In vivo filtering of in vitro MyoD target data: An approach for identification of biologically relevant novel downstream targets of transcription factors (2003)

Abstract

We report a novel approach to identification of downstream targets of MyoD, where a published set of candidate targets from a well-controlled in vitro experiment [1] is filtered for relevance to muscle regeneration using a 27 time point in vivo murine regeneration series. Using both interactive hierarchical clustering (HCE) [2], and Bayes soft clustering (VISDA) [3,4]. We show that only a minority of in vitroefined candidates can be confirmed in vivo (~50% of induced transcripts, and none of repressed transcripts). The concordance of the in vitro, in vivo datasets, and both HCE and VISDA analytical techniques showed strong support for 18 targets (13 no vel) of MyoD that are biologically relevant during myoblast differentiation, including Cdh15, L-myc, Hes6, Stam, Tnnt2, Fyn, Rapsn, Nestin, Osp94, Pep4, Mef2a, Sh3glb1 and Rb1.