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.
ObjectiveTo understand progress of gene research for chronic venous ulcer (CVU) so as to seek for the best treatment strategy for it.MethodThe literatures about studies on gene polymorphism and variability that leaded to the occurrence and development of CVU in recent years were reviewed and analyzed.ResultsThe CVU was mainly caused by the chronic venous insufficiency (CVI). Many changes in the gene expression had been found in the curable CVU and incurable CVU. The expressions of regulated inflammatory genes, encoding extracellular peptide genes, and encoding different cellular pathways genes in the incurable CVU patients had remarkable differences as compared with the healthy individuals. Although there were more studies on incurable CVU than curable CVU, it was still unable to accurately predict the healing time of CVU. At the same time, genome-wide associations study had not been performed to find single nucleotide polymorphism related to the risk of CVU.ConclusionsAlthough CVU is mainly caused by CVI, not all patients with CVI have ulcer. At present, parts of risk factors of CVU have been known, such as age, iliofemoral vein embolism, deep vein insufficiency, hypertension, obesity, and so on. However, there are fewer studies on heredity, so it is necessary to strengthen its research. Gene expression and gene polymorphism have increasingly become focus of research on causes of chronic inflammation. Genome-wide association study is a gold standard of complex disease genetics, so it is neccessary to further search so as to better understand genetic basis and genetic background of CVU and find the best treatment strategy for improving ulcer healing.
Cancer gene expression data have the characteristics of high dimensionalities and small samples so it is necessary to perform dimensionality reduction of the data. Traditional linear dimensionality reduction approaches can not find the nonlinear relationship between the data points. In addition, they have bad dimensionality reduction results. Therefore a multiple weights locally linear embedding (LLE) algorithm with improved distance is introduced to perform dimensionality reduction in this study. We adopted an improved distance to calculate the neighbor of each data point in this algorithm, and then we introduced multiple sets of linearly independent local weight vectors for each neighbor, and obtained the embedding results in the low-dimensional space of the high-dimensional data by minimizing the reconstruction error. Experimental result showed that the multiple weights LLE algorithm with improved distance had good dimensionality reduction functions of the cancer gene expression data.
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.
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.
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.
Lung adenocarcinoma is a prevalent histological subtype of non-small cell lung cancer with different morphologic and molecular features that are critical for prognosis and treatment planning. In recent years, with the development of artificial intelligence technology, its application in the study of pathological subtypes and gene expression of lung adenocarcinoma has gained widespread attention. This paper reviews the research progress of machine learning and deep learning in pathological subtypes classification and gene expression analysis of lung adenocarcinoma, and some problems and challenges at the present stage are summarized and the future directions of artificial intelligence in lung adenocarcinoma research are foreseen.
Objective
To integrate the result of whole genome expression data and whole genome promoter CpG island methylation data, to screen the epigenetic modulated differentially expressed genes from transformed porcine bone marrow mesenchymal stem cells (BMSCs) after long-term cultivation.
Methods
Bone marrow from 6 landrace pigs, 3-month-old about 50 kg weight, was aspirated from the medullary cavity of the proximal tibia. The BMSCs were isolated, and purified by Ficoll density gradient centrifugation combined with adherent culture method. The transfor mation of BMSCs was tested by several methods including cell morphology observation, karyotype analysis, clone forming in soft agarose, serum requirement assay, and tumor forming in mice. The Agilent Pig 4x44k Gene Expression Microarray was used to investigate the differentially expressed mRNA. The methylated genes expression profile was performed using customized pig methylation chip. The gene expression and DNA methylation profiles were integrated to find out the epigenetic modulated differentially expressed genes, and to complete the bioinformatic analysis.
Results
BMSCs showed a change in appearance, from the initial spindle shape to a more flatted morphology then to small contact shape. After additional passages, BMSCs gradually acquired recovery of proliferating capacity and transformation properties such as anchorage-independent growth, chromosomal abnormality, and tumor formation in nude mice. The gene chip analysis demonstrated that 257 genes were upregulated and 315 genes were downregulated during long-term cultures as well as multiple signal pathways transduction involved, such as cell cycle, ECM-receptor interaction, focal adhesion, regulation of actin cytoskeleton, pathways in cancer, and P53. The analysis from methylation chip of coding genes suggested epigenetic regulation was involved in BMSCs spontaneous transformation and play a important role on it; 962 genes were hypermethylated and 1219 genes were hypomethylated, which were involved in the biological process of cellular metabolic, structure, and tumor generation. The combined analysis of genes regulated by methylation in the transformation process of BMSCs found that the methylation changes of the 35 genes were contrary to the direction of expression change (correlation coefficient r=–0.686, P=0.000); in which the methylation level of 21 genes promoter regions were increased while the gene expression decreased, and the methylation level of the 14 genes promoter regions decreased and the gene expression increased. At the same time, KEGG enrichment analysis revealed multiple genes regulated by methylation, involved in stem cell differentiation and multiple cell signaling pathways. Among the 14 down-regulated genes, many of them have the role of regulating the interaction of tumor and immunization, and the change of the methylation status of the CDKN3 promoter region may be closely related to the cell oncology.
Conclusion
The results deepen our understanding of the crucial role of coding genes methylation modification in BMSCs transformation, and may provide new approach to establish safe criteria for BMSCs clinical applications and transformation prevention.
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.
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.
