scholarly journals Network-Based Approaches to Explore Complex Biological Systems towards Network Medicine

Genes ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 437 ◽  
Author(s):  
Giulia Fiscon ◽  
Federica Conte ◽  
Lorenzo Farina ◽  
Paola Paci
Keyword(s):  
Gene Expression ◽  
Regulatory Networks ◽  
Detection Algorithm ◽  
Cell Phenotype ◽  
Disease Genes ◽  
Network Medicine ◽  
Topological Properties ◽  
Systematic Analysis ◽  
Ppi Networks ◽  
Non Coding Rnas

Network medicine relies on different types of networks: from the molecular level of protein–protein interactions to gene regulatory network and correlation studies of gene expression. Among network approaches based on the analysis of the topological properties of protein–protein interaction (PPI) networks, we discuss the widespread DIAMOnD (disease module detection) algorithm. Starting from the assumption that PPI networks can be viewed as maps where diseases can be identified with localized perturbation within a specific neighborhood (i.e., disease modules), DIAMOnD performs a systematic analysis of the human PPI network to uncover new disease-associated genes by exploiting the connectivity significance instead of connection density. The past few years have witnessed the increasing interest in understanding the molecular mechanism of post-transcriptional regulation with a special emphasis on non-coding RNAs since they are emerging as key regulators of many cellular processes in both physiological and pathological states. Recent findings show that coding genes are not the only targets that microRNAs interact with. In fact, there is a pool of different RNAs—including long non-coding RNAs (lncRNAs) —competing with each other to attract microRNAs for interactions, thus acting as competing endogenous RNAs (ceRNAs). The framework of regulatory networks provides a powerful tool to gather new insights into ceRNA regulatory mechanisms. Here, we describe a data-driven model recently developed to explore the lncRNA-associated ceRNA activity in breast invasive carcinoma. On the other hand, a very promising example of the co-expression network is the one implemented by the software SWIM (switch miner), which combines topological properties of correlation networks with gene expression data in order to identify a small pool of genes—called switch genes—critically associated with drastic changes in cell phenotype. Here, we describe SWIM tool along with its applications to cancer research and compare its predictions with DIAMOnD disease genes.

scholarly journals NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis

2019 ◽  
Vol 47 (W1) ◽  
pp. W234-W241 ◽  
Author(s):  
Guangyan Zhou ◽  
Othman Soufan ◽  
Jessica Ewald ◽  
Robert E W Hancock ◽  
Niladri Basu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Visual Analytics ◽  
Regulatory Networks ◽  
Meta Analysis ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Perform Gene Expression ◽  
And Performance

Abstract The growing application of gene expression profiling demands powerful yet user-friendly bioinformatics tools to support systems-level data understanding. NetworkAnalyst was first released in 2014 to address the key need for interpreting gene expression data within the context of protein-protein interaction (PPI) networks. It was soon updated for gene expression meta-analysis with improved workflow and performance. Over the years, NetworkAnalyst has been continuously updated based on community feedback and technology progresses. Users can now perform gene expression profiling for 17 different species. In addition to generic PPI networks, users can now create cell-type or tissue specific PPI networks, gene regulatory networks, gene co-expression networks as well as networks for toxicogenomics and pharmacogenomics studies. The resulting networks can be customized and explored in 2D, 3D as well as Virtual Reality (VR) space. For meta-analysis, users can now visually compare multiple gene lists through interactive heatmaps, enrichment networks, Venn diagrams or chord diagrams. In addition, users have the option to create their own data analysis projects, which can be saved and resumed at a later time. These new features are released together as NetworkAnalyst 3.0, freely available at https://www.networkanalyst.ca.

Label-free proteomic analysis reveals large dynamic changes to the cellular proteome upon expression of the miRNA-23a-27a-24-2 microRNA cluster

2020 ◽  
Vol 98 (1) ◽  
pp. 61-69 ◽  
Author(s):  
Ramanaguru S. Piragasam ◽  
S. Faraz Hussain ◽  
Steven G. Chaulk ◽  
Zaeem A. Siddiqi ◽  
Richard P. Fahlman
Keyword(s):  
Gene Expression ◽  
Proteomic Analysis ◽  
Small Rnas ◽  
Regulatory Networks ◽  
Mirna Cluster ◽  
Label Free ◽  
Dynamic Changes ◽  
Mrna Targets ◽  
Order Of Magnitude ◽  
Non Coding Rnas

In deciphering the regulatory networks of gene expression controlled by the small non-coding RNAs known as microRNAs (miRNAs), a major challenge has been with the identification of the true mRNA targets by these RNAs within the context of the enormous numbers of predicted targets for each of these small RNAs. To facilitate the system-wide identification of miRNA targets, a variety of system wide methods, such as proteomics, have been implemented. Here we describe the utilization of quantitative label-free proteomics and bioinformatics to identify the most significant changes to the proteome upon expression of the miR-23a-27a-24-2 miRNA cluster. In light of recent work leading to the hypothesis that only the most pronounced regulatory events by miRNAs may be physiologically relevant, our data reveal that label-free analysis circumvents the limitations of proteomic labeling techniques that limit the maximum differences that can be quantified. The result of our analysis identifies a series of novel candidate targets that are reduced in abundance by more than an order of magnitude upon the expression of the miR-23a-27a-24-2 cluster.

scholarly journals Highly Divergent Neuropeptide - non-coding RNA regulatory networks underpin variant host-finding behaviours in Steinernema species infective juveniles

2020 ◽  
Author(s):  
Neil D. Warnock ◽  
Erwan Atcheson ◽  
Ciaran McCoy ◽  
Johnathan J. Dalzell
Keyword(s):  
Gene Expression ◽  
Small Rna ◽  
Regulatory Networks ◽  
Host Finding ◽  
Protein Coding ◽  
Rna Analysis ◽  
New Family ◽  
Non Coding Rna ◽  
Non Coding Rnas ◽  
Shared Gene

AbstractWe conducted a transcriptomic and small RNA analysis of infective juveniles (IJs) from three behaviourally distinct Steinernema species. Substantial variation was found in the expression of shared gene orthologues, revealing gene expression signatures that correlate with behavioural states. 97% of predicted microRNAs are novel to each species. Surprisingly, our data provide evidence that isoform variation can effectively convert protein-coding neuropeptide genes into non-coding transcripts, which may represent a new family of long non-coding RNAs. These data suggest that differences in neuropeptide gene expression, isoform variation, and small RNA interactions could contribute to behavioural differences within the Steinernema genus.

scholarly journals Gene modules and non-coding RNAs involved in pancreatic tumorigenesis through acinar ductal metaplasia 

2020 ◽  
Author(s):  
Huairong Zhang ◽  
Bingyin Shi ◽  
ZU-HUA GAO ◽  
BO GAO
Keyword(s):  
Gene Expression ◽  
Intraepithelial Neoplasia ◽  
Genetic Alterations ◽  
Ductal Adenocarcinoma ◽  
Nucleosome Assembly ◽  
Go Analysis ◽  
Functional Studies ◽  
Ppi Networks ◽  
Gene Modules ◽  
Non Coding Rnas

Abstract Background: Acinar ductal metaplasia (ADM) is a recently identified precursor lesion that can progress through pancreatic ductal intraepithelial neoplasia (PanIN) to pancreatic ductal adenocarcinoma (PDAC). However, the genetic alterations and the transcriptional regulators at work during the process of ADM-driven PDAC tumorigenesis are largely unknown. We applied a multidimensional integration strategy to unveil the gene modules and non-coding RNAs that drive the ADM-PanIN-PDAC process. Methods: GSE40895 and the microarray datasets were integrated to unmask the regulators linked to ADM, PanIN and PDAC. Based on the differentially expressed genes and protein–protein interaction (PPI) networks for each stage, overlapping and crosstalk gene modules in ADM-PanIN-PDAC were identified using the search tool for the retrieval of interacting genes (STRING) and Cytoscape. The functions of these modules were elucidated by gene ontology (GO) analysis. The expression levels of hub genes and survival analysis were investigated in human PDAC via gene expression profiling interactive analysis (GEPIA). The MiRDB database was used to predict potential non-coding RNAs (ncRNAs) capable of regulating overlap and crosstalk genes.Results: We found several bridging ADM gene modules (e.g. SMARCA1 and H2AFZ), PanIN gene modules (e.g. HDAC11 and SMARCA2) and PDAC gene modules (e.g. OLFR239 and CLIP3). They were enriched in nucleosome assembly, chromatin organization and G-protein coupled receptor signalling pathways by GO analysis. MicroRNAs (e.g. mmu-miR-335-5p and mmu-miR-669n) and lncRNAs (e.g. H19 and Gm14207) took part in this ample crosstalk by regulating the gene expression. Conclusions: SMARCA1, SMARCA2 and CLIP3 were identified as novel crosstalk genes and potential prognostic biomarkers for ADM-driven PDAC carcinogenesis. After validation in clinical and functional studies, transcriptional regulatory non-coding RNAs targeting crosstalk and overlapping genes could represent effective targets for early PDAC intervention.

scholarly journals Identification of Key Genes and miRNA-mRNA Regulatory Pathways in Bronchopulmonary Dysplasia in Preterm Infants by Bioinformatics Methods

2020 ◽  
Author(s):  
Tong Sun ◽  
Haiyang Yu ◽  
Jianhua Fu
Keyword(s):  
Gene Expression ◽  
Preterm Infants ◽  
Bronchopulmonary Dysplasia ◽  
Regulatory Networks ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Regulatory Pathways ◽  
Ppi Networks ◽  
Key Genes

Abstract Background: Bronchopulmonary dysplasia (BPD) remains a severe respiratory complication of preterm infants in neonatal intensive care units (NICUs). However, its pathogenesis has been unclear. Bioinformatics analysis, which can help us explore genetic alternations and recognize latent diagnostic biomarkers, has recently promoted the comprehension of the molecular mechanisms underlying disease occurrence and development. Methods: In this study, we identified key genes and miRNA-mRNA regulatory networks in BPD in preterm infants to elucidate the pathogenesis of BPD. We downloaded and analyzed miRNA and gene expression microarray datasets from the Gene Expression Omnibus database (GEO). Differentially expressed miRNA (DEMs) and differentially expressed genes (DEGs) were obtained through NetworkAnalyst. We performed pathway enrichment analysis using the Database for Annotation, Visualization and Integrated Discovery program (DAVID), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG). Then we used the STRING to establish protein–protein interactions and the Cytoscape tool to establish miRNA–mRNA regulatory networks. Results: We identified 19 significant DEMs and 140 and 33 significantly upregulated and downregulated DEGs, respectively. Functional enrichment analysis indicated that significant DEGs were associated with the antigen processing and presentation, and B-cell receptor signaling pathways in BPD. Key DEGs, such as CD19, CD79B, MS4A1, and FCGR2B were selected as hub genes in PPI networks. Conclusions: In this study, we screened out 19 DEMs that might play important roles in the regulatory networks of BPD. Higher expression of miRNAs such as miR-15b-5p, hsa-miR-32-5p, miR-3613-3p, and miR-33a-5p and lower expression of miRNAs such as miR-3960, miR-425-5p, and miR-3202 might be correlated with the process of BPD.

scholarly journals A Novel LncRNA-miRNA-mRNA Triple Network Identifies LncRNA RP11-363E7.4 as An Important Regulator of miRNA and Gene Expression in Gastric Cancer

2018 ◽  
Vol 47 (3) ◽  
pp. 1025-1041 ◽  
Author(s):  
Pengcheng Wang ◽  
Jing Li ◽  
Wei Zhao ◽  
Chunyang Shang ◽  
Xian Jiang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Regulatory Networks ◽  
Functional Enrichment ◽  
Microrna Target ◽  
Kaplan Meier ◽  
Important Regulator ◽  
Non Coding Rnas ◽  
Gc Gene ◽  
Insight Into

Background/Aims: Recent evidence has shown that some long non-coding RNAs (lncRNAs) play important roles in various biological processes. However, the regulatory mechanism of lncRNA in gastric cancer (GC) remains unclear. Methods: We reannotated the GC gene expression profile into a lncRNA-mRNA biphasic profile and integrated the microRNA target data to construct a global GC triple network. A further clustering and random walk with restart analyses was performed on the triple network from the level of topology analyses. Quantitative real-time PCR was used to determine expression of lncRNA RP11-363E7.4. Kaplan-Meier analyses was performed to evaluate the prognostic value of lncRNA RP11-363E7.4. Results: We constructed a gastric cancer lncRNA-miRNA-mRNA network (GCLMN) including six lncRNAs, 332 mRNAs, and 3,707 edges. For the shared lncRNA RP11-363E7.4, the interacting gene and microRNA functional enrichment studies implied that lncRNA RP11-363E7.4 might function as a new regulator in GC. The expression of lncRNA RP11-363E7.4 was downregulated compared with that of paracarcinoma tissues in five GC samples. High expression of lncRNA RP11-363E7.4 was found to be correlated to better overall survival (OS) for GC patients. Conclusions: This study focused on GC lncRNA-miRNA-mRNA regulatory networks, and found that lncRNA RP11-363E7.4 was a new GC risk lncRNA, which might provide novel insight into a better understanding of the pathogenesis of GC.

scholarly journals Molecular Mediators of RNA Loading into Extracellular Vesicles

Cells ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 3355
Author(s):  
Chiara Corrado ◽  
Maria Magdalena Barreca ◽  
Chiara Zichittella ◽  
Riccardo Alessandro ◽  
Alice Conigliaro
Keyword(s):  
Gene Expression ◽  
Extracellular Vesicles ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Translation Regulation ◽  
Circular Rnas ◽  
Non Coding Rna ◽  
Gene Regulatory ◽  
Non Coding Rnas ◽  
Different Levels

In the last decade, an increasing number of studies have demonstrated that non-coding RNA (ncRNAs) cooperate in the gene regulatory networks with other biomolecules, including coding RNAs, DNAs and proteins. Among them, microRNAs (miRNAs), long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) are involved in transcriptional and translation regulation at different levels. Intriguingly, ncRNAs can be packed in vesicles, released in the extracellular space, and finally internalized by receiving cells, thus affecting gene expression also at distance. This review focuses on the mechanisms through which the ncRNAs can be selectively packaged into extracellular vesicles (EVs).

scholarly journals Revisiting the Role of Transcription Factors in Coordinating the Defense Response Against Citrus Bark Cracking Viroid Infection in Commercial Hop (Humulus Lupulus L.)

Viruses ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 419 ◽  
Author(s):  
Vishnu Sukumari Nath ◽  
Ajay Kumar Mishra ◽  
Atul Kumar ◽  
Jaroslav Matoušek ◽  
Jernej Jakše
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Regulatory Networks ◽  
Evolutionary History ◽  
Digital Gene Expression ◽  
Defense Responses ◽  
Integrated Analysis ◽  
Systematic Analysis ◽  
Viroid Infection ◽  
External Stimuli

Transcription factors (TFs) play a major role in controlling gene expression by intricately regulating diverse biological processes such as growth and development, the response to external stimuli and the activation of defense responses. The systematic identification and classification of TF genes are essential to gain insight into their evolutionary history, biological roles, and regulatory networks. In this study, we performed a global mining and characterization of hop TFs and their involvement in Citrus bark cracking viroid CBCVd infection by employing a digital gene expression analysis. Our systematic analysis resulted in the identification of a total of 3,818 putative hop TFs that were classified into 99 families based on their conserved domains. A phylogenetic analysis classified the hop TFs into several subgroups based on a phylogenetic comparison with reference TF proteins from Arabidopsis thaliana providing glimpses of their evolutionary history. Members of the same subfamily and subgroup shared conserved motif compositions. The putative functions of the CBCVd-responsive hop TFs were predicted using their orthologous counterparts in A. thaliana. The analysis of the expression profiling of the CBCVd-responsive hop TFs revealed a massive differential modulation, and the expression of the selected TFs was validated using qRT-PCR. Together, the comprehensive integrated analysis in this study provides better insights into the TF regulatory networks associated with CBCVd infections in the hop, and also offers candidate TF genes for improving the resistance in hop against viroids.

scholarly journals Identification of the Key MicroRNAs and the miRNA-mRNA Regulatory Pathways in Prostate Cancer by Bioinformatics Methods

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Dongyang Li ◽  
Xuanyu Hao ◽  
Yongsheng Song
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Signaling Pathway ◽  
Regulatory Networks ◽  
Experimental Studies ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Tgf Beta ◽  
Regulatory Pathways ◽  
Ppi Networks

Objective. To identify key microRNAs (miRNAs) and their regulatory networks in prostate cancer.Methods. Four miRNA and three gene expression microarray datasets were downloaded for analysis from Gene Expression Omnibus database. The differentially expressed miRNA and genes were accessed by a GEO2R. Functional and pathway enrichment analyses were performed using the DAVID program. Protein-protein interaction (PPI) and miRNA-mRNA regulatory networks were constructed using the STRING and Cytoscape tool. Moreover, the results and clinical significance were validated in TCGA data.Results. We identified 26 significant DEMs, 633 upregulated DEGs, and 261 downregulated DEGs. Functional enrichment analysis indicated that significant DEGs were related to TGF-beta signaling pathway and TNF signaling pathway in PCa. Key DEGs such as HSPA8, PPP2R1A, CTNNB1, ADCY5, ANXA1, and COL9A2 were found as hub genes in PPI networks. TCGA data supported our results and the miRNAs were correlated with clinical stages and overall survival.Conclusions. We identified 26 miRNAs that may take part in key pathways like TGF-beta and TNF pathways in prostate cancer regulatory networks. MicroRNAs like miR-23b, miR-95, miR-143, and miR-183 can be utilized in assisting the diagnosis and prognosis of prostate cancer as biomarkers. Further experimental studies are required to validate our results.

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