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Letter

Decay Rates of Human mRNAs: Correlation With Functional Characteristics and Sequence Attributes

¹ Laboratory of Molecular Cell Biology, The Rockefeller University, New York, New York 10021-6399, USA ² Laboratory of Computational Genomics, The Rockefeller University, New York, New York 10021-6399, USA ³ Laboratory of Mathematical Physics, The Rockefeller University, New York, New York 10021-6399, USA ⁴ Center for the Study of Physics and Biology, The Rockefeller University, New York, New York 10021-6399, USA ⁵ Department of Biology and Courant Institute of Mathematical Sciences, New York University, New York, New York 10012, USA

Although mRNA decay rates are a key determinant of the steady-state concentration for any given mRNA species, relatively little is known, on a population level, about what factors influence turnover rates and how these rates are integrated into cellular decisions. We decided to measure mRNA decay rates in two human cell lines with high-density oligonucleotide arrays that enable the measurement of decay rates simultaneously for thousands of mRNA species. Using existing annotation and the Gene Ontology hierarchy of biological processes, we assign mRNAs to functional classes at various levels of resolution and compare the decay rate statistics between these classes. The results show statistically significant organizational principles in the variation of decay rates among functional classes. In particular, transcription factor mRNAs have increased average decay rates compared with other transcripts and are enriched in "fast-decaying" mRNAs with half-lives <2 h. In contrast, we find that mRNAs for biosynthetic proteins have decreased average decay rates and are deficient in fast-decaying mRNAs. Our analysis of data from a previously published study of Saccharomyces cerevisiae mRNA decay shows the same functional organization of decay rates, implying that it is a general organizational scheme for eukaryotes. Additionally, we investigated the dependence of decay rates on sequence composition, that is, the presence or absence of short mRNA motifs in various regions of the mRNA transcript. Our analysis recovers the positive correlation of mRNA decay with known AU-rich mRNA motifs, but we also uncover further short mRNA motifs that show statistically significant correlation with decay. However, we also note that none of these motifs are strong predictors of mRNA decay rate, indicating that the regulation of mRNA decay is more complex and may involve the cooperative binding of several RNA-binding proteins at different sites.

⁶ These authors contributed equally to this work.

⁷ Corresponding author. E-MAIL darnell{at}mail.rockefeller.edu; FAX (212) 327-8801.

[Supplemental material is available online at www.genome.org, and also at http://genomes.rockefeller.edu/~yange.]

⁸ The Affymetrix MAS 5.0 software reports logarithms base 2. We follow this convention, which also simplifies the relation between decay rate and half-life. That is, half-life is just the inverse of decay rate.

⁹ Affymetrix calculates this mean and variance using a procedure that down-weighs the contribution of "outliers" to the average, but we ignore this technical complication here.

¹⁰ The cumulative of the above Student-t distribution can be expressed in terms of a so-called regularized -function. We invert the regularized -function numerically for each probe set to obtain the _i.

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