SUMMARY Translation elongation efficiency is largely thought of as the sum of decoding efficiencies for individual codons. Here, we find that adjacent codon pairs modulate translation efficiency. Deploying an approach in Saccharomyces cerevisiae that scored the expression of over 35,000 GFP variants in which three adjacent codons were randomized, we identified 17 pairs of adjacent codons associated with reduced expression. For many pairs, codon order is obligatory for inhibition, implying a more complex interaction than a simple additive effect. Inhibition mediated by adjacent codons occurs during translation itself as GFP expression is restored by increased tRNA levels or by non-native tRNAs with exact-matching anticodons. Inhibition operates in endogenous genes, based on analysis of ribosome profiling data. Our findings suggest translation efficiency is modulated by an interplay between tRNAs at adjacent sites in the ribosome, and that this concerted effect needs to be considered in predicting the functional consequences of codon choice.

The RNA recognition motif (RRM) is the most common RNA-binding domain in eukaryotes. Differences in RRM sequences dictate, in part, both RNA and protein-binding specificities and affinities. We used a deep mutational scanning approach to study the sequence-function relationship of the RRM2 domain of the Saccharomyces cerevisiae poly(A)-binding protein (Pab1). By scoring the activity of more than 100,000 unique Pab1 variants, including 1246 with single amino acid substitutions, we delineated the mutational constraints on each residue. Clustering of residues with similar mutational patterns reveals three major classes, composed principally of RNA-binding residues, of hydrophobic core residues, and of the remaining residues. The first class also includes a highly conserved residue not involved in RNA binding, G150, which can be mutated to destabilize Pab1. A comparison of the mutational sensitivity of yeast Pab1 residues to their evolutionary conservation reveals that most residues tolerate more substitutions than are present in the natural sequences, although other residues that tolerate fewer substitutions may point to specialized functions in yeast. An analysis of ∼40,000 double mutants indicates a preference for a short distance between two mutations that display an epistatic interaction. As examples of interactions, the mutations N139T, N139S, and I157L suppress other mutations that interfere with RNA binding and protein stability. Overall, this study demonstrates that living cells can be subjected to a single assay to analyze hundreds of thousands of protein variants in parallel.

Transcriptional enhancers are a primary mechanism by which tissue-specific gene expression is achieved. Despite the importance of these regulatory elements in development, responses to environmental stresses, and disease, testing enhancer activity in animals remains tedious, with a minority of enhancers having been characterized. Here, we have developed ‘enhancer-FACS-Seq’ (eFS) technology for highly parallel identification of active, tissue-specific enhancers in Drosophila embryos. Analysis of enhancers identified by eFS to be active in mesodermal tissues revealed enriched DNA binding site motifs of known and putative, novel mesodermal transcription factors (TFs). Naïve Bayes classifiers using TF binding site motifs accurately predicted mesodermal enhancer activity. Application of eFS to other cell types and organisms should accelerate the cataloging of enhancers and understanding how transcriptional regulation is encoded within them.

Contemporary high-throughput technologies permit the rapid identification of transcription factor (TF) target genes on a genome-wide scale, yet the functional significance of TFs requires knowledge of target gene expression patterns, cooperating TFs, and cis-regulatory element (CRE) structures. Here we investigated the myogenic regulatory network downstream of the Drosophila zinc finger TF Lame duck (Lmd) by combining both previously published and newly performed genomic data sets, including ChIP sequencing (ChIP-seq), genome-wide mRNA profiling, cell-specific expression patterns of putative transcriptional targets, analysis of histone mark signatures, studies of TF cooccupancy by additional mesodermal regulators, TF binding site determination using protein binding microarrays (PBMs), and machine learning of candidate CRE motif compositions. Our findings suggest that Lmd orchestrates an extensive myogenic regulatory network, a conclusion supported by the identification of Lmd-dependent genes, histone signatures of Lmd-bound genomic regions, and the relationship of these features to cell-specific gene expression patterns. The heterogeneous cooccupancy of Lmd-bound regions with additional mesodermal regulators revealed that different transcriptional inputs are used to mediate similar myogenic gene expression patterns. Machine learning further demonstrated diverse combinatorial motif patterns within tissue-specific Lmdbound regions. PBM analysis established the complete spectrum of Lmd DNA binding specificities, and site-directed mutagenesis of Lmd and additional newly discovered motifs in known enhancers demonstrated the critical role of these TF binding sites in supporting full enhancer activity. Collectively, these findings provide insights into the transcriptional codes regulating muscle gene expression and offer a generalizable approach for similar studies in other systems.