Fenglin Lv

Heme activates TLR4-mediated inflammatory injury via MyD88/TRIF signaling pathway in intracerebral hemorrhage

BackgroundInflammatory injury plays a critical role in intracerebral hemorrhage (ICH)-induced neurological deficits; however, the signaling pathways are not apparent by which the upstream cellular events trigger innate immune and inflammatory responses that contribute to neurological impairments. Toll-like receptor 4 (TLR4) plays a role in inflammatory damage caused by brain disorders.MethodsIn this study, we investigate the role of TLR4 signaling in ICH-induced inflammation. In the ICH model, a significant upregulation of TLR4 expression in reactive microglia has been demonstrated using real-time RT-PCR. Activation of microglia was detected by immunohistochemistry, cytokines were measured by ELISA, MyD88, TRIF and NF-κB were measured by Western blot and EMSA, animal behavior was evaluated by animal behavioristics.ResultsCompared to WT mice, TLR4−/− mice had restrained ICH-induced brain damage showing in reduced cerebral edema and lower neurological deficit scores. Quantification of cytokines including IL-6, TNF-α and IL-1β and assessment of macrophage infiltration in perihematoma tissues from TLR4−/−, MyD88−/− and TRIF−/− mice showed attenuated inflammatory damage after ICH. TLR4−/− mice also exhibited reduced MyD88 and TRIF expression which was accompanied by decreased NF-κB activity. This suggests that after ICH both MyD88 and TRIF pathways might be involved in TLR4-mediated inflammatory injury possibly via NF-κB activation. Exogenous hemin administration significantly increased TLR4 expression and microglial activation in cultures and also exacerbated brain injury in WT mice but not in TLR4−/− mice. Anti-TLR4 antibody administration suppressed hemin-induced microglial activation in cultures and in the mice model of ICH.ConclusionsOur findings suggest that heme potentiates microglial activation via TLR4, in turn inducing NF-κB activation via the MyD88/TRIF signaling pathway, and ultimately increasing cytokine expression and inflammatory injury in ICH. Targeting TLR4 signaling may be a promising therapeutic strategy for ICH.

Geometric characteristics of hydrogen bonds involving sulfur atoms in proteins

Sulfur atoms have been known to participate in hydrogen bonds (H‐bonds) and these sulfur‐containing H‐bonds (SCHBs) are suggested to play important roles in certain biological processes. This study aims to comprehensively characterize all the SCHBs in 500 high‐resolution protein structures (≤1.8 Å). We categorized SCHBs into six types according to donor/acceptor behaviors and used explicit hydrogen approach to distinguish SCHBs from those of nonhydrogen bonding interactions. It is revealed that sulfur atom is a very poor H‐bond acceptor, but a moderately good H‐bond donor. In α‐helix, considerable SCHBs were found between the sulphydryl group of cysteine residue i and the carbonyl oxygen of residue i‐4, and these SCHBs exert effects in stabilizing helices. Although for other SCHBs, they possess no specific secondary structural preference, their geometric characteristics in proteins and in free small compounds are significantly distinct, indicating the protein SCHBs are geometrically distorted. Interestingly, sulfur atom in the disulfide bond tends to form bifurcated H‐bond whereas in cysteine‐cysteine pairs prefer to form dual H‐bond. These special H‐bonds remarkably boost the interaction between H‐bond donor and acceptor. By oxidation/reduction manner, the mutual transformation between the dual H‐bonds and disulfide bonds for cysteine‐cysteine pairs can accurately adjust the structural stability and biological function of proteins in different environments. Furthermore, few loose H‐bonds were observed to form between the sulphydryl groups and aromatic rings, and in these cases the donor H is almost over against the rim rather than the center of the aromatic ring. Proteins 2009.

Toll-like receptor 2/4 heterodimer mediates inflammatory injury in intracerebral hemorrhage

Inflammatory injury plays a critical role in intracerebral hemorrhage (ICH)‐induced secondary brain injury. However, the upstream events that initiate inflammatory responses following ICH remain elusive. Our previous studies suggested that Toll‐like receptor 4 (TLR4) may be the upstream signal that triggers inflammatory injury in ICH. In addition, recent clinical findings indicated that both TLR2 and TLR4 may participate in ICH‐induced brain injury. However, it is unclear how TLR2 functions in ICH‐induced inflammatory injury and how TLR2 interacts with TLR4.

Comprehensive comparison of eight statistical modelling methods used in quantitative structure-retention relationship studies for liquid chromatographic retention times of peptides generated by protease digestion of the Escherichia coli proteome

In this study, we propose a new peptide characterization method that gives attention to both the amino acid composition and the residue local environment. Using this approach, structural characteristics of peptides derived from Escherichia coli proteome were parameterized and, based upon that, the performance profile of eight statistical modelling methods were validated rigorously and compared comprehensively by applying them to modelling relationship between the sequence structure and retention ability for 816 experimentally measured peptides and to predicting normalized retention times for 121,273 unmeasured peptides in liquid chromatography. Results show that the regression models constructed by nonlinear approaches are more robust and predictable but time-consuming than those by linear ones. In these modelling methods, Gaussian process and back-propagation neural network possess the best stability, unbiased ability and predictive power, thus they can be used to accurately model the peptide structure-retention relationships; multiple linear regression and partial least squares regression perform worse compared to nonlinear modelling techniques but they are computationally efficient, so they are promising candidates for solving the qualitative problems involved in massive data. In addition, by investigating the descriptor importance in different models we found that the amino acid composition presents a significantly linear correlation with the retention time of peptides, whereas the residue environment is mainly correlated nonlinearly with peptide retention. The polar Arg and strongly hydrophobic amino acids such as Leu, Ile, Phe, Trp and Val are the critical factors influencing peptide retention behavior.

Why OppA protein can bind sequence-independent peptides? A combination of QM/MM, PB/SA, and structure-based QSAR analyses

Periplasmic oligopeptide-binding protein (OppA) is the initial receptor in the ATP-binding cassette (ABC) system of bacteria, which exhibits a broad specificity in binding oligopeptides without regard to sequence. Here, we present a computational study on the structural properties and energetic landscapes of OppA protein interacting with its cognate ligands on the basis of 28 structure/affinity-known OppA–tripeptide complexes. By employing a well-designed protocol that couples the hybrid quantum mechanical/molecular mechanical (QM/MM) scheme and the sophisticated Poisson–Boltzmann/surface area (PB/SA) solvent model together to analyze and decompose the energy components associated with the OppA–peptide binding, we demonstrate that the broad specificity of OppA-recognizing peptides is originated from a series of exquisite balances between the free energy contributions from, for example, the direct nonbonded interactions and indirect desolvation effects, the main chains and side chains, and the different residue positions of the tripeptide ligands. We also show that, in a framework of structure-based quantitative structure–activity relationship (SB-QSAR) methodology, the QM/MM–PB/SA-derived energy terms could be used as a good descriptor to characterize the interaction profile of OppA with peptides and correlate pretty well with the experimentally measured affinities of the binding.

Predicting liquid chromatographic retention times of peptides from the Drosophila melanogaster proteome by machine learning approaches

Three machine learning algorithms as least-squares support vector machine (LSSVM), random forest (RF) and Gaussian process (GP) were used to model the quantitative structure-retention relationship (QSRR) for predicting and explaining the retention behavior of proteome-wide peptides in the reverse-phase liquid chromatography. Peptides were parameterized using CODESSA approach and 145 descriptors were obtained for each peptide, including diverse structural information such as constitutional, topological, geometrical and physicochemical property. Based upon that, the nonlinear LSSVM, RF and GP as well as another sophisticated linear method (partial least-squares regression (PLS)) were employed in the QSRR model development. By a series of systematic validations as internal cross-validation, external test and Monte Carlo cross-validation, the stability and predictive power of the constructed models were confirmed. Results show that regression models developed using nonlinear approaches such as LSSVM, RF and GP predict better than linear PLS models. Considering the retention times used in this work were measured in different columns and thus have a relatively large uncertainty (reproducibility within 7%), the optimal statistics obtained from GP modeling are satisfactory, with the coefficients of determination (R2) for training set and test set of 0.894 and 0.866, respectively.

Toward Prediction of Binding Affinities Between the MHC Protein and Its Peptide Ligands Using Quantitative Structure-Affinity Relationship Approach

It is important but challenging to determine the binding specificity of MHC-peptide interactions accurately and to predict their binding affinity quantitatively. In this paper, we discuss the application of an effective amino acid descriptor to model and predict the binding affinities between the MHC protein and its peptide ligands. This amino acid descriptor was derived from 23 electronic properties, 37 steric properties, 54 hydrophobic properties and 5 hydrogen bond properties of coded amino acids using principal component analysis (PCA), called the divided physicochemical property scores (DPPS). The DPPS descriptor was used to characterize a set of mouse MHC (H-2K(K)) binding peptides, and genetic algorithm-partial least square (GA-PLS) models were then constructed. In analyses, these models were statistically consistent with previous reports and molecular graphics exhibition. Hydrophobic interactions and hydrogen bonds were important to antigen recognition and presentation, especially exerting effects on anchor residues of peptides.

FGF19 promotes epithelial-mesenchymal transition in hepatocellular carcinoma cells by modulating the GSK3β/β- catenin signaling cascade via FGFR4 activation.

Compelling evidence suggests that the epithelial-mesenchymal transition (EMT) correlates with aggressiveness of tumors and poor survival. FGF19 has been shown to be involved in EMT in cholangiocarcinoma and colorectal cancer, however, molecular mechanisms underlying FGF19-induced EMT process in hepatocellular carcinoma (HCC) remain largely unknown. Here, we show the expression of FGF19 is significantly elevated and negatively associated with the expression of E-cadherin in HCC tissues and cell lines. Ectopic FGF19 expression promotes EMT and invasion in epithelial-like HCC cells through repression of E-cadherin expression, whereas FGF19 knockdown enhances E-cadherin expression and hence diminishes EMT traits in mesenchymal-like HCC cells, suggesting FGF19 exerts its tumor progressing functions as an EMT inducer. Interestingly, depletion of FGF19 cannot abrogate EMT traits in the presence of GSK3β inhibitors. Furthermore, FGF19-induced EMT can be markedly attenuated when FGFR4 is knocked out. These observations clearly indicate that FGFR4/GSK3β/β-catenin axis may play a pivotal role in FGF19-induced EMT in HCC cells. As FGF19 and its specific receptor FGFR4 are frequently amplified in HCC cells, selective targeting this signaling node may lend insights into a potential effective therapeutic approach for blocking metastasis of HCC.

Development of QSAR‐Improved Statistical Potential for the Structure‐Based Analysis of ProteinPeptide Binding Affinities

Proteinpeptide interactions have recently been found to play an essential role in constructing intracellular signaling networks. Understanding the molecular mechanism of such interactions and identification of the interacting partners would be of great value for developing peptide therapeutics against many severe diseases such as cancer. In this study, we describe a structure‐based, general‐purpose strategy for fast and reliably predicting proteinpeptide binding affinities. This strategy combines unsupervised knowledge‐based statistical potential derived from 505 interfacially diverse, non‐redundant proteinpeptide complex structures and supervised quantitative structure‐activity relationship (QSAR) modeling trained by 250 proteinpeptide interactions with known structure and affinity data. The built partial least squares (PLS) model is confirmed to have high stability and predictive power by using internal 5‐fold cross‐validation and rigorous Monte Carlo cross‐validation (MCCV). The model is further employed to analyze two large groups of HLA‐ and SH3‐binding peptides based upon computationally modeled structures. Satisfactorily, although the PLS model is originally trained with dissociation constants (Kd) of proteinpeptide binding, it shows a good correlation with other two affinity qualities, i.e. SPOT signal intensities (BLU) and half maximal competitive concentrations (IC50). Furthermore, we perform systematic comparisons of our method with several widely used, representative affinity predictors, including molecular mechanics‐based MM‐PB/SA, knowledge‐based DFIRE and docking score HADDOCK, on a small panel of elaborately selected proteinpeptide systems. It is demonstrated that (i) the QSAR‐improved statistical potential exhibits a comparable predictive performance with but can work faster than these traditional methods, and (ii) the crystal structure‐derived statistical potential also supports the modeled and solution structures of proteinpeptide complexes. We expect that this hybrid method can be exploited as a new scoring tool to facilitate, for example, peptide docking and virtual screening.

The Inhibitory Effects of RFamide-Related Peptide 3 on Luteinizing Hormone Release Involves an Estradiol-Dependent Manner in Prepubertal but Not in Adult Female Mice

ABSTRACT The mammalian gonadotropin-inhibitory hormone (GnIH) ortholog, RFamide-related peptide (RFRP), is considered to act on gonadotropin-releasing hormone (GnRH) neurons and the pituitary to inhibit gonadotropin synthesis and release. However, there is little evidence documenting whether RFamide-related peptide 3 (RFRP-3) plays a primary role in inhibition of the hypothalamo-pituitary-gonadal (HPG) axis prior to the onset of puberty. The present study aimed to understand the functional significance of the neuropeptide on pubertal development. The developmental changes in reproductive-related gene expression at the mRNA level were investigated in the hypothalamus of female mice. The results indicated that RFRP-3 may be an endogenous inhibitory factor for the activation of the HPG axis prior to the onset of puberty. In addition, centrally administered RFRP-3 significantly suppressed plasma luteinizing hormone (LH) levels in prepubertal female mice. Surprisingly, centrally administered RFRP-3 had no effects on plasma LH levels in ovariectomized (OVX) prepubescent female mice. In contrast, RFRP-3 also inhibited plasma LH levels in OVX prepubescent female mice that were treated with 17beta-estradiol replacement. Our study also examined the effects of RFRP-3 on plasma LH release in adult female mice that were ovariectomized at dioestrus, with or without estradiol (E2). Our results showed that the inhibitory effects of RFRP-3 were independent of E2 status. Quantitative real-time PCR and immunohistochemistry analyses showed that RFRP-3 inhibited GnRH expression at both the mRNA and protein levels in the hypothalamus. These data demonstrated that RFRP-3 could effectively suppress pituitary LH release, via the inhibition of GnRH transcription and translation in prepubescent female mice, which is associated with estrogen signaling pathway and developmental stages.