Ingenuity’s IPA was featured prominently at the Eighth Annual Cambridge Healthtech Institute conference on microRNA in Human Disease and Development, held in Cambridge, MA from March 12-13, 2012. The conference brought together academic, industrial, and clinical researchers focused on the development of microRNA diagnostics and therapeutics. Below is a synopsis of the research highlighting the use of IPA to advance these research endeavors.
By Aimee L. Jackson, Ph.D., Translational Genomics Consultant
This year’s CHI conference on microRNA in Human Disease and Development emphasized the role of microRNAs in the development of disease, and the potential of microRNAs as both disease biomarkers and as novel therapeutic agents. It was exciting to see the depth and diversity of research being pursued in these areas. A common theme of the conference was the challenge of assigning mechanism of action for a microRNA and identifying the key targets for a particular phenotype, given the large number of targets regulated by a microRNA. Target prediction algorithms can provide some insight, but are limited for a number of reasons: 1) not all microRNA binding site rules have been identified, 2) a single transcript can have binding sites for, and therefore be a target of, more than one microRNA, and 3) algorithms differ in prediction approaches, yielding different subsets of predicted targets. Furthermore, these algorithms do not necessarily prioritize the key targets for a particular phenotype of interest. Therefore, the identification of functional microRNA targets is best approached experimentally in the desired context.
To illustrate this point, I presented the use of the microRNA target filter in IPA to facilitate mechanistic insight into the role of microRNAs in hypertensive kidneys. miR-16 was the most up-regulated microRNA and let-7c was the most down-regulated microRNA in medullas of hypertensive kidneys. Combined, these 2 microRNAs have >2000 predicted targets. I used multiple filters available in IPA’s microRNA target filter, including a corresponding mRNA dataset from the same kidney medullas, inverse expression pairing, highly predicted or experimentally observed targets, and mRNA expression in the target organ (kidney), to identify a total of 5 mRNAs for the 2 microRNAs. Using these modular and customizable filters, therefore, one can rapidly filter from a large number of predicted targets to a number that can be validated experimentally. Network analysis and canonical pathways capabilities in IPA were subsequently employed to provide a hypothesized role for the let-7c and miR-16 microRNAs in regulating the NF-kB pathway in hypertensive kidney.
Two speakers presented their experience incorporating Ingenuity IPA core analysis, canonical pathways and network analysis to prioritize targets and provide mechanistic information on microRNAs and mRNAs dysregulated in the disease states under investigation.
David von Schack from Pfizer discussed microRNAs for mechanistic biomarker identification in the setting of neuropathic pain. Dr. von Schack identified 63 microRNAs significantly changed after spinal nerve ligation in rats. He then performed sequence-based analysis of predicted targets to identify predicted targets enriched for multiple microRNA binding sites, and used IPA core analysis to reveal that these predicted targets are enriched for functional annotation in ‘Organ development’ and ‘Nervous system development and function.’ Subsequent IPA core analysis of mRNAs differentially expressed after spinal nerve ligation confirmed enrichment for annotation in neurological pathways and functions, and enabled focus on the microRNA seed-matched transcripts that are actually regulated in this biological context. Correlating the differentially regulated microRNAs with the key targets dysregulated in neuropathic pain can help to identify 1) pharmacodynamic biomarkers of drug functionally engaging its target, 2) proof-of-principle biomarkers that the drug-target interaction results in the desired response, and 3) efficacy or surrogate biomarkers.
Fazlul Sarkar from Wayne State University is exploring microRNAs as targets to overcome resistance to conventional cancer therapeutics. Since therapeutic resistance is the cause of treatment failure in cancer, novel approaches to overcoming this resistance would provide significant benefit and improved survival for cancer patients. Dr. Sarkar is focused on cancer stem cells, which tend to be spared by conventional cancer therapeutics, and on the epithelial-to-mesenchymal (EMT) transition to produce drug-resistant cancer cells. As microRNAs can regulate multiple cancer-relevant pathways (Wnt, Shh, NF-kB, Notch, etc), Dr. Sarkar has become interested the role of microRNAs in drug resistance, and the use of systems biology approaches to identify microRNAs that can be modulated therapeutically. Dr. Sarkar also discussed the interesting concept of network pharmacology to design strategies to modulate multiple microRNAs simultaneously for a more comprehensive approach to cancer therapy. Using mouse pancreatic cancer as a model system, Dr. Sarkar demonstrated the use of Ingenuity IPA for microRNA target pathway and network identification for dysregulated microRNAs, especially ‘driver’ microRNAs, which he defined as central hubs in these networks.
Using systems biology approaches and integrated data analysis tools, researches are exploring the fascinating biology of microRNAs and their diverse roles in development and disease. With the recent success of the first microRNA-based therapeutic, anti-miR-122, in patients with hepatitis C virus, there is great enthusiasm for microRNAs as a novel therapeutic modality. In addition, advances in monitoring microRNA expression in blood and other bodily fluids as well as in circulating tumor cells have built significant enthusiasm for microRNAs as circulating biomarkers. Tools to define and interpret the biological function of microRNAs in specific biological contexts (e.g., cells, tissues, disease states) will facilitate progress toward therapeutic application of microRNAs.
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