The Genomic Pattern of tDNA Operon Expression in E. coli

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Abstract
In fast-growing microorganisms, a tRNA concentration profile enriched in major isoacceptors selects for the biased usage of cognate codons. This optimizes translational rate for the least mass invested in the translational apparatus. Such translational streamlining is thought to be growth-regulated, but its genetic basis is poorly understood. First, we found in reanalysis of the E. coli tRNA profile that the degree to which it is translationally streamlined is nearly invariant with growth rate. Then, using least squares multiple regression, we partitioned tRNA isoacceptor pools to predicted tDNA operons from the E. coli K12 genome. Co-expression of tDNAs in operons explains the tRNA profile significantly better than tDNA gene dosage alone. Also, operon expression increases significantly with proximity to the origin of replication, oriC, at all growth rates. Genome location explains about 15% of expression variation in a form, at a given growth rate, that is consistent with replication-dependent gene concentration effects. Yet the change in the tRNA profile with growth rate is less than would be expected from such effects. We estimated per-copy expression rates for all tDNA operons that were consistent with independent estimates for rDNA operons. We also found that tDNA operon location, and the location dependence of expression, were significantly different in the leading and lagging strands. The operonic organization and genomic location of tDNA operons are significant factors influencing their expression. Nonrandom patterns of location and strandedness shown by tDNA operons in E. coli suggest that their genomic architecture may be under selection to satisfy physiological demand for tRNA expression at high growth rates. The concentrations of tRNAs are co-adapted to codon usage frequencies in the transcriptomes of E. coli and other diverse organisms. But how are tRNA concentrations determined? Here, the researchers analyzed the E. coli tRNA concentration profile in its genomic context, using clustering and regression methods to partition tRNA concentration data to tDNA operons that were defined semi-automatically. They found that co-expression in operons explains the tRNA profile much better than tDNA gene dosage alone. Furthermore, they could significantly explain the total expression from tDNA operons by their distance from the genomic origin of replication. Per-copy transcription initiation rates from tDNA operons were also estimated. Although there is some evidence for replication-dependent effects on tDNA operon expression, this cannot explain how constant the tRNA profile is with growth rate. As a consequence, tDNA promoters are predicted to compensate for the location of their operons. Finally, the researchers found pronounced asymmetries between the leading and lagging genomic strands in the locations of tDNA operons, and on the effect of location on their expression. These nonrandom patterns suggest that the genomic location and strandedness of tDNA operons may be under some selection in E. coli to satisfy physiological demand for tRNAs at high growth rates.