miRNA theraupetics by means of digital circuits modelling pathways

Motivation: MicroRNA (miRNAs) are non coding RNA molecules, 18-22 nucleotide long, responsible of gene regulation at post transcriptional level. It has been validated their role as regulators of different physiological and pathological conditions as cancer. Indeed, their expression is often deregulated in tumors, and they can have an oncogenic or tumor-suppressive role, depending on their target gene. On the basis of the dual role of miRNAs as oncogenes or tumor-suppressors, two possible miRNA-based drugs, called antagomiR and miRNA-mimics, respectively, have been investigated. The former are syntetic compounds that inhibit miRNA action, by acting as their antagonists; the latter are molecules that mimic miRNAs behaviour. In this work, following the same principles of drug discovery experiments based on computer simulations of logical circuits, a pathway modeling approach based on Boolean networks for miRNA therapeutics studies is presented. Methods: The proposed modeling and analysis approach can be applied to each human disease starting from the corresponding signaling pathway. Given a biological pathway, retrieved for example from KEGG repository, transformation rules from the pathway to the digital circuit are obtained. Accordingly with KEGG pathway component, we modelled all the relationships, such as activation, repression, macromolecular complex, combined activation and dissociation. In addition to KEGG relationships, the proposed Boolean network model also contains miRNA-target interactions and gene mutations respectively from mirTarBase and ClinVar databases. A logic value of 1 is linked to the presence (expression) of the corresponding gene. A logic value of 0 is linked to the absence (not expressed) gene. The activation relationship is represented by a buffer gate, meaning that if the input gene is expressed, the output gene will be expressed and vice versa. The repression relationship is represented by a NOT gate, meaning that if the input gene is expressed, the output gene will not be expressed and vice versa. A combined activation relationship is represented by a logic OR gate, meaning that almost one among the input genes must be expressed to express the output gene. The macromolecular complex is modeled by an AND logic gate, meaning that both genes must be simultaneously expressed to express the output gene. KEGG relationship of dissociation is considered as an activation. Indeed, in a macromolecular complex a protein inhibits the other. In order to activate the latter protein, the macromolecular complex should be dissociated. The input of the signaling pathway is represented by a binary vector whose size is equal to the number of input genes. In cancer signalling pathways, inputs are growth factors and tumor suppressors; outputs consists of genes playing a key role in proliferation or apoptosis. The output is modeled by a binary vector including all output genes. A test vector is identified to enumerate and analyze the effect of all mutations of the pathway, modelled as faults in the Boolean network. The test vector is identified by forcing to zero each growth factor and forcing to 1 the tumor suppressors. If no growth factor is present and tumor suppressors are active, the output is non proliferative. Mutations can alter the functionality of the logic circuit thus leading to proliferative outputs even without growth factors and with active tumor suppressors. By applying the test vector and analyzing the circuit under each mutation while observing the output vector, the level of proliferation caused by the mutation can be identified. Effects of drugs can be analyzed by the output of the digital circuit. As seen in literature, drugs modify the circuit functionality altering the output of each mutation, thus recovering faults and leading to less proliferative output. A score is assigned to each drug and to each output. The lowest score corresponds to less dangerous condition. The best drug combination is obtained for cancer therapy design based on the minimum score. Results: The novelty of this work relies in addressing miRNA therapeutics with the digital approach. As a case study, the non-small-cell lung cancer is analyzed. First of all, the related pathway is converted to the equivalent digital circuit. miRNAa are introduced in the circuit considering their target gene. The study is then focused on determining the best miRNA for each mutation and the best combination of two miRNAs for all introduced mutations. The best combination is identified as let-7c/let-7g. The analyzed circuit validates the let-7 family as the most promising treatment for non-small cell lung cancer since the best combination is identified as let-7c/let-7g . Results are validated form literature, where the let-7 family is treated as a whole not distinguishing the wide variety of let-7 miRNAs. Instead, in silico experiments highlight two specific miRNAs of the let-7 family, which are identified as the best combination.