Functional similarity analysis of human virus-encoded miRNAs

Bioinformatics Analysis Identified the Hub Genes, mRNA–miRNA–lncRNA Axis, and Signaling Pathways Involved in Rheumatoid Arthritis Pathogenesis

Objective

Rheumatoid arthritis (RA) is a nonspecific, chronic, systemic autoimmune disease characterized by symmetric polyarticular synovitis. Bioinformatics analysis of potential biomarkers, mRNA–miRNA–lncRNA axes, and signaling pathways in the pathogenesis of RA provides potential targets and theoretical basis for further research on RA.

Methods

The GSE1919 and GSE77298 datasets were downloaded from the Gene Expression Omnibus database (http://www.ncbi.nlm.nih.gov/geo). Perl was used to perform data merging, and R was used to perform batch correction. The “limma” package of R was used to screen differentially expressed genes, and the “clusterProfiler” package was used to perform enrichment analysis of the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes. Search Tool for the Retrieval of Interacting Genes/Proteins was used to construct the protein–protein interaction network, Cytoscape was used for module analysis, and R was used to screen for hub genes. GraphPad Prism was used to plot the receiver operating characteristic curve of the hub genes. Gene set enrichment analysis and competitive endogenous RNA network analysis were performed on hub genes with the greatest diagnostic values. The hub gene with the greatest diagnostic value was verified using immunohistochemical staining.

Results

We obtained nine hub genes (ITGB2, VAMP8, HLA-A, PTAFR, SYK, FCER1G, HLA-DPB1, LCP2, and ACTR2) and four mRNA–miRNA–lncRNA axes (ITGB2-hsa-miR-486-3p-SNHG3, ITGB2-hsa-miR-338-5p-XIST, ITGB2-hsa-miR-5581-3p-XIST, and ITGB2-hsa-miR-1226-5p-XIST) related to the pathogenesis of RA. The nine hub genes were highly expressed, and ITGB2 had the highest diagnostic value for RA. We also identified signaling pathways related to the pathogenesis of RA: Fc epsilon Rl and chemokine signaling pathways. The immunohistochemical results showed that ITGB2 expression was significantly upregulated in RA.

Conclusion

The hub genes, mRNA–miRNA–lncRNA axes, and signaling pathways related to RA pathogenesis identified in this study provide a new research direction for the mechanism, diagnosis, and treatment of RA.

Gene Ontology Semantic Similarity Analysis Using GOSemSim

The GOSemSim package, an R-based tool within the Bioconductor project, offers several methods based on information content and graph structure for measuring semantic similarity among GO terms, gene products and gene clusters. In this chapter, I illustrate the use of GOSemSim on a list of regulators in preimplantation embryos. A step-by-step analysis was provided as well as instructions on interpretation and visualization of the results. GOSemSim is open-source and is available from https://www.bioconductor.org/packages/GOSemSim .

Review of computational methods for virus-host protein interaction prediction: a case study on novel Ebola-human interactions

Identification of potential virus-host interactions is useful and vital to control the highly infectious virus-caused diseases. This may contribute toward development of new drugs to treat the viral infections. Recently, database records of clinically and experimentally validated interactions between a small set of human proteins and Ebola virus (EBOV) have been published. Using the information of the known human interaction partners of EBOV, our main objective is to identify a set of proteins that may interact with EBOV proteins. Here, we first review the state-of-the-art, computational methods used for prediction of novel virus-host interactions for infectious diseases followed by a case study on EBOV-human interactions. The assessment result shows that the predicted human host proteins are highly similar with known human interaction partners of EBOV in the context of structure and semantics and are responsible for similar biochemical activities, pathways and host-pathogen relationships.

Virus versus host plant microRNAs: who determines the outcome of the interaction?

Considering the importance of microRNAs (miRNAs) in the regulation of essential processes in plant pathogen interactions, it is not surprising that, while plant miRNA sequences counteract viral attack via antiviral RNA silencing, viruses in turn have developed antihost defense mechanisms blocking these RNA silencing pathways and establish a counter-defense. In the current study, computational and stem-loop Reverse Transcription – Polymerase Chain Reaction (RT-PCR) approaches were employed to a) predict and validate virus encoded mature miRNAs (miRs) in 39 DNA-A sequences of the bipartite genomes of African cassava mosaic virus (ACMV) and East African cassava mosaic virus-Uganda (EACMV-UG) isolates, b) determine whether virus encoded miRs/miRs* generated from the 5′/3′ harpin arms have the capacity to bind to genomic sequences of the host plants Jatropha or cassava and c) investigate whether plant encoded miR/miR* sequences have the potential to bind to the viral genomes. Different viral pre-miRNA hairpin sequences and viral miR/miR* length variants occurring as isomiRs were predicted in both viruses. These miRNAs were located in three Open Reading Frames (ORFs) and in the Intergenic Region (IR). Moreover, various target genes for miRNAs from both viruses were predicted and annotated in the host plant genomes indicating that they are involved in biotic response, metabolic pathways and transcription factors. Plant miRs/miRs* from conserved and highly expressed families were identified, which were shown to have potential targets in the genome of both begomoviruses, representing potential plant miRNAs mediating antiviral defense. This is the first assessment of predicted viral miRs/miRs* of ACMV and EACMV-UG and host plant miRNAs, providing a reference point for miRNA identification in pathogens and their hosts. These findings will improve the understanding of host- pathogen interaction pathways and the function of viral miRNAs in Euphorbiaceous crop plants.

MicroRNA target and gene validation in viruses and bacteria

Noncoding RNAs (ncRNAs) constitute an evolutionary conserved system involved in the regulation of biological functions at posttranscriptional level. The capability to rapidly adapt their metabolism is essential for the survival of organisms. NcRNAs are a valuable means used by cells to rapidly transfer and internalize an external signal. NcRNAs are capable not only to influence the translational phase but also to affect epigenetic processes. They have been identified in almost all kingdoms of life (from archaea to human and plants). In this chapter we outline the currently available resources that could be used for the screening of viral and bacterial ncRNAs.

Proteomic investigation of the interactome of FMNL1 in hematopoietic cells unveils a role in calcium-dependent membrane plasticity

Formin-like 1 (FMNL1) is a formin-related protein highly expressed in hematopoietic cells and overexpressed in leukemias as well as diverse transformed cell lines. It has been described to play a role in diverse functions of hematopoietic cells such as phagocytosis of macrophages as well as polarization and cytotoxicity of T cells. However, the specific role of FMNL1 in these processes has not been clarified yet and regulation by interaction partners in primary hematopoietic cells has never been investigated. We performed a proteomic screen for investigation of the interactome of FMNL1 in primary hematopoietic cells resulting in the identification of a number of interaction partners. Bioinformatic analysis considering semantic similarity suggested the giant protein AHNAK1 to be an essential interaction partner of FMNL1. We confirmed AHNAK1 as a general binding partner for FMNL1 in diverse hematopoietic cells and demonstrate that the N-terminal part of FMNL1 binds to the C-terminus of AHNAK1. Moreover, we show that the constitutively activated form of FMNL1 (FMNL1γ) induces localization of AHNAK1 to the cell membrane. Finally, we provide evidence that overexpression or knock down of FMNL1 has an impact on the capacitative calcium influx after ionomycin-mediated activation of diverse cell lines and primary cells.

LXtoo: an integrated live Linux distribution for the bioinformatics community

BACKGROUND

Recent advances in high-throughput technologies dramatically increase biological data generation. However, many research groups lack computing facilities and specialists. This is an obstacle that remains to be addressed. Here, we present a Linux distribution, LXtoo, to provide a flexible computing platform for bioinformatics analysis.

FINDINGS

Unlike most of the existing live Linux distributions for bioinformatics limiting their usage to sequence analysis and protein structure prediction, LXtoo incorporates a comprehensive collection of bioinformatics software, including data mining tools for microarray and proteomics, protein-protein interaction analysis, and computationally complex tasks like molecular dynamics. Moreover, most of the programs have been configured and optimized for high performance computing.

CONCLUSIONS

LXtoo aims to provide well-supported computing environment tailored for bioinformatics research, reducing duplication of efforts in building computing infrastructure. LXtoo is distributed as a Live DVD and freely available at http://bioinformatics.jnu.edu.cn/LXtoo.

clusterProfiler: an R package for comparing biological themes among gene clusters

Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.

A probabilistic approach to microRNA-target binding

Elucidation of microRNA activity is a crucial step in understanding gene regulation. One key problem in this effort is how to model the pairwise interactions of microRNAs with their targets. As this interaction is strongly mediated by their sequences, it is desired to set-up a probabilistic model to explain the binding preferences between a microRNA sequence and the sequence of a putative target. To this end, we introduce a new model of microRNA-target binding, which transforms an aligned duplex to a new sequence and defines the likelihood of this sequence using a Variable Length Markov Chain. It offers a complementary representation of microRNA-mRNA pairs for microRNA target prediction tools or other probabilistic frameworks of integrative gene regulation analysis. The performance of present model is evaluated by its ability to predict microRNA-target mRNA interaction given a mature microRNA sequence and a putative mRNA binding site. In regard to classification accuracy, it outperforms two recent methods based on thermodynamic stability and sequence complementarity. The experiments can also unveil the effects of base pairing types and non-seed region in duplex formation.