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Biology subjects

Lin, K. S.

Publications and source records attributed to Lin, K. S..

2 recordsLinked to original sources

The Dynamics of Cytoplasmic mRNA Metabolism

For all but a few mRNAs, the dynamics of metabolism are unknown. Here, we developed an experimental and analytical framework for examining these dynamics for mRNAs from thousands of genes. mRNAs of mouse fibroblasts exit the nucleus with diverse intragenic and intergenic poly(A)-tail lengths. Once in the cytoplasm, they have a broad (1000-fold) range of deadenylation rate constants, which correspond to cytoplasmic lifetimes. Indeed, with few exceptions, degradation appears to occur primarily through deadenylation-linked mechanisms, with little contribution from either endonucleolytic cleavage or deadenylation-independent decapping. Most mRNA molecules degrade only after their tail lengths fall below 25 nt. Decay rate constants of short-tailed mRNAs vary broadly (1000-fold) and are more rapid for short-tailed mRNAs that had previously undergone more rapid deadenylation. This coupling helps clear rapidly deadenylated mRNAs, enabling the large range in deadenylation rate constants to impart a similarly large range in stabilities.\n\nHighlightsO_LImRNAs enter the cytoplasm with diverse intra- and intergenic lengths\nC_LIO_LImRNA deadenylation rates span a 1000-fold range and correspond to mRNA half-lives\nC_LIO_LIAfter their tails become short, mRNAs decay at rates that span a 1000-fold range\nC_LIO_LIMore rapidly deadenylated mRNAs decay more rapidly upon reaching short tail lengths\nC_LI

genomics↗

The biochemical basis of microRNA targeting efficacy

MicroRNAs (miRNAs) act within Argonaute proteins to guide repression of mRNA targets. Although various approaches have provided insight into target recognition, the sparsity of miRNA-target affinity measurements has limited understanding and prediction of targeting efficacy. Here, we adapted RNA bind-n-seq to enable measurement of relative binding affinities between Argonaute-miRNA complexes and all [≤]12-nucleotide sequences. This approach revealed noncanonical target sites unique to each miRNA, miRNA-specific differences in canonical target-site affinities, and a 100-fold impact of dinucleotides flanking each site. These data enabled construction of a biochemical model of miRNA-mediated repression, which was extended to all miRNA sequences using a convolutional neural network. This model substantially improved prediction of cellular repression, thereby providing a biochemical basis for quantitatively integrating miRNAs into gene-regulatory networks.

biochemistry↗