Random walks on mutual microRNA-target gene interaction network improve the prediction of disease-associated microRNAs

Background: MicroRNAs (miRNAs) have been shown to play an important role in pathological initiation, progression and maintenance. Because identification in the laboratory of disease-related miRNAs is not straightforward, numerous network-based methods have been developed to predict novel miRNAs in silico. Homogeneous networks (in which every node is a miRNA) based on the targets shared between miRNAs have been widely used to predict their role in disease phenotypes. Although such homogeneous networks can predict potential disease-associated miRNAs, they do not consider the roles of the target genes of the miRNAs. Here, we introduce a novel method based on a heterogeneous network that not only considers miRNAs but also the corresponding target genes in the network model. Results: Instead of constructing homogeneous miRNA networks, we built heterogeneous miRNA networks consisting of both miRNAs and their target genes, using databases of known miRNA-target gene interactions. In addition, as recent studies demonstrated reciprocal regulatory relations between miRNAs and their target genes, we considered these heterogeneous miRNA networks to be undirected, assuming mutual miRNA-target interactions. Next, we introduced a novel method (RWRMTN) operating on these mutual heterogeneous miRNA networks to rank candidate disease-related miRNAs using a random walk with restart (RWR) based algorithm. Using both known disease-associated miRNAs and their target genes as seed nodes, the method can identify additional miRNAs involved in the disease phenotype. Experiments indicated that RWRMTN outperformed two existing state-of-the-art methods: RWRMDA, a network-based method that also uses a RWR on homogeneous (rather than heterogeneous) miRNA networks, and RLSMDA, a machine learning-based method. Interestingly, we could relate this performance gain to the emergence of "disease modules" in the heterogeneous miRNA networks used as input for the algorithm. Moreover, we could demonstrate that RWRMTN is stable, performing well when using both experimentally validated and predicted miRNA-target gene interaction data for network construction. Finally, using RWRMTN, we identified 76 novel miRNAs associated with 23 disease phenotypes which were present in a recent database of known disease-miRNA associations. Conclusions: Summarizing, using random walks on mutual miRNA-target networks improves the prediction of novel disease-associated miRNAs because of the existence of "disease modules" in these networks

MicroRNA co-expression networks exhibit increased complexity in pancreatic ductal compared to Vater’s papilla adenocarcinoma

iRNA expression abnormalities in adenocarcinoma arising from pancreatic ductal system (PDAC) and Vater’s papilla (PVAC) could be associated with distinctive pathologic features and clinical cancer behaviours. Our previous miRNA expression profiling data on PDAC (n=9) and PVAC (n=4) were revaluated to define differences/ similarities in miRNA expression patterns. Afterwards, in order to uncover target genes and core signalling pathways regulated by specific miRNAs in these two tumour entities, miRNA interaction networks were wired for each tumour entity, and experimentally validated target genes underwent pathways enrichment analysis. One hundred and one miRNAs were altered, mainly over-expressed, in PDAC samples. Twenty-six miRNAs were deregulated in PVAC samples, where more miRNAs were down-expressed in tumours compared to normal tissues. Four miRNAs were significantly altered in both subgroups of patients, while 27 miRNAs were differentially expressed between PDAC and PVAC. Although miRNA interaction networks were more complex and dense in PDAC than in PVAC, pathways enrichment analysis uncovered a functional overlapping between PDAC and PVAC. However, shared signalling events were influenced by different miRNA and/or genes in the two tumour entities. Overall, specific miRNA expression patterns were involved in the regulation of a limited core signalling pathways in the biology landscape of PDAC and PVAC

miR-196b target screen reveals mechanisms maintaining leukemia stemness with therapeutic potential.

We have shown that antagomiR inhibition of miRNA miR-21 and miR-196b activity is sufficient to ablate MLL-AF9 leukemia stem cells (LSC) in vivo. Here, we used an shRNA screening approach to mimic miRNA activity on experimentally verified miR-196b targets to identify functionally important and therapeutically relevant pathways downstream of oncogenic miRNA in MLL-r AML. We found Cdkn1b (p27Kip1) is a direct miR-196b target whose repression enhanced an embryonic stem cell–like signature associated with decreased leukemia latency and increased numbers of leukemia stem cells in vivo. Conversely, elevation of p27Kip1 significantly reduced MLL-r leukemia self-renewal, promoted monocytic differentiation of leukemic blasts, and induced cell death. Antagonism of miR-196b activity or pharmacologic inhibition of the Cks1-Skp2–containing SCF E3-ubiquitin ligase complex increased p27Kip1 and inhibited human AML growth. This work illustrates that understanding oncogenic miRNA target pathways can identify actionable targets in leukemia

Analysing multiple types of molecular profiles simultaneously: connecting the needles in the haystack

It has been shown that a random-effects framework can be used to test the association between a gene's expression level and the number of DNA copies of a set of genes. This gene-set modelling framework was later applied to find associations between mRNA expression and microRNA expression, by defining the gene sets using target prediction information. Here, we extend the model introduced by Menezes et al (2009) to consider the effect of not just copy number, but also of other molecular profiles such as methylation changes and loss-of-heterozigosity (LOH), on gene expression levels. We will consider again sets of measurements, to improve robustness of results and increase the power to find associations. Our approach can be used genome-wide to find associations, yields a test to help separate true associations from noise and can include confounders. We apply our method to colon and to breast cancer samples, for which genome-wide copy number, methylation and gene expression profiles are available. Our findings include interesting gene expression-regulating mechanisms, which may involve only one of copy number or methylation, or both for the same samples. We even are able to find effects due to different molecular mechanisms in different samples. Our method can equally well be applied to cases where other types of molecular (high-dimensional) data are collected, such as LOH, SNP genotype and microRNA expression data. Computationally efficient, it represents a flexible and powerful tool to study associations between high-dimensional datasets. The method is freely available via the SIM BioConductor package

Bioinformatic interpretation of microRNA role in three phenotypically related genodermatoses

The well-known regulatory function of microRNAs seems to play an important role in disease mechanism. Recent hypotheses have positioned them as a promising option for the study of genetic disorders. In this context, the microRNA profile of three phenotypically related rare genodermatoses, namely Recessive Dystrophic Epidermolysis Bullosa, Kindler Syndrome and Xeroderma Pigmentosum type C, is going to be analyzed taking as a reference healthy controls within the frame of network medicine concepts. Bioinformatics tools have proven to be essential to keep track of the alteration of the dysregulated microRNAs all over the organism. This thesis provides a direct correlation between the real and observable symptoms of the three conditions and the distortion at molecular level caused by pathogenic pathways. From the network analysis and functional enrichment analysis, a small selection of microRNAs and target genes are proposed as potential candidates for further research. Thus, with this project, it becomes evident the need of powerful predictive research tools previous to laboratory validation. In the considerably new field of microRNAs, the design and elaboration of more precise treatments as well as the discovery of biomarkers for early detection prevail among the possible different applications.Ingeniería Biomédic

NETWORK ANALYTICS FOR THE MIRNA REGULOME AND MIRNA-DISEASE INTERACTIONS

miRNAs are non-coding RNAs of approx. 22 nucleotides in length that inhibit gene expression at the post-transcriptional level. By virtue of this gene regulation mechanism, miRNAs play a critical role in several biological processes and patho-physiological conditions, including cancers. miRNA behavior is a result of a multi-level complex interaction network involving miRNA-mRNA, TF-miRNA-gene, and miRNA-chemical interactions; hence the precise patterns through which a miRNA regulates a certain disease(s) are still elusive. Herein, I have developed an integrative genomics methods/pipeline to (i) build a miRNA regulomics and data analytics repository, (ii) create/model these interactions into networks and use optimization techniques, motif based analyses, network inference strategies and influence diffusion concepts to predict miRNA regulations and its role in diseases, especially related to cancers. By these methods, we are able to determine the regulatory behavior of miRNAs and potential causal miRNAs in specific diseases and potential biomarkers/targets for drug and medicinal therapeutics

Could be FOXO3a, miR-96-5p and miR-182-5p useful for Brazilian women with luminal A and triple negative breast cancers prognosis and target therapy?

FOXO3a dysregulation is frequently implicated in tumorigenesis, and its inhibition can occur by several molecular mechanisms. Among these, post-transcriptional suppression by miRNAs has been associated with various cancers initiation. Here, we assessed the expression profiles of the most relevant miRNAs for breast tumorigenesis, using Luminal A (LA) and Triple-Negative (TN) breast cancer from Brazilian patients, by the quantitative real time-PCR method. Their potential prognostic role for the patients was also evaluated. We identified the miRNAs miR-96-5p and miR-182-5p, de-scribed as negative regulators of FOXO3A, with differential expression both in LA and TN tumors when compared to normal tissue. The miR-96-5p and miR-182-5p miRNAs were upregulated in LA (7.82 times, p < 0.005; 6.12 times, p < 0.005, respectively) and TN breast cancer samples (9.42 times, p < 0.0001; 8.51 times, p < 0.0001) compared to normal tissues. The samples with higher miR-96-5p and miR-182-5p expression (FR ≥ 4) were submitted for FOXO3a immunostaining. Reduced protein detection was observed in all of the tumors compared to normal tissues. The most prominent miRNA expression and FOXO3a protein suppression were observed in TN samples (p < 0.001), indicating the relevant role of these molecules in this tumor biology and clinical behavior. Our results corroborate the literature regarding to the relevance of FOXO3a in the breast cancer, and they open new perspectives for alternative target therapy options for Brazilian patients expressing both FOXO3a and its regulatory miRNAs

Disease gene recognition and editing optimization through knowledge learned from domain feature spaces

University of Technology Sydney. Faculty of Engineering and Information Technology.This thesis presents computational methods used for the recognition of disease genes and for the optimal design of disease gene CRISPR/Cas9 editing systems. The key innovation in these computational methods is the feature space and characteristics captured from the biology domain knowledge through machine learning algorithms. The disease-gene association prediction problems are studied in Chapters 3-5. Disease gene recognition is a hot topic in various fields, especially in biology, medicine and pharmacology. Non-coding genes, a type of genes without protein products, have been proved to play important roles in disease development. Particularly, the two kinds of non-coding gene products such as microRNA (miRNA) and long non-coding RNA (lncRNA) have caught much attention as they are abundantly expressed in various tissues and frequently interact with other biomolecules, e.g. DNA, RNA and protein. The disease-ncRNA relationships remain largely unknown. Computational methods can immensely help replenish this kind of knowledge. To overcome existing computational methods’ limitations such as significantly relying on network structures and similarity measurements, or lacking reliable negative samples, this thesis presents two novel methods. One is the precomputed kernel matrix support vector machine (SVM) method to predict disease related miRNAs in Chapter 3. The precomputed kernel matrix was built by integrating several kinds of similarities computed with effective characteristics for miRNAs and diseases. The reliable negative samples were collected through analyzing the published array and sequencing data. This binary classification method accurately predicts disease-miRNA associations, which outperforms those state-of-the-art methods. In Chapter 4, the predicted novel disease-miRNA associations were combined with known relationships of diseases, miRNAs and genes to reconstruct a disease-gene-miRNA (DGR) tripartite network. Reliable multi-disease associated co-functional miRNA pairs were extracted from this DGR for cross-disease analysis by defining the co-function score. This not only proves the proposed method’s effectiveness but also contributes to the study of multi-purpose miRNA therapeutics. Another is the bagging SVM-based positive-unlabeled learning method for disease-lncRNA prioritizing that is described in Chapter 5. It creatively characterized a disease with its related genes’ chromosome distribution and pathway enrichment properties. The disease-lncRNA pairs were represented as novel feature vectors to train the bagging SVM for predicting disease-lncRNA associations. This novel representation contributes to the superior performance of the proposed method in disease-lncRNA prediction even when a given disease has no currently recognized lncRNA genes. After confirming the relationships between genes and diseases, one of the most difficult tasks is to investigate the molecular mechanism and treatment of the diseases considering their related genes. The CRISPR/Cas9 system is a promising gene editing tool for operating the genes to achieve the goals of disease-gene function clarification and genetic disease curing. Designing an optimal CRISPR/Cas9 system can not only improve its editing efficiency but also reduce its side effect, i.e. off-target editing. Furthermore, the off-target site detection problem involves genome-wide sequence observing which makes it a more challenging job. The CRISPR/Cas9 system on-target cutting efficiency prediction and off-target site detection questions are discussed in Chapters 6 and 7 respectively. To accurately measure the CRISPR/Cas9 system’s cutting efficiency, the profiled Markov properties and some cutting position related features were merged into the feature space for representing the single-guide RNAs (sgRNAs). These features were learned by a two-step averaging method where an XGBoost’s predictions and an SVM’s predictions were averaged as the final results. Later performance evaluations and comparisons demonstrate that this method can predict a sgRNA’s cutting efficiency with consistently good performance no matter it is expressed from a U6 promoter in cells or from a T7 promoter in vitro. In the off-target site detection, a sample was defined as an on-target-off-target site sequence pair to turn this problem into a classification issue. Each sample was numerically depicted with the nucleotide composition change features and the mismatch distribution properties. An ensemble classifier was constructed to distinguish real off-target sites and no-editing sites of a given sgRNA. Its excellent performance was confirmed with different test scenarios and case studies