Identification of the yeast cytidine deaminase CDD1 as an orphan C→U RNA editase

Yeast co-expressing rat APOBEC-1 and a fragment of human apolipoprotein B (apoB) mRNA assembled functional editosomes and deaminated C6666 to U in a mooring sequence-dependent fashion. The occurrence of APOBEC-1-complementing proteins suggested a naturally occurring mRNA editing mechanism in yeast. Previously, a hidden Markov model identified seven yeast genes encoding proteins possessing putative zinc-dependent deaminase motifs. Here, only CDD1, a cytidine deaminase, is shown to have the capacity to carry out C→U editing on a reporter mRNA. This is only the second report of a cytidine deaminase that can use mRNA as a substrate. CDD1-dependent editing was growth phase regulated and demonstrated mooring sequence-dependent editing activity. Candidate yeast mRNA substrates were identified based on their homology with the mooring sequence-containing tripartite motif at the editing site of apoB mRNA and their ability to be edited by ectopically expressed APOBEC-1. Naturally occurring yeast mRNAs edited to a significant extent by CDD1 were, however, not detected. We propose that CDD1 be designated an orphan C→U editase until its native RNA substrate, if any, can be identified and that it be added to the CDAR (cytidine deaminase acting on RNA) family of editing enzymes.

A stochastic approximation algorithm with Markov chain Monte-Carlo method for incomplete data estimation problems

We propose a general procedure for solving incomplete data estimation problems. The procedure can be used to find the maximum likelihood estimate or to solve estimating equations in difficult cases such as estimation with the censored or truncated regression model, the nonlinear structural measurement error model, and the random effects model. The procedure is based on the general principle of stochastic approximation and the Markov chain Monte-Carlo method. Applying the theory on adaptive algorithms, we derive conditions under which the proposed procedure converges. Simulation studies also indicate that the proposed procedure consistently converges to the maximum likelihood estimate for the structural measurement error logistic regression model.