scholarly journals Comprehensive circular RNA expression profile in radiation-treated HeLa cells and analysis of radioresistance-related circRNAs

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5011 ◽  
Author(s):  
Duo Yu ◽  
Yunfeng Li ◽  
Zhihui Ming ◽  
Hongyong Wang ◽  
Zhuo Dong ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
High Throughput ◽  
Protein Interaction ◽  
Hela Cells ◽  
High Throughput Sequencing ◽  
Interaction Network ◽  
Circular Rna ◽  
Protein Protein Interaction ◽  
Hub Proteins

Background Cervical cancer is one of the most common cancers in women worldwide. Malignant tumors develop resistance mechanisms and are less sensitive to or do not respond to irradiation. With the development of high-throughput sequencing technologies, circular RNA (circRNA) has been identified in an increasing number of diseases, especially cancers. It has been reported that circRNA can compete with microRNAs (miRNAs) to change the stability or translation of target RNAs, thus regulating gene expression at the transcriptional level. However, the role of circRNAs in cervical cancer and the radioresistance mechanisms of HeLa cells are unknown. The objective of this study is to investigate the role of circRNAs in radioresistance in HeLa cells. Methods High-throughput sequencing and bioinformatics analysis of irradiated and sham-irradiated HeLa cells. The reliability of high-throughput RNA sequencing was validated using quantitative real-time polymerase chain reaction. The most significant circRNA functions and pathways were selected by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. A circRNA–miRNA–target gene interaction network was used to find circRNAs associated with radioresistance. Moreover, a protein–protein interaction network was constructed to identify radioresistance-related hub proteins. Results High-throughput sequencing allowed the identification of 16,893 circRNAs involved in the response of HeLa cells to radiation. Compared with the control group, there were 153 differentially expressed circRNAs, of which 76 were up-regulated and 77 were down-regulated. GO covered three domains: biological process (BP), cellular component (CC) and molecular function (MF). The terms assigned to the BP domain were peptidyl-tyrosine dephosphorylation and regulation of cell migration. The identified CC terms were cell–cell adherens junction, nucleoplasm and cytosol, and the identified MF terms were protein binding and protein tyrosine phosphatase activity. The top five KEGG pathways were MAPK signaling pathway, endocytosis, axon guidance, neurotrophin signaling pathway, and SNARE interactions in vesicular transport. The protein–protein interaction analysis indicated that 19 proteins might be hub proteins. Conclusions CircRNAs may play a major role in the response to radiation. These findings may improve our understanding of the role of circRNAs in radioresistance in HeLa cells and allow the development of novel therapeutic approaches.

scholarly journals Male differentiation in the marine copepod Oithona nana reveals the development of a new nervous ganglion linked to Lin12-Notch-Repeat protein-associated proteolysis

2021 ◽  
Author(s):  
Kevin Sugier ◽  
Romuald Laso-Jadart ◽  
Benoit Vacherie ◽  
Jos Kafer ◽  
Laurie Bertrand ◽  
...  
Keyword(s):  
Extracellular Matrix ◽  
Protein Interaction ◽  
System Development ◽  
Interaction Network ◽  
Nervous System Development ◽  
Protein Coding ◽  
Protein Protein Interaction ◽  
Marine Copepod ◽  
Male Specific

Background: Copepods are among the most numerous animals, and play an essential role in the marine trophic web and biogeochemical cycles. The genus Oithona is described as having the highest density of copepods, and as being the most cosmopolite copepods. The Oithona male paradox describes the activity states of males, which are obliged to alternate between immobile and mobile phases for ambush feeding and mate searching, respectively, while the female is typically less mobile and often feeding. To characterize the molecular basis of this sexual dimorphism, we combined immunofluorescence, genomics, transcriptomics, and protein-protein interaction approaches. Results: Immunofluorescence of β3- and α-tubulin revealed two male-specific nervous ganglia in the lateral first segment of the Oithona nana male's prosome. In parallel, transcriptomic analysis showed male-specific enrichment for nervous system development-related transcripts. Twenty-seven Lin12-Notch Repeat domain-containing protein coding genes (LDPGs) of the 75 LDPGs identified in the genome were specifically expressed only in males. Furthermore, most of the LDPGs (27%) coded for proteins having predicted proteolytic activity, and non-LDPG proteolysis-associated transcripts showed a male-specific enrichment. Using yeast double-hybrid assays, we constructed a protein-protein interaction network involving two LDPs with proteases, extracellular matrix proteins, and neurogenesis-related proteins. Conclusions: For the first time, our study describes the lateral nervous ganglia of O. nana males, unique to copepods. We also demonstrated a role of LDPGs and their associated proteolysis in male-specific physiology, and we hypothesize a role of the LDPGs in the development of the lateral ganglia through directed lysis of the extracellular matrix for the growth of neurites and genesis of synapses.

scholarly journals Computational identification of signaling pathways in protein interaction networks

F1000Research ◽  
2015 ◽  
Vol 4 ◽  
pp. 1522
Author(s):  
Angela U. Makolo ◽  
Temitayo A. Olagunju
Keyword(s):  
Signaling Pathways ◽  
High Throughput ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Protein Interaction Data ◽  
Interaction Data ◽  
Protein Protein Interaction

The knowledge of signaling pathways is central to understanding the biological mechanisms of organisms since it has been identified that in eukaryotic organisms, the number of signaling pathways determines the number of ways the organism will react to external stimuli. Signaling pathways are studied using protein interaction networks constructed from protein-protein interaction data obtained from high-throughput experiments. However, these high-throughput methods are known to produce very high rates of false positive and negative interactions. To construct a useful protein interaction network from this noisy data, computational methods are applied to validate the protein-protein interactions. In this study, a computational technique to identify signaling pathways from a protein interaction network constructed using validated protein-protein interaction data was designed.A weighted interaction graph of Saccharomyces Cerevisiae was constructed. The weights were obtained using a Bayesian probabilistic network to estimate the posterior probability of interaction between two proteins given the gene expression measurement as biological evidence. Only interactions above a threshold were accepted for the network model.We were able to identify some pathway segments, one of which is a segment of the pathway that signals the start of the process of meiosis in S. Cerevisiae.

scholarly journals Proteinarium: Multi-Sample Protein-Protein Interaction Analysis and Visualization Tool

2019 ◽  
Author(s):  
David Armanious ◽  
Jessica Schuster ◽  
George F. Tollefson ◽  
Anthony Agudelo ◽  
Andrew T. DeWan ◽  
...  
Keyword(s):  
High Throughput ◽  
Protein Interaction ◽  
Protein Interactions ◽  
High Throughput Screening ◽  
Interaction Analysis ◽  
Interaction Network ◽  
Visualization Tool ◽  
Sequencing Data ◽  
Protein Protein Interaction ◽  
Ppi Networks

AbstractBackgroundData analysis has become crucial in the post genomic era where the accumulation of genomic information is mounting exponentially. Analyzing protein-protein interactions in the context of the interactome is a powerful approach to understanding disease phenotypes.ResultsWe describe Proteinarium, a multi-sample protein-protein interaction network analysis and visualization tool. Proteinarium can be used to analyze data for samples with dichotomous phenotypes, multiple samples from a single phenotype or a single sample. Then, by similarity clustering, the network-based relations of samples are identified and clusters of related samples are presented as a dendrogram. Each branch of the dendrogram is built based on network similarities of the samples. The protein-protein interaction networks can be analyzed and visualized on any branch of the dendrogram. Proteinarium’s input can be derived from transcriptome analysis, whole exome sequencing data or any high-throughput screening approach. Its strength lies in use of gene lists for each sample as a distinct input which are further analyzed through protein interaction analyses. Proteinarium output includes the gene lists of visualized networks and PPI interaction files where users can analyze the network(s) on other platforms such as Cytoscape. In addition, since the dendrogram is written in Newick tree format, users can visualize it in other software platforms like Dendroscope, ITOL.ConclusionsProteinarium, through the analysis and visualization of PPI networks, allows researchers to make important observations on high throughput data for a variety of research questions. Proteinarium identifies significant clusters of patients based on their shared network similarity for the disease of interest and the associated genes. Proteinarium is a command-line tool written in Java with no external dependencies and it is freely available at https://github.com/Armanious/Proteinarium.

scholarly journals IHP-PING—generating integrated human protein–protein interaction networks on-the-fly

2020 ◽  
Author(s):  
Gaston K Mazandu ◽  
Christopher Hooper ◽  
Kenneth Opap ◽  
Funmilayo Makinde ◽  
Victoria Nembaware ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
High Throughput Sequencing ◽  
Current Knowledge ◽  
Interaction Network ◽  
Human Protein ◽  
Online Resources ◽  
Ppi Network ◽  
Genomic Context ◽  
Protein Protein Interaction

Abstract Advances in high-throughput sequencing technologies have resulted in an exponential growth of publicly accessible biological datasets. In the ‘big data’ driven ‘post-genomic’ context, much work is being done to explore human protein–protein interactions (PPIs) for a systems level based analysis to uncover useful signals and gain more insights to advance current knowledge and answer specific biological and health questions. These PPIs are experimentally or computationally predicted, stored in different online databases and some of PPI resources are updated regularly. As with many biological datasets, such regular updates continuously render older PPI datasets potentially outdated. Moreover, while many of these interactions are shared between these online resources, each resource includes its own identified PPIs and none of these databases exhaustively contains all existing human PPI maps. In this context, it is essential to enable the integration of or combining interaction datasets from different resources, to generate a PPI map with increased coverage and confidence. To allow researchers to produce an integrated human PPI datasets in real-time, we introduce the integrated human protein–protein interaction network generator (IHP-PING) tool. IHP-PING is a flexible python package which generates a human PPI network from freely available online resources. This tool extracts and integrates heterogeneous PPI datasets to generate a unified PPI network, which is stored locally for further applications.

scholarly journals Circular RNA circYPEL2: A Novel Biomarker in Cervical Cancer

Genes ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 38
Author(s):  
Xinyang Zhang ◽  
Siqi Yang ◽  
Wenbo Chen ◽  
Xin Dong ◽  
Rongyu Zhang ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Cell Lines ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Strong Stability ◽  
Circular Rna ◽  
Migration And Invasion ◽  
Circular Rnas ◽  
Cancer Hallmarks ◽  
Potential Biomarker

Cervical cancer (CC) is one of the most threatening diseases in women. Circular RNAs (circRNAs) have been reported to be cancer hallmarks, but typical circRNAs in CC were rarely indicated. Through high-throughput sequencing in CC and normal cervix tissues, circYPEL2 (hsa_circ_0005600) was proposed as a candidate circRNA. CircYPEL2 exhibited significantly high expression in CC tissue and strong stability in CC cell lines. Furthermore, knockdown and overexpression of circYPEL2 indicated the potential involvement in CC proliferation, migration and invasion. Finally, the downstream regulatory genes of circYPEL2 were investigated by knockdown experiment in CC cell lines with high-throughput sequencing. In summary, our work identified circYPEL2 as a potential biomarker for clinical research of cervical cancer.

scholarly journals Circular RNA hsa_circ_0000376 Participates in Tumorigenesis of Breast Cancer by Targeting miR-1285-3p

2020 ◽  
Vol 19 ◽  
pp. 153303382092847
Author(s):  
Ziqi Peng ◽  
Boyang Xu ◽  
Feng Jin
Keyword(s):  
Breast Cancer ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Circular Rna ◽  
Differentially Expressed ◽  
Circular Rnas ◽  
Messenger Rnas ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

This study was designed to identify novel circular RNAs and the related regulatory axis to provide research targets for the diagnosis and treatment of breast cancer. The circular RNA expression microarray “GSE101123” related to breast cancer was downloaded from the Gene Expression Omnibus database. The differentially expressed circular RNAs between tumor and normal samples were screened using Limma package. The targeted microRNAs of the differentially expressed circular RNAs and the targeted messenger RNAs of the microRNAs were predicted using miRanda and miRWalk, respectively, and a circular RNAs–microRNAs–messenger RNAs network was constructed. Then, functional enrichment analysis, protein–protein interaction network construction, and drug–gene interaction analysis were conducted for the messenger RNAs. A total of 11 differentially expressed circular RNAs were identified between the breast cancer and normal samples, of which 3 were upregulated, while 8 were downregulated. The circular RNA–microRNA–messenger RNA network contained 1 circular RNA (hsa_circ_0000376), 2 microRNAs (miR-1285-3p and miR-1286), and 353 messenger RNAs. The protein–protein interaction network contained 150 nodes and 240 interactions. The hub genes in the protein–protein interaction network were all targeted messenger RNAs of miR-1285-3p that were significantly enriched in the ubiquitin–proteasome system, apoptosis, cell cycle arrest–related pathways, and cancer-related pathways involving SMAD specific E3 ubiquitin protein ligase 1, β-transducin repeat containing E3 ubiquitin protein ligase, tumor protein P53 among others. Twenty-two drugs were predicted to target 4 messenger RNAs, including tumor protein P53. A novel circular RNA, hsa_circ_0000376, was identified in breast cancer that may act as a sponge targeting miR-1285-3p expression which through its target genes, SMURF1, BTRC, and TP53, may further regulate tumorigenesis.

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