Evaluation of the combination mode and features of p38 MAPK inhibitors: construction of different pharmacophore models and molecular docking

Abnormal expression of tumour necrosis factor-α (TNF-α) can lead to various pathological reactions, such as arthritis, psoriasis, krone disease, etc. p38 mitogen-activated protein kinase (p38 MAPK) is an important signal transduction enzyme that plays important roles in influencing the release of intracellular TNF-α factor. It is very meaningful to study the targeting kinase with specific inhibitors in the treatment of related diseases. In order to achieve a deeper insight, it is necessary to analyse the structural characteristics and the action mode of the p38 MAPK inhibitors in the active site. In the study, a ligand-based common feature pharmacophore model and the receptor structure-based pharmacophore model were constructed, respectively. Their common chemical features consisted of the hydrophobic groups (H) and the hydrogen bond acceptors (A), and kept the consistency of spatial structure distribution. Then, the molecular docking and molecular dynamics simulation were performed with the eight training set compounds. The binding characteristics of molecules binding were described in the topological region of the active site. Finally, the structure–activity relationship (SAR) was obtained by analysing docking results with the different pharmacophore models. This research leads to the proposal of an interaction model in the p38 MAPK active site and provides guidance for the screening and design of more potent and selective p38 MAPK inhibitors.     

Synthetic biology and metabolic engineering of actinomycetes for natural product discovery

Actinomycetes are one of the most valuable sources of natural products with industrial and medicinal importance. After more than half a century of exploitation, it has become increasingly challenging to find novel natural products with useful properties as the same known compounds are often repeatedly re-discovered when using traditional approaches. Modern genome mining approaches have led to the discovery of new biosynthetic gene clusters, thus indicating that actinomycetes still harbor a huge unexploited potential to produce novel natural products. In recent years, innovative synthetic biology and metabolic engineering tools have greatly accelerated the discovery of new natural products and the engineering of actinomycetes. In the first part of this review, we outline the successful application of metabolic engineering to optimize natural product production, focusing on the use of multi-omics data, genome-scale metabolic models, rational approaches to balance precursor pools, and the engineering of regulatory genes and regulatory elements. In the second part, we summarize the recent advances of synthetic biology for actinomycetal metabolic engineering including cluster assembly, cloning and expression, CRISPR/Cas9 technologies, and chassis strain development for natural product overproduction and discovery. Finally, we describe new advances in reprogramming biosynthetic pathways through polyketide synthase and non-ribosomal peptide synthetase engineering. These new developments are expected to revitalize discovery and development of new natural products with medicinal and other industrial applications.     

Tribolium castaneum as a whole‐animal screening system for the detection and characterization of neuroprotective substances

Parkinson's disease (PD) is a movement disorder caused by the progressive loss of dopaminergic neurons. Natural antioxidants and plant extracts with neuroprotective properties offer a promising new therapeutic approach for PD patients, but a suitable large‐scale screening system is required for their discovery and preclinical analysis. Here we used the red flour beetle (Tribolium castaneum ) as a whole‐animal screening system for the detection and characterization of neuroprotective substances. Paraquat was added to the diet of adult beetles to induce PD‐like symptoms, which were quantified using a novel positive geotaxis behavioral assay. These paraquat‐induced behavioral changes were reduced in beetles fed on diets supplemented with l‐ dihydroxyphenylalanine, ascorbic acid, curcumin, hempseed flour, or the Chinese herb gou‐teng. T. castaneum is, therefore, a valuable model for the screening of neuroprotective substances in chemical libraries and plant extracts and could be developed as a model for the preclinical testing of therapeutic candidates for the treatment of neurodegenerative diseases, such as PD.     

Context‐dependent biotic interactions control plant abundance across altitudinal environmental gradients

Many biotic interactions influence community structure, yet most distribution models for plants have focused on plant competition or used only abiotic variables to predict plant abundance. Furthermore, biotic interactions are commonly context‐dependent across abiotic gradients. For example, plant–plant interactions can grade from competition to facilitation over temperature gradients. We used a hierarchical Bayesian framework to predict the abundances of 12 plant species across a mountain landscape and test hypotheses on the context‐dependency of biotic interactions over abiotic gradients. We combined field‐based estimates of six biotic interactions (foliar herbivory and pathogen damage, fungal root colonization, fossorial mammal disturbance, plant cover and plant diversity) with abiotic data on climate and soil depth, nutrients and moisture. All biotic interactions were significantly context‐dependent along temperature gradients. Results supported the stress gradient hypothesis: as abiotic stress increased, the strength or direction of the relationship between biotic variables and plant abundance generally switched from negative (suggesting suppressed plant abundance) to positive (suggesting facilitation/mutualism). For half of the species, plant cover was the best predictor of abundance, suggesting that the prior focus on plant–plant interactions is well‐justified. Explicitly incorporating the context‐dependency of biotic interactions generated novel hypotheses about drivers of plant abundance across abiotic gradients and may improve the accuracy of niche models.     

The retinol-retinoic acid metabolic pathway is impaired in the lumbar spine of a rat model of congenital kyphoscoliosis

Although congenital scoliosis is defined as a genetic disease characterized by a congenital and abnormal curvature of the spinal vertebrae, our knowledge of the genetic underpinnings of the disease is insufficient. We herein show that the downregulation of the retinol-retinoic acid metabolism pathway is involved in the pathogenesis of congenital scoliosis. By analyzing DNA microarray data, we found that the expression levels of genes associated with the retinol metabolism pathway were decreased in the lumbar spine of Ishibashi rats (IS), a rat model of congenital kyphoscoliosis. The expression of Adh1 and Aldh1a2 (alcohol dehydrogenase), two enzymes that convert retinol to retinoic acid in this pathway, were decreased at both the gene and protein levels. Rarα, a receptor of retinoic acid and bone morphogenetic protein 2, which play a central role in bone formation and are located downstream of this pathway, were also downregulated. Interestingly, the serum retinol levels of IS rats were higher than those of wild-type control rats. These results indicate that the adequate conversion from retinol to retinoic acid is extremely important in the regulation of normal bone formation and it may also be a key factor for understanding the pathogenesis of congenital scoliosis.     

The discovery of a novel eight-mRNA-lncRNA signature predicting survival of hepatocellular carcinoma patients

Increasing evidence indicates that the expressions of messenger RNAs (mRNAs) and long non-coding RNAs (lncRNAs) undergo a frequent and aberrant change in carcinogenesis and cancer development. But some research was carried out on mRNA-lncRNA signatures for prediction of hepatocellular carcinoma (HCC) prognosis. We aimed to establish an mRNA-lncRNA signature to improve the ability to predict HCC patients’ survival. The subjects from the cancer genome atlas (TCGA) data set were randomly divided into two parts: training data set (n = 246) and testing data set (n = 124). Using computational methods, we selected eight gene signatures (five mRNAs and three lncRNAs) to generate the risk score model, which were significantly correlated with overall survival of patients with HCC in both training and testing data set. The signature had the ability to classify the patients in training data set into a high-risk group and low-risk group with significantly different overall survival (hazard ratio = 4.157, 95% confidence interval = 2.648-6.526, P < 0.001). The prognostic value was further validated in testing data set and the entire data set. Further analysis revealed that this signature was independent of tumor stage. In addition, Gene Set Enrichment Analysis suggested that high risk score group was associated with cell proliferation and division related pathways. Finally, we developed a well-performed nomogram integrating the prognostic signature and other clinical information to predict 3- and 5-year overall survival. In conclusion, the prognostic mRNAs and lncRNAs identified in our study indicate their potential role in HCC biogenesis. The risk score model based on the mRNA-lncRNA may be an efficient classification tool to evaluate the prognosis of patients’ with HCC.     

NetLogoR: a package to build and run spatially explicit agent‐based models in R

NetLogoR is an R package to build and run spatially explicit agent‐based models (SE‐ABMs) using the R language. SE‐ABMs are models that simulate the fate of entities at the individual level within a spatial context and where patterns emerge at the population level. NetLogoR follows the same framework as the NetLogo software (Wilensky 1999). Rather than a call function to use the NetLogo software, NetLogoR is a translation into the R language of the structure and functions of NetLogo. Models built with NetLogoR are written in R language and are run on the R platform; no other software or language has to be involved. NetLogoR provides new R classes to define model agent objects and functions to implement spatially explicit agent‐based models in the R environment. Users of this package benefit from the fast and easy coding provided by the highly developed NetLogo framework, coupled with the versatility, power and massive resources of the R language.