Assistant Professor of Internal Medicine
Building upon established techniques that utilize cell and animal models, together with RNA-sequencing and bioinformatic analyses, we seek to better understand the role of microRNAs (miRs) in regulating insulin secretion, the development of peripheral tissue insulin resistance, and the role of miRs in hypertrophied and diabetic hearts.
miRs have emerged in recent years as a fundamental part of the mammalian transcriptome that comprise critical regulators of mRNA translation. These endogenous, non-coding RNAs are expressed in distinct patterns during development and are highly regulated in disease states. To best explore their biology, their in vivo messenger RNA (mRNA) targets need to be known. However, accurate prediction of the mRNA targets that will be subject to regulation by a particular miR is complicated: 1) miR regulation of mammalian mRNAs only requires partial Watson-Crick base-pairing, 2) miR-mRNA pairing is only likely to occur at mRNA sites free of complex secondary structure, and 3) the degree of miR suppression will depend on the expression level of both the miR and its mRNA target, and in this respect miRs expressed in more than one cell type may well have different preferred targets in different cells.
Many groups are working to explore the therapeutic potential of miRs, since artificial molecules able to inhibit the normal activity of miRs (antagomiRs) can be readily introduced in vivo. Understanding the mRNA networks regulated by miRs in cell- and tissue-specific contexts will be critical to effective use of such therapies, and requires unbiased investigation of potential mRNA targets. A key component of our research strategy is to use RISC-sequencing, a novel next-generation sequencing technique that examines the flux of mRNAs between the transcriptome and the RISC-associated RNA pool and identifies mRNAs that are targeted to the RISC. In combination with miR and translationally competent mRNA expression analyses using deep sequencing, and both sequence-based and structure-based algorithms for predicting miR-binding sites, we aim to discover new regulatory events mediated by miRs in cardiovascular disease and diabetes.
We welcome the involvement of cell and molecular biologists, bioinformaticians and computational biologists as we seek to build quantitative, predictive networks of miR and mRNA signaling in two diseases that represent significant public health burdens.