scholarly journals CROSSalive: a web server for predicting the in vivo structure of RNA molecules

2019 ◽  
Author(s):  
Riccardo Delli Ponti ◽  
Alexandros Armaos ◽  
Gian Gaetano Tartaglia
Keyword(s):  
Protein Interactions ◽  
Rna Structure ◽  
Rna Secondary Structure ◽  
Sequence Length ◽  
Test Set ◽  
Rna Molecules ◽  
Non Coding Rna ◽  
Map Data ◽  
Long Non Coding Rna

ABSTRACTHere we introduce CROSSalive, an algorithm to predict the RNA secondary structure profile (double and single stranded regions) in vivo and without sequence length limitations. Using predictions of protein interactions CROSSalive predicts the effect of N6 adenosine methylation (m6a) on RNA structure. Trained on icSHAPE data in presence (m6a+) and absence (m6a-) of methylation CROSSalive achieves an accuracy of 0.88 on the test set. The algorithm was also applied to the murine long non-coding RNA Xist (17900 nt, not present in the training) and shows a Pearson’s correlation of 0.45 with SHAPE-map data. CROSSalive webserver is freely accessible at the following page: http://service.tartaglialab.com/new_submission/crossalive

scholarly journals CROSSalive: a web server for predicting the in vivo structure of RNA molecules

2019 ◽  
Author(s):  
Riccardo Delli Ponti ◽  
Alexandros Armaos ◽  
Andrea Vandelli ◽  
Gian Gaetano Tartaglia
Keyword(s):  
Protein Interactions ◽  
Rna Structure ◽  
Cross Validation ◽  
Supplementary Information ◽  
Supplementary Data ◽  
High Confidence ◽  
Rna Molecules ◽  
Non Coding Rna ◽  
Long Non Coding Rna

Abstract Motivation RNA structure is difficult to predict in vivo due to interactions with enzymes and other molecules. Here we introduce CROSSalive, an algorithm to predict the single- and double-stranded regions of RNAs in vivo using predictions of protein interactions. Results Trained on icSHAPE data in presence (m6a+) and absence of N6 methyladenosine modification (m6a-), CROSSalive achieves cross-validation accuracies between 0.70 and 0.88 in identifying high-confidence single- and double-stranded regions. The algorithm was applied to the long non-coding RNA Xist (17 900 nt, not present in the training) and shows an Area under the ROC curve of 0.83 in predicting structured regions. Availability and implementation CROSSalive webserver is freely accessible at http://service.tartaglialab.com/new_submission/crossalive Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Insights into the structure-driven protein interactivity of RNA molecules

2018 ◽  
Author(s):  
Natalia Sanchez de Groot ◽  
Alexandros Armaos ◽  
Ricardo Graña Montes ◽  
Marion Alriquet ◽  
Giulia Calloni ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Rna Structure ◽  
High Throughput Sequencing ◽  
Interaction Network ◽  
Recent Experimental Data ◽  
Rna Molecules ◽  
Rna Granules ◽  
Complex Interdependence

ABSTRACTThe combination of high-throughput sequencing and in vivo crosslinking approaches leads to the progressive uncovering of the complex interdependence between cellular transcriptome and proteome. Yet the molecular determinants that govern interactions in protein-RNA networks are poorly known at present. Here we used the most recent experimental data to investigate the relationship between RNA structure and protein interactions. Our results show that, independently of the particular technique, the amount of structure in RNA molecules correlates with the capacity of binding to proteins in vitro and in vivo. To validate this observation, we generated an in vitro network that mimics the composition of phase-separated RNA granules. We observed that RNA, when structured, competes with protein binding and can rearrange the interaction network. The simplicity of the principle bears great potential to boost the understanding and modelling of cellular processes involving RNA-protein interactions.

Recent Advances on the Semi-Supervised Learning for Long Non-Coding RNA-Protein Interactions Prediction: A Review

2020 ◽  
Vol 27 (5) ◽  
pp. 385-391
Author(s):  
Lin Zhong ◽  
Zhong Ming ◽  
Guobo Xie ◽  
Chunlong Fan ◽  
Xue Piao
Keyword(s):  
Supervised Learning ◽  
Protein Interactions ◽  
Computational Models ◽  
Prediction Models ◽  
Chromatin Modification ◽  
Computational Prediction ◽  
Human Diseases ◽  
Future Research ◽  
Non Coding Rna ◽  
Long Non Coding Rna

: In recent years, more and more evidence indicates that long non-coding RNA (lncRNA) plays a significant role in the development of complex biological processes, especially in RNA progressing, chromatin modification, and cell differentiation, as well as many other processes. Surprisingly, lncRNA has an inseparable relationship with human diseases such as cancer. Therefore, only by knowing more about the function of lncRNA can we better solve the problems of human diseases. However, lncRNAs need to bind to proteins to perform their biomedical functions. So we can reveal the lncRNA function by studying the relationship between lncRNA and protein. But due to the limitations of traditional experiments, researchers often use computational prediction models to predict lncRNA protein interactions. In this review, we summarize several computational models of the lncRNA protein interactions prediction base on semi-supervised learning during the past two years, and introduce their advantages and shortcomings briefly. Finally, the future research directions of lncRNA protein interaction prediction are pointed out.

scholarly journals LncRNA PVT1 promotes cervical cancer progression by sponging miR-503 to upregulate ARL2 expression

2021 ◽  
Vol 16 (1) ◽  
pp. 1-13
Author(s):  
Weiwei Liu ◽  
Dongmei Yao ◽  
Bo Huang
Keyword(s):  
Cervical Cancer ◽  
Cancer Progression ◽  
Luciferase Reporter ◽  
Migration And Invasion ◽  
Nude Mouse Model ◽  
Western Blot Assay ◽  
Non Coding Rna ◽  
Long Non Coding Rna

Abstract Cervical cancer (CC) is a huge threat to the health of women worldwide. Long non-coding RNA plasmacytoma variant translocation 1 gene (PVT1) was proved to be associated with the development of diverse human cancers, including CC. Nevertheless, the exact mechanism of PVT1 in CC progression remains unclear. Levels of PVT1, microRNA-503 (miR-503), and ADP ribosylation factor-like protein 2 (ARL2) were measured by quantitative reverse transcription-polymerase chain reaction or western blot assay. 3-(4,5)-Dimethylthiazole-2-y1)-2,5-biphenyl tetrazolium bromide (MTT) and flow cytometry were used to examine cell viability and apoptosis, respectively. For migration and invasion detection, transwell assay was performed. The interaction between miR-503 and PVT1 or ARL2 was shown by dual luciferase reporter assay. A nude mouse model was constructed to clarify the role of PVT1 in vivo. PVT1 and ARL2 expressions were increased, whereas miR-503 expression was decreased in CC tissues and cells. PVT1 was a sponge of miR-503, and miR-503 targeted ARL2. PVT1 knockdown suppressed proliferation, migration, and invasion of CC cells, which could be largely reverted by miR-503 inhibitor. In addition, upregulated ARL2 could attenuate si-PVT1-mediated anti-proliferation and anti-metastasis effects on CC cells. Silenced PVT1 also inhibited CC tumor growth in vivo. PVT1 knockdown exerted tumor suppressor role in CC progression via the miR-503/ARL2 axis, at least in part.

Long Non-Coding RNA ARAP1-AS1 Facilitates the Progression of Cervical Cancer by Regulating miR-149-3p and POU2F2

Pathobiology ◽  
2021 ◽  
pp. 1-12
Author(s):  
Ling Zhou ◽  
Xiao-li Xu
Keyword(s):  
Cervical Cancer ◽  
Tumor Model ◽  
Luciferase Reporter ◽  
Migration And Invasion ◽  
Non Coding Rna ◽  
Injection Model ◽  
Rna Immunoprecipitation ◽  
Long Non Coding Rna

<b><i>Background:</i></b> Emerging research has demonstrated that long non-coding RNAs (lncRNAs) attach great importance to the progression of cervical cancer (CC). LncRNA ARAP1-AS1 was involved in the development of several cancers; however, its role in CC is far from being elucidated. <b><i>Methods:</i></b> Real-time PCR (RT-PCR) was employed to detect ARAP1-AS1 and miR-149-3p expression in CC samples. CC cell lines (HeLa and C33A cells) were regarded as the cell models. The biological effect of ARAP1-AS1 on cancer cells was measured using CCK-8 assay, colony formation assay, flow cytometry, Transwell assay and wound healing assay in vitro, and subcutaneous xenotransplanted tumor model and tail vein injection model in vivo. Furthermore, interactions between ARAP1-AS1 and miR-149-3p, miR-149-3p and POU class 2 homeobox 2 (POU2F2) were determined by bioinformatics analysis, qRT-PCR, Western blot, luciferase reporter and RNA immunoprecipitation assay, respectively. <b><i>Results:</i></b> The expression of ARAP1-AS1 was enhanced in CC samples, while miR-149-3p was markedly suppressed. Additionally, ARAP1-AS1 overexpression enhanced the viability, migration, and invasion of CC cells. ARAP1-AS1 downregulated miR-149-3p via sponging it. ARAP1-AS1 and miR-149-3p exhibited a negative correlation in CC samples. On the other hand, ARAP1-AS1 enhanced the expression of POU2F2, which was validated as a target gene of miR-149-3p. <b><i>Conclusion:</i></b> ARAP1-AS1 was abnormally upregulated in CC tissues and indirectly modulated the POU2F2 expression via reducing miR-149-3p expression. Our study identified a novel axis, ARAP1-AS1/miR-149-3p/POU2F2, in CC tumorigenesis.

scholarly journals Long Non-coding RNA FEZF1-AS1 Promotes Growth and Reduces Apoptosis Through Regulation of miR-363-3p/PAX6 Axis in Retinoblastoma

2021 ◽  
Author(s):  
Xiuming Liu ◽  
Xiaofeng Li ◽  
Jianchang Li
Keyword(s):  
Cell Viability ◽  
Antisense Rna ◽  
Tumor Formation ◽  
Retinoblastoma Cell ◽  
Non Coding Rna ◽  
High Incidence ◽  
Non Coding Rnas ◽  
Long Non Coding Rna ◽  
Common Malignancy

AbstractRetinoblastoma is the most common malignancy in children's eyes with high incidence. Long non-coding RNAs (lncRNAs) play important roles in the progression of retinoblastoma. LncRNA FEZF1 antisense RNA 1 (FEZF1-AS1) has been found to stimulate retinoblastoma. However, the mechanism of FEZF1-AS1 underlying progression of retinoblastoma is still unclear. In current study, FEZF1-AS1 was up-regulated in retinoblastoma tissues and cells. FEZF1-AS1 overexpression enhanced retinoblastoma cell viability, promoted cell cycle, and inhibited apoptosis. Conversely, FEZF1-AS1 knockdown reduced cell viability, cycle, and elevated apoptosis. The interaction between FEZF1-AS1 and microRNA-363-3p (miR-363-3p) was confirmed. FEZF1-AS1 down-regulated miR-363-3p and up-regulated PAX6. PAX6 was a target gene of miR-363-3p. EZF1-AS1 promoted retinoblastoma cell viability and suppressed apoptosis via PAX6. Further, we demonstrated that FEZF1-AS1 contribute to tumor formation in vivo. In conclusion, FEZF1-AS1 elevated growth and inhibited apoptosis by regulating miR-363-3p/PAX6 in retinoblastoma, which provide a new target for retinoblastoma treatment.

scholarly journals Long non-coding RNA TUG1 knockdown hinders the tumorigenesis of multiple myeloma by regulating the microRNA-34a-5p/NOTCH1 signaling pathway

2020 ◽  
Vol 15 (1) ◽  
pp. 284-295
Author(s):  
Yongtian Zhang ◽  
Dandan Zhao ◽  
Shumei Li ◽  
Meng Xiao ◽  
Hongjing Zhou ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Signaling Pathway ◽  
Notch Receptor ◽  
Cell Counting ◽  
Luciferase Reporter ◽  
Notch1 Signaling ◽  
Non Coding Rna ◽  
Notch1 Signaling Pathway ◽  
Long Non Coding Rna

AbstractMultiple myeloma (MM) is a serious health issue in hematological malignancies. Long non-coding RNA taurine-upregulated gene 1 (TUG1) has been reported to be highly expressed in the plasma of MM patients. However, the functions of TUG1 in MM tumorigenesis along with related molecular basis are still undefined. In this study, increased TUG1 and decreased microRNA-34a-5p (miR-34a-5p) levels in MM tissues and cells were measured by the real-time quantitative polymerase reaction assay. The expression of relative proteins was determined by the Western blot assay. TUG1 knockdown suppressed cell viability, induced cell cycle arrest and cell apoptosis in MM cells, as shown by Cell Counting Kit-8 and flow cytometry assays. Bioinformatics analysis, luciferase reporter assay, and RNA pull-down assay indicated that miR-34a-5p was a target of TUG1 and directly bound to notch receptor 1 (NOTCH1), and TUG1 regulated the NOTCH1 expression by targeting miR-34a-5p. The functions of miR-34a-5p were abrogated by TUG1 upregulation. Moreover, TUG1 loss impeded MM xenograft tumor growth in vivo by upregulating miR-34a-5p and downregulating NOTCH1. Furthermore, TUG1 depletion inhibited the expression of Hes-1, Survivin, and Bcl-2 protein in MM cells and xenograft tumors. TUG1 knockdown inhibited MM tumorigenesis by regulating the miR-34a-5p/NOTCH1 signaling pathway in vitro and in vivo, deepening our understanding of the TUG1 function in MM.

scholarly journals CoBold: a method for identifying different functional classes of transient RNA structure features that can impact RNA structure formation in vivo

2020 ◽  
Author(s):  
Adrián López Martín ◽  
Mohamed Mounir ◽  
Irmtraud M Meyer
Keyword(s):  
Structure Formation ◽  
Rna Structure ◽  
Rna Secondary Structure ◽  
Computational Method ◽  
Data Visualisation ◽  
Multiple Sequence ◽  
Functional Roles ◽  
Functional Classes ◽  
The Given

Abstract RNA structure formation in vivo happens co-transcriptionally while the transcript is being made. The corresponding co-transcriptional folding pathway typically involves transient RNA structure features that are not part of the final, functional RNA structure. These transient features can play important functional roles of their own and also influence the formation of the final RNA structure in vivo. We here present CoBold, a computational method for identifying different functional classes of transient RNA structure features that can either aid or hinder the formation of a known reference RNA structure. Our method takes as input either a single RNA or a corresponding multiple-sequence alignment as well as a known reference RNA secondary structure and identifies different classes of transient RNA structure features that could aid or prevent the formation of the given RNA structure. We make CoBold available via a web-server which includes dedicated data visualisation.

Nano-Coated si-SNHG14 Regulated PD-L1 Expression and Decreased Epithelial-Mesenchymal Transition in Nasopharyngeal Carcinoma Cells

2021 ◽  
Vol 17 (10) ◽  
pp. 1993-2002
Author(s):  
Haoran Yu ◽  
Chen Zhang ◽  
Wanpeng Li ◽  
Xicai Sun ◽  
Quan Liu ◽  
...  
Keyword(s):  
Nasopharyngeal Carcinoma ◽  
Epithelial Mesenchymal Transition ◽  
Animal Experiments ◽  
Loss Of Function ◽  
Mesenchymal Transition ◽  
Non Coding Rna ◽  
Long Non Coding Rna ◽  
Tumor Agent

To investigate the expression characteristics of long non-coding RNA SNHG14 in nasopharyngeal carcinoma (NPC) and its effects on epithelial-mesenchymal transition and development of nano-coated si-SNHG14 as an anti-tumor agent. The SNHG14 expression in cancerous and adjacent non-cancerous tissues was monitored using reverse transcriptionpolymerase chain reaction (RT-PCR). Gain- and loss-of-function experiments tested the regulation of SNHG14, miR- 5590-3p, and ZEB1 on PD-L1. The binding association between the above three factors was verified using bioinformatics analysis. EMT-related E-cadherin, N-cadherin, and Vimentin were tested using Western blot. Animal experiments in nude mice verified the function of SNHG14 in the EMT of NPC in vivo. The nano-coated si-SNHG14 was developed as an anti-tumor agent and was verified NPC cell in vitro. SNHG14 was upregulated in NPC tissues. Knocking down SNHG14 markedly inhibited the EMT of NPC. Additionally, the expression of ZEB1 was positively related to that of the SNHG14, while it was inversely correlated with that of miR-5590-3p. Moreover, ZEB1 transcription upregulated PD-L1 and promoted the EMT, while SNHG14 could accelerate the EMT of NPC in vivo by regulating the PD-1 and PD-L1. SNHG14-miR-5590- 3p-ZEB1 positively regulated PD-L1 and facilitate the EMT of NPC. Nano-coated si-SNHG14 significantly downregulated PD-L1 expression and decreased EMT.

scholarly journals Long-non Coding RNAs Related to Fat Deposition in Pigs Included lncRNA Corresponding to Human MALAT1

2021 ◽  
Author(s):  
Katarzyna Piórkowska ◽  
Kacper Żukowski ◽  
Katarzyna Ropka-Molik ◽  
Mirosław Tyra
Keyword(s):  
Regulatory Elements ◽  
Fat Deposition ◽  
Differentially Expressed ◽  
Rna Molecules ◽  
Non Coding Rna ◽  
Sequencing Method ◽  
Non Coding Rnas ◽  
Potential Interactions ◽  
Long Non Coding Rna ◽  
Generation Sequencing

Obesity is a problem in the last decades since the development of different technologies forced the submission of a faster pace of life, resulting in nutrition style changes. In turn, domestic pigs are an excellent animal model in recognition of adiposity-related processes, corresponding to the size of individual organs, the distribution of body fat in the organism, and similar metabolism. The present study applied the next-generation sequencing method to identify adipose tissue (AT) transcriptomic signals related to increased fat content by identifying differentially expressed genes (DEGs), included long-non coding RNA molecules. The Freiburg RNA tool was applied to recognise predicting hybridisation energy of RNA-RNA interactions. The results indicated several long non-coding RNAs (lncRNAs) whose expression was significantly positively or negatively associated with fat deposition. lncRNAs play an essential role in regulating gene expression by sponging miRNA, binding transcripts, facilitating translation, or coding other smaller RNA regulatory elements. In the pig fat tissue of obese group, increased expression of lncRNAs corresponding to human MALAT1 was observed that previously recognised in the obesity-related context. Moreover, hybridisation energy analyses pinpointed numerous potential interactions between identified differentially expressed lncRNAs, and obesity-related genes and miRNAs expressed in AT.

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