Current Molecular Medicine - Volume 20, Issue 6, 2020

Objective: To investigate the relationship between genetic polymorphisms of RXRG rs1467664, rs3753898 and the genetic susceptibility of type 2 diabetes in the Chinese Han population from South China. Methods: In our case-control study, single-nucleotide polymorphisms (SNPs) rs1467664 and rs3753898 were genotyped by SNPscanTM kit in 1092 patients with T2D as cases and 1092 normal persons as controls. The distributions of genotype and allele frequencies in two groups were analyzed by the SPSS 20.0 software. Results: The distribution of genotypes and alleles of RXRG rs3753898 was statistically significant between the two groups, but there was no significant difference in the distribution of genotypes and alleles of the rs1467664. Before and after the adjustment of age, sex and BMI, rs3753898 in the two groups had statistical significance under the additive, dominant and recessive models (P<0.05), but no statistical differences were found under the overdominance and co-dominant genetic models (P>0.05). There was no significant difference in the genetic models of rs1467664 between the two groups (P>0.05). The haplotype, which consists of rs1467664 allele T and rs3753898 allele A was a high-risk factor for T2D, OR=1.27, 95% CI (1.09-1.47), Padj=0.002. Conclusion: Our results showed that the single nucleotide polymorphism of RXRG rs3753898 may be related to genetic susceptibility of type 2 diabetes. The haplotype consisting of the allele T of rs1467664 and the allele A of rs3753898 is a risk factor for type 2 diabetes, suggesting that the genetic variation of RXRG gene may be the genetic cause of diabetes mellitus in the Chinese Han population.

Background: Colorectal cancer (CRC) is the third most common cancer worldwide. Cancer discrimination is a typical application of gene expression analysis using a microarray technique. However, microarray data suffer from the curse of dimensionality and usual imbalanced class distribution between the majority (tumor samples) and minority (normal samples) classes. Feature gene selection is necessary and important for cancer discrimination. Objectives: To select feature genes for the discrimination of CRC. Methods: We improve the feature selection algorithm based on differential evolution, DEFSw by using RUSBoost classifier and weight accuracy instead of the common classifier and evaluation measure for selecting feature genes from imbalance data. We firstly extract differently expressed genes (DEGs) from the CRC dataset of the TCGA and then select the feature genes from the DEGs using the improved DEFSw algorithm. Finally, we validate the selected feature gene sets using independent datasets and retrieve the cancer related information for these genes based on text mining through the Coremine Medical online database. Results: We select out 16 single-gene feature sets for colorectal cancer discrimination and 19 single-gene feature sets only for colon cancer discrimination. Conclusions: In summary, we find a series of high potential candidate biomarkers or signatures, which can discriminate either or both of colon cancer and rectal cancer with high sensitivity and specificity.

Background: Bipolar disorder (BD) is a type of chronic emotional disorder with a complex genetic structure. However, its genetic molecular mechanism is still unclear, which makes it insufficient to be diagnosed and treated. Methods and Results: In this paper, we proposed a model for predicting BD based on single nucleotide polymorphisms (SNPs) screening by genome-wide association study (GWAS), which was constructed by a convolutional neural network (CNN) that predicted the probability of the disease. According to the difference of GWAS threshold, two sets of data were named: group P001 and group P005. And different convolutional neural networks are set for the two sets of data. The training accuracy of the model trained with group P001 data is 96%, and the test accuracy is 91%. The training accuracy of the model trained with group P005 data is 94.5%, and the test accuracy is 92%. At the same time, we used gradient weighted class activation mapping (Grad-CAM) to interpret the prediction model, indirectly to identify high-risk SNPs of BD. In the end, we compared these high-risk SNPs with human gene annotation information. Conclusion: The model prediction results of the group P001 yielded 137 risk genes, of which 22 were reported to be associated with the occurrence of BD. The model prediction results of the group P005 yielded 407 risk genes, of which 51 were reported to be associated with the occurrence of BD.

Background: Drug repositioning refers to discovering new indications for the existing drugs, which can improve the efficiency of drug research and development. Methods: In this work, a novel drug repositioning approach based on integrative multiple similarity measure, called DR_IMSM, is proposed. The process of integrative similarity measure contains three steps. First, a heterogeneous network can be constructed based on known drug-disease association, shared entities information for drug pairwise and diseases pairwise. Second, a deep learning method, DeepWalk, is used to capture the topology similarity for drug and disease. Third, a similarity integration and adjusting process is further conducted to obtain more comprehensive drug and disease similarity measure, respectively. Results: On this basis, a Bi-random walk algorithm is implemented in the constructed heterogeneous network to rank diseases for each drug. Compared with other approaches, the proposed DR_IMSM can achieve superior performance in terms of AUC on the gold standard datasets. Case studies further confirm the practical significance of DR_IMSM.

Background: Accumulating experimental studies demonstrated that long non-coding RNAs (LncRNAs) play crucial roles in the occurrence and development progress of various complex human diseases. Nonetheless, only a small portion of LncRNA–disease associations have been experimentally verified at present. Automatically predicting LncRNA–disease associations based on computational models can save the huge cost of wet-lab experiments. Methods and Result: To develop effective computational models to integrate various heterogeneous biological data for the identification of potential disease-LncRNA, we propose a hierarchical extension based on the Boolean matrix for LncRNA-disease association prediction model (HEBLDA). HEBLDA discovers the intrinsic hierarchical correlation based on the property of the Boolean matrix from various relational sources. Then, HEBLDA integrates these hierarchical associated matrices by fusion weights. Finally, HEBLDA uses the hierarchical associated matrix to reconstruct the LncRNA– disease association matrix by hierarchical extending. HEBLDA is able to work for potential diseases or LncRNA without known association data. In 5-fold cross-validation experiments, HEBLDA obtained an area under the receiver operating characteristic curve (AUC) of 0.8913, improving previous classical methods. Besides, case studies show that HEBLDA can accurately predict candidate disease for several LncRNAs. Conclusion: Based on its ability to discover the more-richer correlated structure of various data sources, we can anticipate that HEBLDA is a potential method that can obtain more comprehensive association prediction in a broad field.

Background: Pain is one of the most common clinical symptoms . This review aims to describe research on herbs and their active ingredients in treating pain and provide a valuable reference for the development and utilization of analgesic traditional Chinese medicine (TCM). Material and Methods: The literature search was performed from 1995 to October 2016, covering the relevant studies that concern the treatment of pain with TCM. Active ingredients extracted from TCM with analgesic activity are summarized and classified into six categories, including polysaccharides, saponins, alkaloids, flavonoids, terpenoids, and other constituents. Results: There are two pathways constituting the analgesic mechanisms of TCM: through the central nervous system and the peripheral nervous system. The former pathway includes increasing the content of endogenous analgesic substances like opiate peptide, cutting down the second messenger of neurotransmitter like nitric oxide (NO), reducing the content of prostaglandin E2 (PGE2) in brain tissues, blocking the central calcium channel, reducing excitatory amino acids in brain tissues, inhibiting their receptors and raising the content of the central 5-hydroxytryptamine (5-HT). The latter one usually involves the decrease in the secretion of peripheral algogenic substances, the induction of pain-sensitive substances, the accumulation of a local algogenic substance, the increase in the release of peripheral endogenous analgesia materials and the regulation of c-Fos gene (immediate early gene).

There is an expanding field of research investigating the instrumental methods to measure the development of affective disorders. The goal of the commentary is to turn the attention of medical practitioners at the molecular spectroscopy techniques (FTIR, Raman and UV-Vis) that can be applied for monitoring and quantification of the phospholipid-protein balance in human blood serum of depressed patients. Even facial overview of cited original research strongly suggests that disturbed phospholipid-protein balance could be one of the biomarkers of affective disorders. The blood serum monitoring of depressed patients would serve as a tool for more effective holistic therapy.

Background: G protein-coupled receptors (GPCRs) represent the largest family of surface proteins and are involved in the regulation of key physiological processes. GPCRs are characterized by seven transmembrane domains, an extracellular N-terminus and an intracellular C-terminus. Cellular response of these receptors to their ligands is largely determined by their surface expression and postactivation behavior including expression, desensitization and resensitization. Objective: To develop a quantitative fluorescence Microscopy assay to study β2- Adrenergic receptor expression and desensitization. Method: β2-Adrenergic receptor cDNA was engineered to put an HA tag at the extracellular N-terminus and GFP Tag at the intracellular C-terminus. GFP fluorescence serves as a measure of total cellular expression; whereas staining with CY3 conjugated anti-HA antibodies without permeabilizing the cells represents the surface expression of β2-AR. The images are quantified and amount of CY3 (surface) and GFP (total) fluorescence for each cell determined using image processing software. Results: The method is sensitive and allows for the simultaneous measurement of surface and total expression of β2-AR. Conclusion: A highly accurate method is described for measuring β2-AR surface and total expression based on single-cell quantitative immunofluorescence. The method can be used to determine agonist-induced desensitization and resensitization process as well as receptor kinetics like endocytosis and exocytosis of β2-Adrenergic receptor and can be applied to essentially any other GPCR.

Background: White matter damage and neuronal cell death are incurred by spinal cord injury (SCI). FBXW7α, an important mediator of cell division and growth was investigated to explore its role in repairing the traumatic spinal cord in rats. Underlying mechanisms such as oxidative stress and inflammasomes signaling were also studied. Methods: Spinal cord injury in rats was established by longitudinal surgical incision from the lower to mid-thoracic vertebrae on the backside, followed by 20-g weight placed on the exposed Th12 surface for 30 min. AAV-delivered FBXW7α and -sh-FBXW7α were intrathecally injected into the rat spinal cord. Indices of oxidation, neurotrophic factors, and pyroptosis were measured by Western blot, Elisa, and RT-PCR. Results: We found the overexpression of FBXW7α in spinal cord rescue neuronal death triggered by the injury. Specifically, the nutritional condition, oxidative stress, and pyroptosis were improved. A synchronization of BNDF and GDNF expression patterns in various groups indicated the secretion of neurotrophic factors affect the outcome of SCI. The SOD1, CAT, and GSH-px were suppressed after trauma but all restored in response to FBXW7α overexpression. Inflammasomes-activated pyroptosis was incurred after the injury, and relevant biomarkers such as GSDMD, caspase-1, caspase- 11, IL-1β, and IL-18 were down-regulated after the introduction of FBXW7α into the injured cord. Additionally, up-regulating FBXW7α also repaired the mitochondria dysfunction. Conclusion: Our data indicate FBXW7α probably serves as an important molecular target for the therapy of spinal cord injury.