Structural characterization and modeling of ncRNA–protein interactions

Long noncoding RNA LOC100911498 is a novel regulator of neuropathic pain in rats

INTRODUCTION

Neuropathic pain (NP) is the most debilitating of all clinical pain syndromes and may be a consequence of dysfunction in the somatosensory nervous system. Unfortunately, the pathogenesis of NP is not fully understood yet and it cannot be cured totally. Long noncoding RNA (lncRNA) is a type of RNA molecule greater than 200 nucleotides, and dysregulated expression of lncRNAs play a critical role in the facilitation of NP. Previous study showed the expression level of LOC100911498 in the spinal cords of spared nerve injury (SNI) rats were increased. This research was aimed at exploring what role LOC100911498 plays in the pathophysiological process of NP.

METHODS

The mechanical withdrawal threshold (MWT) of rats was measured by the von Frey test. The expression levels of P2X4 receptor (P2X4R), ionized calcium-binding adaptor molecule 1 (Iba-1), p-p38 and brain-derived neurotrophic factor (BDNF) in spinal cords were detected, respectively.

RESULTS

Our results suggested that the level of LOC100911498 in SNI rats was markedly higher than that in the sham group; the MWT values in rats were treated with LOC100911498siRNA were increased, and the expression levels of P2X4R, Iba-1, p-p38 and BDNF in SNI+ LOC100911498siRNA group were reduced compared with those in the SNI group.

CONCLUSION

Our study indicated the effects lncRNA LOC100911498 siRNA exerted on NP were mediated by P2X4R on microglia in the spinal cords of rats. Further, LOC100911498 may be a novel positive regulator of NP by regulating the expression and function of the P2X4R.

A Novel LncRNA, MuLnc1, Associated With Environmental Stress in Mulberry (Morus multicaulis)

Environmental stresses are major constraints that limit the leaf productivity and quality of mulberry. LncRNAs have emerged as important regulators in response to biotic and abiotic stresses in plants. However, the functions and mechanisms of most lncRNAs remain largely unknown. A novel lncRNA designated as MuLnc1 was found to be cleaved by mul-miR3954 and produce secondary siRNAs in a 21 nt phase in mulberry. It was demonstrated that one of the siRNAs produced, si161579, can silence the expression of the calmodulin-like protein gene CML27 of mulberry (MuCML27). When MuCML27 was heterologously expressed in Arabidopsis, the transgenic plants exhibited enhanced resistance to Botrytis cinerea and Pseudomonas syringae pv tomato DC3000. In addition, the transgenic MuCML27-overexpressing Arabidopsis plants are more tolerant to salt and drought stresses. Furthermore, the network of mul-miR3954-MuLnc1-siRNAs-mRNAs was modeled to elucidate the interaction between lncRNAs and sRNAs with mRNAs. All of these, taken together, suggest that MuLnc1 was associated with environmental stress in mulberry and may be considered as a potential genetic improvement target gene of mulberry. The information provided may shed light on the complicated gene expression regulatory mechanisms in mulberry stress responses.

LncRNA NONRATT021972 siRNA normalized the dysfunction of hepatic glucokinase through AKT signaling in T2DM rats

Hepatic glucokinase (GK) expression and activity are decreased in type 2 diabetes mellitus (T2DM), and glycogen synthase kinase-3 (GSK-3) inhibits the synthesis of GK. In hepatocytes, the activation of the protein kinase B (PKB/AKT) signaling pathway enhances GK expression and inhibits the phosphorylation of GSK-3β. The dysfunction of certain long noncoding RNAs (lncRNAs) has been associated with a variety of diseases.

AIMS

This study explored the effects of the lncRNA NONRATT021972 small interfering RNA (siRNA) on the dysfunction of hepatic GK through AKT signaling in T2DM rats.

METHODS

Livers from type 2 diabetic rats and hepatocytes cultured in high glucose and high fatty acid media were studied. The changes in expression of AKT, GK and GSK 3β were detected by western blotting or RT-PCR. The application of bioinformatics technology (CatRAPID) was used to identify the targets of NONRATT021972 RNA.

RESULTS

We found that lncRNA NONRATT021972 levels in the liver were increased in type 2 diabetic rats, and the increase was associated with an increase in the blood glucose levels. The NONRATT021972 siRNA enhanced phospho-AKT (p-AKT) levels, GK expression and hepatic glycogen synthesis. This siRNA also reduced phospho-glycogen synthase kinase-3β (p-GSK-3β) levels and hyperglycemia in T2DM rats, as well as in hepatocytes cultured in high glucose media with fatty acids. CatRAPID predicted that there was the interaction between NONRATT021972 and p-AKT.

CONCLUSIONS

LncRNA NONRATT021972 siRNA may have beneficial effects on T2DM.

LncRNA NONRATT021972 siRNA regulates neuropathic pain behaviors in type 2 diabetic rats through the P2X7 receptor in dorsal root ganglia

BACKGROUND

Long non-protein-coding RNAs (lncRNAs) are involved in the pathological processes of nervous system diseases. NONRATT021972 is an lncRNA. This study explores the effects of lncRNA NONRATT021972 small interference RNA (siRNA) on diabetic neuropathic pain (DNP) mediated by the P2X7 receptor in the rat dorsal root ganglia (DRG).

RESULTS

Our results show that NONRATT021972 expression was significantly higher in the DRG of diabetes mellitus (DM) group compared with control group. NONRATT021972 expression in the DRG was reduced when DM rats were treated with NONRATT021972 siRNA. NONRATT021972 siRNA treatment in type 2 DM rats increased the mechanical withdrawal threshold (MWT), the thermal withdrawal latency (TWL) and the sensory nerve conduction velocity (SNCV) of rat tail nerves. After intravenous injection with NONRATT021972 siRNA in DM rats, the P2X7, GFAP and TNF-ɑ expression levels in DRG were decreased. An interaction between the RNA (NONRATT021972) and protein (P2X7) was predicted by the application of bioinformatics technology. The BzATP-activated currents in DRG non-neurons (satellite glial cells) of DM rats were significantly increased compared to control rats. NONRATT021972 siRNA treatment inhibited the ATP-activated currents in HEK293 cells transfected with pEGFP-P2X7.

CONCLUSIONS

NONRATT021972 siRNA treatment can decrease the expression levels of P2X7 mRNA and protein and inhibit the activation of satellite glial cells (SGCs) in the DRG of type 2 DM rats. Moreover, NONRATT021972 siRNA treatment reduced the release of inflammatory factors (TNF-α), thereby inhibiting the excitability of DRG neurons and reducing mechanical and thermal hyperalgesia in type 2 DM rats.

RPI-Pred: predicting ncRNA-protein interaction using sequence and structural information

RNA-protein complexes are essential in mediating important fundamental cellular processes, such as transport and localization. In particular, ncRNA-protein interactions play an important role in post-transcriptional gene regulation like mRNA localization, mRNA stabilization, poly-adenylation, splicing and translation. The experimental methods to solve RNA-protein interaction prediction problem remain expensive and time-consuming. Here, we present the RPI-Pred (RNA-protein interaction predictor), a new support-vector machine-based method, to predict protein-RNA interaction pairs, based on both the sequences and structures. The results show that RPI-Pred can correctly predict RNA-protein interaction pairs with ∼94% prediction accuracy when using sequence and experimentally determined protein and RNA structures, and with ∼83% when using sequences and predicted protein and RNA structures. Further, our proposed method RPI-Pred was superior to other existing ones by predicting more experimentally validated ncRNA-protein interaction pairs from different organisms. Motivated by the improved performance of RPI-Pred, we further applied our method for reliable construction of ncRNA-protein interaction networks. The RPI-Pred is publicly available at: http://ctsb.is.wfubmc.edu/projects/rpi-pred.

Predicting RNA-protein interactions using only sequence information

Background

RNA-protein interactions (RPIs) play important roles in a wide variety of cellular processes, ranging from transcriptional and post-transcriptional regulation of gene expression to host defense against pathogens. High throughput experiments to identify RNA-protein interactions are beginning to provide valuable information about the complexity of RNA-protein interaction networks, but are expensive and time consuming. Hence, there is a need for reliable computational methods for predicting RNA-protein interactions.

Results

We propose RPISeq, a family of classifiers for predicting RNA-protein interactions using only sequence information. Given the sequences of an RNA and a protein as input, RPIseq predicts whether or not the RNA-protein pair interact. The RNA sequence is encoded as a normalized vector of its ribonucleotide 4-mer composition, and the protein sequence is encoded as a normalized vector of its 3-mer composition, based on a 7-letter reduced alphabet representation. Two variants of RPISeq are presented: RPISeq-SVM, which uses a Support Vector Machine (SVM) classifier and RPISeq-RF, which uses a Random Forest classifier. On two non-redundant benchmark datasets extracted from the Protein-RNA Interface Database (PRIDB), RPISeq achieved an AUC (Area Under the Receiver Operating Characteristic (ROC) curve) of 0.96 and 0.92. On a third dataset containing only mRNA-protein interactions, the performance of RPISeq was competitive with that of a published method that requires information regarding many different features (e.g., mRNA half-life, GO annotations) of the putative RNA and protein partners. In addition, RPISeq classifiers trained using the PRIDB data correctly predicted the majority (57-99%) of non-coding RNA-protein interactions in NPInter-derived networks from E. coli, S. cerevisiae, D. melanogaster, M. musculus, and H. sapiens.

Conclusions

Our experiments with RPISeq demonstrate that RNA-protein interactions can be reliably predicted using only sequence-derived information. RPISeq offers an inexpensive method for computational construction of RNA-protein interaction networks, and should provide useful insights into the function of non-coding RNAs. RPISeq is freely available as a web-based server at http://pridb.gdcb.iastate.edu/RPISeq/.