scholarly journals Quantitative Proteomic Approach for MicroRNA Target Prediction Based on 18O/16O Labeling

2015 ◽  
Vol 14s5 ◽  
pp. CIN.S30563 ◽  
Author(s):  
Xuepo Ma ◽  
Ying Zhu ◽  
Yufei Huang ◽  
Tony Tegeler ◽  
Shou-Jiang Gao ◽  
...  
Keyword(s):  
Large Scale ◽  
Dynamic Range ◽  
Primary Effusion Lymphoma ◽  
Statistical Tests ◽  
Target Prediction ◽  
Prediction Algorithm ◽  
Luciferase Reporter ◽  
Microrna Target ◽  
Gene Expressions ◽  
Proteomic Approach

Motivation Among many large-scale proteomic quantification methods, 18O/16O labeling requires neither specific amino acid in peptides nor label incorporation through several cell cycles, as in metabolic labeling; it does not cause significant elution time shifts between heavy- and light-labeled peptides, and its dynamic range of quantification is larger than that of tandem mass spectrometry-based quantification methods. These properties offer 18O/16O labeling the maximum flexibility in application. However, 18O/16O labeling introduces large quantification variations due to varying labeling efficiency. There lacks a processing pipeline that warrants the reliable identification of differentially expressed proteins (DEPs). This motivates us to develop a quantitative proteomic approach based on 18O/16O labeling and apply it on Kaposi sarcoma-associated herpesvirus (KSHV) microRNA (miR) target prediction. KSHV is a human pathogenic y-herpesvirus strongly associated with the development of B-cell proliferative disorders, including primary effusion lymphoma. Recent studies suggest that miRs have evolved a highly complex network of interactions with the cellular and viral transcriptomes, and relatively few KSHV miR targets have been characterized at the functional level. While the new miR target prediction method, photoactivatable ribonucleoside-enhanced cross-linking and immunoprecipitation (PAR-CLIP), allows the identification of thousands of miR targets, the link between miRs and their targets still cannot be determined. We propose to apply the developed proteomic approach to establish such links. Method We integrate several 18O/16O data processing algorithms that we published recently and identify the messenger RNAs of downregulated proteins as potential targets in KSHV miR-transfected human embryonic kidney 293T cells. Various statistical tests are employed for picking DEPs, and we select the best test by examining the enrichment of PAR-CLIP-reported targets with seed match to the miRs of interest among top ranked DEPs returned by statistical tests. Subsequently, the list of DEPs picked by the selected statistical test is filtered with the criteria that they must have downregulated gene expressions, must have reported as targets by an miR target prediction algorithm SVMcrio, and must have reported as targets by PAR-CLIP. Result We test the developed approach in the problem of finding targets of KSHV miR-K1. The RNAs of three DEPs are identified as miR-K1 targets, among which RAB23 and HNRNPU are novel. Results from both Western blotting and Luciferase reporter assays confirm the novel targets. These results show that the developed quantitative approach based on 18O/16O labeling can be combined with genomic, PAR-CLIP, and target prediction algorithms for the confident identification of KSHV miR targets. The developed approach could also be applied in other applications.

MiRACLe: an individual-specific approach to improve microRNA-target prediction based on a random contact model

2020 ◽  
Author(s):  
Pan Wang ◽  
Qi Li ◽  
Nan Sun ◽  
Yibo Gao ◽  
Jun S Liu ◽  
...  
Keyword(s):  
Predictive Power ◽  
Expression Profiles ◽  
Target Prediction ◽  
Contact Model ◽  
Prediction Algorithm ◽  
Large Sample Size ◽  
Messenger Rnas ◽  
Expression Data ◽  
Mirna Regulation ◽  
Microrna Target

Abstract Deciphering microRNA (miRNA) targets is important for understanding the function of miRNAs as well as miRNA-based diagnostics and therapeutics. Given the highly cell-specific nature of miRNA regulation, recent computational approaches typically exploit expression data to identify the most physiologically relevant target messenger RNAs (mRNAs). Although effective, those methods usually require a large sample size to infer miRNA–mRNA interactions, thus limiting their applications in personalized medicine. In this study, we developed a novel miRNA target prediction algorithm called miRACLe (miRNA Analysis by a Contact modeL). It integrates sequence characteristics and RNA expression profiles into a random contact model, and determines the target preferences by relative probability of effective contacts in an individual-specific manner. Evaluation by a variety of measures shows that fitting TargetScan, a frequently used prediction tool, into the framework of miRACLe can improve its predictive power with a significant margin and consistently outperform other state-of-the-art methods in prediction accuracy, regulatory potential and biological relevance. Notably, the superiority of miRACLe is robust to various biological contexts, types of expression data and validation datasets, and the computation process is fast and efficient. Additionally, we show that the model can be readily applied to other sequence-based algorithms to improve their predictive power, such as DIANA-microT-CDS, miRanda-mirSVR and MirTarget4. MiRACLe is publicly available at https://github.com/PANWANG2014/miRACLe.

scholarly journals MicroRNA-194 Inhibition Promotes SUMO-2-Dependent Neuro-Protection Against Oxygen-Glucose Deprivation

2021 ◽  
Author(s):  
Xiao-Hua Wang ◽  
Si-Yuan Zhang ◽  
Yi Huang ◽  
Yunqian Guan ◽  
Yi Zheng ◽  
...  
Keyword(s):  
Cortical Neurons ◽  
Target Prediction ◽  
Glucose Deprivation ◽  
Cell Types ◽  
Prediction Algorithm ◽  
Luciferase Reporter ◽  
Oxygen Glucose Deprivation ◽  
Protective Mechanisms ◽  
Novel Approach ◽  
Underlying Mechanisms

Abstract Background Hypothermia is a powerful neuroprotectant. However, clinical translation has been difficult partly because the underlying mechanisms remain to be fully elucidated. Recently, it has been suggested that hypothermic neuroprotection may be linked with specific microRNA signatures, specifically the downregulation of miR-194-5p. Here, we attempt a reverse translation study to define the novel neuroprotective mechanism of miR-194-5p downregulation. Methods Our research designed experiments to determine the expression of miR-194-5p in hypothermic neuroprotection. After transfected with miRNA-194-5p inhibitor or control miRNA, neuron cells were performed Oxygen-glucose deprivation (OGD) and reoxygenation. Cell viability was determined by staining with propidium iodide (PI) and expression of SUMO-2 were quantified using Western blot. Luciferase reporter assays were performed to verify the direct binding of miR-194-5p to SUMO2 transcripts. Puromycin and recombinant lentivirus (pLKD-CMV-eGFP-sumo2-shRNA) were used to suppressed SUMOylation non-specifically and specifically. Results First, we documented that miR-194-5p was highly expressed in rat primary cortical neurons and astrocytes, compared with other glial and vascular cell types. Blockade with anti-miR-194-5p did not affect astrocytes, but significantly protected neurons against oxy-glucose deprivation. Using a miRNA target prediction algorithm, we found that SUMO-2, a known endogenous neuroprotective regulator, possessed the binding site for miR-194-5p. When miR-194-5p was inhibited, SUMO-2 mRNA was increased in neurons along with the enhancement of SUMO-2 conjugation. Finally, downregulation of SUMO-2 with none-special puromycin and special lentiviral shRNA-mediated knockdown of SUMO2 both canceled the neuroprotection mediated by miR194-5p inhibition. Conclusion Taken together, this study provides the first proof-of-concept that miR194-5p may be a negative controller of endogenous SUMO-2 protective mechanisms, and therefore miR194-5p inhibition may provide a novel approach for leveraging hypothermic neuroprotection against ischemic stress.

scholarly journals MiR-541-3p suppresses gastric cancer via negative regulation of HSF1

2021 ◽  
Vol 20 (11) ◽  
pp. 2293-2298
Author(s):  
Zihan Zheng ◽  
Peng Zhou ◽  
Yangyang Xiao ◽  
Qian Liu ◽  
Tao Wan
Keyword(s):  
Gastric Cancer ◽  
Cell Lines ◽  
Caspase 3 ◽  
Target Prediction ◽  
Luciferase Reporter ◽  
Migration And Invasion ◽  
Mrna Levels ◽  
Microrna Target ◽  
Gastric Cancer Cells

Purpose: To explore the effects of miR-541-3P on the expression of heat shock transcription factor 1 (HSF1) in gastric cancer cells (GC).Methods: The MicroRNA Target Prediction Database was used to predict whether miR-541-3p interacts with HSF1. Interaction was assessed by dual-luciferase reporter assays. Furthermore, miR-541-3p mRNA levels in GC cell lines were determined by qRT-PCR. Human GC cell lines MKN45 and NCI-N87 were transfected with miR-541-3p mimic. Cell apoptosis, proliferation, invasion, and migration were evaluated using flow cytometry, apoptosis assays, Edu assays, CCK-8 assays, and transwell assays, respectively. Caspase-3, Bcl-2, and cleaved caspase-3 expression levels were determined by western blot.Results: Expression of miR-541-3p was significantly down-regulated in GC cells. Functionally, miR-541-3p mimic inhibited GC cell proliferation, migration, and invasion and induced apoptosis in vitro (p <0.01). Mechanistically, miR-541-3p interacted with HSF1 and inhibited its expression. Overexpression of HSF1 counteracted the effects of miR-541-3p mimic in GC cells.Conclusion: These results indicate that miR-541-3p suppresses the development of GC by targeting HSF1 and thus, is a possible strategy for for the management of GC.

ANIMAL MICRORNA TARGET PREDICTION USING DIVERSE SEQUENCE-SPECIFIC DETERMINANTS

2010 ◽  
Vol 08 (04) ◽  
pp. 763-788 ◽  
Author(s):  
YUN ZHENG ◽  
WEIXIONG ZHANG
Keyword(s):  
Drosophila Melanogaster ◽  
Protein Level ◽  
Mirna Target ◽  
Homo Sapiens ◽  
Target Prediction ◽  
Prediction Algorithm ◽  
Mirna Target Prediction ◽  
Microrna Target ◽  
Microrna Target Prediction ◽  
Target Prediction Algorithm

Many recent studies have shown that access of animal microRNAs (miRNAs) to their complementary sites in target mRNAs is determined by several sequence-specific determinants beyond the seed regions in the 5′ end of miRNAs. These factors have been related to the repressive power of miRNAs and used in some programs to predict the efficacy of miRNA complementary sites. However, these factors have not been systematically examined regarding their capacities for improving miRNA target prediction. We develop a new miRNA target prediction algorithm, called Hitsensor, by incorporating many sequence-specific features that determine complementarities between miRNAs and their targets, in addition to the canonical seed regions in the 5′ ends of miRNAs. We evaluate the performance of our algorithm on 720 known animal miRNA:target pairs in four species, Homo sapiens, Mus musculus, Drosophila melanogaster and Caenorhabditis elegans. Our experimental results show that Hitsensor outperforms five popular existing algorithms, indicating that our unique scheme for quantifying the determinants of complementary sites is effective in improving the performance of a miRNA target prediction algorithm. We also examine the effectiveness of miRNA-mediated repression for the predicted targets by using a published quantitative protein expression dataset of miR-223 knockout in mouse neutrophils. Hitsensor identifies more targets than the existing algorithms, and the predicted targets of Hitsensor show comparable protein level changes to those of the existing algorithms.

scholarly journals An improvement of ComiR algorithm for microRNA target prediction by exploiting coding region sequences of mRNAs

2020 ◽  
Vol 21 (S8) ◽  
Author(s):  
Giorgio Bertolazzi ◽  
Panayiotis V. Benos ◽  
Michele Tumminello ◽  
Claudia Coronnello
Keyword(s):  
Binding Sites ◽  
Target Prediction ◽  
Prediction Algorithm ◽  
Messenger Rnas ◽  
Microrna Target ◽  
Coding Region ◽  
Web Tool ◽  
Coding Regions ◽  
Microrna Target Prediction ◽  
Microrna Targets

Abstract MicroRNA are small non-coding RNAs that post-transcriptionally regulate the expression levels of messenger RNAs. MicroRNA regulation activity depends on the recognition of binding sites located on mRNA molecules. ComiR is a web tool realized to predict the targets of a set of microRNAs, starting from their expression profile. ComiR was trained with the information regarding binding sites in the 3’utr region, by using a reliable dataset containing the targets of endogenously expressed microRNA in D. melanogaster S2 cells. This dataset was obtained by comparing the results from two different experimental approaches, i.e., inhibition, and immunoprecipitation of the AGO1 protein--a component of the microRNA induced silencing complex. In this work, we tested whether including coding region binding sites in ComiR algorithm improves the performance of the tool in predicting microRNA targets. We focused the analysis on the D. melanogaster species and updated the ComiR underlying database with the currently available releases of mRNA and microRNA sequences. As a result, we find that ComiR algorithm trained with the information related to the coding regions is more efficient in predicting the microRNA targets, with respect to the algorithm trained with 3’utr information. On the other hand, we show that 3’utr based predictions can be seen as complementary to the coding region based predictions, which suggests that both predictions, from 3’utr and coding regions, should be considered in comprehensive analysis. Furthermore, we observed that the lists of targets obtained by analyzing data from one experimental approach only, that is, inhibition or immunoprecipitation of AGO1, are not reliable enough to test the performance of our microRNA target prediction algorithm. Further analysis will be conducted to investigate the effectiveness of the tool with data from other species, provided that validated datasets, as obtained from the comparison of RISC proteins inhibition and immunoprecipitation experiments, will be available for the same samples. Finally, we propose to upgrade the existing ComiR web-tool by including the coding region based trained model, available together with the 3’utr based one.

Epigenetic Target Prediction with Accurate Machine Learning Models

2021 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
Jose L. Medina-Franco
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Predictive Models ◽  
Large Scale ◽  
Target Prediction ◽  
Quantitative Measure ◽  
Learning Models ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Machine Learning Models

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

Construction and Functional Analysis of the Recombinant Bacteriocins Weissellicin-MBF from Weissella confusa MBF8-1

2020 ◽  
Vol 22 (1) ◽  
pp. 115-122
Author(s):  
Amarila Malik ◽  
Elita Yuliantie ◽  
Nisa Yulianti Suprahman ◽  
Theresa Linardi ◽  
Angelina Wening Widiyanti ◽  
...  
Keyword(s):  
Functional Analysis ◽  
Large Scale ◽  
Micrococcus Luteus ◽  
Affinity Column ◽  
Gene Expressions ◽  
His Tag ◽  
Recombinant Peptides ◽  
Weak Activity ◽  
Potential Alternative ◽  
Weissella Confusa

Background: Bacteriocins (Bac1, Bac2, and Bac3) from Weissella confusa MBF8-1, weissellicin- MBF, have been reported as potential alternative substances as well as complements to the existing antibiotics against many antimicrobial-resistant pathogens. Previously, the genes encoded in the large plasmid, pWcMBF8-1, and the spermicidal activity of their synthetic peptides, originally discovered Indonesia, have been studied. Three synthetic bacteriocins peptides of this weissellicin-MBF have been reported for their potential activities, i.e. antibacterial and spermicidal. Objective: The aim of this study was to construct the recombinant Bacteriocin (r-Bac) genes, as well as to investigate the gene expressions and their functional analysis. Method: Here, the recombinant Bacteriocin (r-Bac) genes were constructed and the recombinant peptides (r-Bac1, r-Bac2, and r-Bac3) in B. subtilis DB403 cells were produced on a large scale. After purification, using the His-tag affinity column, their potential bioactivities were measured as well as their antibacterial minimum inhibitory concentrations against Leuconostoc mesenteroides and Micrococcus luteus, were determined. Results: Pure His-tag-recombinant Bac1, Bac2, and Bac3 were obtained and they could inhibit the growth of L. mesenteroides and M. luteus. Conclusion: The recombinant bacteriocin could be obtained although with weak activity in inhibiting gram-positive bacterial growth.

In Silico Study of Potential Cross-Kingdom Plant MicroRNA Based Regulation in Chronic Myeloid Leukemia

2020 ◽  
Vol 17 (2) ◽  
pp. 125-132
Author(s):  
Marjanu Hikmah Elias ◽  
Noraziah Nordin ◽  
Nazefah Abdul Hamid
Keyword(s):  
Targeted Therapy ◽  
In Silico ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
Target Prediction ◽  
In Silico Analysis ◽  
Microrna Target ◽  
Good Target

Background: Chronic Myeloid Leukaemia (CML) is associated with the BCRABL1 gene, which plays a central role in the pathogenesis of CML. Thus, it is crucial to suppress the expression of BCR-ABL1 in the treatment of CML. MicroRNA is known to be a gene expression regulator and is thus a good candidate for molecularly targeted therapy for CML. Objective: This study aims to identify the microRNAs from edible plants targeting the 3’ Untranslated Region (3’UTR) of BCR-ABL1. Methods: In this in silico analysis, the sequence of 3’UTR of BCR-ABL1 was obtained from Ensembl Genome Browser. PsRNATarget Analysis Server and MicroRNA Target Prediction (miRTar) Server were used to identify miRNAs that have binding conformity with 3’UTR of BCR-ABL1. The MiRBase database was used to validate the species of plants expressing the miRNAs. The RNAfold web server and RNA COMPOSER were used for secondary and tertiary structure prediction, respectively. Results: In silico analyses revealed that cpa-miR8154, csi-miR3952, gma-miR4414-5p, mdm-miR482c, osa-miR1858a and osa-miR1858b show binding conformity with strong molecular interaction towards 3’UTR region of BCR-ABL1. However, only cpa-miR- 8154, osa-miR-1858a and osa-miR-1858b showed good target site accessibility. Conclusion: It is predicted that these microRNAs post-transcriptionally inhibit the BCRABL1 gene and thus could be a potential molecular targeted therapy for CML. However, further studies involving in vitro, in vivo and functional analyses need to be carried out to determine the ability of these miRNAs to form the basis for targeted therapy for CML.

scholarly journals Large-Scale Road Network Congestion Pattern Analysis and Prediction Using Deep Convolutional Autoencoder

2021 ◽  
Vol 13 (9) ◽  
pp. 5108
Author(s):  
Navin Ranjan ◽  
Sovit Bhandari ◽  
Pervez Khan ◽  
Youn-Sik Hong ◽  
Hoon Kim
Keyword(s):  
Traffic Congestion ◽  
Network Architecture ◽  
Road Network ◽  
Large Scale ◽  
Pattern Analysis ◽  
Image Data ◽  
Urban Life ◽  
Prediction Algorithm ◽  
Rapid Urbanization ◽  
Convolutional Autoencoder

The transportation system, especially the road network, is the backbone of any modern economy. However, with rapid urbanization, the congestion level has surged drastically, causing a direct effect on the quality of urban life, the environment, and the economy. In this paper, we propose (i) an inexpensive and efficient Traffic Congestion Pattern Analysis algorithm based on Image Processing, which identifies the group of roads in a network that suffers from reoccurring congestion; (ii) deep neural network architecture, formed from Convolutional Autoencoder, which learns both spatial and temporal relationships from the sequence of image data to predict the city-wide grid congestion index. Our experiment shows that both algorithms are efficient because the pattern analysis is based on the basic operations of arithmetic, whereas the prediction algorithm outperforms two other deep neural networks (Convolutional Recurrent Autoencoder and ConvLSTM) in terms of large-scale traffic network prediction performance. A case study was conducted on the dataset from Seoul city.

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