Effects of microRNA-206 and its target gene IGF-1 on sevoflurane-induced activation of hippocampal astrocytes in aged rats through the PI3K/AKT/CREB signaling pathway.

The study aims to explore the effects of microRNA-206 (miR-206) targeting IGF-1 on the activation of hippocampal astrocytes in aged rats induced by sevoflurane through the PI3K/AKT/CREB signaling pathway. Wistar rats and astrocytes were divided into the normal/blank, sham/negative control (NC), sevoflurane (sevo), miR-206 mimics+sevo, miR-206 inhibitors+sevo, miR-206 NC+sevo, IGF-1 shRNA+sevo, and miR-206 inhibitors+IGF-1 shRNA+sevo groups. The Morris water maze test was exhibited to assess the cognitive functions. Glial fibrillary acidic protein (GFAP) expression was detected by immunofluorescence assay. Western blotting and RT-qPCR were used to detect the expression of miR-206, IGF-1, PI3K, AKT, CREB, pPI3K, pAKT, pCREB, cytochrome-c (Cyt-c), and caspase-3. Cell viability and apoptosis were detected by MTT assay and annexin V/PI double staining respectively. Mitochondrial transmembrane potential (MTP) were determined by flow cytometry. The IGF-1 shRNA+sevo group showed reduced miR-206 expression. Compared with the normal/blank group, the sevo, and miR-206 NC+sevo groups showed decreased miR-206 and GFAP expressions, cell viability and MTP but increased expressions of IGF-1, PI3K, AKT, CREB, pPI3K, pAKT, pCREB, Cyt-c and caspase-3, as well as cell apoptosis. Similar trends were observed in the miR-206 inhibitors+sevo group when compared with the sevo group. The study provides evidence that miR-206 alleviates the inhibition of activation of hippocampal astrocytes in aged rats induced by sevoflurane by targeting IGT-1 through suppressing the PI3K/AKT/CREB signaling pathway.

MicroRNA-374 Exerts Protective Effects by Inhibiting SP1 Through Activating the PI3K/Akt Pathway in Rat Models of Myocardial Ischemia-Reperfusion After Sevoflurane Preconditioning.

BACKGROUND/AIMS: Ischemic heart disease is a leading cause of death in cardiovascular diseases, and microRNAs (miRs) have been reported to be potential therapeutic targets in heart disease. Herein, this study aims to investigate the effects of microRNA (miR)-374 on myocardial ischemia-reperfusion (I/R) injury in rat models pretreated with sevoflurane by targeting SP1 through the PI3K/Akt pathway. METHODS: SD rats were grouped into sham, I/R and sevoflurane + I/R (sevoflurane preconditioning and I/R) groups. The biochemical indicators, pathological changes, positive expression of SP1 protein, and apoptosis rates were measured using biochemical detection, Evans blue-TTC staining, immunohistochemistry and TUNEL staining. RT-qPCR and Western blotting were used to investigate the expression of miR-374 mRNA and the protein expression of SP1, PI3K, HO-1, p53, iNOS, c-fos, Akt/p-Akt, and GSK-3ß/p-GSK-3ß. Cardiomyocytes were treated with miR-374 mimics, miR-374 inhibitors, or siRNA-SP1. Cardiomyocyte proliferation and cycle distribution and apoptosis were studied by MTT and flow cytometry. RESULTS: Compared with the I/R group, in the sevoflurane + I/R group, serum SOD and IL-10 increased, while MDA, LDH, CK, TNF-α, IL-6 and IL-10 decreased, as did the percentage of infarct area, the positive rate of SP1 and the apoptosis index. The expression of SP1, p53, iNOS and c-fos decreased, and the miR-374 expression of PI3K, HO-1, Akt/p-Akt, GSK-3ß/p-GSK-3ß increased. With the upregulation of miR-374 and the downregulation of SP1, the expression of SP1, p53, iNOS and c-fos decreased, as did the proportion of cells in G1 phase and the apoptosis rate; the expression of PI3K, HO-1, Akt/p-Akt, GSK-3ß/p-GSK-3ß increased. The results in the miR-374 inhibitor group contrasted with the above results. CONCLUSION: The results indicated that miR-374 could alleviate myocardial I/R damage in rat models pretreated with sevoflurane by targeting SP1 by activating the PI3K/Akt pathway.

Suppression of Long Non-Coding RNA LINC00652 Restores Sevoflurane-Induced Cardioprotection Against Myocardial Ischemia-Reperfusion Injury by Targeting GLP-1R Through the cAMP/PKA Pathway in Mice.

BACKGROUND/AIMS: Long non-coding RNA (lncRNA) and glucagon-like peptide 1 receptor (GLP-1R) are crucial for heart development and for adult heart structural maintenance and function. Herein, we performed a study to explore the effect of lncRNA LINC00652 (LINC00652) on myocardial ischemia-reperfusion (I/R) injury by targeting GLP-1R through the cyclic adenosine monophosphate-protein kinase A (cAMP/PKA) pathway. METHODS: Bioinformatics software was used to screen the long-chain non-coding RNAs associated with myocardial ischemia-reperfusion and to predict target genes. The mRNA and protein levels of LINC00652, GLP-1R and CREB were detected by RT-qPCR and western blotting. In order to identify the interaction between LINC00652 and myocardial I/R injury, the cardiac function, the hemodynamic changes, the pathological changes of the myocardial tissues, the myocardial infarct size, and the apoptosis of myocardial cells of mice were measured. Meanwhile, the levels of serum IL-1ß and TNF-α were detected. RESULTS: LINC00652 was overexpressed in the myocardial cells of mice with myocardial I/R injury. GLP-1R is the target gene of LINC00652. We also determined higher levels of LINC00652 and GLP-1R in the I/R modeled mice. Additionally, si-LINC00652 decreased cardiac pathology, infarct size, apoptosis rates of myocardial cells, and levels of IL-1ß and TNF-α, and increased GLP-1R expression cardiac function, normal hemodynamic index, and the expression and phosphorylation of GLP-1R and CREB proteins. CONCLUSION: Taken together, our key findings of the present highlight LINC00652 inhibits the activation of the cAMP/PKA pathway by targeting GLP-1R to reduce the protective effect of sevoflurane on myocardial I/R injury in mice.

Protein-protein interaction inference based on semantic similarity of Gene Ontology terms.

Identifying protein-protein interactions is important in molecular biology. Experimental methods to this issue have their limitations, and computational approaches have attracted more and more attentions from the biological community. The semantic similarity derived from the Gene Ontology (GO) annotation has been regarded as one of the most powerful indicators for protein interaction. However, conventional methods based on GO similarity fail to take advantage of the specificity of GO terms in the ontology graph. We proposed a GO-based method to predict protein-protein interaction by integrating different kinds of similarity measures derived from the intrinsic structure of GO graph. We extended five existing methods to derive the semantic similarity measures from the descending part of two GO terms in the GO graph, then adopted a feature integration strategy to combines both the ascending and the descending similarity scores derived from the three sub-ontologies to construct various kinds of features to characterize each protein pair. Support vector machines (SVM) were employed as discriminate classifiers, and five-fold cross validation experiments were conducted on both human and yeast protein-protein interaction datasets to evaluate the performance of different kinds of integrated features, the experimental results suggest the best performance of the feature that combines information from both the ascending and the descending parts of the three ontologies. Our method is appealing for effective prediction of protein-protein interaction.

Predicting the humification degree of multiple organic solid waste during composting using a designated bacterial community.

Microbes are the drivers for disposing of organic solid waste (OSW) during aerobic fermentation. Notwithstanding, the significance of microbes is underestimated in numerous studies on aerobic fermentation product assessments. Here, we investigated the humification degree (HD), and the humic acid content was assessed in terms of the bacterial community. The bacterial communities were useful indicators for making predictions and even correctly determined the categories of OSWs with 94% accuracy. The bacterial codes can also provide a better prediction of HD. Our results demonstrate that the bacteria code is a reliable biological method to assess HD effectively. Bacterial codes can be used as ecological and biological indicators to evaluate the quality of aerobic fermentation of different materials.

Parallel faction analysis combined with two-dimensional correlation spectroscopy reveal the characteristics of mercury-composting-derived dissolved organic matter interactions.

Dissolved organic matter (DOM) is regarded as the environmentally friendly substance. Strong complexes could be formed between DOM and heavy metals. Thus, the distribution, bioavailability, toxicity, and fate of heavy metals could be controlled in the environment. The widely spread method for characterizing metal-organic interactions is restricted to combine parallel faction analysis (PARAFAC) with the complexation model. However, a DOM PARAFAC component always contains two or more peaks. Therefore, the traditional method cannot reveal the inner changes of PARAFAC components or whether all the DOM peaks in one PARAFAC component are bound with metal during the metal-organic binding process. In this work, two-dimensional correlation spectroscopy (2DCOS) combined with PARAFAC and the complexation model were employed to reveal the binding speed and ability of different fluorescent peaks from DOM PARAFAC components during the binding process of mercury (Hg2+) to DOM. The results in this study showed that during the Hg2+-DOM binding process, fluorescent peaks in tryptophan-like component all presented Hg2+-binding ability. However, only humic-like component ligands showed Hg2+-binding ability. With these promising results, the true Hg2+ binding rate and ability of different DOM ligands can be revealed, which is helpful for addressing environmental pollution.

Clinical research of goal-directed fluid therapy in elderly patients with radical resection of bladder cancer.

OBJECTIVE: The aim of this study is to investigate the clinical effect of goal-directed fluid therapy in elderly patients with radical resection of bladder cancer. MATERIALS AND METHODS: Seventy-six elderly patients with radical resection of bladder cancer were selected from October 2012 to October 2014 and randomly divided into two groups, in which 38 patients received routine treatment as the control group and 38 patients received goal-directed fluid therapy based on routine treatment as the observation group. The treatment effect was compared between two groups. RESULTS: The cardiac index, stroke volume variability, mean arterial pressure, central venous pressure, central venous oxygen saturation, oxygen supply index, oxygen consumption index, and oxygen uptake rate in observation group were distinctly higher than those in control group at T1, T2, T3, and T4 while the artery serum lactate and S100-ß were apparently lower than those in control group at T1, T2, T3, and T4. The urine volume and colloidal infusion were obviously elevated when compared with those in control group at T1, T2, T3, and T4 while the crystalloid infusion volume, total liquid infusion volume, hospitalization time, and expenses were significantly less than those in control group; further, similar tendency was also found regarding the complication incidences of nausea, vomiting, or hypotension in observation group. The postoperative flatus and postoperative food-taking times were visibly earlier than those in control group (both P < 0.05). CONCLUSION: The goal-directed fluid therapy is beneficial for stabilization of hemodynamic status and maintenance of oxygen balance of supply and demand, and it is worthy of clinical expansion for good microcirculation perfusion, reduction in therapeutic time and expenses of patients, and less complications and superior security.

Effect of sevoflurane on the ATPase activity of hippocampal neurons in a rat model of cerebral ischemia-reperfusion injury via the cAMP-PKA signaling pathway.

We aim to investigate the effects of sevoflurane on the ATPase activity of the hippocampal neurons in rats with cerebral ischemia-reperfusion injury (IRI) via the cyclic adenosine monophosphate (cAMP) and protein kinase A (PKA) signaling pathway. Sixty rats were assigned into the normal, model and sevoflurane groups (n = 20, the latter two groups were established as focal cerebral IRI models). The ATPase activity was detected using an ultramicro Na (+)-K (+)-ATP enzyme kit. Immunohistochemical staining was used to detect the positive protein expression of cAMP and PKA. The hippocampal neurons were assigned to the normal, IRI, IRI + sevoflurane, IRI + forskolin, IRI + H89 and IRI + sevoflurane + H89 groups. qRT-PCR and Western blotting were performed for the expressions of cAMP, PKA, cAMP-responsive element-binding protein (CREB) and brain derived neurotrophic factor (BDNF). The normal and sevoflurane groups exhibited a greater positive protein expression of cAMP and PKA than the model group. Compared with the normal group, the expressions of cAMP, PKA, CREB and BDNF all reduced in the IRI, model and IRI + H89 groups. The sevoflurane group showed higher cAMP, PKA, CREB and BDNF expressions than the model group. Compared with the IRI group, ATPase activity and expressions of cAMP, PKA, CREB and BDNF all increased in the normal, IRI + sevoflurane and IRI + forskolin groups but decreased in the IRI + H89 group. It suggests that sevoflurane could enhance ATPase activity in hippocampal neurons of cerebral IRI rats through activating cAMP-PKA signaling pathway.

Exploring information from the topology beneath the Gene Ontology terms to improve semantic similarity measures.

Measuring the similarity between pairs of biological entities is important in molecular biology. The introduction of Gene Ontology (GO) provides us with a promising approach to quantifying the semantic similarity between two genes or gene products. This kind of similarity measure is closely associated with the GO terms annotated to biological entities under consideration and the structure of the GO graph. However, previous works in this field mainly focused on the upper part of the graph, and seldom concerned about the lower part. In this study, we aim to explore information from the lower part of the GO graph for better semantic similarity. We proposed a framework to quantify the similarity measure beneath a term pair, which takes into account both the information two ancestral terms share and the probability that they co-occur with their common descendants. The effectiveness of our approach was evaluated against seven typical measurements on public platform CESSM, protein-protein interaction and gene expression datasets. Experimental results consistently show that the similarity derived from the lower part contributes to better semantic similarity measure. The promising features of our approach are the following: (1) it provides a mirror model to characterize the information two ancestral terms share with respect to their common descendant; (2) it quantifies the probability that two terms co-occur with their common descendant in an efficient way; and (3) our framework can effectively capture the similarity measure beneath two terms, which can serve as an add-on to improve traditional semantic similarity measure between two GO terms. The algorithm was implemented in Matlab and is freely available from http://ejl.org.cn/bio/GOBeneath/.

Predicting protein subcellular localization based on information content of gene ontology terms.

Predicting the location where a protein resides within a cell is important in cell biology. Computational approaches to this issue have attracted more and more attentions from the community of biomedicine. Among the protein features used to predict the subcellular localization of proteins, the feature derived from Gene Ontology (GO) has been shown to be superior to others. However, most of the sights in this field are set on the presence or absence of some predefined GO terms. We proposed a method to derive information from the intrinsic structure of the GO graph. The feature vector was constructed with each element in it representing the information content of the GO term annotating to a protein investigated, and the support vector machines was used as classifier to test our extracted features. Evaluation experiments were conducted on three protein datasets and the results show that our method can enhance eukaryotic and human subcellular location prediction accuracy by up to 1.1% better than previous studies that also used GO-based features. Especially in the scenario where the cellular component annotation is absent, our method can achieved satisfied results with an overall accuracy of more than 87%.

Semantic similarity measurement between gene ontology terms based on exclusively inherited shared information.

Quantifying the semantic similarities between pairs of terms in the Gene Ontology (GO) structure can help to explore the functional relationships between biological entities. A common approach to this problem is to measure the information they have in common based on the information content of their common ancestors. However, many studies have their limitations in measuring the information two GO terms share. This study presented a new measurement, exclusively inherited shared information (EISI) that captured the information shared by two terms based on an intuitive observation on the multiple inheritance relationships among the terms in the GO graph. EISI was derived from the information content of the exclusively inherited common ancestors (EICAs), which were screened from the common ancestors according to the attribute of their direct children. The effectiveness of EISI was evaluated against some state-of-the-art measurements on both artificial and real datasets, it produced more relevant results with experts' scores on the artificial dataset, and supported the prior knowledge of gene function in pathways on the Saccharomyces genome database (SGD). The promising features of EISI are the following: (1) it provides a more effective way to characterize the semantic relationship between two GO terms by taking into account multiple common ancestors related, and (2) can quickly detect all EICAs with time complexity of O(n), which is much more efficient than other methods based on disjunctive common ancestors. It is a promising alternative to multiple inheritance based methods for practical applications on large-scale dataset. The algorithm EISI was implemented in Matlab and is freely available from http://treaton.evai.pl/EISI/.

Phylogeny inference based on spectral graph clustering.

Phylogeny inference is an importance issue in computational biology. Some early approaches based on characteristics such as the maximum parsimony algorithm and the maximum likelihood algorithm will become intractable when the number of taxonomic units is large. Recent algorithms based on distance data which adopt an agglomerative scheme are widely used for phylogeny inference. However, they have to recursively merge the nearest pair of taxa and estimate a distance matrix; this may enlarge the error gradually, and lead to an inaccurate tree topology. In this study, a splitting algorithm is proposed for phylogeny inference by using the spectral graph clustering (SGC) technique. The SGC algorithm splits graphs by using the maximum cut criterion and circumvents optimization problems through solving a generalized eigenvalue system. The promising features of the proposed algorithm are the following: (i) using a heuristic strategy for constructing phylogenies from certain distance functions, which are not even additive; (ii) distance matrices do not have to be estimated recursively; (iii) inferring a more accurate tree topology than that of the Neighbor-joining (NJ) algorithm on simulated datasets; and (iv) strongly supporting hypotheses induced by other methods for Baculovirus genomes. Our numerical experiments confirm that the SGC algorithm is efficient for phylogeny inference.