The Assembly of miRNA-mRNA-protein Regulatory Networks Using High-throughput Expression Data
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE60433 | GEO | 2015/03/01
SECONDARY ACCESSION(S): PRJNA258590
REPOSITORIES: GEO
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Project description:MotivationInference of gene regulatory networks from high throughput measurement of gene and protein expression is particularly attractive because it allows the simultaneous discovery of interactive molecular signals for numerous genes and proteins at a relatively low cost.ResultsWe developed two score-based local causal learning algorithms that utilized the Markov blanket search to identify direct regulators of target mRNAs and proteins. These two algorithms were specifically designed for integrated high throughput RNA and protein data. Simulation study showed that these algorithms outperformed other state-of-the-art gene regulatory network learning algorithms. We also generated integrated miRNA, mRNA, and protein expression data based on high throughput analysis of primary trophoblasts, derived from term human placenta and cultured under standard or hypoxic conditions. We applied the new algorithms to these data and identified gene regulatory networks for a set of trophoblastic proteins found to be differentially expressed under the specified culture conditions.
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Project description:Analyze miRNA expression levels in primary trophoblasts, derived from term human placenta and cultured under standard or hypoxic conditions Human placental trophoblasts were dispersed using a trypsin-deoxyribonuclease-dispase/Percoll method, plated in 6-well plates, and maintained in standard culture conditions (O2=20%). After 4 h (defined as time 0), the plates were divided to those in continued standard culture conditions, or to culture in hypoxia (O2=0%). Cells were then harvested at 6 h, 12 h, 24 h, 48 h and 72 h, and processed for miRNA arrays
2015-03-01 | E-GEOD-60431 | biostudies-arrayexpress
Project description:Analyze gene expression levels in primary trophoblasts, derived from term human placenta and cultured under standard or hypoxic conditions Human placental trophoblasts were dispersed using a trypsin-deoxyribonuclease-dispase/Percoll method, plated in 6-well plates, and maintained in standard culture conditions (O2=20%). After 4 h (defined as time 0), the plates were divided to those in continued standard culture conditions, or to culture in hypoxia (O2=0%). Cells were then harvested at 6 h, 12 h, 24 h, 48 h and 72 h, and processed for mRNA arrays
2015-03-01 | E-GEOD-60432 | biostudies-arrayexpress
Project description:This SuperSeries is composed of the SubSeries listed below. Refer to individual Series
2015-03-01 | E-GEOD-60433 | biostudies-arrayexpress
Project description:Analyze miRNA expression levels in primary trophoblasts, derived from term human placenta and cultured under standard or hypoxic conditions
2015-03-01 | GSE60431 | GEO
Project description:Analyze gene expression levels in primary trophoblasts, derived from term human placenta and cultured under standard or hypoxic conditions
2015-03-01 | GSE60432 | GEO
Project description:MicroRNAs (miRNAs) regulate gene expression through translational repression and RNA degradation. Recently developed high-throughput proteomic methods measure gene expression changes at protein level and therefore can reveal the direct effects of miRNAs' translational repression. Here, we present a web server, ProteoMirExpress, that integrates proteomic and mRNA expression data together to infer miRNA-centered regulatory networks. With both types of high-throughput data from the users, ProteoMirExpress is able to discover not only miRNA targets that have decreased mRNA, but also subgroups of targets with suppressed proteins whose mRNAs are not significantly changed or with decreased mRNA whose proteins are not significantly changed, which are usually ignored by most current methods. Furthermore, both direct and indirect targets of miRNAs can be detected. Therefore, ProteoMirExpress provides more comprehensive miRNA-centered regulatory networks. We used several published data to assess the quality of our inferred networks and prove the value of our server. ProteoMirExpress is available online, with free access to academic users.
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Project description:BackgroundIdentifying cancer subtypes is an important component of the personalised medicine framework. An increasing number of computational methods have been developed to identify cancer subtypes. However, existing methods rarely use information from gene regulatory networks to facilitate the subtype identification. It is widely accepted that gene regulatory networks play crucial roles in understanding the mechanisms of diseases. Different cancer subtypes are likely caused by different regulatory mechanisms. Therefore, there are great opportunities for developing methods that can utilise network information in identifying cancer subtypes.ResultsIn this paper, we propose a method, weighted similarity network fusion (WSNF), to utilise the information in the complex miRNA-TF-mRNA regulatory network in identifying cancer subtypes. We firstly build the regulatory network where the nodes represent the features, i.e. the microRNAs (miRNAs), transcription factors (TFs) and messenger RNAs (mRNAs) and the edges indicate the interactions between the features. The interactions are retrieved from various interatomic databases. We then use the network information and the expression data of the miRNAs, TFs and mRNAs to calculate the weight of the features, representing the level of importance of the features. The feature weight is then integrated into a network fusion approach to cluster the samples (patients) and thus to identify cancer subtypes. We applied our method to the TCGA breast invasive carcinoma (BRCA) and glioblastoma multiforme (GBM) datasets. The experimental results show that WSNF performs better than the other commonly used computational methods, and the information from miRNA-TF-mRNA regulatory network contributes to the performance improvement. The WSNF method successfully identified five breast cancer subtypes and three GBM subtypes which show significantly different survival patterns. We observed that the expression patterns of the features in some miRNA-TF-mRNA sub-networks vary across different identified subtypes. In addition, pathway enrichment analyses show that the top pathways involving the most differentially expressed genes in each of the identified subtypes are different. The results would provide valuable information for understanding the mechanisms characterising different cancer subtypes and assist the design of treatment therapies. All datasets and the R scripts to reproduce the results are available online at the website: http://nugget.unisa.edu.au/Thuc/cancersubtypes/.
| S-EPMC4818025 | biostudies-literature
Project description:Parkinson's disease (PD) is the second-most common neurodegenerative disease, and its pathophysiology is associated with alpha-synuclein accumulation, oxidative stress, mitochondrial dysfunction, and neuroinflammation. MicroRNAs are small non-coding RNAs that regulate gene expression, and many previous studies have described their dysregulation in plasma, CSF, and in the brain of patients with PD. In this study, we aimed to provide a regulatory network analysis on differentially expressed miRNAs in the brain of patients with PD. Based on our systematic review with a focus on the substantia nigra and the putamen, we found 99 differentially expressed miRNAs in brain samples from patients with PD, which regulate 135 target genes. Five genes associated with neuronal survival (BCL2, CCND1, FOXO3, MYC, and SIRT1) were modulated by dysregulated miRNAs found in the substantia nigra and the putamen of patients with PD. The functional enrichment analysis found FoxO and PI3K-AKT signaling as pathways related to PD. In conclusion, our comprehensive analysis of brain-related miRNA-mRNA regulatory networks in PD showed that mechanisms involving neuronal survival signaling, such as cell cycle control and regulation of autophagy/apoptosis, may be crucial for the neurodegeneration of PD, being a promising way for novel disease-modifying therapies.
| S-EPMC8228551 | biostudies-literature
Project description:IntroductionDiabetic retinopathy (DR) is a major complication of diabetes and a leading cause of visual loss. This study aimed to explore biomarkers for DR that may provide additional reference to DR pathogenesis and development.MethodsThe differentially expressed genes (DEGs) between the DR and control samples in the GSE53257 dataset were identified. Logistics analyses were performed to identify DR-associated miRNAs and genes, and correlation analysis was performed to determine the correlation between them in GSE160306.ResultsA total of 114 DEGs in DR were identified in GSE53257. Three genes, including ATP5A1 (down), DAUFV2 (down), and OXA1L (down), were differentially expressed between DR and control samples in GSE160306. Univariate logistics analysis identified that ATP5A1 (OR=0.007, p = 1.40E-02), NDUFV2 (OR = 0.003, p = 6.40E-03), and OXA1L (OR = 0.093, p = 3.08E-02) were DR-associated genes. ATP5A1 and OXA1L were regulated by multiple miRNAs, of which hsa-let- 7b-5p (OR = 26.071, p = 4.40E-03) and hsa-miR-31-5p (OR = 4.188, p = 5.09E-02) were related to DR. ATP5A1 and OXA1L were closely correlated with each other in DR.ConclusionThe hsa-miR-31-5p-ATP5A1 and hsa-let-7b-5p-OXA1L axes might play novel and important roles in the pathogenesis and development of DR.
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