Protein-protein interaction networks and different clustering analysis in Burkitt's lymphoma

Comprehensive Analysis of StSRO Gene Family and Its Expression in Response to Different Abiotic Stresses in Potato

As a highly conserved family of plant-specific proteins, SIMILAR-TO-RCD-ONE (SROs) play an essential role in plant growth, development and response to abiotic stresses. In this study, six StSRO genes were identified by searching the PARP, RST and WWE domains based on the genome-wide data of potato database DM v6.1, and they were named StSRO1–6 according to their locations on chromosomes. StSRO genes were comprehensively analyzed using bioinformatics methods. The results showed that six StSRO genes were irregularly distributed on five chromosomes. Phylogenetic analysis showed that 30 SRO genes of four species were distributed in three groups, while StSRO genes were distributed in groups II and III. The promoter sequence of StSRO genes contained many cis-acting elements related to hormones and stress responses. In addition, the expression level of StSRO genes in different tissues of doubled monoploid (DM) potato, as well as under salt, drought stresses and hormone treatments, was analyzed by RNA-seq data from the online database and quantitative real-time polymerase chain reaction (qRT-PCR) analysis. Furthermore, the expression level of StSRO genes was analyzed by transcriptome analysis under mild, moderate and severe salt stress. It was concluded that StSRO genes could respond to different abiotic conditions, but their expression level was significantly different. This study lays a foundation for further studies on the biological functions of the StSRO gene family.

Proteome Composition of Bovine Amniotic Membrane and Its Potential Role in Corneal Healing

Purpose

To investigate the protein profile of bovine amniotic membranes (bAM) and to determine putative associations between protein composition in bAM and known corneal healing pathways.

Methods

The bAM were acquired from normal full-term births (n = 10), processed, and stored at -80°C for two days. Subsequently, the frozen membranes were thawed at room temperature and prepared for proteomic exploration using high-resolution liquid chromatography-mass spectrometry, followed by bioinformatics analysis. Recently identified corneal healing pathways were contrasted with protein profiles and pathways present in bAM.

Results

The analyses identified 2105 proteins, with an interactive network of 1271 nodes (proteins) and 8757 edges (interactions). The proteins with higher betweenness centrality measurements include microfibril-associated protein 4, HSD3B1, CAPNS1, ATP1B3, CAV1, ANXA2, YARS, and GAPDH. The top four pathways in Kyoto Encyclopedia of Genes and Genomes were ribosome, metabolic pathway, spliceosome, and oxidative phosphorylation. The bAM and cornea shared abundant proteins, genome ontology, and signaling pathways.

Conclusions

The high-throughput proteomic profile of the bAM demonstrated that numerous proteins present in the cornea are also present in this fetal membrane. Our findings collectively demonstrate the similarity between bAM and the cornea's protein composition, supporting our hypothesis that bAM can be used to treat corneal diseases.

Evolution of Sequence-based Bioinformatics Tools for Protein-protein Interaction Prediction

Protein-protein interactions (PPIs) are the physical connections between two or more proteins via electrostatic forces or hydrophobic effects. Identification of the PPIs is pivotal, which contributes to many biological processes including protein function, disease incidence, and therapy design. The experimental identification of PPIs via high-throughput technology is time-consuming and expensive. Bioinformatics approaches are expected to solve such restrictions. In this review, our main goal is to provide an inclusive view of the existing sequence-based computational prediction of PPIs. Initially, we briefly introduce the currently available PPI databases and then review the state-of-the-art bioinformatics approaches, working principles, and their performances. Finally, we discuss the caveats and future perspective of the next generation algorithms for the prediction of PPIs.

A systems biology-driven approach to construct a comprehensive protein interaction network of influenza A virus with its host

BACKGROUND

Influenza A virus (IAV) infection is a serious public health problem not only in South East Asia but also in European and African countries. Scientists are using network biology to dig deep into the essential host factors responsible for regulation of virus infections. Researchers can explore the virus invasion into the host cells by studying the virus-host relationship based on their protein-protein interaction network.

METHODS

In this study, we present a comprehensive IAV-host protein-protein interaction network that is obtained based on the literature-curated protein interaction datasets and some important interaction databases. The network is constructed in Cytoscape and analyzed with its plugins including CytoHubba, CytoCluster, MCODE, ClusterViz and ClusterOne. In addition, Gene Ontology and KEGG enrichment analyses are performed on the highly IAV-associated human proteins. We also compare the current results with those from our previous study on Hepatitis C Virus (HCV)-host protein-protein interaction network in order to find out valuable information.

RESULTS

We found out 1027 interactions among 829 proteins of which 14 are viral proteins and 815 belong to human proteins. The viral protein NS1 has the highest number of associations with human proteins followed by NP, PB2 and so on. Among human proteins, LNX2, MEOX2, TFCP2, PRKRA and DVL2 have the most interactions with viral proteins. Based on KEGG pathway enrichment analysis of the highly IAV-associated human proteins, we found out that they are enriched in the KEGG pathway of basal cell carcinoma. Similarly, the result of KEGG analysis of the common host factors involved in IAV and HCV infections shows that these factors are enriched in the infection pathways of Hepatitis B Virus (HBV), Viral Carcinoma, measles and certain other viruses.

CONCLUSION

It is concluded that the list of proteins we identified might be used as potential drug targets for the drug design against the infectious diseases caused by Influenza A Virus and other viruses.

Long non-coding RNA-SRA promotes neointimal hyperplasia and vascular smooth muscle cells proliferation via MEK-ERK-CREB pathway

Long noncoding RNA-steroid receptor RNA activator (LncRNA-SRA) is transcribed from a class of noncoding genes, and plays a critical role in regulating cell proliferation. However, the effect of lncRNA-SRA remains unclear in vascular proliferative diseases. In the present study, we overexpressed lncRNA-SRA in vitro, then investigated the biological consequences. A vascular damage mice model was constructed by performing femoral artery wire injury. LncRNA-SRA was overexpressed in the injured arteries, and significantly promoted the expression of ki67, thereby caused an overall increase in neointima formation. LncRNA-SRA overexpression led to the proliferation and migration of vascular smooth muscle cells (VSMCs). By stimulating the phosphorylation of MEK, ERK and CREB (cyclic nucleotide responsive element binding protein), lncRNA-SRA promoted VSMC proliferation. Meanwhile, these effects were blocked by the MEK inhibitor U0126. Therefore, lncRNA-SRA promoted VSMC proliferation by activating the MEK-ERK-CREB pathway. LncRNA-SRA could be a promising therapeutic target in vascular diseases characterized by neointimal hyperplasia.