It is generally considered that various regulatory activities between genes are contained in the gene expression datasets. Therefore, the underlying gene regulatory relationship and the biologically useful information can be found by modeling the gene regulatory network from the gene expression data. In our study, two unsupervised matrix factorization methods, independent component analysis (ICA) and nonnegative matrix factorization (NMF), were proposed to identify significant genes and model the regulatory network using the microarray gene expression data of Alzheimer's disease (AD). By bio-molecular analyzing of the pathways, the differences between ICA and NMF have been explored and the fact, which the inflammatory reaction is one of the main pathological mechanisms of AD, is also emphasized. It was demonstrated that our study gave a novel and valuable method for the research of early detection and pathological mechanism, biomarkers' findings of AD.
Ion channels are involved in the mechanism of anesthetic action and side effect. The transcription and expression of ion channel genes can be modulated by general anesthetics. The adverse effect of continuous infusion of etomidate has been concerned. However, the effects of etomidate on mRNA expressions of ion channel genes remain unclear. In this study, we exposed Daphnia pulex in 250 μmol/L of etomidate for 240 min and observed the change of heart rate, phototactic behavior and blood glucose during the period of exposure, as well as the mRNA expressions of 120 ion channel genes at the end of the experiment. Compared to the controls, heart rate, phototactic behavior and blood glucose were not influenced by 250 μmol/L of etomidate. According to the quantitative PCR results, 18 of 120 Daphnia pulex ion channel genes transcripts were affected by persistent 240 min exposure to 250 μmol/L of etomidate: 2 genes were upregulated and 16 genes were down-regulated, suggesting that etomidate showed effects on many different ion channels in transcription level. Systematical exploration of transcriptional changes of ion channels could contribute to understanding of the pharmacological mechanism of etomidate.
Objective The aim of this study is to review the association between long non-coding RNA (lncRNA) and papillary thyroid carcinoma (PTC). Method The relevant literatures about lncRNA associated with PTC were retrospectively analyzed and summarized. Results The expression levels of noncoding RNA associated with MAP kinase pathway and growth arrest (NAMA), PTC susceptibility candidate 3 (PTCSC3), BRAF activated non-coding RNA (BANCR), maternally expressed gene 3 (MEG3), NONHSAT037832, and GAS8-AS1 in PTC tissues were significantly lower than those in non-thyroid carcinoma tissues. The expression levels of ENST00000537266, ENST00000426615, XLOC051122, XLOC006074, HOX transcript antisense RNA (HOTAIR), antisense noncoding RNA in the INK4 locus (ANRIL), and metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) in PTC tissues were upregulated in PTC tissues, comparing with the non-thyroid carcinoma tissues. These lncRNAs were possibly involved in cell proliferation, migration, and apoptosis of PTC. Conclusion LncRNAs may provide new insights into the molecular mechanism and gene-targeted therapy of PTC and become new molecular marker for the diagnosis of PTC.
Objective To study the effect of silencing the NOD-like receptor family, pyrin domain containing protein 3 (NLRP3) gene on the production of inflammatory factors induced by lipopolysaccharide (LPS) and adenosine triphosphate (ATP) in rat brain microvascular endothelial cells (BMECs), and whether NLRP3 inflammasome signaling pathway plays a role in the BMEC model of cerebral small vessel disease induced by proinflammatory agents. Methods BMECs from male Wistar rats were extracted in vitro and the morphology and purity of endothelial cells were identified. BMECs in normal culture were divided into blank control group and LPS+ATP group. The expression levels of NLRP3 inflammasome and downstream inflammatory factor Caspase-1 were detected by Western blot and real-time polymerase chain reaction, and compared by student’s t test between the two groups. Small interfering RNA (siRNA) was used to silence the specific gene NLRP3 in BMECs. After transfection of siRNA NLRP3 and siRNA plasmid negative control into BMECs, the transfected cells were divided into four groups, namely, siNC group (non silenced target gene), siNLRP3 group (silenced target gene), siNC+LPS+ATP group (non silenced target gene and added proinflammatory agents) and siNLRP3+LPS+ATP group (silenced target gene and added proinflammatory agents). The expression levels of NLRP3 and Caspase-1 were detected by Western blot and real-time polymerase chain reaction, and analyzed by analysis of variance for 2-factor factorial design. Results The microvascular segments of rat BMECs were “beaded” after 24 h of isolation and culture; after 48 h, “island” cell clusters were formed; after 72 h, “paving stone” like monolayer cells adhered to the wall and grew. After that, the cells gradually became dense and reached the convergence degree of 80%. The positive rate of BMECs detected by immunofluorescence staining was 96%. In the normally cultured cells, the protein and mRNA expression levels of NLRP3 and Caspase-1 in the LPS+ATP group were higher than those in the blank control group (P<0.05). In the RNA interference cultured cells, the protein and mRNA expression levels of NLRP3 and Caspase-1 in the siNLRP3 group were lower than those in the siNC group, and those expression levels in the siNLRP3+LPS+ATP group were lower than those in the siNC+LPS+ATP group (P<0.05); the protein and mRNA expression levels of NLRP3 and Caspase-1 in the siNC+LPS+ATP group were higher than those in the siNC group, and those expression levels in the siNLRP3+LPS+ATP group were higher than those in the siNLRP3 group (P<0.05). Plasmid transfection and proinflammatory agents intervention had statistically significant interaction effect on the mRNA expression of NLRP3 and Caspase-1 (P<0.05). Conclusions LPS and ATP can promote the release of NLRP3 and Caspase-1 in BMECs. Silencing NLRP3 gene expression can reduce the induction of proinflammatory agents. NLRP3 inflammasome signaling pathway may play a role in the cerebral small vessel disease cell model of rat BMECs induced by proinflammatory agents.
Synergistic effects of drug combinations are very important in improving drug efficacy or reducing drug toxicity. However, due to the complex mechanism of action between drugs, it is expensive to screen new drug combinations through trials. It is well known that virtual screening of computational models can effectively reduce the test cost. Recently, foreign scholars successfully predicted the synergistic value of new drug combinations on cancer cell lines by using deep learning model DeepSynergy. However, DeepSynergy is a two-stage method and uses only one kind of feature as input. In this study, we proposed a new end-to-end deep learning model, MulinputSynergy which predicted the synergistic value of drug combinations by integrating gene expression, gene mutation, gene copy number characteristics of cancer cells and anticancer drug chemistry characteristics. In order to solve the problem of high dimension of features, we used convolutional neural network to reduce the dimension of gene features. Experimental results showed that the proposed model was superior to DeepSynergy deep learning model, with the mean square error decreasing from 197 to 176, the mean absolute error decreasing from 9.48 to 8.77, and the decision coefficient increasing from 0.53 to 0.58. This model could learn the potential relationship between anticancer drugs and cell lines from a variety of characteristics and locate the effective drug combinations quickly and accurately.
Recombinant protein SMBPRG4 containing two Somatomedin B domains and a small amount of glycosylation of repetitive sequences of proteoglycan 4 was cloned according to PGR4 gene polymorphism. Mature purification process was established and recombinant protein SMBPRG4, with high-level expression was purified. By using size-exclusion chromatogaraphy and dynamic light scattering, we found that the recombinant protein self-aggregate to dimeric form. Structure prediction and non-reducing electrophoresis revealed that SMBPRG4 was a non-covalently bonded dimer.
The microarray technology used in biological and medical research provides a new idea for the diagnosis and treatment of cancer. To find different types of cancer and to classify the cancer samples accurately, we propose a new cluster ensemble framework Dual Neural Gas Cluster Ensemble (DNGCE), which is based on neural gas algorithm, to discover the underlying structure of noisy cancer gene expression profiles. This framework DNGCE applies the neural gas algorithm to perform clustering not only on the sample dimension, but also on the attribute dimension. It also adopts the normalized cut algorithm to partition off the consensus matrix constructed from multiple clustering solutions. We obtained the final accurate results. Experiments on cancer gene expression profiles illustrated that the proposed approach could achieve good performance, as it outperforms the single clustering algorithms and most of the existing approaches in the process of clustering gene expression profiles.
RNA can be labeled by more than 170 chemical modifications after transcription, and these chemical modifications are collectively referred to as RNA modifications. It opened a new chapter of epigenetic research and became a major research hotspot in recent years. RNA modification regulates the expression of genes from the transcriptome level by regulating the fate of RNA, thus participating in many biological processes and disease occurrence and development. With the deepening of research, the diversity and complexity of RNA modification, as well as its physiological significance and potential as a therapeutic target, can not be ignored.
Objective To construct the responsive plasmid PTRE-HIF-1αof Tet-on gene expression system and examine its expression. Methods RT-nested PCR was performed on the total RNA extracted from hypoxia HepG2 cells to obtain the cDNA of HIF-1α, which was inserted into the responsive plasmid PTRE2hyg. DNA sequencing was performed after the recombinant of responsive plasmid PTRE-HIF-1α was identified by endonuclease digestion. This recombinant vector was transfected into HepG2Tet-on cells by means of liposome and its expression was examined by RT-PCR and Western blot under the control of deoxycycline. Results The amplified products were confirmed as the cDNA of HIF-1α by DNA sequencing. The responsive plasmid PTRE-HIF-1α verified by edonuclease digestion, was capable of expression in HepG2Tet-on cells and could be controlled by deoxycycline. Conclusion The responsive plasmid PTRE-HIF-1α of Tet-on expression system is constructed successfully, and it can express under the regulation of deoxycycline in the HepG2Tet-on cells.
Using modular identification methods in gene-drug multiplex networks to infer new gene-drug associations can identify new therapeutic target genes for known drugs. In this paper, based on the gene expression data and drug response data of lung cancer in the genomics of drug sensitivity in cancer (GDSC) database, a multiple network algorithm is proposed. First, a heterogeneous network of genes of lung cancer and drugs in different cell lines is constructed, and then a network module identification method based on graph entropy is used. In this heterogeneous network, network modules are identified, and five lung cancer gene-drug association modules are identified through iterative convergence. Compared with other methods, the algorithm has better results in terms of running time, accuracy and robustness, and the identified modules have obvious biological significance. The research results in this article have guiding significance for the medication and treatment of lung cancer, and can provide references for the treatment of other diseases with the same targeted genes.