Combined Stress Conditions in Melon Induce Non-additive Effects in the Core miRNA Regulatory Network.
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ABSTRACT:
SUBMITTER: Villalba-Bermell P
PROVIDER: S-EPMC8656716 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND:MiRNAs have emerged as key regulators of stress response in plants, suggesting their potential as candidates for knock-in/out to improve stress tolerance in agricultural crops. Although diverse assays have been performed, systematic and detailed studies of miRNA expression and function during exposure to multiple environments in crops are limited. RESULTS:Here, we present such pioneering analysis in melon plants in response to seven biotic and abiotic stress conditions. Deep-sequencing and computational approaches have identified twenty-four known miRNAs whose expression was significantly altered under at least one stress condition, observing that down-regulation was preponderant. Additionally, miRNA function was characterized by high scale degradome assays and quantitative RNA measurements over the intended target mRNAs, providing mechanistic insight. Clustering analysis provided evidence that eight miRNAs showed a broad response range under the stress conditions analyzed, whereas another eight miRNAs displayed a narrow response range. Transcription factors were predominantly targeted by stress-responsive miRNAs in melon. Furthermore, our results show that the miRNAs that are down-regulated upon stress predominantly have as targets genes that are known to participate in the stress response by the plant, whereas the miRNAs that are up-regulated control genes linked to development. CONCLUSION:Altogether, this high-resolution analysis of miRNA-target interactions, combining experimental and computational work, Illustrates the close interplay between miRNAs and the response to diverse environmental conditions, in melon.
| S-EPMC6379984 | biostudies-other
Project description:miRNAs are small RNAs that regulate mRNAs at both transcriptional and posttranscriptional level. In plants, miRNAs are involved in the regulation of different processes including development and stress-response. Elucidating how stress-responsive miRNAs are regulated is key to understand the global response to stress but also to develop efficient biotechnological tools that could help to cope with stress. Here, we describe a computational approach based on sRNA sequencing, transcript quantification and degradome data to analyse the accumulation, function and structural organization of melon miRNAs reactivated under seven biotic and abiotic stress conditions at two and four days post-treatment. Our pipeline allowed us to identify fourteen stress-responsive miRNAs (including evolutionary conserved such as miR156, miR166, miR172, miR319, miR398, miR399, miR894 and miR408) at both analysed times. According to our analysis miRNAs were categorized in three groups showing a broad-, intermediate- or narrow- response range. miRNAs reactive to a broad range of environmental cues appear as central components in the stress-response network. The strictly coordinated response of miR398 and miR408 (broad response-range) to the seven stress treatments during the period analysed here reinforces this notion. Although both, the amplitude and diversity of the miRNA-related response to stress changes during the exposition time, the architecture of the miRNA-network is conserved. This organization of miRNA response to stress is also conserved in rice and soybean supporting the conservation of miRNA-network organization in other crops. Overall, our work sheds light into how miRNA networks in plants organize and function during stress.
| S-EPMC6973316 | biostudies-literature
Project description:Gene-on-gene regulations are key components of every living organism. Dynamical abstract models of genetic regulatory networks help explain the genome's evolvability and robustness. These properties can be attributed to the structural topology of the graph formed by genes, as vertices, and regulatory interactions, as edges. Moreover, the actual gene interaction of each gene is believed to play a key role in the stability of the structure. With advances in biology, some effort was deployed to develop update functions in boolean models that include recent knowledge. We combine real-life gene interaction networks with novel update functions in a boolean model. We use two sub-networks of biological organisms, the yeast cell-cycle and the mouse embryonic stem cell, as topological support for our system. On these structures, we substitute the original random update functions by a novel threshold-based dynamic function in which the promoting and repressing effect of each interaction is considered. We use a third real-life regulatory network, along with its inferred boolean update functions to validate the proposed update function. Results of this validation hint to increased biological plausibility of the threshold-based function. To investigate the dynamical behavior of this new model, we visualized the phase transition between order and chaos into the critical regime using Derrida plots. We complement the qualitative nature of Derrida plots with an alternative measure, the criticality distance, that also allows to discriminate between regimes in a quantitative way. Simulation on both real-life genetic regulatory networks show that there exists a set of parameters that allows the systems to operate in the critical region. This new model includes experimentally derived biological information and recent discoveries, which makes it potentially useful to guide experimental research. The update function confers additional realism to the model, while reducing the complexity and solution space, thus making it easier to investigate.
| S-EPMC3221653 | biostudies-literature
Project description:Proteasomes consist of a 19-subunit regulatory particle (RP) and 28-subunit core particle (CP), an ?(7)?(7)?(7)?(7) structure. The RP recognizes substrates and translocates them into the CP for degradation. At the RP-CP interface, a heterohexameric Rpt ring joins to a heteroheptameric CP ? ring. Rpt C termini insert individually into the ? ring pockets to form a salt bridge with a pocket lysine residue. We report that substitutions of ? pocket lysine residues produce an unexpected block to CP assembly, arising from a late stage defect in ? ring assembly. Substitutions ?5(K66A) and ?6(K62A) resulted in abundant incorporation of immature CP ? subunits, associated with a complete ? ring, into proteasome holoenzymes. Incorporation of immature CP into the proteasome depended on a proteasome-associated protein, Ecm29. Using ump1 mutants, we identified Ecm29 as a potent negative regulator of RP assembly and confirmed our previous findings that proper RP assembly requires the CP. Ecm29 was enriched on proteasomes of pocket lysine mutants, as well as those of rpt4-?1 and rpt6-?1 mutants, in which the C-terminal residue, thought to contact the pocket lysine, is deleted. In both rpt6-?1 and ?6(K62A) proteasomes, Ecm29 suppressed opening of the CP substrate translocation channel, which is gated through interactions between Rpt C termini and the ? pockets. The ubiquitin ligase Hul5 was recruited to these proteasomes together with Ecm29. Proteasome remodeling through the addition of Ecm29 and Hul5 suggests a new layer of the proteasome stress response and may be a common response to structurally aberrant proteasomes or deficient proteasome function.
| S-EPMC3196138 | biostudies-literature
Project description:Post-transcriptional regulation by miRNAs is a widespread and highly conserved phenomenon in metazoans, with several hundreds to thousands of conserved binding sites for each miRNA, and up to two thirds of all genes under miRNA regulation. At the same time, the effect of miRNA regulation on mRNA and protein levels is usually quite modest and associated phenotypes are often weak or subtle. This has given rise to the notion that the highly interconnected miRNA regulatory network exerts its function less through any individual link and more via collective effects that lead to a functional interdependence of network links. We present a Bayesian framework to quantify conservation of miRNA target sites using vertebrate whole-genome alignments. The increased statistical power of our phylogenetic model allows detection of evolutionary correlation in the conservation patterns of site pairs. Such correlations could result from collective functions in the regulatory network. For instance, co-conservation of target site pairs supports a selective benefit of combinatorial regulation by multiple miRNAs. We find that some miRNA families are under pronounced co-targeting constraints, indicating a high connectivity in the regulatory network, while others appear to function in a more isolated way. By analyzing coordinated targeting of different curated gene sets, we observe distinct evolutionary signatures for protein complexes and signaling pathways that could reflect differences in control strategies. Our method is easily scalable to analyze upcoming larger data sets, and readily adaptable to detect high-level selective constraints between other genomic loci. We thus provide a proof-of-principle method to understand regulatory networks from an evolutionary perspective.
| S-EPMC4191876 | biostudies-literature
Project description:BackgroundMicroRNAs (miRNAs) are a class of non-coding regulatory RNAs approximately 22 nucleotides in length that play a role in a wide range of biological processes. Abnormal miRNA function has been implicated in various human cancers including prostate cancer (PCa). Altered miRNA expression may serve as a biomarker for cancer diagnosis and treatment. However, limited data are available on the role of cancer-specific miRNAs. Integrative computational bioinformatics approaches are effective for the detection of potential outlier miRNAs in cancer.MethodsThe human miRNA-mRNA target network was reconstructed by integrating multiple miRNA-mRNA interaction datasets. Paired miRNA and mRNA expression profiling data in PCa versus benign prostate tissue samples were used as another source of information. These datasets were analyzed with an integrated bioinformatics framework to identify potential PCa miRNA signatures. In vitro q-PCR experiments and further systematic analysis were used to validate these prediction results.ResultsUsing this bioinformatics framework, we identified 39 miRNAs as potential PCa miRNA signatures. Among these miRNAs, 20 had previously been identified as PCa aberrant miRNAs by low-throughput methods, and 16 were shown to be deregulated in other cancers. In vitro q-PCR experiments verified the accuracy of these predictions. miR-648 was identified as a novel candidate PCa miRNA biomarker. Further functional and pathway enrichment analysis confirmed the association of the identified miRNAs with PCa progression.ConclusionsOur analysis revealed the scale-free features of the human miRNA-mRNA interaction network and showed the distinctive topological features of existing cancer miRNA biomarkers from previously published studies. A novel cancer miRNA biomarker prediction framework was designed based on these observations and applied to prostate cancer study. This method could be applied for miRNA biomarker prediction in other cancers.
| S-EPMC4007708 | biostudies-literature
Project description:Artificial neural network is an efficient and accurate fitting method. It has the function of self-learning, which is particularly important for prediction, and it could take advantage of the computer's high-speed computing capabilities and find the optimal solution quickly. In this paper, four culture conditions: agar concentration, light time, culture temperature, and humidity were selected. And a three-layer neural network was used to predict the differentiation rate of melon under these four conditions. Ten-fold cross validation revealed that the optimal back propagation neural network was established with traingdx as the training function and the final architecture of 4-3-1 (four neurons in the input layer, three neurons in the hidden layer and one neuron in the output layer), which yielded a high coefficient of correlation (R2, 0.9637) between the actual and predicted outputs, and a root-mean-square error (RMSE) of 0.0108, suggesting that the artificial neural network worked well. According to the optimal culture conditions generated by genetic algorithm, tissue culture experiments had been carried out. The results showed that the actual differentiation rate of melon reached 90.53%, and only 1.59% lower than the predicted value of genetic algorithm. It was better than the optimization by response surface methodology, which the predicted induced differentiation rate is 86.04%, the actual value is 83.62%, and was 2.89% lower than the predicted value. It can be inferred that the combination of artificial neural network and genetic algorithm can optimize the plant tissue culture conditions well and with high prediction accuracy, and this method will have a good application prospect in other biological experiments.
| S-EPMC7044330 | biostudies-literature
Project description:Background: Glioblastoma multiforme (GBM), the most common and aggressive human malignant brain tumor, is notorious for its limited treatment options and poor prognosis. MicroRNAs (miRNAs) are found to be involved in tumorigenesis of GBM. However, a comprehensive miRNA-mRNA regulatory network has still not been established. Methods: A miRNA microarray dataset (GSE90603) was obtained from GEO database. Then, we employed GEO2R tool to perform differential expression analysis. Potential transcription factors and target genes of screened differentially expressed miRNAs (DE-miRNAs) were predicted. The GBM mRNA dataset were downloaded from TCGA database for identifying differentially expressed genes (DEGs). Next, GO annotation and KEGG pathway enrichment analysis was conducted. PPI network was then established, and hub genes were identified via Cytoscape software. The expression and prognostic roles of hub genes was further evaluated. Results: Total 33 DE-miRNAs, consisting of 10 upregulated DE-miRNAs and 23 downregulated DE-miRNAs, were screened. SP1 was predicted to potentially regulate most of screened DE-miRNAs. Three thousand and twenty seven and 3,879 predicted target genes were obtained for upregulated and downregulated DE-miRNAs, respectively. Subsequently, 1,715 upregulated DEGs and 1,259 downregulated DEGs were identified. Then, 149 and 295 potential downregulated and upregulated genes commonly appeared in target genes of DE-miRNAs and DEGs were selected for GO annotation and KEGG pathway enrichment analysis. The downregulated genes were significantly enriched in cGMP-PKG signaling pathway and calcium signaling pathway whereas the upregulated genes were enriched in pathways in cancer and PI3K-Akt signaling pathway. Construction and analysis of PPI network showed that STXBP1 and TP53 were recognized as hub genes with the highest connectivity degrees. Expression analytic result of the top 20 hub genes in GBM using GEPIA database was generally identical with previous differential expression analysis for TCGA data. EGFR, PPP3CB, and MYO5A expression was significantly associated with patients' OS. Conclusions: In this study, we established a potential GBM-related miRNA-mRNA regulatory network, which explores a comprehensive understanding of the molecular mechanisms and provides key clues in seeking novel therapeutic targets for GBM. In the future, more experiments need to be performed to validate our current findings.
| S-EPMC6444190 | biostudies-literature
Project description:Deregulation of gene expression is associated with the pathogenesis of numerous human diseases including cancer. Current data analyses on gene expression are mostly focused on differential gene/transcript expression in big data-driven studies. However, a poor connection to the proteome changes is a widespread problem in current data analyses. This is partly due to the complexity of gene regulatory pathways at the post-transcriptional level. In this study, we overcome these limitations and introduce a graph-based learning model, PTNet, which simulates the microRNAs (miRNAs) that regulate gene expression post-transcriptionally in silico. Our model does not require large-scale proteomics studies to measure the protein expression and can successfully predict the protein levels by considering the miRNA-mRNA interaction network, the mRNA expression, and the miRNA expression. Large-scale experiments on simulations and real cancer high-throughput datasets using PTNet validated that (i) the miRNA-mediated interaction network affects the abundance of corresponding proteins and (ii) the predicted protein expression has a higher correlation with the proteomics data (ground-truth) than the mRNA expression data. The classification performance also shows that the predicted protein expression has an improved prediction power on cancer outcomes compared to the prediction done by the mRNA expression data only or considering both mRNA and miRNA. Availability: PTNet toolbox is available at http://github.com/CompbioLabUCF/PTNet.
| S-EPMC8575005 | biostudies-literature
Project description:we aimed to construct a bioinformatics-based co-regulatory network of mRNAs and non coding RNAs (ncRNAs), which is implicated in the pathogenesis of non-alcoholic fatty liver disease (NAFLD), followed by its validation in a NAFLD animal model. The mRNAs-miRNAs-lncRNAs regulatory network involved in NAFLD was retrieved and constructed utilizing bioinformatics tools. Then, we validated this network using an NAFLD animal model, high sucrose and high fat diet (HSHF)-fed rats. Finally, the expression level of the network players was assessed in the liver tissues using reverse transcriptase real-time polymerase chain reaction. in-silico constructed network revealed six mRNAs (YAP1, FOXA2, AMOTL2, TEAD2, SMAD4 and NF2), two miRNAs (miR-650 and miR-1205), and two lncRNAs (RPARP-AS1 and SRD5A3-AS1) that play important roles as a co-regulatory network in NAFLD pathogenesis. Moreover, the expression level of these constructed network-players was significantly different between NAFLD and normal control. Conclusion and future perspectives: this study provides new insight into the molecular mechanism of NAFLD pathogenesis and valuable clues for the potential use of the constructed RNA network in effective diagnostic or management strategies of NAFLD.
| S-EPMC8269036 | biostudies-literature