Transcriptional gene silencing of dopamine D3 receptor caused by let-7d mimics in immortalized renal proximal tubule cells of rats

Ensemble of kernel ridge regression-based small molecule-miRNA association prediction in human disease

MicroRNAs (miRNAs) play crucial roles in human disease and can be targeted by small molecule (SM) drugs according to numerous studies, which shows that identifying SM-miRNA associations in human disease is important for drug development and disease treatment. We proposed the method of Ensemble of Kernel Ridge Regression-based Small Molecule-MiRNA Association prediction (EKRRSMMA) to uncover potential SM-miRNA associations by combing feature dimensionality reduction and ensemble learning. First, we constructed different feature subsets for both SMs and miRNAs. Then, we trained homogeneous base learners based on distinct feature subsets and took the average of scores obtained from these base learners as SM-miRNA association score. In EKRRSMMA, feature dimensionality reduction technology was employed in the process of construction of feature subsets to reduce the influence of noisy data. Besides, the base learner, namely KRR_avg, was the combination of two classifiers constructed under SM space and miRNA space, which could make full use of the information of SM and miRNA. To assess the prediction performance of EKRRSMMA, we conducted Leave-One-Out Cross-Validation (LOOCV), SM-fixed local LOOCV, miRNA-fixed local LOOCV and 5-fold CV based on two datasets. For Dataset 1 (Dataset 2), EKRRSMMA got the Area Under receiver operating characteristic Curves (AUCs) of 0.9793 (0.8871), 0.8071 (0.7705), 0.9732 (0.8586) and 0.9767 ± 0.0014 (0.8560 ± 0.0027). Besides, we conducted four case studies. As a result, 32 (5-Fluorouracil), 19 (17β-Estradiol), 26 (5-Aza-2'-deoxycytidine) and 11 (cyclophosphamide) out of top 50 predicted potentially associated miRNAs were confirmed by database or experimental literature. Above evaluation results demonstrated that EKRRSMMA is reliable for predicting SM-miRNA associations.

Regulation of ABO blood group antigen expression by miR-331-3p and miR-1908-5p during hematopoietic stem cell differentiation

The ABO blood group system is the most important factor in clinical transfusion medicine and is implicated in a number of human diseases. ABO antigens are not confined to red blood cells (RBCs) and are widely expressed in a variety of human cells and tissues. To date, many alleles with variant ABO expression have been identified and in many cases traced to one of the >250 reported genetic variations in the respective glycosyltransferase. The role of microRNAs (miRNAs) in the regulation of blood group antigens during erythropoiesis has not been addressed, however. Here, we show that miR-331-3p and miR-1908-5p directly target the mRNA of glycosyltransferases A and B. Expression levels of miR-331-3p and miR-1908-5p inversely correlated with levels of blood group A antigen. In addition, we found that overexpression of these miRNAs in hematopoietic stem cells led to a significantly reduced number of blood group A antigens per RBC. Simultaneous targeting of the transcription factor (TF) SP1 by miR-331-3p further enhanced these effects. The targeting rendered SP1 incapable of binding to the ABO gene promoter, causing further downregulation of blood group A antigen expression by up to 70%. Taken together, expression changes in these miRNAs may account for rare cases of weak A/B phenotypes that genetic variations in the glycosyltransferase coding region cannot explain. These results also suggest an explanation for the disappearance of ABH antigens during carcinogenesis and point to new therapeutic targets in ABO mismatched organ transplantation.

SNMFSMMA: using symmetric nonnegative matrix factorization and Kronecker regularized least squares to predict potential small molecule-microRNA association

Accumulating studies have shown that microRNAs (miRNAs) could be used as targets of small-molecule (SM) drugs to treat diseases. In recent years, researchers have proposed many computational models to reveal miRNA-SM associations due to the huge cost of experimental methods. Considering the shortcomings of the previous models, such as the prediction accuracy of some models is low or some cannot be applied for new SMs (miRNAs), we developed a novel model named Symmetric Nonnegative Matrix Factorization for Small Molecule-MiRNA Association prediction (SNMFSMMA). Different from some models directly applying the integrated similarities, SNMFSMMA first performed matrix decomposition on the integrated similarity matrixes, and calculated the Kronecker product of the new integrated similarity matrixes to obtain the SM-miRNA pair similarity. Further, we applied regularized least square to obtain the mapping function of the SM-miRNA pairs to the associated probabilities by minimizing the objective function. On the basis of Dataset 1 and 2 extracted from SM2miR v1.0 database, we implemented global leave-one-out cross validation (LOOCV), miRNA-fixed local LOOCV, SM-fixed local LOOCV and 5-fold cross-validation to evaluate the prediction performance. Finally, the AUC values obtained by SNMFSMMA in these validation reached 0.9711 (0.8895), 0.9698 (0.8884), 0.8329 (0.7651) and 0.9644 ± 0.0035 (0.8814 ± 0.0033) based on Dataset 1 (Dataset 2), respectively. In the first case study, 5 of the top 10 associations predicted were confirmed. In the second, 7 and 8 of the top 10 predicted miRNAs related with 5-FU and 5-Aza-2'-deoxycytidine were confirmed. These results demonstrated the reliable predictive power of SNMFSMMA.

RFSMMA: A New Computational Model to Identify and Prioritize Potential Small Molecule-MiRNA Associations

More and more studies found that many complex human diseases occur accompanied by aberrant expression of microRNAs (miRNAs). Small molecule (SM) drugs have been utilized to treat complex human diseases by affecting the expression of miRNAs. Several computational methods were proposed to infer underlying associations between SMs and miRNAs. In our study, we proposed a new calculation model of random forest based small molecule-miRNA association prediction (RFSMMA) which was based on the known SM-miRNA associations in the SM2miR database. RFSMMA utilized the similarity of SMs and miRNAs as features to represent SM-miRNA pairs and further implemented the machine learning algorithm of random forest to train training samples and obtain a prediction model. In RFSMMA, integrating multiple kinds of similarity can avoid the bias of single similarity and choosing more reliable features from original features can represent SM-miRNA pairs more accurately. We carried out cross validations to assess predictive accuracy of RFSMMA. As a result, RFSMMA acquired AUCs of 0.9854, 0.9839, 0.7052, and 0.9917 ± 0.0008 under global leave-one-out cross validation (LOOCV), miRNA-fixed local LOOCV, SM-fixed local LOOCV, and 5-fold cross validation, respectively, under data set 1. Based on data set 2, RFSMMA obtained AUCs of 0.8456, 0.8463, 0.6653, and 0.8389 ± 0.0033 under four cross validations according to the order mentioned above. In addition, we implemented a case study on three common SMs, namely, 5-fluorouracil, 17β-estradiol, and 5-aza-2'-deoxycytidine. Among the top 50 associated miRNAs of these three SMs predicted by RFSMMA, 31, 32, and 28 miRNAs were verified, respectively. Therefore, RFSMMA is shown to be an effective and reliable tool for identifying underlying SM-miRNA associations.

In Silico Prediction of Small Molecule-miRNA Associations Based on the HeteSim Algorithm

Targeting microRNAs (miRNAs) with drug small molecules (SMs) is a new treatment method for many human complex diseases. Unsurprisingly, identification of potential miRNA-SM associations is helpful for pharmaceutical engineering and disease therapy in the field of medical research. In this paper, we developed a novel computational model of HeteSim-based inference for SM-miRNA Association prediction (HSSMMA) by implementing a path-based measurement method of HeteSim on a heterogeneous network combined with known miRNA-SM associations, integrated miRNA similarity, and integrated SM similarity. Through considering paths from an SM to a miRNA in the heterogeneous network, the model can capture the semantics information under each path and predict potential miRNA-SM associations based on all the considered paths. We performed global, miRNA-fixed local and SM-fixed local leave one out cross validation (LOOCV) as well as 5-fold cross validation based on the dataset of known miRNA-SM associations to evaluate the prediction performance of our approach. The results showed that HSSMMA gained the corresponding areas under the receiver operating characteristic (ROC) curve (AUCs) of 0.9913, 0.9902, 0.7989, and 0.9910 ± 0.0004 based on dataset 1 and AUCs of 0.7401, 0.8466, 0.6149, and 0.7451 ± 0.0054 based on dataset 2, respectively. In case studies, 2 of the top 10 and 13 of the top 50 predicted potential miRNA-SM associations were confirmed by published literature. We further implemented case studies to test whether HSSMMA was effective for new SMs without any known related miRNAs. The results from cross validation and case studies showed that HSSMMA could be a useful prediction tool for the identification of potential miRNA-SM associations.

MicroRNAs and Target Genes As Biomarkers for the Diagnosis of Early Onset of Parkinson Disease

Among the neurodegenerative disorders, Parkinson's disease (PD) ranks as the second most common disorder with a higher prevalence in individuals aged over 60 years old. Younger individuals may also be affected with PD which is known as early onset PD (EOPD). Despite similarities between the characteristics of EOPD and late onset PD (LODP), EOPD patients experience much longer disease manifestations and poorer quality of life. Although some individuals are more prone to have EOPD due to certain genetic alterations, the molecular mechanisms that differentiate between EOPD and LOPD remains unclear. Recent findings in PD patients revealed that there were differences in the genetic profiles of PD patients compared to healthy controls, as well as between EOPD and LOPD patients. There were variants identified that correlated with the decline of cognitive and motor symptoms as well as non-motor symptoms in PD. There were also specific microRNAs that correlated with PD progression, and since microRNAs have been shown to be involved in the maintenance of neuronal development, mitochondrial dysfunction and oxidative stress, there is a strong possibility that these microRNAs can be potentially used to differentiate between subsets of PD patients. PD is mainly diagnosed at the late stage, when almost majority of the dopaminergic neurons are lost. Therefore, identification of molecular biomarkers for early detection of PD is important. Given that miRNAs are crucial in controlling the gene expression, these regulatory microRNAs and their target genes could be used as biomarkers for early diagnosis of PD. In this article, we discussed the genes involved and their regulatory miRNAs, regarding their roles in PD progression, based on the findings of significantly altered microRNAs in EOPD studies. We also discussed the potential of these miRNAs as molecular biomarkers for early diagnosis.

MiR-1254 suppresses HO-1 expression through seed region-dependent silencing and non-seed interaction with TFAP2A transcript to attenuate NSCLC growth

MicroRNAs (miRNAs) are a class of small non-coding RNAs, which direct post-transcriptional gene silencing (PTGS) and function in a vast range of biological events including cancer development. Most miRNAs pair to the target sites through seed region near the 5’ end, leading to mRNA cleavage and/or translation repression. Here, we demonstrated a miRNA-induced dual regulation of heme oxygenase-1 (HO-1) via seed region and non-seed region, consequently inhibited tumor growth of NSCLC. We identified miR-1254 as a negative regulator inhibiting HO-1 translation by directly targeting HO-1 3’UTR via its seed region, and suppressing HO-1 transcription via non-seed region-dependent inhibition of transcriptional factor AP-2 alpha (TFAP2A), a transcriptional activator of HO-1. MiR-1254 induced cell apoptosis and cell cycle arrest in human non-small cell lung carcinoma (NSCLC) cells by inhibiting the expression of HO-1, consequently suppressed NSCLC cell growth. Consistently with the in vitro studies, mouse xenograft studies validated that miR-1254 suppressed NSCLC tumor growth in vivo. Moreover, we found that HO-1 expression was inversely correlated with miR-1254 level in human NSCLC tumor samples and cell lines. Overall, these findings identify the dual inhibition of HO-1 by miR-1254 as a novel functional mechanism of miRNA, which results in a more effective inhibition of oncogenic mRNA, and leads to a tumor suppressive effect.

It is generally accepted that miRNAs bind to 3`UTR of target mRNAs and direct post-transcriptional gene silencing (PTGS) via its seed sequence. Here we report a new dual regulatory mechanism of miRNA. We described that miR-1254 repressed HO-1 at post-transcriptional level by directly targeting HO-1 3’UTR via its seed sequence and also inhibited HO-1 transcription by suppressing the transcriptional factor AP-2 alpha (TFAP2A) via its non-seed sequence. MiR-1254 induced cell apoptosis and cell cycle arrest in human non-small cell lung carcinoma (NSCLC) cells by inhibiting the expression of HO-1, consequently suppressed NSCLC cell growth. Moreover, in vivo mouse xenograft studies also supported the inhibitory effect of miR-1254 on NSCLC growth. These findings identify the dual regulation of miR-1254 on HO-1 as a novel functional mechanism of miRNA, which results in a more effective inhibition on the oncogenic mRNA, and leads to a suppressive effect on NSCLC growth, thus significantly advance our understanding of miRNA-directed gene regulation.

Noncoding RNA Regulation of Dopamine Signaling in Diseases of the Central Nervous System

Dopaminergic neurotransmission mediates a majority of the vital central nervous system functions. Disruption of these synaptic events provokes a multitude of neurological pathologies, including Parkinson's, schizophrenia, depression, and addiction. Growing evidence supports a key role for noncoding RNA (ncRNA) regulation in the synapse. This review will discuss the role of both short and long ncRNAs in dopamine signaling, including bioinformatic examination of the pathways they target. Specifically, we focus on the contribution of ncRNAs to dopaminergic dysfunction in neurodegenerative as well as psychiatric disease.