Computational prediction of microRNA networks incorporating environmental toxicity and disease etiology

Quantitative prediction model for affinity of drug-target interactions based on molecular vibrations and overall system of ligand-receptor

Background

The study of drug–target interactions (DTIs) affinity plays an important role in safety assessment and pharmacology. Currently, quantitative structure–activity relationship (QSAR) and molecular docking (MD) are most common methods in research of DTIs affinity. However, they often built for a specific target or several targets, and most QSAR and MD methods were based either on structure of drug molecules or on structure of receptors with low accuracy and small scope of application. How to construct quantitative prediction models with high accuracy and wide applicability remains a challenge. To this end, this paper screened molecular descriptors based on molecular vibrations and took molecule-target as a whole system to construct prediction models with high accuracy-wide applicability based on dissociation constant (Kd) and concentration for 50% of maximal effect (EC50), and to provide reference for quantifying affinity of DTIs.

Results

After comprehensive comparison, the results showed that RF models are optimal models to analyze and predict DTIs affinity with coefficients of determination (R²) are all greater than 0.94. Compared to the quantitative models reported in literatures, the RF models developed in this paper have higher accuracy and wide applicability. In addition, E-state molecular descriptors associated with molecular vibrations and normalized Moreau-Broto autocorrelation (G3), Moran autocorrelation (G4), transition-distribution (G7) protein descriptors are of higher importance in the quantification of DTIs.

Conclusion

Through screening molecular descriptors based on molecular vibrations and taking molecule-target as whole system, we obtained optimal models based on RF with more accurate-widely applicable, which indicated that selection of molecular descriptors associated with molecular vibrations and the use of molecular-target as whole system are reliable methods for improving performance of models. It can provide reference for quantifying affinity of DTIs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04389-w.

A comprehensive review of online resources for microRNA-diseases associations: the state of the art

MicroRNAs (miRNAs) as small 19- to 24-nucleotide noncoding RNAs regulate several mRNA targets and signaling pathways. Therefore, miRNAs are considered key regulators in cellular pathways as well as various pathologies. There is substantial interest in the relationship between disease and miRNAs, which made that one of the important research topics. Interestingly, miRNAs emerged as an attractive approach for clinical application, not only as biomarkers for diagnosis and prognosis or in the prediction of therapy response but also as therapeutic tools. For these purposes, the identification of crucial miRNAs in disease is very important. Databases provided valuable experimental and computational miRNAs-disease information in an accessible and comprehensive manner, such as miRNA target genes, miRNA related in signaling pathways and miRNA involvement in various diseases. In this review, we summarized miRNAs-disease databases in two main categories based on the general or specific diseases. In these databases, researchers could search diseases to identify critical miRNAs and developed that for clinical applications. In another way, by searching particular miRNAs, they could recognize in which disease these miRNAs would be dysregulated. Despite the significant development that has been done in these databases, there are still some limitations, such as not being updated and not providing uniform and detailed information that should be resolved in future databases. This survey can be helpful as a comprehensive reference for choosing a suitable database by researchers and as a guideline for comparing the features and limitations of the database by developer or designer. Short abstract We summarized miRNAs-disease databases that researchers could search disease to identify critical miRNAs and developed that for clinical applications. This survey can help choose a suitable database for researchers.

Analysis of MicroRNA Expression Changes During the Course of Therapy In Rectal Cancer Patients

MicroRNAs (miRNAs) regulate gene expression in a tissue-specific manner. However, little is known about the miRNA expression changes induced by the therapy in rectal cancer (RC) patients. We evaluated miRNA expression levels before and after therapy and identified specific miRNA signatures reflecting disease course and treatment responses of RC patients. First, miRNA expression levels were assessed by next-generation sequencing in two plasma samplings (at the time of diagnosis and a year after) from 20 RC patients. MiR-122-5p and miR-142-5p were classified for subsequent validation in plasma and plasma extracellular vesicles (EVs) on an independent group of RC patients (n=107). Due to the intrinsic high differences in miRNA expression levels between samplings, cancer-free individuals (n=51) were included in the validation phase to determine the baseline expression levels of the selected miRNAs. Expression levels of these miRNAs were significantly different between RC patients and controls (for all p <0.001). A year after diagnosis, miRNA expression profiles were significantly modified in patients responding to treatment and were no longer different from those measured in cancer-free individuals. On the other hand, patients not responding to therapy maintained low expression levels in their second sampling (miR-122-5p: plasma: p=0.05, EVs: p=0.007; miR-142-5p: plasma: p=0.008). Besides, overexpression of miR-122-5p and miR-142-5p in RC cell lines inhibited cell growth and survival. This study provides novel evidence that circulating miR-122-5p and miR-142-5p have a high potential for RC screening and early detection as well as for the assessment of patients' outcomes and the effectiveness of treatment schedule.

Genetic variations in microRNA-binding sites of solute carrier transporter genes as predictors of clinical outcome in colorectal cancer

One of the principal mechanisms of chemotherapy resistance in highly frequent solid tumors, such as colorectal cancer (CRC), is the decreased activity of drug transport into tumor cells due to low expression of important membrane proteins, such as solute carrier (SLC) transporters. Sequence complementarity is a major determinant for target gene recognition by microRNAs (miRNAs). Single-nucleotide polymorphisms (SNPs) in target sequences transcribed into messenger RNA may therefore alter miRNA binding to these regions by either creating a new site or destroying an existing one. miRSNPs may explain the modulation of expression levels in association with increased/decreased susceptibility to common diseases as well as in chemoresistance and the consequent inter-individual variability in drug response. In the present study, we investigated whether miRSNPs in SLC transporter genes may modulate CRC susceptibility and patient's survival. Using an in silico approach for functional predictions, we analyzed 26 miRSNPs in 9 SLC genes in a cohort of 1368 CRC cases and 698 controls from the Czech Republic. After correcting for multiple tests, we found several miRSNPs significantly associated with patient's survival. SNPs in SLCO3A1, SLC22A2 and SLC22A3 genes were defined as prognostic factors in the classification and regression tree analysis. In contrast, we did not observe any significant association between miRSNPs and CRC risk. To the best of our knowledge, this is the first study investigating miRSNPs potentially affecting miRNA binding to SLC transporter genes and their impact on CRC susceptibility or patient's prognosis.

MSFSP: A Novel miRNA-Disease Association Prediction Model by Federating Multiple-Similarities Fusion and Space Projection

Growing evidences have indicated that microRNAs (miRNAs) play a significant role relating to many important bioprocesses; their mutations and disorders will cause the occurrence of various complex diseases. The prediction of miRNAs associated with underlying diseases via computational approaches is beneficial to identify biomarkers and discover specific medicine, which can greatly reduce the cost of diagnosis, cure, prognosis, and prevention of human diseases. However, how to further achieve a more reliable prediction of potential miRNA-disease associations with effective integration of different biological data is a challenge for researchers. In this study, we proposed a computational model by using a federated method of combined multiple-similarities fusion and space projection (MSFSP). MSFSP firstly fused the integrated disease similarity (composed of disease semantic similarity, disease functional similarity, and disease Hamming similarity) with the integrated miRNA similarity (composed of miRNA functional similarity, miRNA sequence similarity, and miRNA Hamming similarity). Secondly, it constructed the weighted network of miRNA-disease associations from the experimentally verified Boolean network of miRNA-disease associations by using similarity networks. Finally, it calculated the prediction results by weighting miRNA space projection scores and the disease space projection scores. Leave-one-out cross-validation demonstrated that MSFSP has the distinguished predictive accuracy with area under the receiver operating characteristics curve (AUC) of 0.9613 better than that of five other existing models. In case studies, the predictive ability of MSFSP was further confirmed as 96 and 98% of the top 50 predictions for prostatic neoplasms and lung neoplasms were successfully validated by experimental evidences and supporting experimental evidences were also found for 100% of the top 50 predictions for isolated diseases.

MicroRNAs as Epigenetic Targets of Cigarette Smoke During Embryonic Development

The adverse developmental effects of exposure to Cigarette Smoke (CS) during pregnancy are documented in this paper. These include low birth weight, congenital anomalies, preterm birth, fetal mortality and morbidity. The current biological thought now recognizes that epigenetics represents a fundamental contributing process in embryogenesis, and that the environment can have a profound effect on shaping the epigenome. It has become increasingly recognized that genes encoding microRNAs (miRNAs) might be potential loci for congenital disabilities. One means by which CS can cause developmental anomalies may be through epigenetic mechanisms involving altered miRNA expression. While several studies have focused on genes affected by CS during embryonic/ fetal development, there is a paucity of knowledge on the involvement of miRNAs in this process. This brief review summarizes the current state of knowledge in this area.

A network-based approach to uncover microRNA-mediated disease comorbidities and potential pathobiological implications

Disease-disease relationships (e.g., disease comorbidities) play crucial roles in pathobiological manifestations of diseases and personalized approaches to managing those conditions. In this study, we develop a network-based methodology, termed meta-path-based Disease Network (mpDisNet) capturing algorithm, to infer disease-disease relationships by assembling four biological networks: disease-miRNA, miRNA-gene, disease-gene, and the human protein-protein interactome. mpDisNet is a meta-path-based random walk to reconstruct the heterogeneous neighbors of a given node. mpDisNet uses a heterogeneous skip-gram model to solve the network representation of the nodes. We find that mpDisNet reveals high performance in inferring clinically reported disease-disease relationships, outperforming that of traditional gene/miRNA-overlap approaches. In addition, mpDisNet identifies network-based comorbidities for pulmonary diseases driven by underlying miRNA-mediated pathobiological pathways (i.e., hsa-let-7a- or hsa-let-7b-mediated airway epithelial apoptosis and pro-inflammatory cytokine pathways) as derived from the human interactome network analysis. The mpDisNet offers a powerful tool for network-based identification of disease-disease relationships with miRNA-mediated pathobiological pathways.

LSGSP: a novel miRNA-disease association prediction model using a Laplacian score of the graphs and space projection federated method

Lots of research findings have indicated that miRNAs (microRNAs) are involved in many important biological processes; their mutations and disorders are closely related to diseases, therefore, determining the associations between human diseases and miRNAs is key to understand pathogenic mechanisms. Existing biological experimental methods for identifying miRNA-disease associations are usually expensive and time consuming. Therefore, the development of efficient and reliable computational methods for identifying disease-related miRNAs has become an important topic in the field of biological research in recent years. In this study, we developed a novel miRNA-disease association prediction model using a Laplacian score of the graphs and space projection federated method (LSGSP). This integrates experimentally validated miRNA-disease associations, disease semantic similarity scores, miRNA functional scores, and miRNA family information to build a new disease similarity network and miRNA similarity network, and then obtains the global similarities of these networks through calculating the Laplacian score of the graphs, based on which the miRNA-disease weighted network can be constructed through combination with the miRNA-disease Boolean network. Finally, the miRNA-disease score was obtained via projecting the miRNA space and disease space onto the miRNA-disease weighted network. Compared with several other state-of-the-art methods, using leave-one-out cross validation (LOOCV) to evaluate the accuracy of LSGSP with respect to a benchmark dataset, prediction dataset and compare dataset, LSGSP showed excellent predictive performance with high AUC values of 0.9221, 0.9745 and 0.9194, respectively. In addition, for prostate neoplasms and lung neoplasms, the consistencies between the top 50 predicted miRNAs (obtained from LSGSP) and the results (confirmed from the updated HMDD, miR2Disease, and dbDEMC databases) reached 96% and 100%, respectively. Similarly, for isolated diseases (diseases not associated with any miRNAs), the consistencies between the top 50 predicted miRNAs (obtained from LSGSP) and the results (confirmed from the above-mentioned three databases) reached 98% and 100%, respectively. These results further indicate that LSGSP can effectively predict potential associations between miRNAs and diseases.

MicroRNAs and complex diseases: from experimental results to computational models

Plenty of microRNAs (miRNAs) were discovered at a rapid pace in plants, green algae, viruses and animals. As one of the most important components in the cell, miRNAs play a growing important role in various essential and important biological processes. For the recent few decades, amounts of experimental methods and computational models have been designed and implemented to identify novel miRNA-disease associations. In this review, the functions of miRNAs, miRNA-target interactions, miRNA-disease associations and some important publicly available miRNA-related databases were discussed in detail. Specially, considering the important fact that an increasing number of miRNA-disease associations have been experimentally confirmed, we selected five important miRNA-related human diseases and five crucial disease-related miRNAs and provided corresponding introductions. Identifying disease-related miRNAs has become an important goal of biomedical research, which will accelerate the understanding of disease pathogenesis at the molecular level and molecular tools design for disease diagnosis, treatment and prevention. Computational models have become an important means for novel miRNA-disease association identification, which could select the most promising miRNA-disease pairs for experimental validation and significantly reduce the time and cost of the biological experiments. Here, we reviewed 20 state-of-the-art computational models of predicting miRNA-disease associations from different perspectives. Finally, we summarized four important factors for the difficulties of predicting potential disease-related miRNAs, the framework of constructing powerful computational models to predict potential miRNA-disease associations including five feasible and important research schemas, and future directions for further development of computational models.

Effects of short-time exposure to atrazine on miRNA expression profiles in the gonad of common carp (Cyprinus carpio)

BACKGROUND

Atrazine is widely used in agriculture and is a known endocrine disrupting chemical. Atrazine can seep into the water body through surface, posing a potential threat to the aquatic ecological environment and human drinking water source. In vertebrate, studies have shown that it can affect reproduction and development seriously, but its molecular mechanism for aquatic animals is unknown. Aquaculture is very common in China, especially common carp, whose females grow faster than males. However, the effects of atrazine on the reproduction of carp, especially miRNA, have not been investigated.

RESULTS

In this study, common carp (Cyprinus carpio) at two key developmental stages were exposed to atrazine in vitro. Sex ratio was observed to analyze the effect of atrazine on the sex. MiRNA expression profiles were analysed to identify miRNAs related to gonad development and to reveal the atrazine mechanisms interfering with gonad differentiation. The results showed that the sex ratio was biased towards females. Atrazine exposure caused significant alteration of multiple miRNAs. Predicted targets of differently-expressed miRNAs were involved in many reproductive biology signalling pathways.

CONCLUSIONS

Our results indicate that atrazine promoted the expression of female-biased genes by decreasing miRNAs in primordial gonad. In addition, our results indicate that atrazine can up-regulate aromatase expression through miRNAs, which supports the hypothesis that atrazine has endocrine-disrupting activity by altering the gene expression profile of the Hypothalamus-Pituitary-Gonad axis through its corresponding miRNAs.

Bipartite Heterogeneous Network Method Based on Co-neighbor for MiRNA-Disease Association Prediction

In recent years, miRNA variation and dysregulation have been found to be closely related to human tumors, and identifying miRNA-disease associations is helpful for understanding the mechanisms of disease or tumor development and is greatly significant for the prognosis, diagnosis, and treatment of human diseases. This article proposes a Bipartite Heterogeneous network link prediction method based on co-neighbor to predict miRNA-disease association (BHCN). According to the structural characteristics of the bipartite network, the concept of bipartite network co-neighbors is proposed, and the co-neighbors were used to represent the probability of association between disease and miRNA. To predict the isolated diseases and the new miRNA based on the association probability expressed by co-neighbors, we utilized the similarity between disease nodes and the similarity between miRNA nodes in heterogeneous networks to represent the association probability between disease and miRNA. The model's predictive performance was evaluated by the leave-one-out cross validation (LOOCV) on different datasets. The AUC value of BHCN on the gold benchmark dataset was 0.7973, and the AUC obtained on the prediction dataset was 0.9349, which was better than that of the classic global algorithm. In this case study, we conducted predictive studies on breast neoplasms and colon neoplasms. Most of the top 50 predicted results were confirmed by three databases, namely, HMDD, miR2disease, and dbDEMC, with accuracy rates of 96 and 82%. In addition, BHCN can be used for predicting isolated diseases (without any known associated diseases) and new miRNAs (without any known associated miRNAs). In the isolated disease case study, the top 50 of breast neoplasm and colon neoplasm potentials associated with miRNAs predicted an accuracy of 100 and 96%, respectively, thereby demonstrating the favorable predictive power of BHCN for potentially relevant miRNAs.

miRNA-17 promotes nasopharyngeal carcinoma radioresistance by targeting PTEN/AKT

Radioresistance remains a challenge during nasopharyngeal carcinoma (NPC) radiotherapy. Numerous studies suggest that the miRNAs may play important roles in the regulation of radioresistance. miRNA-17-5p, which is located within the miR-17-92a cluster, could modulate tumor progression in different tissues by targeting multiple tumor associated genes. However, whether it is correlated with the radioresistance of tumor cells has not yet been elucidated. In our study, we have observed increasing miR-17-5p expression in radioresistant NPC tissues. The functional experiments suggested that miR-17-5p could clearly promote NPC cell proliferation and the cell cycle even after X-ray irradiation. Irradiation leads to tumor cell damage and death via ROS generation. The overexpression of miR-17-5p could protect NPC cells from apoptosis induced by irradiation. In addition, an in vivo experiment indicated that miR-17-5p promoted tumor growth with radiotherapy using the xenograft tumor model. A bioinformatics analysis and reporter assay were carried out to demonstrate that PTEN, which is a key regulator of AKT phosphorylation, is a target of miR-17-5p. The overexpression of miR-17-5p directly suppresses the mRNA and protein expression of PTEN. In addition, the rescue experiments showed that the AKT inhibitor can diminish the proliferation, promotion, and apoptosis inhibition effects on radioresistant NPC cells mediated by miR-17-5p. In conclusion, our findings demonstrated that miR-17-5p can enhance the radioresistance of NPC through the PTEN/AKT pathway, which is a biomarker of radioresistant NPC and a potential target for new therapeutic strategies.

In Silico Oncology Drug Repositioning and Polypharmacology

Network-aided in silico approaches have been widely used for prediction of drug-target interactions and evaluation of drug safety to increase the clinical efficiency and productivity during drug discovery and development. Here we review the advances and new progress in this field and summarize the translational applications of several new network-aided in silico approaches we developed recently. In addition, we describe the detailed protocols for a network-aided drug repositioning infrastructure for identification of new targets for old drugs, failed drugs in clinical trials, and new chemical entities. These state-of-the-art network-aided in silico approaches have been used for the discovery and development of broad-acting and targeted clinical therapies for various complex diseases, in particular for oncology drug repositioning. In this chapter, the described network-aided in silico protocols are appropriate for target-centric drug repositioning to various complex diseases, but expertise is still necessary to perform the specific oncology projects based on the cancer targets of interest.

Inferring microRNA-Environmental Factor Interactions Based on Multiple Biological Information Fusion

Accumulated studies have shown that environmental factors (EFs) can regulate the expression of microRNA (miRNA) which is closely associated with several diseases. Therefore, identifying miRNA-EF associations can facilitate the study of diseases. Recently, several computational methods have been proposed to explore miRNA-EF interactions. In this paper, a novel computational method, MEI-BRWMLL, is proposed to uncover the relationship between miRNA and EF. The similarities of miRNA-miRNA are calculated by using miRNA sequence, miRNA-EF interaction, and the similarities of EF-EF are calculated based on the anatomical therapeutic chemical information, chemical structure and miRNA-EF interaction. The similarity network fusion is used to fuse the similarity between miRNA and the similarity between EF, respectively. Further, the multiple-label learning and bi-random walk are employed to identify the association between miRNA and EF. The experimental results show that our method outperforms the state-of-the-art algorithms.

Dme-Hsa Disease Database (DHDD): Conserved Human Disease-Related miRNA and Their Targeting Genes in Drosophila melanogaster

Abnormal expressions of microRNA (miRNA) can result in human diseases such as cancer and neurodegenerative diseases. MiRNA mainly exert their biological functions via repressing the expression of their target genes. Drosophila melanogaster (D. melanogaster) is an ideal model for studying the molecular mechanisms behind biological phenotypes, including human diseases. In this study, we collected human and D. melanogaster miRNA as well as known human disease-related genes. In total, we identified 136 human disease-related miRNA that are orthologous to 83 D. melanogaster miRNA by mapping "seed sequence", and 677 human disease-related genes that are orthologous to 734 D. melanogaster genes using the DRSC Integrative Ortholog Prediction Tool Furthermore, we revealed the target relationship between genes and miRNA using miRTarBase database and target prediction software, including miRanda and TargetScan. In addition, we visualized interaction networks and signalling pathways for these filtered miRNA and target genes. Finally, we compiled all the above data and information to generate a database designated DHDD This is the first comprehensive collection of human disease-related miRNA and their targeting genes conserved in a D. melanogaster database. The DHDD provides a resource for easily searching human disease-related miRNA and their disease-related target genes as well as their orthologs in D. melanogaster, and conveniently identifying the regulatory relationships among them in the form of a visual network.

Combined Biomarkers Composed of Environment and Genetic Factors in Stroke

It was widely accepted that stroke onset was the result of interactions between environment and genetic factors. However, the combined biomarkers covering environment and genetic factors and their interplay information in stroke were still lacking. In this study, we proposed a framework to identify the targeting or indicating role each factor played in the combined stroke biomarkers. A combined set of 36 biomarkers were identified based on evaluation and importance scores. Validations on three independent microarray data sets justified that the obtained markers were pervasively effective in discriminating stroke patients of different stages from healthy people on genetic levels. 8 and 3 genetic factors were identified as biomarkers in the acute and recovery phases of stroke, respectively. For example, the expression changing of SERPINH1 only appeared in the acute phase of stroke showing its targeting role in the combined biomarker. Compared with this, 11 genetic factors such as MMP9 were found to be differentially expressed in both acute and recovery phases of stroke showing their indicating roles in stroke. Functional analyses further revealed that the biomarkers could be grouped into 4 closely related processes of stroke including prevention, occurrence, processing, and recovery, respectively. These results indicated that the adoption of interactions between environment and genetic factors would be helpful in selecting robust and biologically relevant biomarkers, which cast a new insight for stroke biomarker identification.

A Computational Systems Pharmacology Approach to Investigate Molecular Mechanisms of Herbal Formula Tian-Ma-Gou-Teng-Yin for Treatment of Alzheimer's Disease

Traditional Chinese medicine (TCM) is typically prescribed as formula to treat certain symptoms. A TCM formula contains hundreds of chemical components, which makes it complicated to elucidate the molecular mechanisms of TCM. Here, we proposed a computational systems pharmacology approach consisting of network link prediction, statistical analysis, and bioinformatics tools to investigate the molecular mechanisms of TCM formulae. Taking formula Tian-Ma-Gou-Teng-Yin as an example, which shows pharmacological effects on Alzheimer’s disease (AD) and its mechanism is unclear, we first identified 494 formula components together with corresponding 178 known targets, and then predicted 364 potential targets for these components with our balanced substructure-drug–target network-based inference method. With Fisher’s exact test and statistical analysis we identified 12 compounds to be most significantly related to AD. The target genes of these compounds were further enriched onto pathways involved in AD, such as neuroactive ligand–receptor interaction, serotonergic synapse, inflammatory mediator regulation of transient receptor potential channel and calcium signaling pathway. By regulating key target genes, such as ACHE, HTR2A, NOS2, and TRPA1, the formula could have neuroprotective and anti-neuroinflammatory effects against the progression of AD. Our approach provided a holistic perspective to study the relevance between TCM formulae and diseases, and implied possible pharmacological effects of TCM components.

Inferring potential small molecule-miRNA association based on triple layer heterogeneous network

Recently, many biological experiments have indicated that microRNAs (miRNAs) are a newly discovered small molecule (SM) drug targets that play an important role in the development and progression of human complex diseases. More and more computational models have been developed to identify potential associations between SMs and target miRNAs, which would be a great help for disease therapy and clinical applications for known drugs in the field of medical research. In this study, we proposed a computational model of triple layer heterogeneous network based small molecule–MiRNA association prediction (TLHNSMMA) to uncover potential SM–miRNA associations by integrating integrated SM similarity, integrated miRNA similarity, integrated disease similarity, experimentally verified SM–miRNA associations and miRNA–disease associations into a heterogeneous graph. To evaluate the performance of TLHNSMMA, we implemented global and two types of local leave-one-out cross validation as well as fivefold cross validation to compare TLHNSMMA with one previous classical computational model (SMiR-NBI). As a result, for Dataset 1, TLHNSMMA obtained the AUCs of 0.9859, 0.9845, 0.7645 and 0.9851 ± 0.0012, respectively; for Dataset 2, the AUCs are in turn 0.8149, 0.8244, 0.6057 and 0.8168 ± 0.0022. As the result of case studies shown, among the top 10, 20 and 50 potential SM-related miRNAs, there were 2, 7 and 14 SM–miRNA associations confirmed by experiments, respectively. Therefore, TLHNSMMA could be effectively applied to the prediction of SM–miRNA associations.

Global Similarity Method Based on a Two-tier Random Walk for the Prediction of microRNA-Disease Association

microRNAs (miRNAs) mutation and maladjustment are related to the occurrence and development of human diseases. Studies on disease-associated miRNA have contributed to disease diagnosis and treatment. To address the problems, such as low prediction accuracy and failure to predict the relationship between new miRNAs and diseases and so on, we design a Laplacian score of graphs to calculate the global similarity of networks and propose a Global Similarity method based on a Two-tier Random Walk for the prediction of miRNA-disease association (GSTRW) to reveal the correlation between miRNAs and diseases. This method is a global approach that can simultaneously predict the correlation between all diseases and miRNAs in the absence of negative samples. Experimental results reveal that this method is better than existing approaches in terms of overall prediction accuracy and ability to predict orphan diseases and novel miRNAs. A case study on GSTRW for breast cancer and conlon cancer is also conducted, and the majority of miRNA-disease association can be verified by our experiment. This study indicates that this method is feasible and effective.

The Role of MicroRNAs in Environmental Risk Factors, Noise-Induced Hearing Loss, and Mental Stress

SIGNIFICANCE

MicroRNAs (miRNAs) are important regulators of gene expression and define part of the epigenetic signature. Their influence on every realm of biomedicine is established and progressively increasing. The impact of environment on human health is enormous. Among environmental risk factors impinging on quality of life are those of chemical nature (toxic chemicals, heavy metals, pollutants, and pesticides) as well as those related to everyday life such as exposure to noise or mental and psychosocial stress. Recent Advances: This review elaborates on the relationship between miRNAs and these environmental risk factors.

CRITICAL ISSUES

The most relevant facts underlying the role of miRNAs in the response to these environmental stressors, including redox regulatory changes and oxidative stress, are highlighted and discussed. In the cases wherein miRNA mutations are relevant for this response, the pertinent literature is also reviewed.

FUTURE DIRECTIONS

We conclude that, even though in some cases important advances have been made regarding close correlations between specific miRNAs and biological responses to environmental risk factors, a need for prospective large-cohort studies is likely necessary to establish causative roles. Antioxid. Redox Signal. 28, 773-796.

A survey on database resources for microRNA-disease relationships

The relationships between microRNAs (miRNAs) and diseases are garnering greater interest in the biological research fields. Recently, miRNA-disease databases have emerged as powerful tools for bioinformatics studies of these relationships. However, guidelines for comparing the features of this type of database have not yet been established. In this article, the details of popular miRNA-disease databases are analyzed, and their features are compared from several different aspects, including database scale, disease classification, miRNA targets, miRNA detection technique, miRNA regulation, quantitative scores, study design and tissue/cell lines. Then, guidelines for choosing a suitable database for specific research interests are provided. This survey will guide computational biology or biological researchers as well as medical and clinical researchers in making better use of miRNA-disease data resources.

The gain and loss of long noncoding RNA associated-competing endogenous RNAs in prostate cancer

Prostate cancer (PC) is one of the most common solid tumors in men. However, the molecular mechanism of PC remains unclear. Numerous studies have demonstrated that long noncoding RNA (lncRNA) can act as microRNA (miRNA) sponge, one type of competing endogenous RNAs (ceRNAs), which offers a novel viewpoint to elucidate the mechanisms of PC. Here, we proposed an integrative systems biology approach to infer the gain and loss of ceRNAs in PC. First, we re-annotated exon microarray data to obtain lncRNA expression profiles of PC. Second, by integrating mRNA and miRNA expression, as well as miRNA targets, we constructed lncRNA-miRNA-mRNA ceRNA networks in cancer and normal samples. The lncRNAs in these two ceRNA networks tended to have a longer transcript length and cover more exons than the lncRNAs not involved in ceRNA networks. Next, we further extracted the gain and loss ceRNA networks in PC. We found that the gain ceRNAs in PC participated in cell cycle, and the loss ceRNAs in PC were associated with metabolism. We also identified potential prognostic ceRNA pairs such as MALAT1-EGR2 and MEG3-AQP3. Finally, we inferred a novel mechanism of known drugs, such as cisplatin, for the treatment of PC through gain and loss ceRNA networks. The potential drugs such as 1,2,6-tri-O-galloyl-beta-D-glucopyranose (TGGP) could modulate lncRNA-mRNA competing relationships, which may uncover new strategy for treating PC. In summary, we systematically investigated the gain and loss of ceRNAs in PC, which may prove useful for identifying potential biomarkers and therapeutics for PC.

Network-based identification of microRNAs as potential pharmacogenomic biomarkers for anticancer drugs

As the recent development of high-throughput technologies in cancer pharmacogenomics, there is an urgent need to develop new computational approaches for comprehensive identification of new pharmacogenomic biomarkers, such as microRNAs (miRNAs). In this study, a network-based framework, namely the SMiR-NBI model, was developed to prioritize miRNAs as potential biomarkers characterizing treatment responses of anticancer drugs on the basis of a heterogeneous network connecting drugs, miRNAs and genes. A high area under the receiver operating characteristic curve of 0.820 ± 0.013 was yielded during 10-fold cross validation. In addition, high performance was further validated in identifying new anticancer mechanism-of-action for natural products and non-steroidal anti-inflammatory drugs. Finally, the newly predicted miRNAs for tamoxifen and metformin were experimentally validated in MCF-7 and MDA-MB-231 breast cancer cell lines via qRT-PCR assays. High success rates of 60% and 65% were yielded for tamoxifen and metformin, respectively. Specifically, 11 oncomiRNAs (e.g. miR-20a-5p, miR-27a-3p, miR-29a-3p, and miR-146a-5p) from the top 20 predicted miRNAs were experimentally verified as new pharmacogenomic biomarkers for metformin in MCF-7 or MDA-MB-231 cell lines. In summary, the SMiR-NBI model would provide a powerful tool to identify potential pharmacogenomic biomarkers characterized by miRNAs in the emerging field of precision cancer medicine, which is available at http://lmmd.ecust.edu.cn/database/smir-nbi/.

A novel information diffusion method based on network consistency for identifying disease related microRNAs

The abnormal expression of miRNAs is directly related to the development of human diseases. Predicting the potential candidate miRNAs associated with diseases can contribute to the detection, diagnosis, treatment and prevention of human complex diseases. The effective inference of the calculation method of the relationship between miRNAs and diseases is an effective supplement to biological experiments. It is of great help in the prevention, treatment and prognosis of complex diseases. This paper proposes a novel information diffusion method based on network consistency (IDNC) for identifying disease related microRNAs. The model first synthesizes the miRNA family information and the miRNA function similarity to reconstruct the miRNA network, and reconstruct the disease network by using the known disease and miRNA-related information and the semantic score between diseases. Then the global similarity of the two networks is obtained by using the Laplacian score of graphs. The global similarity score is a measure of the similarity between diseases and miRNAs. The disease–miRNA relation network was reconstructed by integrating the global similarity relation. The network consistency diffusion seed is then obtained by combining the global similarity network with the reconstructed disease–miRNA association network. Thereafter, the stable diffusion spectrum is generated as the prediction score by using the restarted random walk algorithm. The AUC value obtained by performing the LOOCV in the gold benchmark dataset is 0.8814. The AUC value obtained by performing the LOOCV in the predictive dataset is 0.9512. Compared with other frontier methods, our method has higher accuracy, which is further illustrated by case studies of breast neoplasms and colon neoplasms to prove that IDNC is valuable.

The abnormal expression of miRNAs is directly related to the development of human diseases.

Non-coding RNA networks in cancer

Thousands of unique non-coding RNA (ncRNA) sequences exist within cells. Work from the past decade has altered our perception of ncRNAs from 'junk' transcriptional products to functional regulatory molecules that mediate cellular processes including chromatin remodelling, transcription, post-transcriptional modifications and signal transduction. The networks in which ncRNAs engage can influence numerous molecular targets to drive specific cell biological responses and fates. Consequently, ncRNAs act as key regulators of physiological programmes in developmental and disease contexts. Particularly relevant in cancer, ncRNAs have been identified as oncogenic drivers and tumour suppressors in every major cancer type. Thus, a deeper understanding of the complex networks of interactions that ncRNAs coordinate would provide a unique opportunity to design better therapeutic interventions.

SDTNBI: an integrated network and chemoinformatics tool for systematic prediction of drug-target interactions and drug repositioning

Computational prediction of drug-target interactions (DTIs) and drug repositioning provides a low-cost and high-efficiency approach for drug discovery and development. The traditional social network-derived methods based on the naïve DTI topology information cannot predict potential targets for new chemical entities or failed drugs in clinical trials. There are currently millions of commercially available molecules with biologically relevant representations in chemical databases. It is urgent to develop novel computational approaches to predict targets for new chemical entities and failed drugs on a large scale. In this study, we developed a useful tool, namely substructure-drug-target network-based inference (SDTNBI), to prioritize potential targets for old drugs, failed drugs and new chemical entities. SDTNBI incorporates network and chemoinformatics to bridge the gap between new chemical entities and known DTI network. High performance was yielded in 10-fold and leave-one-out cross validations using four benchmark data sets, covering G protein-coupled receptors, kinases, ion channels and nuclear receptors. Furthermore, the highest areas under the receiver operating characteristic curve were 0.797 and 0.863 for two external validation sets, respectively. Finally, we identified thousands of new potential DTIs via implementing SDTNBI on a global network. As a proof-of-principle, we showcased the use of SDTNBI to identify novel anticancer indications for nonsteroidal anti-inflammatory drugs by inhibiting AKR1C3, CA9 or CA12. In summary, SDTNBI is a powerful network-based approach that predicts potential targets for new chemical entities on a large scale and will provide a new tool for DTI prediction and drug repositioning. The program and predicted DTIs are available on request.

Quantitative and Systems Pharmacology 3. Network-Based Identification of New Targets for Natural Products Enables Potential Uses in Aging-Associated Disorders

Aging that refers the accumulation of genetic and physiology changes in cells and tissues over a lifetime has been shown a high risk of developing various complex diseases, such as neurodegenerative disease, cardiovascular disease and cancer. Over the past several decades, natural products have been demonstrated as anti-aging interveners via extending lifespan and preventing aging-associated disorders. In this study, we developed an integrated systems pharmacology infrastructure to uncover new indications for aging-associated disorders by natural products. Specifically, we incorporated 411 high-quality aging-associated human genes or human-orthologous genes from mus musculus (MM), saccharomyces cerevisiae (SC), caenorhabditis elegans (CE), and drosophila melanogaster (DM). We constructed a global drug-target network of natural products by integrating both experimental and computationally predicted drug-target interactions (DTI). We further built the statistical network models for identification of new anti-aging indications of natural products through integration of the curated aging-associated genes and drug-target network of natural products. High accuracy was achieved on the network models. We showcased several network-predicted anti-aging indications of four typical natural products (caffeic acid, metformin, myricetin, and resveratrol) with new mechanism-of-actions. In summary, this study offers a powerful systems pharmacology infrastructure to identify natural products for treatment of aging-associated disorders.

Quantitative and Systems Pharmacology. 1. In Silico Prediction of Drug-Target Interactions of Natural Products Enables New Targeted Cancer Therapy

Natural products with diverse chemical scaffolds have been recognized as an invaluable source of compounds in drug discovery and development. However, systematic identification of drug targets for natural products at the human proteome level via various experimental assays is highly expensive and time-consuming. In this study, we proposed a systems pharmacology infrastructure to predict new drug targets and anticancer indications of natural products. Specifically, we reconstructed a global drug-target network with 7,314 interactions connecting 751 targets and 2,388 natural products and built predictive network models via a balanced substructure-drug-target network-based inference approach. A high area under receiver operating characteristic curve of 0.96 was yielded for predicting new targets of natural products during cross-validation. The newly predicted targets of natural products (e.g., resveratrol, genistein, and kaempferol) with high scores were validated by various literature studies. We further built the statistical network models for identification of new anticancer indications of natural products through integration of both experimentally validated and computationally predicted drug-target interactions of natural products with known cancer proteins. We showed that the significantly predicted anticancer indications of multiple natural products (e.g., naringenin, disulfiram, and metformin) with new mechanism-of-action were validated by various published experimental evidence. In summary, this study offers powerful computational systems pharmacology approaches and tools for the development of novel targeted cancer therapies by exploiting the polypharmacology of natural products.

MicroRNA response of inhalation exposure to hexanal in lung tissues from Fischer 344 rats

In previous studies, we have investigated the relationships between environmental chemicals and health risk based on omics analysis and identified significant biomarkers. Our current findings indicate that hexanal may be an important toxicant of the pulmonary system in epigenetic insights. MicroRNA (miRNA) is an important indicator of biomedical risk assessment and target identification. Hexanal is highly detectable in the exhaled breath of patients with chronic obstructive pulmonary disease (COPD) and chronic inflammatory lung disease. In this study, we aimed to identify hexanal-characterized miRNA-mRNA correlations involved in lung toxicity. Microarray analysis identified 56 miRNAs that commonly changed their expression more than 1.3-fold in three doses (600, 1000, and 1500 ppm) within hexanal-exposed Fischer 344 rats by inhalation, and 226 genes were predicted to be target genes of miRNAs through TargetScan analysis. By integrating analyses of miRNA and mRNA expression profiles, we identified one anti-correlated target gene (Chga; chromogranin A; parathyroid secretory protein 1). Comparative toxicogenomics database (CTD) analysis of this gene showed that Chga is involved with several disease categories such as cancer, respiratory tract disease, nervous system disease, and cardiovascular disease. Further research is necessary to elucidate the mechanisms of hexanal-responsive toxicologic pathways at the molecular level. This study concludes that our integrated approach to miRNA and mRNA enables us to identify molecular events in disease development induced by hexanal in an in vivo rat model. © 2015 Wiley Periodicals, Inc. Environ Toxicol 31: 1909-1921, 2016.

sNebula, a network-based algorithm to predict binding between human leukocyte antigens and peptides

Understanding the binding between human leukocyte antigens (HLAs) and peptides is important to understand the functioning of the immune system. Since it is time-consuming and costly to measure the binding between large numbers of HLAs and peptides, computational methods including machine learning models and network approaches have been developed to predict HLA-peptide binding. However, there are several limitations for the existing methods. We developed a network-based algorithm called sNebula to address these limitations. We curated qualitative Class I HLA-peptide binding data and demonstrated the prediction performance of sNebula on this dataset using leave-one-out cross-validation and five-fold cross-validations. This algorithm can predict not only peptides of different lengths and different types of HLAs, but also the peptides or HLAs that have no existing binding data. We believe sNebula is an effective method to predict HLA-peptide binding and thus improve our understanding of the immune system.

Prediction of miRNA-disease associations with a vector space model

MicroRNAs play critical roles in many physiological processes. Their dysregulations are also closely related to the development and progression of various human diseases, including cancer. Therefore, identifying new microRNAs that are associated with diseases contributes to a better understanding of pathogenicity mechanisms. MicroRNAs also represent a tremendous opportunity in biotechnology for early diagnosis. To date, several in silico methods have been developed to address the issue of microRNA-disease association prediction. However, these methods have various limitations. In this study, we investigate the hypothesis that information attached to miRNAs and diseases can be revealed by distributional semantics. Our basic approach is to represent distributional information on miRNAs and diseases in a high-dimensional vector space and to define associations between miRNAs and diseases in terms of their vector similarity. Cross validations performed on a dataset of known miRNA-disease associations demonstrate the excellent performance of our method. Moreover, the case study focused on breast cancer confirms the ability of our method to discover new disease-miRNA associations and to identify putative false associations reported in databases.

Early BMP, Wnt and Ca(2+)/PKC pathway activation predicts the bone forming capacity of periosteal cells in combination with calcium phosphates

The development of osteoinductive calcium phosphate- (CaP) based biomaterials has, and continues to be, a major focus in the field of bone tissue engineering. However, limited insight into the spatiotemporal activation of signalling pathways has hampered the optimisation of in vivo bone formation and subsequent clinical translation. To gain further knowledge regarding the early molecular events governing bone tissue formation, we combined human periosteum derived progenitor cells with three types of clinically used CaP-scaffolds, to obtain constructs with a distinct range of bone forming capacity in vivo. Protein phosphorylation together with gene expression for key ligands and target genes were investigated 24 hours after cell seeding in vitro, and 3 and 12 days post ectopic implantation in nude mice. A computational modelling approach was used to deduce critical factors for bone formation 8 weeks post implantation. The combined Ca(2+)-mediated activation of BMP-, Wnt- and PKC signalling pathways 3 days post implantation were able to discriminate the bone forming from the non-bone forming constructs. Subsequently, a mathematical model able to predict in vivo bone formation with 96% accuracy was developed. This study illustrates the importance of defining and understanding CaP-activated signalling pathways that are required and sufficient for in vivo bone formation. Furthermore, we demonstrate the reliability of mathematical modelling as a tool to analyse and deduce key factors within an empirical data set and highlight its relevance to the translation of regenerative medicine strategies.

Prediction of drug–target interaction by label propagation with mutual interaction information derived from heterogeneous network

By implementing label propagation on drug/target similarity network with mutual interaction information derived from drug–target heterogeneous network, LPMIHN algorithm identifies potential drug–target interactions.

Biomarker-based drug safety assessment in the age of systems pharmacology: from foundational to regulatory science

Improved biomarker-based assessment of drug safety is needed in drug discovery and development as well as regulatory evaluation. However, identifying drug safety-related biomarkers such as genes, proteins, miRNA and single-nucleotide polymorphisms remains a big challenge. The advances of 'omics' and computational technologies such as genomics, transcriptomics, metabolomics, proteomics, systems biology, network biology and systems pharmacology enable us to explore drug actions at the organ and organismal levels. Computational and experimental systems pharmacology approaches could be utilized to facilitate biomarker-based drug safety assessment for drug discovery and development and to inform better regulatory decisions. In this article, we review the current status and advances of systems pharmacology approaches for the development of predictive models to identify biomarkers for drug safety assessment.

miR-124, -128, and -137 Orchestrate Neural Differentiation by Acting on Overlapping Gene Sets Containing a Highly Connected Transcription Factor Network

The ventricular-subventricular zone harbors neural stem cells (NSCs) that can differentiate into neurons, astrocytes, and oligodendrocytes. This process requires loss of stem cell properties and gain of characteristics associated with differentiated cells. miRNAs function as important drivers of this transition; miR-124, -128, and -137 are among the most relevant ones and have been shown to share commonalities and act as proneurogenic regulators. We conducted biological and genomic analyses to dissect their target repertoire during neurogenesis and tested the hypothesis that they act cooperatively to promote differentiation. To map their target genes, we transfected NSCs with antagomiRs and analyzed differences in their mRNA profile throughout differentiation with respect to controls. This strategy led to the identification of 910 targets for miR-124, 216 for miR-128, and 652 for miR-137. The target sets show extensive overlap. Inspection by gene ontology and network analysis indicated that transcription factors are a major component of these miRNAs target sets. Moreover, several of these transcription factors form a highly interconnected network. Sp1 was determined to be the main node of this network and was further investigated. Our data suggest that miR-124, -128, and -137 act synergistically to regulate Sp1 expression. Sp1 levels are dramatically reduced as cells differentiate and silencing of its expression reduced neuronal production and affected NSC viability and proliferation. In summary, our results show that miRNAs can act cooperatively and synergistically to regulate complex biological processes like neurogenesis and that transcription factors are heavily targeted to branch out their regulatory effect.

Computational Prediction of Alzheimer's and Parkinson's Disease MicroRNAs in Domestic Animals

As the most common neurodegenerative diseases, Alzheimer’s disease (AD) and Parkinson’s disease (PD) are two of the main health concerns for the elderly population. Recently, microRNAs (miRNAs) have been used as biomarkers of infectious, genetic, and metabolic diseases in humans but they have not been well studied in domestic animals. Here we describe a computational biology study in which human AD- and PD-associated miRNAs (ADM and PDM) were utilized to predict orthologous miRNAs in the following domestic animal species: dog, cow, pig, horse, and chicken. In this study, a total of 121 and 70 published human ADM and PDM were identified, respectively. Thirty-seven miRNAs were co-regulated in AD and PD. We identified a total of 105 unrepeated human ADM and PDM that had at least one 100% identical animal homolog, among which 81 and 54 showed 100% sequence identity with 241 and 161 domestic animal miRNAs, respectively. Over 20% of the total mature horse miRNAs (92) showed perfect matches to AD/PD-associated miRNAs. Pigs, dogs, and cows have similar numbers of AD/PD-associated miRNAs (63, 62, and 59). Chickens had the least number of perfect matches (34). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses suggested that humans and dogs are relatively similar in the functional pathways of the five selected highly conserved miRNAs. Taken together, our study provides the first evidence for better understanding the miRNA-AD/PD associations in domestic animals, and provides guidance to generate domestic animal models of AD/PD to replace the current rodent models.

DISMIRA: Prioritization of disease candidates in miRNA-disease associations based on maximum weighted matching inference model and motif-based analysis

BACKGROUND

MicroRNAs (miRNAs) have increasingly been found to regulate diseases at a significant level. The interaction of miRNA and diseases is a complex web of multilevel interactions, given the fact that a miRNA regulates upto 50 or more diseases and miRNAs/diseases work in clusters. The clear patterns of miRNA regulations in a disease are still elusive.

METHODS

In this work, we approach the miRNA-disease interactions from a network scientific perspective and devise two approaches - maximum weighted matching model (a graph theoretical algorithm which provides the result by solving an optimization equation of selecting the most prominent set of diseases) and motif-based analyses (which investigates the motifs of the miRNA-disease network and selects the most prominent set of diseases based on their maximum number of participation in motifs, thereby revealing the miRNA-disease interaction dynamics) to determine and prioritize the set of diseases which are most certainly impacted upon the activation of a group of queried miRNAs, in a miRNA-disease network.

RESULTS AND CONCLUSION

Our tool, DISMIRA implements the above mentioned approaches and presents an interactive visualization which helps the user in exploring the networking dynamics of miRNAs and diseases by analyzing their neighbors, paths and topological features. A set of miRNAs can be used in this analysis to get the associated diseases for the input group of miRs with ranks and also further analysis can be done to find key miRs or diseases, shortest paths etc. DISMIRA can be accessed online for free at http://bnet.egr.vcu.edu:8080/dismira.

FXR antagonism of NSAIDs contributes to drug-induced liver injury identified by systems pharmacology approach

Non-steroidal anti-inflammatory drugs (NSAIDs) are worldwide used drugs for analgesic, antipyretic, and anti-inflammatory therapeutics. However, NSAIDs often cause several serious liver injuries, such as drug-induced liver injury (DILI), and the molecular mechanisms of DILI have not been clearly elucidated. In this study, we developed a systems pharmacology approach to explore the mechanism-of-action of NSAIDs. We found that the Farnesoid X Receptor (FXR) antagonism of NSAIDs is a potential molecular mechanism of DILI through systematic network analysis and in vitro assays. Specially, the quantitative real-time PCR assay reveals that indomethacin and ibuprofen regulate FXR downstream target gene expression in HepG2 cells. Furthermore, the western blot shows that FXR antagonism by indomethacin induces the phosphorylation of STAT3 (signal transducer and activator of transcription 3), promotes the activation of caspase9, and finally causes DILI. In summary, our systems pharmacology approach provided novel insights into molecular mechanisms of DILI for NSAIDs, which may propel the ways toward the design of novel anti-inflammatory pharmacotherapeutics.