Abstract
MicroRNAs are a family of short (~21 nucleotide) noncoding RNAs that serve key roles in cellular growth and differentiation and the response of the heart to stress stimuli. As the sequence-specific recognition element of RNA-induced silencing complexes (RISCs), microRNAs bind mRNAs and prevent their translation via mechanisms that may include transcript degradation and/or prevention of ribosome binding. Short microRNA sequences and the ability of microRNAs to bind to mRNA sites having only partial/imperfect sequence complementarity complicate purely computational analyses of microRNA-mRNA interactomes. Furthermore, computational microRNA target prediction programs typically ignore biological context, and therefore the principal determinants of microRNA-mRNA binding: the presence and quantity of each. To address these deficiencies we describe an empirical method, developed via studies of stressed and failing hearts, to determine disease-induced changes in microRNAs, mRNAs, and the mRNAs targeted to the RISC, without cross-linking mRNAs to RISC proteins. Deep sequencing methods are used to determine RNA abundances, delivering unbiased, quantitative RNA data limited only by their annotation in the genome of interest. We describe the laboratory bench steps required to perform these experiments, experimental design strategies to achieve an appropriate number of sequencing reads per biological replicate, and computer-based processing tools and procedures to convert large raw sequencing data files into gene expression measures useful for differential expression analyses.