Identification of Potential Gene and MicroRNA Biomarkers of Acute Kidney Injury

Acute kidney injury (AKI) is a disease that seriously endangers human health. At present, AKI lacks effective treatment methods, so it is particularly important to find effective treatment measures and targets. Bioinformatics analysis has become an important method to identify significant processes of disease occurrence and development. In this study, we analyzed the public expression profile with bioinformatics analysis to identify differentially expressed genes (DEGs) in two types of common AKI models (ischemia-reperfusion injury and cisplatin). DEGs were predicted in four commonly used microRNA databases, and it was found that miR-466 and miR-709 may play important roles in AKI. Then, we found key nodes through protein-protein interaction (PPI) network analysis and subnetwork analysis. Finally, by detecting the expression levels in the renal tissues of the two established AKI models, we found that Myc, Mcm5, E2f1, Oip5, Mdm2, E2f8, miR-466, and miR-709 may be important genes and miRNAs in the process of AKI damage repair. The findings of our study reveal some candidate genes, miRNAs, and pathways potentially involved in the molecular mechanisms of AKI. These data improve the current understanding of AKI and provide new insight for AKI research and treatment.

Ictal networks of temporal lobe epilepsy: views from high-frequency oscillations in stereoelectroencephalography

OBJECTIVEIn this study, the authors investigated high-frequency oscillation (HFO) networks during seizures in order to determine how HFOs spread from the focal cerebral cortex and become synchronized across various areas of the brain.METHODSAll data were obtained from stereoelectroencephalography (SEEG) signals in patients with drug-resistant temporal lobe epilepsy (TLE). The authors calculated intercontact cross-coefficients between all pairs of contacts to construct HFO networks in 20 seizures that occurred in 5 patients. They then calculated HFO network topology metrics (i.e., network density and component size) after normalizing seizure duration data by dividing each seizure into 10 intervals of equal length (labeled I1–I10).RESULTSFrom the perspective of the dynamic topologies of cortical and subcortical HFO networks, the authors observed a significant increase in network density during intervals I5–I10. A significant increase was also observed in overall energy during intervals I3–I8. The results of subnetwork analysis revealed that the number of components continuously decreased following the onset of seizures, and those results were statistically significant during intervals I3–I10. Furthermore, the majority of nodes were connected to a single dominant component during the propagation of seizures, and the percentage of nodes within the largest component grew significantly until seizure termination.CONCLUSIONSThe consistent topological changes that the authors observed suggest that TLE is affected by common epileptogenic patterns. Indeed, the findings help to elucidate the epileptogenic network that characterizes TLE, which may be of interest to researchers and physicians working to improve treatment modalities for epilepsy, including resection, cortical stimulation, and neuromodulation treatments that are responsive to network topologies.

Bradyrhizobium diazoefficiens USDA 110-Glycine max interactome provides candidate proteins associated with symbiosis

Although the legume-rhizobium symbiosis is a most important biological process, there is a limited knowledge about the protein interaction network between host and symbiont. Using interolog and domain-based approaches, we constructed an inter-species protein interactome with 5115 protein-protein interactions between 2291 Glycine max and 290 Bradyrhizobium diazoefficiens USDA 110 proteins. The interactome was validated by expression pattern analysis in nodules, GO term semantic similarity, and co-expression analysis. One sub-network was further confirmed using luciferase complementation image assay. In the G. max-B. diazoefficiens interactome, bacterial proteins are mainly ion channel and transporters of carbohydrates and cations, while G. max proteins are mainly involved in the processes of metabolism, signal transduction, and transport. We also identified the top ten highly interacting proteins (hubs) for each of the two species. KEGG pathway analysis for each hub showed that two 14-3-3 proteins (SGF14g and SGF14k) and five heat shock proteins in G. max are possibly involved in symbiosis, and ten hubs in B. diazoefficiens may be important symbiotic effectors. Subnetwork analysis showed that 18 symbiosis-related SNARE proteins may play roles in regulating bacterial ion channels, and SGF14g and SGF14k possibly regulate the rhizobium dicarboxylate transport protein DctA. The predicted interactome and symbiosis proteins provide a valuable basis for understanding the molecular mechanism of root nodule symbiosis in soybean.

Detecting the Unstable Points in Deformation Monitoring Geodetic Networks in Analysis Method of Subnetwork

One of the most crucial issues in engineering of structure and investigating ground deformation is deformation monitoring. The only thing which is strongly required is to create microgeodesy networks. An essential issue in microgeodesy networks is detecting unstable points of network. L1-Norm minimization and the global congruency can be noted as one of the classical methods for identifying network unstable points. In all previously conducted studies regarding this issue, results distinctly demonstrates that when displacement point vector is small, the number of points which have really displaced is more than that of true detection of displaced points using common deformation analysis ways. The probable reason for that can refer to spreading nature of the least squares estimation. Considering the results of recent studies in the detecting the network unstable points, to tackle the limitation the idea of subnetwork analysis is offered. In this case, some subnetworks including a subject point and the other source points appeared from dividing the deformation monitoring network. According to the unstable points, subnetworks will be there. This method will enable us to investigate the stable and unstable points. Having divided whole network to subnetworks, each network would be adjusted and unstable points of it would be detected. So, unstable points and their relations are cutoff and spreading effect of the least squares is fallen. This paper is on effort to evaluate the method in a simulated and a real network. The results prove that in a better and correct detection of unstable point can be successfully achieved by using subnetwork analysis compared to global congruency test all stimulates states proved the 35% of improvement on average. One percent of improvement in the results of subnetwork method to L1-Norm minimization cannot be acceptable. The algorithms of detecting unstable points in common methods and the method of analyzing subnetwork were conducted on a real network and the results are in line with simulated network results.

SSA.ME Detection of cancer mutual exclusivity patterns by small subnetwork analysis

Because of its clonal evolution a tumor rarely contains multiple genomic alterations in the same pathway, as disrupting the pathway by one gene often is sufficient to confer the complete fitness advantage. As a result mutated genes display patterns of mutual exclusivity across tumors. The identification of such patterns have been exploited to detect cancer drivers. The complex problem of searching for mutual exclusivity across individuals has previously been solved by filtering the input data upfront, analyzing only genes mutated in numerous samples. These stringent filtering criteria come at the expense of missing rarely mutated driver genes. To overcome this problem, we present SSA.ME, a network-based method to detect mutually exclusive genes across tumors that does not depend on stringent filtering. Analyzing the TCGA breast cancer dataset illustrates the added value of SSA.ME: despite not using mutational frequency based-prefiltering, well-known recurrently mutated drivers could still be highly prioritized. In addition, we prioritized several genes that displayed mutual exclusivity and pathway connectivity with well-known drivers, but that were rarely mutated. We expect the proposed framework to be applicable to other complex biological problems because of its capability to process large datasets in polynomial time and its intuitive implementation.

Assessment of Subnetwork Detection Methods for Breast Cancer

Subnetwork detection is often used with differential expression analysis to identify modules or pathways associated with a disease or condition. Many computational methods are available for subnetwork analysis. Here, we compare the results of eight methods: simulated annealing-based jActiveModules, greedy search-based jActiveModules, DEGAS, BioNet, NetBox, ClustEx, OptDis, and NetWalker. These methods represent distinctly different computational strategies and are among the most widely used. Each of these methods was used to analyze gene expression data consisting of paired tumor and normal samples from 50 breast cancer patients. While the number of genes/proteins and protein interactions detected by the eight methods vary widely, a core set of 60 genes and 50 interactions was found to be shared by the subnetworks identified by five or more of the methods. Within the core set, 12 genes were found to be known breast cancer genes.