[Genotype-environment interaction on arterial stiffness: A pedigree-based study].

OBJECTIVE: To utilized the baseline data of the Beijing Fangshan Family Cohort Study, and to estimate whether the association between a healthy lifestyle and arterial stiffness might be modified by genetic effects. METHODS: Probands and their relatives from 9 rural areas in Fangshan district, Beijing were included in this study. We developed a healthy lifestyle score based on five lifestyle behaviors: smoking, alcohol consumption, body mass index (BMI), dietary pattern, and physical activity. The measurements of arterial stiffness were brachial-ankle pulse wave velocity (baPWV) and ankle-brachial index (ABI). A variance component model was used to determine the heritability of arterial stiffness. Genotype-environment interaction effects were performed by the maximum likelihood methods. Subsequently, 45 candidate single nucleotide polymorphisms (SNPs) located in the glycolipid metabolism pathway were selected, and generalized estimated equations were used to assess the gene-environment interaction effects between particular genetic loci and healthy lifestyles. RESULTS: A total of 6 302 study subjects across 3 225 pedigrees were enrolled in this study, with a mean age of 56.9 years and 45.1% male. Heritability of baPWV and ABI was 0.360 (95%CI: 0.302-0.418) and 0.243 (95%CI: 0.175-0.311), respectively. Significant genotype-healthy diet interaction on baPWV and genotype-BMI interaction on ABI were observed. Following the findings of genotype-environment interaction analysis, we further identified two SNPs located in ADAMTS9-AS2 and CDH13 might modify the association between healthy dietary pattern and arterial stiffness, indicating that adherence to a healthy dietary pattern might attenuate the genetic risk on arterial stiffness. Three SNPs in CDKAL1, ATP8B2 and SLC30A8 were shown to interact with BMI, implying that maintaining BMI within a healthy range might decrease the genetic risk of arterial stiffness. CONCLUSION: The current study discovered that genotype-healthy dietary pattern and genotype-BMI interactions might affect the risk of arterial stiffness. Furthermore, we identified five genetic loci that might modify the relationship between healthy dietary pattern and BMI with arterial stiffness. Our findings suggested that a healthy lifestyle may reduce the genetic risk of arterial stiffness. This study has laid the groundwork for future research exploring mechanisms of arterial stiffness.

[The role of the high-level public health school in the development of the Center for Disease Control and Prevention].

The Ministry of Education and other four departments jointly issued the Notice on the Construction of high-level schools of public Health, proposing that "it will take ten years to build a number of high-level schools of public health, and form a high-quality education development system to adapt to the construction of modern public health system". At present, the construction of high-level public health schools in various universities in China is in full swing. The high-level School of Public Health and the CDC have played an important role in constructing the national public health system and the human health community. The high-level public health schools are of strategic significance and important value to the development of the CDC. The review presents reflections and insights on the role of high-level public health schools in the development of the CDC and the challenges they might face.

Mechanism of lncRNA FEZF1-AS1 in promoting the occurrence and development of oral squamous cell carcinoma through targeting miR-196a.

OBJECTIVE: Previous studies have demonstrated that long non-coding ribonucleic acid (lncRNA) FEZF1-AS1 acts as a cancer-promoting gene. However, no reports have investigated the role of FEZF1-AS1 in oral squamous cell carcinoma (OSCC) yet. Therefore, the aim of this study was to explore whether FEZF1-AS1 promoted the expression characteristics of OSCC by targeting miR-196a and to further elucidate the underlying mechanism of FEZF1-AS1 in promoting the metastasis of OSCC. PATIENTS AND METHODS: The expression levels of FEZF1-AS1 and miR-196a in 42 pairs of OSCC tissues and para-carcinoma tissues were detected via quantitative Real Time-Polymerase Chain Reaction (qRT-PCR). The correlation of FEZF1-AS1 expression with clinical indexes and prognosis of OSCC patients was analyzed. Moreover, the expression levels of FEZF1-AS1 and miR-196a in OSCC cells were detected via qRT-PCR. FEZF1-AS1 knockdown and miR-196a over-expression models were established using lentivirus transfection in OSCC cell lines (CAL-27 and Tca8113). Subsequently, the influences of FEZF1-AS1 and miR-196a on the biological functions of OSCC cells were analyzed via Cell Counting Kit-8 (CCK-8) assay, colony formation assay and 5-Ethynyl-2'-deoxyuridine (EdU) assay, respectively. Furthermore, the potential mechanism was explored using the Luciferase reporter gene and recovery assays. RESULTS: The results of qRT-PCR proved that the expression level of FEZF1-AS1 in OSCC tissues was significantly higher than that of para-carcinoma tissues, and the difference was statistically significant. The pathological stage was significantly higher in patients with high-expression FEZF1-AS1 than those with low-expression FEZF1-AS1, while the overall survival rate was remarkably lower. The proliferation ability of cells in FEZF1-AS1 silencing group declined significantly when compared with the NC group. Similarly, qRT-PCR results verified that the expression of miR-196a in OSCC cell lines and tissues was significantly reduced as well. Meanwhile, the miR-196a expression was negatively correlated with FEZF1-AS1. Subsequent Luciferase reporter gene assay confirmed that overexpression of miR-196a could markedly reduce the activity of Luciferase containing wild-type FEZF1-AS1 vector rather than decrease the activity of Luciferase containing mutant-type vector or empty vector. These findings further indicated that FEZF1-AS1 could be targeted by miR-196a through this binding site. In addition, recovery assay demonstrates that there was a mutual regulatory effect between FEZF1-AS1 and miR-196a, jointly affecting the malignant progression of OSCC. CONCLUSIONS: The expression of lncRNA FEZF1-AS1 was markedly up-regulated in OSCC, which was significantly correlated with pathological stage and poor prognosis of OSCC patients. Therefore, it was believed that FEZF1-AS1 might promote the malignant progression of OSCC by regulating miR-196a.

Computational modeling of structure-function of g protein-coupled receptors with applications for drug design.

G protein-coupled receptors (GPCRs) mediate senses such as odor, taste, vision, and pain in mammals. In addition, important cell recognition and communication processes often involve GPCRs. Many diseases involve malfunction of GPCRs, making them important targets for drug development. Indeed, greater than 50 % of all marketed therapeutics act on those receptors. Unfortunately, the atomic-level structures are only available for rhodopsin, beta2AR, beta1AR, A2A adenosin and opsin. In silico computational methods, employing receptor-based modeling, offer a rational approach in the design of drugs targeting GPCRs. These approaches can be used to understand receptor selectivity and species specificity of drugs that interact with GPCRs. This review gives an overview of current computational approaches to GPCR model building; ligand-receptor interaction for drug design; and molecular mechanism of GPCR activation from simulation.

ADME evaluation in drug discovery. 2. Prediction of partition coefficient by atom-additive approach based on atom-weighted solvent accessible surface areas.

A novel method for the calculations of 1-octanol/water partition coefficient (log P) of organic molecules has been presented here. The method, SLOGP v1.0, estimates the log P values by summing the contribution of atom-weighted solvent accessible surface areas (SASA) and correction factors. Altogether 100 atom/group types were used to classify atoms with different chemical environments, and two correlation factors were used to consider the intermolecular hydrophobic interactions and intramolecular hydrogen bonds. Coefficient values for 100 atom/group and two correction factors have been derived from a training set of 1850 compounds. The parametrization procedure for different kinds of atoms was performed as follows: first, the atoms in a molecule were defined to different atom/group types based on SMARTS language, and the correction factors were determined by substructure searching; then, SASA for each atom/group type was calculated and added; finally, multivariate linear regression analysis was applied to optimize the hydrophobic parameters for different atom/group types and correction factors in order to reproduce the experimental log P. The correlation based on the training set gives a model with the correlation coefficient (r) of 0.988, the standard deviation (SD) of 0.368 log units, and the absolute unsigned mean error of 0.261. Comparison of various procedures of log P calculations for the external test set of 138 organic compounds demonstrates that our method bears very good accuracy and is comparable or even better than the fragment-based approaches. Moreover, the atom-additive approach based on SASA was compared with the simple atom-additive approach based on the number of atoms. The calculated results show that the atom-additive approach based on SASA gives better predictions than the simple atom-additive one. Due to the connection between the molecular conformation and the molecular surface areas, the atom-additive model based on SASA may be a more universal model for log P estimation especially for large molecules.

ADME evaluation in drug discovery. 3. Modeling blood-brain barrier partitioning using simple molecular descriptors.

In this paper, QSPR models were developed for in vivo blood-brain partitioning data (logBB) of a large data set consisting of 115 diverse organic compounds. The best model is based on three descriptors: n-octanol/water partition coefficient calculated using the SLOGP approach, logP; high-charged polar surface areas based on the Gasteiger partial charges, HCPSA, and the excessive molecular weight larger than 360, MW(360). The model bears good statistical significance, n = 78, r = 0.88, q = 0.86, s = 0.36, F = 81.5. The actual prediction potential of the model was validated through two external validation sets of 37 diverse compounds. The predicted results demonstrate that the model bears better prediction potential than many other models and can be used for logBB estimations for drug and drug-like molecules. Comparison of several logP calculation approaches suggests that logP calculated by SLOGP can be used as a significant descriptor for the prediction of molecular transport properties because SLOGP gives the most similar results with CLOGP. The QSPR model indicates that larger polar surface areas have a more negative contribution to logBB, but the absolute partial charges on the atoms surrounded by the polar surfaces should be larger than 0.10|e|. Meanwhile, tight junction membranes limit the size of hydrophilic molecules that can cross the membrane with a molecular weight of approximately 360, because when a molecule's weight is larger than 360 it shows a negative contribution to logBB. The computations of molecular surface, partial charges, logP, and logBB have been accomplished using a program called Drug-BB. Moreover, to improve the efficiency of the computations of logP, we made an extensive reparametrization of SLOGP, and the newly developed SLOGP model is only based on simple atomic addition. Further, we developed a set of parameters to calculate the topological polar surface area (TPSA), thus the high-charged topological polar surface area (HCTPSA) could be estimated from the 2D connection information of a molecule. Adopting the new strategies, the estimations of logP, HCTPSA, and logBB are only based on the topological structure of a molecule and therefore, can be used for fast screening of virtual libraries having millions of molecules.

Three-dimensional quantitative structure-activity relationship analysis of the new potent sulfonylureas using comparative molecular similarity indices analysis.

The present study describes the implementation of a new three-dimensional quantitative structure-activity relationship (3D-QSAR) technique: comparative molecular similarity indices analysis (CoMSIA) to a set of novel herbicidal sulfonylureas targeted acetolactate synthase. Field expressions in terms of similarity indices in CoMSIA were applied instead of the usually used Lennard-Jones and Coulomb-type potentials in CoMFA. Two different kinds of alignment techniques including field-fit alignment and atom-by-atom fits were used to produce the molecular aggregate. The results indicated that those two alignment rules generated comparative 3D-QSAR models with similar statistical significance. However, from the predictive ability of the test set, the models from the alignment after maximal steric and electrostatic optimization were slightly better than those from the simple atom-by-atom fits. Moreover, systematic variations of some parameters in CoMSIA were performed to search the best 3D-QSAR model. A significant cross-validated q2 was obtained, indicating the predictive potential of the model for the untested compounds; meanwhile the predicted biological activities of the five compounds in the test set were in good agreement with the experimental values. The CoMSIA coefficient contour plots identified several key features explaining the wide range of activities, which were very valuable for us in tracing the properties that really matter and getting insight into the potential mechanisms of the intermolecular interactions between inhibitor and receptor, especially with respect to the design of new compounds.

ADME evaluation in drug discovery. 4. Prediction of aqueous solubility based on atom contribution approach.

A novel method for the estimation of aqueous solubility was solely based on simple atom contribution. Each atom in a molecule has its own contribution to aqueous solubility and was developed. Altogether 76 atom types were used to classify atoms with different chemical environments. Moreover, two correction factors, including hydrophobic carbon and square of molecular weight, were used to account for the inter-/intramolecular hydrophobic interactions and bulkiness effect. The contribution coefficients of different atom types and correction factors were generated based on a multiple linear regression using a learning set consisting of 1290 organic compounds. The obtained linear regression model possesses good statistical significance with an overall correlation coefficient (r) of 0.96, a standard deviation (s) of 0.61, and an unsigned mean error (UME) of 0.48. The actual prediction potential of the model was validated through an external test set with 21 pharmaceutically and environmentally interesting compounds. For the test set, a predictive r=0.94, s=0.84, and UME=0.52 were achieved. Comparisons among eight procedures of solubility calculation for those 21 molecules demonstrate that our model bears very good accuracy and is comparable to or even better than most reported techniques based on molecular descriptors. Moreover, we compared the performance of our model to a test set of 120 molecules with a popular group contribution method developed by Klopman et al. For this test set, our model gives a very effective prediction (r=0.96, s=0.79, UME=0.57), which is obviously superior to the predicted results (r=0.96, s=0.84, UME=0.70) given by the Klopman's group contribution approach. Because of the adoption of atoms as the basic units, our addition model does not contain a "missing fragment" problem and thus may be more simple and universal than the group contribution models and can give predictions for any organic molecules. A program, drug-LOGS, had been developed to identify the occurrence of atom types and estimate the aqueous solubility of a molecule.

ADME evaluation in drug discovery. 5. Correlation of Caco-2 permeation with simple molecular properties.

The correlations between Caco-2 permeability (logPapp) and molecular properties have been investigated. A training set of 77 structurally diverse organic molecules was used to construct significant QSAR models for Caco-2 cell permeation. Cellular permeation was found to depend primarily upon experimental distribution coefficient (logD) at pH = 7.4, high charged polar surface area (HCPSA), and radius of gyration (rgyr). Among these three descriptors, logD may have the largest impact on diffusion through Caco-2 cell because logD shows obvious linear correlation with logPapp (r=0.703) when logD is smaller than 2.0. High polar surface area will be unfavorable to achieve good Caco-2 permeability because higher polar surface area will introduce stronger H-bonding interactions between Caco-2 cells and drugs. The comparison among HCPSA, PSA (polar surface area), and TPSA (topological polar surface area) implies that high-charged atoms may be more important to the interactions between Caco-2 cell and drugs. Besides logD and HCPSA, rgyr is also closely connected with Caco-2 permeabilities. The molecules with larger rgyr are more difficult to cross Caco-2 monolayers than those with smaller rgyr. The descriptors included in the prediction models permit the interpretation in structural terms of the passive permeability process, evidencing the main role of lipholiphicity, H-bonding, and bulk properties. Besides these three molecular descriptors, the influence of other molecular descriptors was also investigated. From the calculated results, it can be found that introducing descriptors concerned with molecular flexibility can improve the linear correlation. The resulting model with four descriptors bears good statistical significance, n = 77, r = 0.82, q = 0.79, s = 0.45, F = 35.7. The actual predictive abilities of the QSAR model were validated through an external validation test set of 23 diverse compounds. The predictions for the tested compounds are as the same accuracy as the compounds of the training set and significantly better than those predicted by using the model reported. The good predictive ability suggests that the proposed model may be a good tool for fast screening of logPapp for compound libraries or large sets of new chemical entities via combinatorial chemistry synthesis.

Applications of genetic algorithms on the structure-activity relationship analysis of some cinnamamides.

Quantitative structure-activity relationships (QSARs) for 35 cinnamamides were studied. By using a genetic algorithm (GA), a group of multiple regression models with high fitness scores was generated. From the statistical analyses of the descriptors used in the evolution procedure, the principal features affecting the anticonvulsant activity were found. The significant descriptors include the partition coefficient, the molar refraction, the Hammet sigma constant of the substituents on the benzene ring, and the formation energy of the molecules. It could be found that the steric complementarity and the hydrophobic interaction between the inhibitors and the receptor were very important to the biological activity, while the contribution of the electronic effect was not so obvious. Moreover, by construction of the spline models for these four principal descriptors, the effective range for each descriptor was identified.

3D QSAR analyses of novel tyrosine kinase inhibitors based on pharmacophore alignment.

In an effort to develop a quantitative ligand-binding model for the receptor tyrosine kinases, a pharmacophore search was first used to identify structural features that are common in two novel sets of 12 molecules of the 3-substituted indolin-2-ones and 19 compounds of the benzylidene malononitriles with low-to-high affinity for HER2, a kind of receptor tyrosine kinase. The common pharmacophore model based on these 31 compounds was used as a template to obtain the aligned molecular aggregate, which provided a good starting point for 3D-QSAR analysis of only the 19 benzylidene malononitriles. Two molecular field analysis (MFA) techniques, including CoMFA and CoMSIA, were used to derive the quantitative structure-activity relationships of the studied molecules. From the studied results, it was obvious that the 3D-QSAR models based on the pharmacophore alignment were superior to those based on the simple atom-by-atom fits. Considering the flexibility of the studied molecules and the difference between the active conformers and the energy-lowest conformers, the pharmacophore model can usually provide the common features for the flexible regions. Moreover, the best CoMSIA model based on the pharmacophore hypothesis gave good statistical measure from partial least-squares analysis (PLS) (q(2) = 0.71), which was slightly better than the CoMFA one. Our study demonstrated that pharmacophore modeling and CoMSIA research could be effectively combined. Results obtained from both methods helped with understanding the specific activity of some compounds and designing new specific HER2 inhibitors.