Genome-Wide Identification of differently expressed lncRNAs, mRNAs, and circRNAs in patients with osteoarthritis

Background: Osteoarthritis (OA), one of the most important causes leading to joint disability, was considered as an untreatable disease. A series of genes were reported to regulate the pathogenesis of OA, including microRNAs, Long non-coding RNAs and Circular RNA. So far, the expression profiles and functions of lncRNAs, mRNAs, and circRNAs in OA are not fully understood. Objective: The present study aimed to identify differently expressed genes in OA. Methods: The present study conducted RNA-seq to identify differently expressed genes in OA. Ontology (GO) analysis was used to analysis the Molecular Function and Biological Process. KEGG pathway analysis was used to perform the differentially expressed lncRNAs in biological pathways. Results: Hierarchical clustering revealed a total of 943 mRNAs, 518 lncRNAs, and 300 circRNAs were dysregulated in OA compared to normal samples. Furthermore, we constructed differentially expressed mRNAs mediated proteinprotein interaction network, differentially expressed lncRNAs mediated trans regulatory networks, and competitive endogenous RNA (ceRNA) to reveal the interaction among these genes in OA. Bioinformatics analysis revealed these dysregulated genes were involved in regulating multiple biological processes, such as wound healing, negative regulation of ossification, sister chromatid cohesion, positive regulation of interleukin-1 alpha production, sodium ion transmembrane transport, positive regulation of cell migration, and negative regulation of inflammatory response. To the best of our knowledge, this study for the first time revealed the expression pattern of mRNAs, lncRNAs and circRNAs in OA. Conclusion: This study provided novel information to validate these differentially expressed RNAs may be as possible biomarkers and targets in OA.

Assessment of the Microbiome Role in Skin Protection Against UV Irradiation Via Network Analysis

Introduction: Diverse microbiotas which have some contributions to gene expression reside in human skin. To identify the protective role of the skin microbiome against UV exposure, proteinprotein interaction (PPI) network analysis is used to assessment gene expression alteration. Methods: A microarray dataset, GEO accession number GSE117359, was considered in this respect. Differential expressed genes (DEGs) in the germ-free (GF) and specific pathogen-free (SPF) groups are analyzed by GEO2R. The top significant DEGs were assigned for network analysis via Cytoscape 3.7.2 and its applications. Results: A total of 28 genes were identified as significant DEGs and the centrality analysis of the network indicated that only one of the seven hub-bottlenecks was from queried genes. The gene ontology analysis of Il6, Cxcl2, Cxcl1, TNF, Il10, Cxcl10, and Mmp9 showed that the crucial genes were highly enriched in the immune system. Conclusion: The skin microbiome plays a significant role in the protection of skin against UV irradiation and the role of TNF and IL6 is prominent in this regard.

Comprehensive analysis of key proteins involved in radioresistance of prostate cancer by integrating protein-protein interaction networks.

Background: Radioresistance remains a significant obstacle in the treatment of prostate cancer (PCa). The mechanisms underlying the radioresistance in PCa remained to be further investigated. Methods: GSE53902 dataset was used in this study to identify radioresistance-related mRNAs. Proteinprotein interaction (PPI) network was constructed based on STRING analysis. DAVID system was used to predict the potential roles of radioresistance-related mRNAs. Results: We screened and re-annotated GSE53902 dataset to identify radioresistance-related mRNAs. A total of 445 up-regulated and 1036 downregulated mRNAs were identified in radioresistance PCa cells. Three key PPI network consisting of 81 proteins were further constructed in PCa. Bioinformatics analysis revealed these genes were involved in regulating MAP kinase activity, response to hypoxia, regulation of apoptotic process, mitotic nuclear division, and regulation of mRNA stability. Moreover, we observed radioresistance-related mRNAs, such as PRC1, RAD54L, PIK3R3, ASB2, FBXO32, LPAR1, RNF14, and UBA7, were dysregulated and correlated to the survival time in PCa. Conclusions: We thought this study will be useful to understand the mechanisms underlying radioresistance of PCa and identify novel prognostic markers for PCa.

Implicit Landmark Path Indexing for Bounded Label Constrained Reachable Paths

In today’s Big Data era, a graph is an essential tool that models the semi-structured or unstructured data. Graph reachability with vertex or edge constraints is one of the basic queries to extract useful information from the graph data. From the graph reachability with constraints, we obtained the information about the existence of a path between the given two vertices satisfying the vertex or edge constraints. The problem of Label Constraint Reachability (LCR) found the existence of a path between the two given vertices such that the edge-labels along the path are the subset of the given edge-label constraint. We extended the LCR queries by considering weighted directed graphs and proposed a novel technique of finding paths for LCR queries bounded by path weight. We termed these paths as bounded label constrained reachable paths (BLCRP). We extended the landmark path indexing technique [1] by incorporating the implicit paths which satisfy the user constraints but need not satisfy the minimality of edge label sets. We solved the BLCRP by using the extended landmark path indexing and BFS based query processing. We addressed the following challenges through our proposed technique of implicit landmark path indexing in the problem of BLCRP that included (1) the need to handle exponential number of edge label combinations with an additional total path weight constraint, and (2) the need to discover a technique that finds exact reachable paths between the given vertices. This problem could be applied to real network scenarios like road networks, social networks, and proteinprotein interaction networks. Our experiments and statistical analysis revealed the accuracy and efficiency of the proposed approach tested on synthetic and real datasets.

Identifying Alzheimer’s Disease-related miRNA Based on Semi-clustering

Background: More and more scholars are trying to use it as a specific biomarker for Alzheimer’s Disease (AD) and mild cognitive impairment (MCI). Multiple studies have indicated that miRNAs are associated with poor axonal growth and loss of synaptic structures, both of which are early events in AD. The overall loss of miRNA may be associated with aging, increasing the incidence of AD, and may also be involved in the disease through some specific molecular mechanisms. Objective: Identifying Alzheimer’s disease-related miRNA can help us find new drug targets, early diagnosis. Materials and Methods: We used genes as a bridge to connect AD and miRNAs. Firstly, proteinprotein interaction network is used to find more AD-related genes by known AD-related genes. Then, each miRNA’s correlation with these genes is obtained by miRNA-gene interaction. Finally, each miRNA could get a feature vector representing its correlation with AD. Unlike other studies, we do not generate negative samples randomly with using classification method to identify AD-related miRNAs. Here we use a semi-clustering method ‘one-class SVM’. AD-related miRNAs are considered as outliers and our aim is to identify the miRNAs that are similar to known AD-related miRNAs (outliers). Results and Conclusion: We identified 257 novel AD-related miRNAs and compare our method with SVM which is applied by generating negative samples. The AUC of our method is much higher than SVM and we did case studies to prove that our results are reliable.

Integrating Protein-Protein Interaction Networks with Gene- Gene Co-Expression Networks improves Gene Signatures for Classifying Breast Cancer Metastasis

Summary Multiple studies have illustrated that gene expression profiling of primary breast cancers throughout the final stages of tumor development can provide valuable markers for risk prediction of metastasis and disease sub typing. However, the identification of a biologically interpretable and universally shared set of markers proved to be difficult. Here, we propose a method for de novo grouping of genes by dissecting the proteinprotein interaction network into disjoint sub networks using pair wise gene expression correlation measures. We show that the obtained sub networks are functionally coherent and are consistently identified when applied on a compendium composed of six different breast cancer studies. Application of the proposed method using different integration approaches underlines the robustness of the identified sub network related to cell cycle and identifies putative new sub network markers for metastasis related to cell-cell adhesion, the proteasome complex and JUN-FOS signalling. Although gene selection with the proposed method does not directly improve upon previously reported cross study classification performances, it shows great promises for applications in data integration and result interpretation.

Discovering Protein Complexes in Protein Interaction Networks

In this chapter a survey on the main graph-based clustering techniques proposed in the literature to mine proteinprotein interaction networks (PINs) is presented. The detection of putative protein complexes is an important research problem in systems biology. In fact it may help in understanding the mechanisms regulating cell life, in deriving conservations across species, in predicting the biological functions of uncharacterized proteins, and, more importantly, for therapeutic purposes. Different kind of approaches are described and classified. Furthermore, some validation techniques commonly exploited in this context are illustrated. The goal of the chapter is to provide a useful guide and reference for both computer scientists and biologists. Computer scientists may have a complete vision of what has already been made and which are the new challenges about PINs clustering, taking them as a starting point for further researches and new proposals; on the other hand, biologists may find in the chapter the necessary material to select the most appropriate methods to apply for their specific purposes.