW264.7 cells. Results: Both Folin-Ciocalteu and 1H nuclear magnetic resonance (NMR) analyses showed that polyphenolic content in PRF was approximately 10 times higher than that of PDF, and this observation reflected in their antioxidative capacities. PRF but not PDF significantly decreased the level of malondialdehyde, suppressed the expression of nicotinamide phosphoribosyltransferase (NAMPT) protein, and improved the severity of ALI in rats. PRF at 10 µg/mL effectively downregulated the expression of proteins NAMPT, HMGB1, TLR4, and p-p65, and scavenged the intracellular reactive oxygen species (ROS) in LPS-primed RAW264.7 cells. N-acetyl-L-cysteine exhibited similar inhibitory effects on ROS production and NAMPT-mediated TLR4/NF-κB activation in vitro, whereas nicotinamide mononucleotide antagonized all the changes induced by PRF during cotreatments. Conclusion: As an antioxidant, PRF exhibited potent anti-inflammatory activity under both in vivo and in vitro conditions by downregulating NAMPT and TLR4/NF-κB. Accordingly, polyphenols were identified as important bioactive constituents in S. inappendiculata targeting oxidative stress-sensitive pro-inflammatory pathways.

Effects of moxibustion and acupuncture at Zusanli (ST 36) and Zhongwan (CV 12) on chronic atrophic gastritis in rats.

OBJECTIVE: To evaluate the effects of moxibustion and acupuncture of Zusanli (ST 36) and Zhongwan (CV 12) acupoints on chronic atrophic gastritis (CAG) in rats, and to study the mechanisms behind their actions. METHODS: Forty-four male Sprague-Dawley rats were induced with CAG by intragastric administration of 40% ethanol combined with free drinking of N-methyl-N'nitro-N-nitrosoguanidine and irregular feeding for 12 weeks, followed by daily treatment with moxibustion or acupuncture for 2 weeks. Histopathologic examination, Western blotting of cytokines [epidermal growth factor (EGF), EGF receptor (EGFR), extracellular signal-regulated kinase (ERK), phosphorylated ERK (p-ERK)], and 1H NMR-based metabolic profiling of gastric tissues were used to measure changes related to CAG modeling and treatment. RESULTS: Moxibustion and acupuncture at Zusanli (ST 36) and Zhongwan (CV 12) each relieved CAG-induced abnormalities in histopathology and cytokine expression of ERK and p-ERK. Only moxibustion treatment regulated the expression of EGF and EGFR. Metabolites that were increased in gastric tissue by CAG induction (alanine, nicotinamide adenine dinucleotide phosphate, uracil DNA glycosylase, lactate, glycerol and adenosine) were restored to normal levels after moxibustion treatment; acupuncture treatment only normalized the levels of adenosine monophosphate and glycerol. CONCLUSION: Our findings suggest that moxibustion or acupuncture at Zusanli (ST 36) and Zhongwan (CV 12) can significantly improve the condition of CAG in rats. These treatments exert their effects on CAG through different mechanisms.

Momordica charantia Suppresses Inflammation and Glycolysis in Lipopolysaccharide-Activated RAW264.7 Macrophages.

Macrophage activation is a key event that triggers inflammatory response. The activation is accompanied by metabolic shift such as upregulated glucose metabolism. There are accumulating evidences showing the anti-inflammatory activity of Momordica charantia. However, the effects of M. charantia on inflammatory response and glucose metabolism in activated macrophages have not been fully established. The present study aimed to examine the effect of M. charantia in modulating lipopolysaccharide (LPS)-induced inflammation and perturbed glucose metabolism in RAW264.7 murine macrophages. The results showed that LPS-induced NF-κB (p65) nuclear translocation was inhibited by M. charantia treatment. In addition, M. charantia was found to reduce the expression of inflammatory genes including IL6, TNF-α, IL1ß, COX2, iNOS, and IL10 in LPS-treated macrophages. Furthermore, the data showed that M. charantia reduced the expression of GLUT1 and HK2 genes and lactate production (-28%), resulting in suppression of glycolysis. Notably, its effect on GLUT1 gene expression was found to be independent of LPS-induced inflammation. A further experiment also indicated that the bioactivities of M. charantia may be attributed to its key bioactive compound, charantin. Taken together, the study provided supporting evidences showing the potential of M. charantia for the treatment of inflammatory disorders.

1,7-Dihydroxy-3,4-Dimethoxyxanthone Inhibits Lipopolysaccharide-Induced Inflammation in RAW264.7 Macrophages by Suppressing TLR4/NF-κB Signaling Cascades.

Securidaca inappendiculata Hassk. is a traditional Chinese anti-rheumatic herbal medicine native to southern China. In this study, we identified a possible TLR4 inhibitor from this plant. General effects of its xanthone-rich fraction (XRF) on inflammation in vitro were investigated by immunoblotting experiments performed on lipopolysaccharides (LPS)-treated RAW264.7 cells, and the possible ligand of TLR4 within was screened out by analyzing chemical composition differences of the XRF containing cell culture medium under different inflammatory circumstances. The interaction between ligand and TLR4 was validated by cellular thermal shift assay (CETSA) and molecular docking simulation, and TLR4/NF-κB pathway status was investigated by immunoprecipitation, ELISA, immunofluorescence, dual-luciferase reporter, and immunoblotting experiments. Treatment with XRF resulted in significant decrease in p-p65 and p-JNK, and the signal accounting for 1,7-dihydroxy-3,4-dimethoxyxanthone (XAN) at 12.5 min with mass of 289.29 was greatly decreased in XRF containing medium after LPS stimulus because of enhanced interaction with increased TLR4. CETSA and molecular docking simulation demonstrated that XAN could bind to TLR4 directly on a smooth region adjacent to its contact interface with MD-2. XAN treatment inhibited the dimerization of TLR4 and transcriptional activity of NF-κB in HEK293T cells and decreased p65 accumulation in nucleus and pro-inflammatory cytokines production in RAW264.7 cells receiving LPS treatment. Overall evidences suggest that XAN could be a selective TLR4 inhibitor with potent anti-inflammatory effects. Also, it indicated that xanthone derivatives could have promising clinical application in many immune-mediated inflammations by acting as TLR4 inhibitors.

Bioactive fractions from Securidaca inappendiculata alleviated collagen-induced arthritis in rats by regulating metabolism-related signaling.

Securidaca inappendiculata is a xanthone rich medicinal plant that has been used in the treatment of inflammation and autoimmune diseases like rheumatoid arthritis (RA) for centuries; however, the material base and mechanism of action responsible for its anti-arthritis effect still remains elusive. The objective of this study is to evaluate the therapeutic effects of xanthone-enriched extract of the plant against collagen-induced arthritis (CIA) in rats and explore the underlying mechanisms. The xanthone-deprived fraction (XDF) and xanthone-rich fraction (XRF) were obtained by using a resin adsorption coupled with acid-base treatment method, and their chemical composition difference was characterized by UPLC-MS/MS analysis. Effects of the two on CIA were analyzed using radiographic, histological, and immunohistochemical analyses. The results indicated that XRF alleviated joint structures destructions with the higher efficacy than XDF, and decreased levels of TNF-α, IL-6, and anti-cyclic citrullinated peptide antibody in CIA rats significantly. Furthermore, XRF inhibited nicotinamide phosphoribosyl transferase (NAMPT) mediated fat biosynthesis and utilization indicated by clinical evidences and metabonomics analysis, which thereby disrupted energy-metabolism feedback. In addition, Toll-like Receptor 4 and High Mobility Group Protein 1 expressions were downregulated in XRF-treated CIA rats. Collective evidences suggest NAMPT could be an ideal target for RA treatments and reveal a novel antirheumatic mechanism of S. inappendiculata by regulating NAMPT controlled fat metabolism.