microRNAs: role in leukemia and their computational perspective

MicroRNAs as powerful tool against COVID-19: Computational perspective

Severe acute respiratory syndrome coronavirus 2 is the virus that is responsible for the current pandemic, COVID-19 (SARS-CoV-2). MiRNAs, a component of RNAi technology, belong to the family of short, noncoding ssRNAs, and may be crucial in the battle against this global threat since they are involved in regulating complex biochemical pathways and may prevent viral proliferation, translation, and host expression. The complicated metabolic pathways are modulated by the activity of many proteins, mRNAs, and miRNAs working together in miRNA-mediated genetic control. The amount of omics data has increased dramatically in recent years. This massive, linked, yet complex metabolic regulatory network data offers a wealth of opportunity for iterative analysis; hence, extensive, in-depth, but time-efficient screening is necessary to acquire fresh discoveries; this is readily performed with the use of bioinformatics. We have reviewed the literature on microRNAs, bioinformatics, and COVID-19 infection to summarize (1) the function of miRNAs in combating COVID-19, and (2) the use of computational methods in combating COVID-19 in certain noteworthy studies, and (3) computational tools used by these studies against COVID-19 in several purposes. This article is categorized under: Infectious Diseases > Computational Models.

A Novel Approach for Potential Human LncRNA-Disease Association Prediction Based on Local Random Walk

In recent years, lncRNAs (long non-coding RNAs) have been proved to be closely related to many diseases that are seriously harmful to human health. Although researches on clarifying the relationships between lncRNAs and diseases are developing rapidly, associations between the lncRNAs and diseases are still remaining largely unknown. In this manuscript, a novel Local Random Walk based prediction model called LRWHLDA is proposed for inferring potential associations between human lncRNAs and diseases. In LRWHLDA, a new heterogeneous network is established first, which allows that LRWHLDA can be implemented in the case of lacking known lncRNA-disease associations. And then, an improved local random walk method is designed for prediction of novel lncRNA-disease associations, which can help LRWHLDA achieve high prediction accuracy but with low time complexity. Finally, in order to evaluate the prediction performance of LRWHLDA, different frameworks such as LOOCV, 2-folds CV, and 5-folds CV have been implemented, simulation results indicate that LRWHLDA can achieve reliable AUCs of 0.8037, 0.8354, and 0.8556 under the frameworks of 2-fold CV, 5-fold CV, and LOOCV, respectively. Hence, it is easy to know that LRWHLDA contains the potential to be a representative of emerging methods in the field of research on potential lncRNA-disease associations prediction.

RWSF-BLP: a novel lncRNA-disease association prediction model using random walk-based multi-similarity fusion and bidirectional label propagation

An increasing number of studies and experiments have demonstrated that long noncoding RNAs (lncRNAs) have a massive impact on various biological processes. Predicting potential associations between lncRNAs and diseases not only can improve our understanding of the molecular mechanisms of human diseases but also can facilitate the identification of biomarkers for disease diagnosis, treatment, and prevention. However, identifying such associations through experiments is costly and demanding, thereby prompting researchers to develop computational methods to complement these experiments. In this paper, we constructed a novel model called RWSF-BLP (a novel lncRNA-disease association prediction model using Random Walk-based multi-Similarity Fusion and Bidirectional Label Propagation), which applies an efficient random walk-based multi-similarity fusion (RWSF) method to fuse different similarity matrices and utilizes bidirectional label propagation to predict potential lncRNA-disease associations. Leave-one-out cross-validation (LOOCV) and 5-fold cross-validation (5-fold-CV) were implemented in the evaluation RWSF-BLP performance. Results showed that, RWSF-BLP has reliable AUCs of 0.9086 and 0.9115 ± 0.0044 under the framework of LOOCV and 5-fold-CV and outperformed other four canonical methods. Case studies on lung cancer and leukemia demonstrated that potential lncRNA-disease associations can be predicted through our method. Therefore, our method can accurately infer potential lncRNA-disease associations and may be a good choice in future biomedical research.

A novel target convergence set based random walk with restart for prediction of potential LncRNA-disease associations

BACKGROUND

In recent years, lncRNAs (long-non-coding RNAs) have been proved to be closely related to the occurrence and development of many serious diseases that are seriously harmful to human health. However, most of the lncRNA-disease associations have not been found yet due to high costs and time complexity of traditional bio-experiments. Hence, it is quite urgent and necessary to establish efficient and reasonable computational models to predict potential associations between lncRNAs and diseases.

RESULTS

In this manuscript, a novel prediction model called TCSRWRLD is proposed to predict potential lncRNA-disease associations based on improved random walk with restart. In TCSRWRLD, a heterogeneous lncRNA-disease network is constructed first by combining the integrated similarity of lncRNAs and the integrated similarity of diseases. And then, for each lncRNA/disease node in the newly constructed heterogeneous lncRNA-disease network, it will establish a node set called TCS (Target Convergence Set) consisting of top 100 disease/lncRNA nodes with minimum average network distances to these disease/lncRNA nodes having known associations with itself. Finally, an improved random walk with restart is implemented on the heterogeneous lncRNA-disease network to infer potential lncRNA-disease associations. The major contribution of this manuscript lies in the introduction of the concept of TCS, based on which, the velocity of convergence of TCSRWRLD can be quicken effectively, since the walker can stop its random walk while the walking probability vectors obtained by it at the nodes in TCS instead of all nodes in the whole network have reached stable state. And Simulation results show that TCSRWRLD can achieve a reliable AUC of 0.8712 in the Leave-One-Out Cross Validation (LOOCV), which outperforms previous state-of-the-art results apparently. Moreover, case studies of lung cancer and leukemia demonstrate the satisfactory prediction performance of TCSRWRLD as well.

CONCLUSIONS

Both comparative results and case studies have demonstrated that TCSRWRLD can achieve excellent performances in prediction of potential lncRNA-disease associations, which imply as well that TCSRWRLD may be a good addition to the research of bioinformatics in the future.

Defective graphene domains in boron nitride sheets

Novel two-dimensional materials have emerged as hybrid structures that combine graphene and hexagonal boron nitride (h-BN) domains. During their growth process, structural defects such as vacancies and change of atoms connectivity are unavoidable. In the present study, we use first-principle calculations to investigate the electronic structure of graphene domains endowed with a single carbon atom vacancy or Stone-Wales defects in h-BN sheets. The results show that both kinds of defects yield localized states within the bandgap. Alongside this change in the bandgap configuration, it occurs a splitting of the spin channels in such a way that electrons with up and down spins populate different energy levels above and below the Fermi level, respectively. Such a spin arrangement is associated to lattice magnetization. Stone-Wales defects solely point to the appearance of new intragap levels. These results demonstrated that vacancies could significantly affect the electronic properties of hybrid graphene/h-BN sheets. Graphical Abstract A Boron-Nitride sheet doped with a vacancy endowed Carbon domain.

miR-9 Enhances the Chemosensitivity of AML Cells to Daunorubicin by Targeting the EIF5A2/MCL-1 Axis

Daunorubicin (Dnr) is at the forefront of acute myeloid leukemia (AML) therapy, but drug resistance poses a major threat to treatment success. MicroRNA (miR)-9 has been shown to have a pivotal role in AML development. However, little is known about the role of miR-9 in Dnr resistance in AML. We explored the potential role of miR-9 in Dnr resistance in AML cells and its mechanism of action. AML cell lines with high half-maximal inhibitory concentration to Dnr in vivo had significantly low miR-9 expression. miR-9 overexpresssion sensitized AML cells to Dnr, inhibited cell proliferation, and enhanced the ability of Dnr to induce apoptosis; miR-9 knockdown had the opposite effects. Mechanistic studies demonstrated that eukaryotic translation initiation factor 5A-2 (EIF5A2) was a putative target of miR-9, which was inversely correlated with the expression and role of miR-9 in AML cells. miR-9 improved the anti-tumor effects of Dnr by inhibiting myeloid cell leukemia-1 (MCL-1) expression, which was dependent on downregulation of EIF5A2 expression. These results suggest that miR-9 has an essential role in Dnr resistance in AML cells through inhibition of the EIF5A2/MCL-1 axis in AML cells. Our data highlight the potential application of miR-9 in chemotherapy for AML patients.

IRWRLDA: improved random walk with restart for lncRNA-disease association prediction

In recent years, accumulating evidences have shown that the dysregulations of lncRNAs are associated with a wide range of human diseases. It is necessary and feasible to analyze known lncRNA-disease associations, predict potential lncRNA-disease associations, and provide the most possible lncRNA-disease pairs for experimental validation. Considering the limitations of traditional Random Walk with Restart (RWR), the model of Improved Random Walk with Restart for LncRNA-Disease Association prediction (IRWRLDA) was developed to predict novel lncRNA-disease associations by integrating known lncRNA-disease associations, disease semantic similarity, and various lncRNA similarity measures. The novelty of IRWRLDA lies in the incorporation of lncRNA expression similarity and disease semantic similarity to set the initial probability vector of the RWR. Therefore, IRWRLDA could be applied to diseases without any known related lncRNAs. IRWRLDA significantly improved previous classical models with reliable AUCs of 0.7242 and 0.7872 in two known lncRNA-disease association datasets downloaded from the lncRNADisease database, respectively. Further case studies of colon cancer and leukemia were implemented for IRWRLDA and 60% of lncRNAs in the top 10 prediction lists have been confirmed by recent experimental reports.

Onco-microRNA miR-130b promoting cell growth in children APL by targeting PTEN

OBJECTIVE

To study the expression of microRNA-130b (miR-130b) in children acute promyelocytic leukemia (APL) and its role for regulating PTEN expression.

METHODS

A total of 50 children APL marrow tissues and 15 normal marrow tissues between January and December in 2012 were collected into our study. The expression of miR-130b in APL and normal marrow tissues were detected by quantitative real-time polymerase chain reaction. MiR-130b inhibitor was transfected into HL-60 cells. Cell Counting Kit-8 assay and flow cytometry were used to measure cell proliferation and apoptosis, respectively. The expression of PTEN, a potential target of miR-130b, and its downstream genes, Bcl-2 and Bax, in transformed cells were detected by quantitative real-time polymerase chain reaction and western-blot.

RESULTS

The expression of miR-130b was significantly higher in children APL marrow tissues than in normal marrow tissues (P < 0.05). Down-regulation of miR-130b could significantly suppress cell proliferation and induce apoptosis in HL-60 cells (P < 0.05). PTEN expression was upregulated when miR-130b was knocking-down (P < 0.05). As downstream genes of PTEN, the expression of Bcl-2 and Bax were regulated as well.

CONCLUSIONS

MiR-130b is overexpressed in children APL marrow tissues and associated with cell growth. MiR-130b may promote children APL progression by inducing cell proliferation and inhibiting apoptosis.