Current Genomics - Volume 20, Issue 8, 2019

MicroRNA (miRNA) is a small non-coding molecule that is involved in gene regulation and RNA silencing by complementary on their targets. Experimental methods for target prediction can be time-consuming and expensive. Thus, the application of the computational approach is implicated to enlighten these complications with experimental studies. However, there is still a need for an optimized approach in miRNA biology. Therefore, machine learning (ML) would initiate a new era of research in miRNA biology towards potential diseases biomarker. In this article, we described the application of ML approaches in miRNA discovery and target prediction with functions and future prospective. The implementation of a new era of computational methodologies in this direction would initiate further advanced levels of discoveries in miRNA.

Background: Even after decades of research, cancer, by and large, remains a challenge and is one of the major causes of death worldwide. For a very long time, it was believed that cancer is simply an outcome of changes at the genetic level but today, it has become a well-established fact that both genetics and epigenetics work together resulting in the transformation of normal cells to cancerous cells. Objective: In the present scenario, researchers are focusing on targeting epigenetic machinery. The main advantage of targeting epigenetic mechanisms is their reversibility. Thus, cells can be reprogrammed to their normal state. Graph theory is a powerful gift of mathematics which allows us to understand complex networks. Methodology: In this study, graph theory was utilized for quantitative analysis of the epigenetic network of hepato-cellular carcinoma (HCC) and subsequently finding out the important vertices in the network thus obtained. Secondly, this network was utilized to locate novel targets for hepato-cellular carcinoma epigenetic therapy. Results: The vertices represent the genes involved in the epigenetic mechanism of HCC. Topological parameters like clustering coefficient, eccentricity, degree, etc. have been evaluated for the assessment of the essentiality of the node in the epigenetic network. Conclusion: The top ten novel epigenetic target genes involved in HCC reported in this study are cdk6, cdk4, cdkn2a, smad7, smad3, ccnd1, e2f1, sf3b1, ctnnb1, and tgfb1.

Background: Helicobacter pylori are gram-negative bacteria, which colonize the human stomach. More than 50% of the world’s population is infected by H. pylori. Based on the high prevalence of H. pylori, it is very likely that HIV and H. pylori infection may coexist. However, the molecular events that occur during HIV-H. pylori co-infection remain unclear. Latent HIV reservoirs are the major obstacle in HIV cure despite effective therapy. Here, we explored the effect of H. pylori stimulation on latently HIV-infected monocytic cell line U1. Methods: High throughput RNA-Seq using Illumina platform was performed to analyse the change in transcriptome between unstimulated and H. pylori-stimulated latently HIV-infected U1 cells. Transcriptome analysis identified potential genes and pathways involved in the reversal of HIV latency using bioinformatic tools that were validated by real-time PCR. Results: H. pylori stimulation increased the expression of HIV-1 Gag, both at transcription (p<0.001) and protein level. H. pylori stimulation also increased the expression of proinflammatory cytokines IL-1β, CXCL8 and CXCL10 (p<0.0001). Heat-killed H. pylori retained their ability to induce HIV transcription. RNA-Seq analysis revealed 197 significantly upregulated and 101 significantly downregulated genes in H. pylori-stimulated U1 cells. IL-1β and CXCL8 were found to be significantly upregulated using transcriptome analysis, which was consistent with real-time PCR data. Conclusion: H. pylori reactivate HIV-1 in latently infected monocytes with the upregulation of IL-1β and CXCL8, which are prominent cytokines involved in the majority of inflammatory pathways. Our results warrant future in vivo studies elucidating the effect of H. pylori in HIV latency and pathogenesis.

Pancreatic cancer (PaC) is one of the most lethal cancers, with an increasing global incidence rate. Unfavorable prognosis largely results from associated difficulties in early diagnosis and the absence of prognostic and predictive biomarkers that would enable an individualized therapeutic approach. In fact, PaC prognosis has not improved for years, even though much efforts and resources have been devoted to PaC research, and the multimodal management of PaC patients has been used in clinical practice. It is thus imperative to develop optimal biomarkers, which would increase diagnostic precision and improve the post-diagnostic management of PaC patients. Current trends in biomarker research envisage the unique opportunity of cell-free microRNAs (miRNAs) present in circulation to become a convenient, non-invasive tool for accurate diagnosis, prognosis and prediction of response to treatment. This review analyzes studies focused on cell-free miRNAs in PaC. The studies provide solid evidence that miRNAs are detectable in serum, blood plasma, saliva, urine, and stool, and that they present easy-to-acquire biomarkers with strong diagnostic, prognostic and predictive potential.

Background: With the rapid development of biological research, microRNAs (miRNAs) have increasingly attracted worldwide attention. The increasing biological studies and scientific experiments have proven that miRNAs are related to the occurrence and development of a large number of key biological processes which cause complex human diseases. Thus, identifying the association between miRNAs and disease is helpful to diagnose the diseases. Although some studies have found considerable associations between miRNAs and diseases, there are still a lot of associations that need to be identified. Experimental methods to uncover miRNA-disease associations are time-consuming and expensive. Therefore, effective computational methods are urgently needed to predict new associations. Methodology: In this work, we propose an integrated method for predicting potential associations between miRNAs and diseases (IMPMD). The enhanced similarity for miRNAs is obtained by combination of functional similarity, gaussian similarity and Jaccard similarity. To diseases, it is obtained by combination of semantic similarity, gaussian similarity and Jaccard similarity. Then, we use these two enhanced similarities to construct the features and calculate cumulative score to choose robust features. Finally, the general linear regression is applied to assign weights for Support Vector Machine, K-Nearest Neighbor and Logistic Regression algorithms. Results: IMPMD obtains AUC of 0.9386 in 10-fold cross-validation, which is better than most of the previous models. To further evaluate our model, we implement IMPMD on two types of case studies for lung cancer and breast cancer. 49 (Lung Cancer) and 50 (Breast Cancer) out of the top 50 related miRNAs are validated by experimental discoveries. Conclusion: We built a software named IMPMD which can be freely downloaded from https:// github.com/Sunmile/IMPMD.

Introduction: Neddylation is a highly dynamic and reversible post-translational modification. The abnormality of neddylation has previously been shown to be closely related to some human diseases. The detection of neddylation sites is essential for elucidating the regulation mechanisms of protein neddylation. Objective: As the detection of the lysine neddylation sites by the traditional experimental method is often expensive and time-consuming, it is imperative to design computational methods to identify neddylation sites. Methods: In this study, a bioinformatics tool named NeddPred is developed to identify underlying protein neddylation sites. A bi-profile bayes feature extraction is used to encode neddylation sites and a fuzzy support vector machine model is utilized to overcome the problem of noise and class imbalance in the prediction. Results: Matthew's correlation coefficient of NeddPred achieved 0.7082 and an area under the receiver operating characteristic curve of 0.9769. Independent tests show that NeddPred significantly outperforms existing lysine neddylation sites predictor NeddyPreddy. Conclusion: Therefore, NeddPred can be a complement to the existing tools for the prediction of neddylation sites. A user-friendly webserver for NeddPred is accessible at 123.206.31.171/NeddPred/.