bims-lorfki Biomed News
on Long non-coding RNA functions in the kidney
Issue of 2020‒12‒06
two papers selected by
Nikita Dewani
Max Delbrück Centre for Molecular Medicine

  1. Diabetes Metab Syndr Obes. 2020 ;13 4507-4517
    Cao X, Fan QL.
      Aim: More than half of microRNAs are located in genes. LncRNAs are host genes of intronic microRNAs that regulate intracellular splicing to form pre-miRNAs that are processed to mature miRNAs. MicroRNAs work as partners or antagonists of their host lncRNAs by fine-tuning their target genes. However, whether lncRNA-MIR503HG (miR-503 host gene) is co-transcribed with miR-503 and affects miR-503 splicing, thereby affecting its target gene Bcl-2 expression and cell mitochondrial apoptotic pathway in diabetic nephropathy (DN) is currently unknown.Methods: Human proximal tubular (HK-2) cells cultured in high glucose were transfected with lncRNA MIR503HG overexpression/inhibition plasmid and miR-503 mimics/inhibitor. Real-time quantitative PCR was used to measure the expression levels of lncRNA MIR503HG, pre-miR-503, miR-503 and Bcl-2. Western blot was used to measure the protein expressions of Bcl-2, Bax, Cytc and cleaved-caspase 9/3. Annexin V/PI flow cytometry was used to measure apoptosis.
    Results: Host lncRNA MIR503HG was co-transcribed with miR-503. MIR503HG regulated the expression of miR-503 by affecting miR-503 splicing synthesis. In the presence of high glucose, the expression levels of lncRNA MIR503HG and miR-503 were up-regulated in HK-2 cells cultured in high glucose. Bcl-2 expression was inhibited and levels of apoptosis-related proteins Cytc and Bax were increased in HK-2 cells cultured in high glucose, all of which promoted the caspase cascade reaction, leading to increased caspase-9 and caspase-3 shear fragments inducing apoptosis of the mitochondrial pathway. Inhibition of MIR503HG led to a reduction in miR-503 expression, up-regulated its target gene Bcl-2, inhibited the expression levels of Bax and other apoptosis-related proteins and attenuated HK-2 cell apoptosis induced by high glucose. Co-transfection of miRNA-503 partially offset the effect of MIR503HG-siRNA.
    Conclusion: MIR503HG indirectly regulates Bcl-2 by promoting the co-transcription of miRNA-503 to participate high-glucose-induced proximal tubular cell apoptosis, providing a new target for diabetic nephropathy treatment.
    Keywords:  apoptosis; diabetic nephropathy; host gene; lncRNA MIR503HG; miR-503
  2. BMC Bioinformatics. 2020 Dec 02. 21(1): 555
    Xiao Y, Xiao Z, Feng X, Chen Z, Kuang L, Wang L.
      BACKGROUND: Accumulating evidence has demonstrated that long non-coding RNAs (lncRNAs) are closely associated with human diseases, and it is useful for the diagnosis and treatment of diseases to get the relationships between lncRNAs and diseases. Due to the high costs and time complexity of traditional bio-experiments, in recent years, more and more computational methods have been proposed by researchers to infer potential lncRNA-disease associations. However, there exist all kinds of limitations in these state-of-the-art prediction methods as well.RESULTS: In this manuscript, a novel computational model named FVTLDA is proposed to infer potential lncRNA-disease associations. In FVTLDA, its major novelty lies in the integration of direct and indirect features related to lncRNA-disease associations such as the feature vectors of lncRNA-disease pairs and their corresponding association probability fractions, which guarantees that FVTLDA can be utilized to predict diseases without known related-lncRNAs and lncRNAs without known related-diseases. Moreover, FVTLDA neither relies solely on known lncRNA-disease nor requires any negative samples, which guarantee that it can infer potential lncRNA-disease associations more equitably and effectively than traditional state-of-the-art prediction methods. Additionally, to avoid the limitations of single model prediction techniques, we combine FVTLDA with the Multiple Linear Regression (MLR) and the Artificial Neural Network (ANN) for data analysis respectively. Simulation experiment results show that FVTLDA with MLR can achieve reliable AUCs of 0.8909, 0.8936 and 0.8970 in 5-Fold Cross Validation (fivefold CV), 10-Fold Cross Validation (tenfold CV) and Leave-One-Out Cross Validation (LOOCV), separately, while FVTLDA with ANN can achieve reliable AUCs of 0.8766, 0.8830 and 0.8807 in fivefold CV, tenfold CV, and LOOCV respectively. Furthermore, in case studies of gastric cancer, leukemia and lung cancer, experiment results show that there are 8, 8 and 8 out of top 10 candidate lncRNAs predicted by FVTLDA with MLR, and 8, 7 and 8 out of top 10 candidate lncRNAs predicted by FVTLDA with ANN, having been verified by recent literature. Comparing with the representative prediction model of KATZLDA, comparison results illustrate that FVTLDA with MLR and FVTLDA with ANN can achieve the average case study contrast scores of 0.8429 and 0.8515 respectively, which are both notably higher than the average case study contrast score of 0.6375 achieved by KATZLDA.
    CONCLUSION: The simulation results show that FVTLDA has good prediction performance, which is a good supplement to future bioinformatics research.
    Keywords:  Artificial neural network; Features; LncRNA-disease association prediction; Multiple linear regression; Random walk