Identifying ligands directly interacting with target protein in medicinal herbs by metabolomic analysis of T2-filtered HSQC spectra.

A protocol for efficiently identifying ligands directly interacting with a target protein in complex extracts of medicinal herbs was proposed by combining an adapted 2D perfect-echo Carr-Purcell-Meiboom-Gill heteronuclear single quantum correlation (PE-CPMG HSQC) spectrum with metabolomic analysis. PE-CPMG HSQC can suppress the signal interference from the target protein, allowing more accurate peak quantification than conventional HSQC. Inspired from untargeted metabolomics, regions of interest (ROIs) are constructed and quantified for the mixture or complex extract samples with and without a target protein, and then a binding index (BI) of each ROI is determined. ROIs or corresponding peaks significantly perturbed by the presence of the target protein (BI ≥1.5) are detected as differential features, and potential binding ligands identified from the differential features can be equated with bioactive markers associated with the 'treatment' of the target protein. Quantifying ROI can inclusively report the ligand bindings to a target protein in fast, intermediate and slow exchange regimes on nuclear magnetic resonance (NMR) time scale. The approach was successfully implemented and identified Angoroside C, Cinnamic acid and Harpagoside from the extract of Scrophularia ningpoensis Hemsl. as ligands binding to peroxisome proliferator-activated receptor γ. The proposed 2D NMR-based approach saves excess steps for sample processing and has a higher chance of detecting the weaker ligands in the complex extracts of medicinal herbs. We expect that this approach can be applied as an alternative to mining the potential ligands binding to a variety of target proteins from traditional Chinese medicines and herbal extracts.

Deciphering m6A methylation in monocyte-mediated cardiac fibrosis and monocyte-hitchhiked erythrocyte microvesicle biohybrid therapy.

Rationale: Device implantation frequently triggers cardiac remodeling and fibrosis, with monocyte-driven inflammatory responses precipitating arrhythmias. This study investigates the role of m6A modification enzymes METTL3 and METTL14 in these responses and explores a novel therapeutic strategy targeting these modifications to mitigate cardiac remodeling and fibrosis. Methods: Peripheral blood mononuclear cells (PBMCs) were collected from patients with ventricular septal defects (VSD) who developed conduction blocks post-occluder implantation. The expression of METTL3 and METTL14 in PBMCs was measured. METTL3 and METTL14 deficiencies were induced to evaluate their effect on angiotensin II (Ang II)-induced myocardial inflammation and fibrosis. m6A modifications were analyzed using methylated RNA immunoprecipitation followed by quantitative PCR. NF-κB pathway activity and levels of monocyte migration and fibrogenesis markers (CXCR2 and TGF-ß1) were assessed. An erythrocyte microvesicle-based nanomedicine delivery system was developed to target activated monocytes, utilizing the METTL3 inhibitor STM2457. Cardiac function was evaluated via echocardiography. Results: Significant upregulation of METTL3 and METTL14 was observed in PBMCs from patients with VSD occluder implantation-associated persistent conduction block. Deficiencies in METTL3 and METTL14 significantly reduced Ang II-induced myocardial inflammation and fibrosis by decreasing m6A modification on MyD88 and TGF-ß1 mRNAs. This disruption reduced NF-κB pathway activation, lowered CXCR2 and TGF-ß1 levels, attenuated monocyte migration and fibrogenesis, and alleviated cardiac remodeling. The erythrocyte microvesicle-based nanomedicine delivery system effectively targeted inflamed cardiac tissue, reducing inflammation and fibrosis and improving cardiac function. Conclusion: Inhibiting METTL3 and METTL14 in monocytes disrupts the NF-κB feedback loop, decreases monocyte migration and fibrogenesis, and improves cardiac function. Targeting m6A modifications of monocytes with STM2457, delivered via erythrocyte microvesicles, reduces inflammation and fibrosis, offering a promising therapeutic strategy for cardiac remodeling associated with device implantation.

Monosaccharide composition analysis by 2D quantitative gsHSQCi.

The physicochemical properties and biological activities of polysaccharides depend on their structures. Monosaccharide composition analysis is indispensable for the structural characterization of polysaccharides and is helpful in the quality control of polysaccharide preparation. Here, using a model mixture and tamarind seed polysaccharide as examples, we demonstrated that a quantitative 2D NMR method, gsHSQCi (three gradient-selective Heteronuclear Single Quantum Coherence spectra acquired with incremented repetition times, i = 1, 2, 3) can directly quantify a variety of monosaccharides in solution with adequate precision and accuracy, requiring no derivatization, postprocessing steps and column separation. Both anomeric and non-anomeric signals of monosaccharides can be utilized for content determination. More accurate quantification of fructose in a mixture containing nine monosaccharides is obtained, which is difficult to achieve by quantitative 1D 1HNMR and the common PMP-HPLC method (high-performance liquid chromatography through pre-column derivatization with 1-phenyl-3-methyl-5-pyrazolone) due to the peak overlapping and the poor derivatization efficiency, respectively. The results also revealed that Na[Fe(EDTA)] can serve as a proper relaxation-enhancing agent for saccharide samples to save experimental time. We expect that this approach can be applied as an alternative to analyzing the monosaccharide composition and be helpful in interpreting the structure of polysaccharides.

The Therapeutic Effectiveness of Laparoscopic Sleeve Gastrectomy Among Individuals with Low BMI Obesity (30-35 Kg/m2) and the Relationship of BMI to Weight Loss.

Purpose: Investigating the therapeutic efficacy of Laparoscopic Sleeve Gastrectomy (LSG) in low BMI (30-35 kg/m2) patients with obesity, and exploring the correlation between patients' preoperative BMI and postoperative weight loss. Methods: Comparing the weight loss, remission of comorbidities, occurrence of complications, and quality of life among the different BMI patients who underwent LSG. Analyzing the relationship between BMI and percentage of excess weight loss (%EWL) by using Spearman correlation analysis and linear regression analysis. Results: The %EWL at 12 months after the surgical procedure was (104.26±16.41)%, (90.36±9.98)%, and (78.30±14.64)% for patients with Class I, II, and III obesity, respectively, P<0.05. Spearman correlation coefficients between %EWL and BMI at 1, 3, 6, and 12 months after surgery were R=-0.334 (P<0.001), R=-0.389 (P<0.001), and R=-0.442 (P<0.001), R=-0.641 (P<0.001), respectively. The remission of hypertension, diabetes and dyslipidaemia did not differ significantly between groups (P>0.05). Conclusion: Individuals with obesity for varying BMI can experience favorable outcomes following LSG surgery. It is advisable to consider LSG treatment for patients with Class I obesity.

Identifying Anti-NSCLC Bioactive Compounds in Scutellaria via 2D NMR-Based Metabolomic Analysis of Pharmacologically Classified Crude Extracts.

We presented a strategy utilizing 2D NMR-based metabolomic analysis of crude extracts, categorized by different pharmacological activities, to rapidly identify the primary bioactive components of TCM. It was applied to identify the potential bioactive components from Scutellaria crude extracts that exhibit anti-non-small cell lung cancer (anti-NSCLC) activity. Four Scutellaria species were chosen as the study subjects because of their close phylogenetic relationship, but their crude extracts exhibit significantly different anti-NSCLC activity. Cell proliferation assay was used to assess the anti-NSCLC activity of four species of Scutellaria. 1H-13C HSQC spectra were acquired for the chemical profiling of these crude extracts. Based on the pharmacological classification (PCA, OPLS-DA and univariate hypothesis test) were performed to identify the bioactive constituents in Scutellaria associated with the anti-NSCLC activity. As a result, three compounds, baicalein, wogonin and scutellarin were identified as bioactive compounds. The anti-NSCLC activity of the three potential active compounds were further confirmed via cell proliferation assay. The mechanism of the anti-NSCLC activity by these active constituents was further explored via flow cytometry and western blot analyses. This study demonstrated 2D NMR-based metabolomic analysis of pharmacologically classified crude extracts to be an efficient approach to the identification of active components of herbal medicine.

Dihydroartemisinin inhibits tumor progress via blocking ROR1-induced STAT3-activation in non-small cell lung cancer.

In non-small cell lung cancer (NSCLC), identifying a component with certain molecular targets can aid research on cancer treatment. Dihydroartemisinin (DHA) is a semisynthetic derivative of artemisinin which induced the anti-cancer effects via the STAT3 signaling pathway, but the underlying molecular mechanism is still elusive. In this study, we first proved that DHA prohibits the growth of tumors both in vitro and in vivo. Data from transcriptomics showed that DHA reduced the expression level of the genes involved in cell cycle-promoting and anti-apoptosis, and most importantly, DHA restricted the expression level of receptor tyrosine kinase-like orphan receptor 1 (ROR1) which has been reported to have abnormal expression on tumor cells and had close interaction with STAT3 signaling. Then, we performed comprehensive experiments and found that DHA remarkably decreased the expression of ROR1 at both mRNA and protein levels and it also diminished the phosphorylation level of STAT3 in NSCLC cell lines. In addition, our data showed that exogenously introduced ROR1 could significantly enhance the phosphorylation of STAT3 while blocking ROR1 had the opposite effects indicating that ROR1 plays a critical role in promoting the activity of STAT3 signaling. Finally, we found that ROR1 overexpression could partially reverse the decreased activity of STAT3 induced by DHA which indicates that DHA-induced anti-growth signaling is conferred, at least in part, through blocking ROR1-mediated STAT3 activation. In summary, our study indicates that in NSCLC, ROR1 could be one of the critical molecular targets mediating DHA-induced STAT3 retardation.

Increased oxidative stress is associated with hyperandrogenemia in polycystic ovary syndrome evidenced by oxidized lipoproteins stimulating rat ovarian androgen synthesis in vitro.

PURPOSE: To determine the relationship between serum total testosterone (TT) levels and oxidative stress indices in patients with polycystic ovary syndrome (PCOS), and to investigate the effect of oxidative stress on androgen synthesis and its mechanism in rat ovarian theca-interstitial (T-I) cells. METHODS: Clinical, hormonal, metabolic, and oxidative stress parameters were analyzed in a cross-sectional case-control study including 626 patients with PCOS and 296 controls. The effects of oxidized low-density lipoprotein (ox-LDL) and oxidized high-density lipoprotein (ox-HDL) on cell proliferation, TT secretion, and expression of key enzymes involved in testosterone synthesis were evaluated in T-I cells. RESULTS: Serum TT levels were elevated with an increase in ox-LDL levels, whereas glutathione concentrations were lower in the high-TT subgroup than in the low-TT subgroup. The average ovarian volume and ox-LDL and malondialdehyde levels were significant predictors of TT levels in the multivariate regression models. In a rat ovarian T-I cell model, lipoprotein and oxidized lipoprotein treatments stimulated proliferation and promoted testosterone secretion. The mRNA and protein levels of 17α-hydroxylase were significantly higher in oxidized lipoprotein-treated cells than those in lipoprotein-treated cells. The mRNA levels of cholesterol side chain cleavage enzyme and steroidogenic acute regulatory protein were also significantly higher in ox-HDL-treated cells than in HDL-treated cells. CONCLUSIONS: Oxidative stress can promote androgen production by up-regulating the expression of testosterone synthesis-related enzymes in vitro and may be an essential factor in elevating serum TT levels in patients with PCOS.

Comparing univariate filtration preceding and succeeding PLS-DA analysis on the differential variables/metabolites identified from untargeted LC-MS metabolomics data.

BACKGROUND: PLS-DA of high-dimensional metabolomics data is frequently employed to capture the most pertinent features to sample classification. But the presence of numerous insignificant input features could distort the PLS-DA model, blow up and scramble the selected differential features. Usually, univariate filtration is subsequently complemented to refine the selected features, but often giving unstable results. Whereas by precluding insignificant features through univariate data prefiltration assessed by FDR adjusted p-value, PLS-DA can generate more stable and reliable differential features. We explored and compared these two data analysis procedures to gain insights into the underlying mechanisms responsible for the disparate results. RESULTS: The effect of univariate data filtration preceding and succeeding PLS-DA analysis on the identified discriminative features/metabolites was investigated using LC-MS data acquired on the samples of human serum and C. elegans extracts, with and without metabolite standards spiked to simulate the treated and control groups of biological samples. It was shown that the univariate data prefiltration before PLS-DA usually gave less but more stable and likely more reliable and meaningful differential features, while PLS-DA applied directly to the original data could be affected by the presence of insignificant features and orthogonal noise. Large number of insignificant variables and orthogonal noise could distort the generated PLS-DA model and affect the p(corr) value, and artificially inflate the calculated VIP values of relevant features due to the increased total number of input features for model construction, thus leading to more false positives selected by the conventional VIP threshold of 1.0. SIGNIFICANCE AND NOVELTY: Univariate data filtration preceding PLS-DA was important for the identification of reliable differential features if using a conventional threshold of VIP of 1.0. Presence of insignificant features could distort the PLS-DA model and inflate VIP values. Appropriate VIP threshold is associated with the numbers of input features and the model components. For PLS-DA without univariate prefiltration, threshold of VIP larger than 1.0 is recommended for the selection of discriminative features to reduce the false positives.

Lightweight colon polyp segmentation algorithm based on improved DeepLabV3.

To address the problems that the current polyp segmentation model is complicated and the segmentation accuracy needs to be further improved, a lightweight polyp segmentation network model Li-DeepLabV3+ is proposed. Firstly, the optimized MobileNetV2 network is used as the backbone network to reduce the model complexity. Secondly, an improved simple pyramid pooling module is used to replace the original Atrous Spatial Pyramid Pooling structure, which improves the model training efficiency of the model while reducing the model parameters. Finally, to enhance the feature representation, in the feature fusion module, the low-level feature and the high-level feature are fused using the improved Unified Attention Fusion Module, which applies both channel and spatial attention to enrich the fused features, thus obtaining more boundary information. The model was combined with transfer learning for training and validation on the CVC-ClinicDB and Kvasir SEG datasets, and the generalization of the model was verified across the datasets. The experiment results show that the Li-DeepLabV3+ model has superior advantages in segmentation accuracy and segmentation speed, and has certain generalization abilities.

Bioinformatics and Network Pharmacology Explore the Role of Immune Cells in the Occurrence of Anti-Vascular Endothelial Growth Factor (VEGF) Resistance in Patients with Neovascular Age-Related Macular Degeneration(nAMD) and the Application of Complementary Medicine Treatment.

PURPOSE: This study explores the immune cells' role in anti-VEGF resistance in nAMD patients, and the potential of Zi-Yin-Jiang-Huo-Tang (ZYJHT), a Traditional Chinese Medicine formula, as complementary therapy. METHODS: Aqueous humor proteomics data from 10 nAMD patients with anti-VEGF resistance and 10 nAMD patients without anti-VEGF resistance were analyzed, investigating immune cells's role in anti-VEGF resistance and its underlying mechanism. Network pharmacology methods are employed to analyze the active ingredients in ZYJHT that contribute to therapeutic effects and their mechanisms. Real-time PCR (polymerase chain reaction) was used to detect changes in the expression of SOD1 (superoxide dismutase 1) after treatment with compounds targeting SOD1 in ARPE-19 cells. RESULTS: nAMD patients with anti-VEGF resistance showed enhancement of biological processes linked to the positive regulation of immune function, along with decreased cellular resistance to oxidative stress. Infiltration of B cells memory, plasma cells, CD8+and γδ-T cells were higher in nAMD patients with anti-VEGF resistance. SOD1 was identified as a hub gene in the occurrence of anti-VEGF resistance and a core therapeutic target of ZYJHT, negatively correlated with B and T cell infiltration. Compounds diosgenin, naringenin, and liquiritin in ZYJHT can bind to SOD1 and upregulating SOD1 expression in ARPE-19 cells.

A large-scale transcriptional analysis reveals herb-derived ginsenoside F2 suppressing hepatocellular carcinoma via inhibiting STAT3.

BACKGROUND: Hepatocellular carcinoma (HCC) is a common type of cancer that shows great morbidity and mortality rates. However, there are limited available drugs to treat HCC. AIM: The present work focused on discovering the potential anti-HCC compounds from traditional Chinese medicine (TCM) by employing high-throughput sequencing-based high-throughput screening (HTS2) together with the liver cancer pathway-associated gene signature. METHODS: HTS2 assay was adopted for identifying herbs. Protein-protein interaction (PPI) network analysis and computer-aided drug design (CADD) were used to identify key targets and screen the candidate natural products of herbs. Molecular docking, network pharmacology analysis, western blotting, immunofluorescent staining, subcellular fractionation experiment, dual-luciferase reporter gene assay, surface plasmon resonance (SPR) as well as nuclear magnetic resonance (NMR) were performed to validate the ability of compound binding with key target and inhibiting its function. Moreover, cell viability, colony-forming, cell cycle assay and animal experiments were performed to examine the inhibitory effect of compound on HCC. RESULTS: We examined the perturbation of 578 herb extracts on the expression of 84 genes from the liver cancer pathway, and identified the top 20 herbs significantly reverting the gene expression of this pathway. Signal transducer and activator of transcription 3  (STAT3)  was identified as one of the key targets of the liver cancer pathway by PPI network analysis. Then, by analyzing compounds from top 20 herbs utilizing CADD, we found ginsenoside F2 (GF2) binds to STAT3 with high affinity, which was further validated by the results from molecular docking, SPR and NMR. Additionally, our results showed that GF2 suppresses the phosphorylation of Y705 of STAT3, inhibits its nuclear translocation, decreases its transcriptional activity and inhibits the growth of HCC in vitro and in vivo. CONCLUSION: Based on this large-scale transcriptional study, a number of anti-HCC herbs were identified. GF2, a compound derived from TCM, was found to be a chemical basis of these herbs in treating HCC. The present work also discovered that GF2 is a new STAT3 inhibitor, which is able to suppress HCC. As such, GF2 represents a new potential anti-HCC therapeutic strategy.

Hyperinsulinemia Influences the Short-Term Efficiency of Laparoscopic Sleeve Gastrectomy for Patients with Obesity and Insulin Resistance.

Purpose: The effect of hyperinsulinemia on short-term outcomes after laparoscopic sleeve gastrectomy (LSG) in patients with obesity combined with insulin resistance is unclear. Material and Methods: This was a retrospective analysis of patients who underwent LSG at our center between January 1, 2020, and December 31, 2021. Patients were divided into hyperinsulinemia (HINS) and nonhyperinsulinemia (NHINS) groups based on fasting insulin levels. The primary endpoint was weight change. Metabolic disease outcomes, postoperative complications, and quality of life score changes were secondary endpoints. Results: A total of 92 patients were included in this study, with 59 in the HINS group and 33 in the NHINS group. At 6 months postoperatively, the median (P25, P75) %EWL was 76.01 (64.40, 86.99)% in the HINS group and 92.02 (86.78, 100.88)% in the NHINS group (P<0.001). The mean %TWL (SD) was 23.26 (7.14)% in the HINS group and 26.80 (6.55)% in the NHINS group (P=0.021). The remission of dyslipidemia and hypertension in the NHINS group and the HINS group were not significantly different (P>0.05 for all). The differences in QOL between groups were not statistically significant (P=0.788). In terms of postoperative complications, there was no statistically significant difference between the groups (P>0.05 for all). Conclusion: HINS negatively influences weight change in patients with obesity and insulin resistance, and the NHINS group had better postoperative weight loss. In terms of hypertension, dyslipidemia, and postoperative complications, there was no significant effect of HINS.

CYP2E1 C-1054T and 96-bp I/D genetic variations and risk of gestational diabetes mellitus in chinese women: a case-control study.

BACKGROUND: Cytochrome P450 2E1 (CYP2E1) plays a key role in the metabolism of xenobiotic and endogenous low-molecular-weight compounds. This study aimed to determine if the genetic variations of 96-bp insertion/deletion (I/D) and C-1054T (rs2031920) in CYP2E1 were associated with the risk of gestational diabetes mellitus (GDM). METHODS: CYP2E1 polymorphisms were genotyped in a case-control study of 1,134 women with uncomplicated pregnancies and 723 women with GDM. The effects of genotype on the clinical, metabolic, and oxidative stress indices were assessed. RESULTS: The CYP2E1 C-1054T variant was associated with an increased risk of GDM based on the genotype, recessive, dominant, and allele genetic models (P < 0.05). The TT + CT genotype remained a significant predictive factor for GDM risk after correcting for maternal age and pre-pregnancy body mass index (OR = 1.277, 95% CI: 1.042-1.563, P = 0.018). Moreover, fasting insulin concentrations and homeostatic model assessment of insulin resistance were significantly higher in GDM patients carrying the T allele than in those with the CC genotype (P < 0.05). Furthermore, the combined genotype II + ID/TT + CT of the 96-bp I/D and C-1054T polymorphisms further increased the risk of GDM when the combined genotype DD/CC was set as the reference category (OR = 1.676, 95% CI: 1.182-2.376, P = 0.004). CONCLUSIONS: The T allele of the C-1054T polymorphism and its combination with the I allele of the 96-bp I/D variation in CYP2E1 are associated with an increased risk of GDM in the Chinese population. The - 1054T allele may be associated with more serious insulin resistance in patients.

GIPMA: Global Intensity-Guided Peak Matching and Alignment for 2D 1H-13C HSQC-Based Metabolomics.

Two-dimensional (2D) 1H-13C heteronuclear single quantum coherence (HSQC) has been increasingly applied to metabolomics studies because it can greatly improve the resolving capability compared with one-dimensional (1D) 1H NMR. However, preprocessing methods such as peak matching and alignment tools for 2D NMR-based metabolomics have lagged behind similar methods for 1D 1H NMR-based metabolomics. Correct matching and alignment of 2D NMR spectral features across multiple samples are particularly important for subsequent multivariate data analysis. Considering different intensity dynamic ranges of a variety of metabolites and the chemical shift variation across the spectra of multiple samples, here, we developed an efficient peak matching and alignment algorithm for 2D 1H-13C HSQC-based metabolomics, called global intensity-guided peak matching and alignment (GIPMA). In GIPMA, peaks identified in all spectra are pooled together and sorted by intensity. Chemical shift of a stronger peak is regarded to be more accurate and reliable than that of a weaker peak. The strongest undesignated peak is chosen as the reference of a new cluster if it is not located within the chemical shift tolerance of any existing peak cluster (PC), or otherwise it is matched to an existing PC and the aligned chemical shift of the PC is updated as the intensity-weighted average of the chemical shifts of all peaks in the cluster. Setting an optimum chemical shift tolerance (Δδo) is critical for the peak matching and alignment across multiple samples. GIPMA dynamically searches for and intelligently selects the Δδo for peak matching to maximize the number of valid peak clusters (vPC), that is, spectral features, among multiple samples. By GIPMA, fully automatic peakwise matching and alignment do not require any spectrum as initial reference, while the chemical shift of each PC is updated as the intensity-weighted average of the chemical shifts of all peaks in the same PC, which is warranted to be statistically more accurate. Accurate chemical shifts for each representative spectral feature will facilitate subsequent peak assignment and are essential for correct metabolite identification and result interpretation. The proposed method was demonstrated successfully on the spectra of six model mixtures consisting of seven typical metabolites, yielding correct matching of all known spectral features. The performance of GIPMA was also demonstrated on 2D 1H-13C HSQC spectra of 87 real extracts of 29 samples of five Dendrobium species. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) of the 87 matched and aligned spectra by GIPMA generates correct classification of the 29 samples into five groups. In summary, the proposed algorithm of GIPMA provided a practical peak matching and alignment method to facilitate 2D NMR-based metabolomics studies.

Myeloperoxidase G-463A and CYBA C242T genetic variants in gestational diabetes mellitus.

Oxidative stress plays an important role in the pathophysiology of gestational diabetes mellitus (GDM). We investigated the relationship between NADPH oxidase p22phox subunit (CYBA) C242T (rs4673) and myeloperoxidase (MPO) G-463A (rs2333227) genetic variants and GDM in 719 patients with GDM and 1205 control women. Clinical, metabolic, and oxidative stress parameters were analyzed. We found that frequencies of the A allele (15.6% vs 12.3%) and GA + AA genotype (28.5% vs 23.2%) of the MPO G-463A variation were significantly higher in patients with GDM than in the control women (OR = 1.318, 95% CI: 1.068-1.625, P = 0.010 for the dominant model; OR = 1.999, 95% CI: 1.040-3.843, P = 0.034 for the recessive model; OR = 1.320, 95% CI: 1.095-1.591, P = 0.004 for the allele model). Genotype GA + AA remained a significant predictor of GDM in a logistic regression model including age and BMI at delivery (OR = 1.282, 95% CI: 1.037‒1.583, P = 0.021). Furthermore, the ‒463A allele was associated with higher TG and the 242T allele was related to higher pre-pregnancy BMI and oxidative stress index in all subjects (P < 0.05). The 242T allele was also associated with higher homeostatic model assessment of insulin resistance but lower serum total antioxidant capacity in patients with GDM (P < 0.05). We conclude that the MPO G-463A, but not the CYBA C242T, genetic variation is associated with an increased risk of GDM in Chinese women. These two genetic polymorphisms may be linked to obesity, dyslipidemia, insulin resistance, and oxidative stress.

Expression, purification, and characterization of c-FLIP tandem death effector domains from Escherichia coli.

Cellular FLICE-like inhibitory protein (c-FLIP) regulates extrinsic apoptosis by controlling procaspase-8 activation through its tandem N-terminal death effector domains (DEDs). Here, we present the expression and purification of c-FLIP tandem DEDs (tDED) from Escherichia coli. We observed that the c-FLIPtDED maintains monomeric form under near-physiological pH condition in vitro. Our results also reveal a significant correlation between the pH conditions and the structure of c-FLIPtDED (F114A). The described methods and results would be helpful for follow-up study on the structural and functional of c-FLIP.

Screening Novel Vaccine Candidates for Leishmania Donovani by Combining Differential Proteomics and Immunoinformatics Analysis.

Visceral leishmaniasis (VL), also known as kala-azar, is the most dangerous form of leishmaniasis. Currently no effective vaccine is available for clinical use. Since the pathogenicity of different Leishmania strains is inconsistent, the differentially expressed proteins in Leishmania strains may play an important role as virulence factors in pathogenesis. Therefore, effective vaccine candidate targets may exist in the differentially expressed proteins. In this study, we used differential proteomics analysis to find the differentially expressed proteins in two Leishmania donovani strains, and combined with immunoinformatics analysis to find new vaccine candidates. The differentially expressed proteins from L. DD8 (low virulent) and L. 9044 (virulent) strains were analyzed by LC-MS/MS, and preliminarily screened by antigenicity, allergenicity and homology evaluation. The binding peptides of MHC II, IFN-γ and MHC I from differentially expressed proteins were then predicted and calculated for the second screening. IFN-γ/IL-10 ratios and conserved domain prediction were performed to choose more desirable differentially expressed proteins. Finally, the 3D structures of three vaccine candidate proteins were produced and submitted for molecular dynamics simulation and molecular docking interaction with TLR4/MD2. The results showed that 396 differentially expressed proteins were identified by LC-MS/MS, and 155 differentially expressed proteins were selected through antigenicity, allergenicity and homology evaluation. Finally, 16 proteins whose percentages of MHC II, IFN-γ and MHC I binding peptides were greater than those of control groups (TSA, LmSTI1, LeIF, Leish-111f) were considered to be suitable vaccine candidates. Among the 16 candidates, amino acid permease, amastin-like protein and the hypothetical protein (XP_003865405.1) simultaneously had the large ratios of IFN-γ/IL-10 and high percentages of MHC II, IFN-γ and MHC I, which should be focused on. In conclusion, our comprehensive work provided a methodological basis to screen new vaccine candidates for a better intervention against VL and associated diseases.

Effect of Different Processing Methods on the Chemical Constituents of Scrophulariae Radix as Revealed by 2D NMR-Based Metabolomics.

Scrophulariae Radix (SR) is one of the oldest and most frequently used Chinese herbs for oriental medicine in China. Before clinical use, the SR should be processed using different methods after harvest, such as steaming, "sweating", and traditional fire-drying. In order to investigate the difference in chemical constituents using different processing methods, the two-dimensional (2D) 1H-13C heteronuclear single quantum correlation (1H-13C HSQC)-based metabolomics approach was applied to extensively characterize the difference in the chemical components in the extracts of SR processed using different processing methods. In total, 20 compounds were identified as potential chemical markers that changed significantly with different steaming durations. Seven compounds can be used as potential chemical markers to differentiate processing by sweating, hot-air drying, and steaming for 4 h. These findings could elucidate the change of chemical constituents of the processed SR and provide a guide for the processing. In addition, our protocol may represent a general approach to characterizing chemical compounds of traditional Chinese medicine (TCM) and therefore might be considered as a promising approach to exploring the scientific basis of traditional processing of TCM.

Discovery of a Novel Small-Molecule Inhibitor Disrupting TRBP-Dicer Interaction against Hepatocellular Carcinoma via the Modulation of microRNA Biogenesis.

MicroRNAs (miRNAs) are key players in human hepatocellular carcinoma (HCC) tumorigenesis. Therefore, small molecules targeting components of miRNA biogenesis may provide new therapeutic means for HCC treatment. By a high-throughput screening and structural simplification, we identified a small molecule, CIB-3b, which suppresses the growth and metastasis of HCC in vitro and in vivo by modulating expression profiles of miRNAome and proteome in HCC cells. Mechanistically, CIB-3b physically binds to transactivation response (TAR) RNA-binding protein 2 (TRBP) and disrupts the TRBP-Dicer interaction, thereby altering the activity of Dicer and mature miRNA production. Structure-activity relationship study via the synthesis of 45 CIB-3b derivatives showed that some compounds exhibited a similar inhibitory effect on miRNA biogenesis to CIB-3b. These results support TRBP as a potential therapeutic target in HCC and warrant further development of CIB-3b along with its analogues as a novel therapeutic strategy for the treatment of HCC.