scholarly journals In silico identification of conserved miRNAs and their selective target gene prediction in indicine (Bos indicus) cattle

PLoS ONE ◽  
2018 ◽  
Vol 13 (10) ◽  
pp. e0206154 ◽  
Author(s):  
Quratulain Hanif ◽  
Muhammad Farooq ◽  
Imran Amin ◽  
Shahid Mansoor ◽  
Yi Zhang ◽  
...  
F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 1933 ◽  
Author(s):  
Ruipeng Lu ◽  
Peter K. Rogan

Background:The distribution and composition ofcis-regulatory modules composed of transcription factor (TF) binding site (TFBS) clusters in promoters substantially determine gene expression patterns and TF targets. TF knockdown experiments have revealed that TF binding profiles and gene expression levels are correlated. We use TFBS features within accessible promoter intervals to predict genes with similar tissue-wide expression patterns and TF targets.Methods:Genes with correlated expression patterns across 53 tissues and TF targets were respectively identified from Bray-Curtis Similarity and TF knockdown experiments. Corresponding promoter sequences were reduced to DNase I-accessible intervals; TFBSs were then identified within these intervals using information theory-based position weight matrices for each TF (iPWMs) and clustered. Features from information-dense TFBS clusters predicted these genes with machine learning classifiers, which were evaluated for accuracy, specificity and sensitivity. Mutations in TFBSs were analyzed toin silicoexamine their impact on cluster densities and the regulatory states of target genes.Results:  We initially chose the glucocorticoid receptor gene (NR3C1), whose regulation has been extensively studied, to test this approach.SLC25A32andTANKwere found to exhibit the most similar expression patterns toNR3C1. A Decision Tree classifier exhibited the largest area under the Receiver Operating Characteristic (ROC) curve in detecting such genes. Target gene prediction was confirmed using siRNA knockdown of TFs, which was found to be more accurate than those predicted after CRISPR/CAS9 inactivation.In-silicomutation analyses of TFBSs also revealed that one or more information-dense TFBS clusters in promoters are required for accurate target gene prediction. Conclusions: Machine learning based on TFBS information density, organization, and chromatin accessibility accurately identifies gene targets with comparable tissue-wide expression patterns. Multiple information-dense TFBS clusters in promoters appear to protect promoters from effects of deleterious binding site mutations in a single TFBS that would otherwise alter regulation of these genes.


2020 ◽  
Vol 17 (4) ◽  
pp. 342-351
Author(s):  
Sergio A. Durán-Pérez ◽  
José G. Rendón-Maldonado ◽  
Lucio de Jesús Hernandez-Diaz ◽  
Annete I. Apodaca-Medina ◽  
Maribel Jiménez-Edeza ◽  
...  

Background: The protozoan Giardia duodenalis, which causes giardiasis, is an intestinal parasite that commonly affects humans, mainly pre-school children. Although there are asymptomatic cases, the main clinical features are chronic and acute diarrhea, nausea, abdominal pain, and malabsorption syndrome. Little is currently known about the virulence of the parasite, but some cases of chronic gastrointestinal alterations post-infection have been reported even when the infection was asymptomatic, suggesting that the cathepsin L proteases of the parasite may be involved in the damage at the level of the gastrointestinal mucosa. Objective: The aim of this study was the in silico identification and characterization of extracellular cathepsin L proteases in the proteome of G. duodenalis. Methods: The NP_001903 sequence of cathepsin L protease from Homo sapienswas searched against the Giardia duodenalisproteome. The subcellular localization of Giardia duodenaliscathepsin L proteases was performed in the DeepLoc-1.0 server. The construction of a phylogenetic tree of the extracellular proteins was carried out using the Molecular Evolutionary Genetics Analysis software (MEGA X). The Robetta server was used for the construction of the three-dimensional models. The search for possible inhibitors of the extracellular cathepsin L proteases of Giardia duodenaliswas performed by entering the three-dimensional structures in the FINDSITEcomb drug discovery tool. Results: Based on the amino acid sequence of cathepsin L from Homo sapiens, 8 protein sequences were identified that have in their modular structure the Pept_C1A domain characteristic of cathepsins and two of these proteins (XP_001704423 and XP_001704424) are located extracellularly. Threedimensional models were designed for both extracellular proteins and several inhibitory ligands with a score greater than 0.9 were identified. In vitrostudies are required to corroborate if these two extracellular proteins play a role in the virulence of Giardia duodenalisand to discover ligands that may be useful as therapeutic targets that interfere in the mechanism of pathogenesis generated by the parasite. Conclusion: In silicoanalysis identified two proteins in the Giardia duodenalisprotein repertoire whose characteristics allowed them to be classified as cathepsin L proteases, which may be secreted into the extracellular medium to act as virulence factors. Three-dimensional models of both proteins allowed the identification of inhibitory ligands with a high score. The results suggest that administration of those compounds might be used to block the endopeptidase activity of the extracellular cathepsin L proteases, interfering with the mechanisms of pathogenesis of the protozoan parasite Giardia duodenalis.


Author(s):  
Wenzhu Zhao ◽  
Jingbo He ◽  
Zhipeng Yu ◽  
Sijia Wu ◽  
Jianrong Li ◽  
...  

3 Biotech ◽  
2021 ◽  
Vol 11 (3) ◽  
Author(s):  
Vaishali Chandel ◽  
Prem Prakash Sharma ◽  
Seema A. Nayar ◽  
Niraj Kumar Jha ◽  
Saurabh Kumar Jha ◽  
...  

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