Ribosomal subunit association is a key checkpoint in translation initiation, but its structural dynamics are poorly understood. Here, we used a recently developed mixing-spraying, time-resolved, cryogenic electron microscopy (cryo-EM) method to study ribosomal subunit association in the sub-second time range. We have improved this method and increased the cryo-EM data yield by tenfold. Pre-equilibrium states of the association reaction were captured by reacting the mixture of ribosomal subunits for 60 ms and 140 ms. We also identified three distinct ribosome conformations in the associated ribosomes. The observed proportions of these conformations are the same in these two time points, suggesting that ribosomes equilibrate among the three conformations within less than 60 ms upon formation. Our results demonstrate that the mixing-spraying method can capture multiple states of macromolecules during a sub-second reaction. Other fast processes, such as translation initiation, decoding and ribosome recycling, are amenable to study with this method.

Summary It has been known for more than 50 years that transcription and translation are physically coupled in bacteria, but whether or not this coupling may be mediated by the two-domain protein N-utilization substance (Nus) G in Escherichia coli is still heavily debated. Here, we combine integrative structural biology and functional analyses to provide conclusive evidence that NusG can physically link transcription with translation by contacting both RNA polymerase and the ribosome. We present a cryo-electron microscopy structure of a NusG:70S ribosome complex and nuclear magnetic resonance spectroscopy data revealing simultaneous binding of NusG to RNAP and the intact 70S ribosome, providing the first direct structural evidence for NusG-mediated coupling. Furthermore, in vivo reporter assays show that recruitment of NusG occurs late in transcription and strongly depends on translation. Thus, our data suggest that coupling occurs initially via direct RNAP:ribosome contacts and is then mediated by NusG.

The regulatory role of the ribosome in gene expression has come into sharper focus. It has been proposed that ribosomes are dynamic complexes capable of changing their protein composition in response to environmental stimuli. MS is applied to identify quantitative changes in the protein composition of S. cerevisiae 80S ribosomes in response to different environmental stimuli. Using quantitative MS, it is found that the paralog yeast ribosomal proteins RPL8A (eL8A) and RPL8B (eL8B) change their relative proportions in the 80S ribosome when yeast is switched from growth in glucose to glycerol. By using yeast genetics and polysome profiling, it is shown that yeast ribosomes containing either RPL8A or RPL8B are not functionally interchangeable. The quantitative proteomic data support the hypothesis that ribosomes are dynamic complexes that alter their composition and functional activity in response to changes in growth or environmental conditions.

Gene regulatory networks (GRNs) have been widely used as a fundamental tool to reveal the genomic mechanisms that underlie the individual’s response to environmental and developmental cues. Standard approaches infer GRNs as holistic graphs of gene co-expression, but such graphs cannot quantify how gene–gene interactions vary among individuals and how they alter structurally across spatiotemporal gradients. Here, we develop a general framework for inferring informative, dynamic, omnidirectional, and personalized networks (idopNetworks) from routine transcriptional experiments. This framework is constructed by a system of quasi-dynamic ordinary differential equations (qdODEs) derived from the combination of ecological and evolutionary theories. We reconstruct idopNetworks using genomic data from a surgical experiment and illustrate how network structure is associated with surgical response to infrainguinal vein bypass grafting and the outcome of grafting. idopNetworks may shed light on genotype–phenotype relationships and provide valuable information for personalized medicine.