When Robert Hooke first discovered cells in a sliver of cork, one would have barely imagined that this microscopic entity is a room to complex and dynamic genetic information. Ever since the discovery of DNA, scientists have decoded huge sets of data describing genetic diversity between individuals.
I made use of microscopes and some other glasses and instruments that improve the senses…, both in surveying the already visible World, and for the discovery of many others hitherto unknown -Micrographia, by Robert Hooke (1665)
The completion of Human Genome Project and the advent of high-throughput genomics delivered an unprecedented insight into the origin of human diseases based on the genetic constitution. It revolutionized the field of medicine by setting the stage for rapid diagnosis and treatment of debilitating diseases, possibly before the symptoms appear.
However, seeping into the human genome to develop better therapeutic strategies comes with the challenge of grappling with complex genetic interaction and its validation. It calls for a paradigm shift from conventional experimentation techniques to computational approaches, where the genetic interactions are predicted before testing in the laboratory, saving time, effort and money. Computational network models have now become a major tool of choice to study the molecular mechanism of various diseases cutting the bench-to-bedside time for the development of a treatment strategy.
In light of these challenges, our laboratory at National Institute of Technology Calicut, India, computationally predicted molecular interactions to particularly understand ischemic stroke mechanism using a novel five-node feed-forward loop. Feed-forward loops were originally simulated in the field of electronics and are now widely used to study biological interactions and networks. The genetic interactions are associated with transcriptional regulatory networks, where specific regulatory proteins called transcription factors (TFs) control gene expression. microRNAs, a group of small non-coding RNAs, explicitly regulate gene expression at the post-transcriptional level. Together with TFs, microRNAs regulate thousands of genes and each regulatory interaction set is represented as a network motif.
Our study demonstrated feed-forward loops built from network motifs with three main components: microRNA, TF, and gene. A massive set of ischemic stroke-related genes, miRNAs, and transcription factors were collected to build three-, four-, and five-node feed-forward loop. We proposed a novel five-node feed-forward loop by introducing miRNA-miRNA interaction. The inclusion of miRNA-miRNA interaction is important owing to the differential expression of multiple miRNAs during ischemic stroke.
Using the proposed computational framework, we identified principal miRNAs, TFs, and genes associated with the innate inflammatory and neuronal survival mechanism following ischemic stroke. The study was extended to identify core regulatory miRNAs in post-stroke neurogenesis, one of the most sought-after processes involved in ischemic stroke recovery. The proposed five-node feed-forward loop contains all possible regulatory interactions between miRNAs, genes, and TFs and might play a vital role in understanding the complexity of any disease, and not just ischemic stroke.
These findings are described in the article A novel five-node feed-forward loop unravels miRNA-Gene-TF regulatory relationships in ischemic stroke, recently published in the journal Molecular Neurobiology. The work was led by Dr. Rajanikant from the National Institute of Technology Calicut.
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