Dataset Information

Dynamical comparison between Drosha and Dicer reveals functional motion similarities and dissimilarities.

ABSTRACT: Drosha and Dicer are RNase III family members of classes II and III, respectively, which play a major role in the maturation of micro-RNAs. The two proteins share similar domain arrangement and overall fold despite no apparent sequence homology. The overall structural and catalytic reaction similarity of both proteins, on the one hand, and differences in the substrate and its binding mechanisms, on the other, suggest that both proteins also share dynamic similarities and dissimilarities. Since dynamics is essential for protein function, a comparison at their dynamics level is fundamental for a complete understanding of the overall relations between these proteins. In this study, we present a dynamical comparison between human Drosha and Giardia Dicer. Gaussian Network Model and Anisotropic Network Model modes of motion of the proteins are calculated. Dynamical comparison is performed using global and local dynamic programming algorithms for aligning modes of motion. These algorithms were recently developed based on the commonly used Needleman-Wunsch and Smith-Waterman algorithms for global and local sequence alignment. The slowest mode of Drosha is different from that of Dicer due to its more bended posture and allow the motion of the double-stranded RNA-binding domain toward and away from its substrate. Among the five slowest modes dynamics similarity exists only for the second slow mode of motion of Drosha and Dicer. In addition, high local dynamics similarity is observed at the catalytic domains, in the vicinity of the catalytic residues. The results suggest that the proteins exert a similar catalytic mechanism using similar motions, especially at the catalytic sites.

SUBMITTER: Aharoni R

PROVIDER: S-EPMC6903759 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Dynamical comparison between Drosha and Dicer reveals functional motion similarities and dissimilarities.

Aharoni Rotem R Tobi Dror D

PloS one 20191210 12

Drosha and Dicer are RNase III family members of classes II and III, respectively, which play a major role in the maturation of micro-RNAs. The two proteins share similar domain arrangement and overall fold despite no apparent sequence homology. The overall structural and catalytic reaction similarity of both proteins, on the one hand, and differences in the substrate and its binding mechanisms, on the other, suggest that both proteins also share dynamic similarities and dissimilarities. Since d ...[more]

PMID: 31821368

Similar Datasets

Project description:BackgroundWith the development of the technology of single-cell sequence, revealing homogeneity and heterogeneity between cells has become a new area of computational systems biology research. However, the clustering of cell types becomes more complex with the mutual penetration between different types of cells and the instability of gene expression. One way of overcoming this problem is to group similar, related single cells together by the means of various clustering analysis methods. Although some methods such as spectral clustering can do well in the identification of cell types, they only consider the similarities between cells and ignore the influence of dissimilarities on clustering results. This methodology may limit the performance of most of the conventional clustering algorithms for the identification of clusters, it needs to develop special methods for high-dimensional sparse categorical data.ResultsInspired by the phenomenon that same type cells have similar gene expression patterns, but different types of cells evoke dissimilar gene expression patterns, we improve the existing spectral clustering method for clustering single-cell data that is based on both similarities and dissimilarities between cells. The method first measures the similarity/dissimilarity among cells, then constructs the incidence matrix by fusing similarity matrix with dissimilarity matrix, and, finally, uses the eigenvalues of the incidence matrix to perform dimensionality reduction and employs the K-means algorithm in the low dimensional space to achieve clustering. The proposed improved spectral clustering method is compared with the conventional spectral clustering method in recognizing cell types on several real single-cell RNA-seq datasets.ConclusionsIn summary, we show that adding intercellular dissimilarity can effectively improve accuracy and achieve robustness and that improved spectral clustering method outperforms the traditional spectral clustering method in grouping cells.

Project description:Nitrogen levels in grape-juices are of major importance in winemaking ensuring adequate yeast growth and fermentation performance. Here we used a comparative transcriptome analysis to uncover wine yeasts responses to nitrogen availability during fermentation. Gene expression was assessed in three genetically and phenotypically divergent commercial wine strains (CEG, VL1 and QA23), under low (67 mg/L) and high nitrogen (670 mg/L) regimes, at three time points during fermentation (12 h, 24 h and 96 h). Two-way ANOVA analysis of each fermentation condition led to the identification of genes whose expression was dependent on strain, fermentation stage and on the interaction of both factors. The high fermenter yeast strain QA23 was more clearly distinct from the other two strains, by differential expression of genes involved in flocculation, mitochondrial functions, energy generation and protein folding and stabilization. For all strains, higher transcriptional variability due to fermentation stage was seen in the high nitrogen fermentations. A positive correlation between maximum fermentation rate and the expression of genes involved in stress response was observed. The finding of common genes correlated with both fermentation activity and nitrogen up-take underlies the role of nitrogen on yeast fermentative fitness. The comparative analysis of genes differentially expressed between both fermentation conditions at 12 h, where the main difference was the level of nitrogen available, showed the highest variability amongst strains revealing strain-specific responses. Nevertheless, we were able to identify a small set of genes whose expression profiles can quantitatively assess the common response of the yeast strains to varying nitrogen conditions. The use of three contrasting yeast strains in gene expression analysis prompts the identification of more reliable, accurate and reproducible biomarkers that will facilitate the diagnosis of deficiency of this nutrient in the grape-musts and the development of strategies to optimize yeast performance in industrial fermentations.

Project description:BackgroundWe studied Dicer and Drosha, components of the RNA-interference machinery, in ovarian cancer.MethodsWe measured messenger RNA (mRNA) levels of Dicer and Drosha in specimens of invasive epithelial ovarian cancer from 111 patients, using a quantitative reverse-transcriptase-polymerase-chain-reaction assay, and compared the results with clinical outcomes. Validation was performed with the use of published microarray data from cohorts of patients with ovarian, breast, and lung cancer. Mutational analyses of genomic DNA from the Dicer and Drosha genes were performed in a subgroup of ovarian-cancer specimens. Dicer-dependent functional assays were performed by means of in vitro transfection with small interfering RNA (siRNA) and short hairpin RNA (shRNA).ResultsLevels of Dicer and Drosha mRNA correlated with the levels of expression of the corresponding protein and were decreased in 60% and 51% of ovarian-cancer specimens, respectively. Low Dicer expression was significantly associated with advanced tumor stage (P=0.007), and low Drosha expression with suboptimal surgical cytoreduction (P=0.02). Cancer specimens with both high Dicer expression and high Drosha expression were associated with increased median survival (>11 years, vs. 2.66 years for other subgroups; P<0.001). We found three independent predictors of reduced disease-specific survival in multivariate analyses: low Dicer expression (hazard ratio, 2.10; P=0.02), high-grade histologic features (hazard ratio, 2.46; P=0.03), and poor response to chemotherapy (hazard ratio, 3.95; P<0.001). Poor clinical outcomes among patients with low Dicer expression were validated in additional cohorts of patients. Rare missense mutations were found in the Dicer and Drosha genes, but their presence or absence did not correlate with the level of expression. Functional assays indicated that gene silencing with shRNA, but not siRNA, may be impaired in cells with low Dicer expression.ConclusionsOur findings indicate that levels of Dicer and Drosha mRNA in ovarian-cancer cells have associations with outcomes in patients with ovarian cancer.

Dataset Information

Dynamical comparison between Drosha and Dicer reveals functional motion similarities and dissimilarities.

Publications

Dynamical comparison between Drosha and Dicer reveals functional motion similarities and dissimilarities.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets