By shaping gene expression profiles, small RNAs (sRNAs) enable bacteria to efficiently adapt to changes in their environment. To better understand how Escherichia coli acclimatizes to nutrient availability, we performed UV cross-linking, ligation and sequencing of hybrids (CLASH) to uncover Hfq-associated RNA-RNA interactions at specific growth stages. We demonstrate that Hfq CLASH robustly captures bona fide RNA-RNA interactions. We identified hundreds of novel sRNA base-pairing interactions, including many sRNA-sRNA interactions and involving 3’UTR-derived sRNAs. We rediscovered known and identified novel sRNA seed sequences. The sRNA-mRNA interactions identified by CLASH have strong base-pairing potential and are highly enriched for complementary sequence motifs, even those supported by only a few reads. Yet, steady state levels of most mRNA targets were not significantly affected upon over-expression of the sRNA regulator. Our results reinforce the idea that the reproducibility of the interaction, not base-pairing potential, is a stronger predictor for a regulatory outcome.

RNA-binding proteins play a key role in shaping gene expression profiles during stress, however, little is known about the dynamic nature of these interactions and how this influences the kinetics of gene expression. To address this, we developed kinetic cross-linking and analysis of cDNAs (χCRAC), an ultraviolet cross-linking method that enabled us to quantitatively measure the dynamics of protein–RNA interactions in vivo on a minute time-scale. Here, using χCRAC we measure the global RNA-binding dynamics of the yeast transcription termination factor Nab3 in response to glucose starvation. These measurements reveal rapid changes in protein–RNA interactions within 1 min following stress imposition. Changes in Nab3 binding are largely independent of alterations in transcription rate during the early stages of stress response, indicating orthogonal transcriptional control mechanisms. We also uncover a function for Nab3 in dampening expression of stress-responsive genes. χCRAC has the potential to greatly enhance our understanding of in vivo dynamics of protein–RNA interactions.

Structure probing coupled with high-throughput sequencing could revolutionize our understanding of the role of RNA structure in regulation of gene expression. Despite recent technological advances, intrinsic noise and high sequence coverage requirements greatly limit the applicability of these techniques. Here we describe a probabilistic modeling pipeline that accounts for biological variability and biases in the data, yielding statistically interpretable scores for the probability of nucleotide modification transcriptome wide. Using two yeast data sets, we demonstrate that our method has increased sensitivity, and thus our pipeline identifies modified regions on many more transcripts than do existing pipelines. Our method also provides confident predictions at much lower sequence coverage levels than those recommended for reliable structural probing. Our results show that statistical modeling extends the scope and potential of transcriptome-wide structure probing experiments.

27By shaping gene expression profiles, small RNAs (sRNAs) enable bacteria to very 28 efficiently adapt to constant changes in their environment. To better understand how 29 Escherichia coli acclimatizes to changes in nutrient availability, we performed UV cross-30 linking, ligation and sequencing of hybrids (CLASH) to uncover sRNA-target interactions. 31Strikingly, we uncovered hundreds of novel Hfq-mediated sRNA-target interactions at specific 32 growth stages, involving many novel 3'UTR-derived sRNAs and a plethora of sRNA-sRNA 33 interactions. We discovered sRNA-target interaction networks that play a role in adaptation to 34 changes in nutrient availability. We characterized a novel 3'UTR-derived sRNA (MdoR), which 35 is part of a regulatory cascade that enhances maltose uptake by (a) inactivating repressive 36 pathways that block the accumulation of maltose transporters and (b) by reducing the flux of 37 general porins to the outer membrane. Our work provides striking examples of how bacteria 38 utilize sRNAs to integrate multiple regulatory pathways to enhance nutrient stress adaptation.39 3 Microorganisms are renowned for their ability to adapt to environmental changes by 40 rapidly rewiring their gene expression program. These responses are mediated through 41 integrated transcriptional and post-transcriptional networks. Control at the transcriptional level 42 dictates which genes are expressed (Balleza et al., 2009; Martínez-Antonio et al., 2008) and 43 is well-characterised in Escherichia coli. Post-transcriptional regulation is key for controlling 44 adaptive responses. By using riboregulators and RNA-binding proteins (RBPs), cells can 45 efficiently integrate multiple pathways and incorporate additional signals into regulatory 46 circuits. E. coli employs many post-transcriptional regulators, including small regulatory RNAs 47 (sRNAs (Waters and Storz, 2009)), cis-acting RNAs (Kortmann and Narberhaus, 2012), and 48 RNA binding proteins (RBPs) (Holmqvist and Vogel, 2018). The sRNAs are the largest class 49 of bacterial regulators, which work in tandem with RBPs to regulate their RNA targets (Storz 50 et al., 2011; Waters and Storz, 2009). The base-pairing interactions are often mediated by 51 RNA chaperones such as Hfq and ProQ, which help to anneal or stabilize the sRNA and 52 sRNA-target duplex (Smirnov et al., 2017(Smirnov et al., , 2016 Updegrove et al., 2016). Small RNAs can 53 repress or stimulate translation and transcription, as well as control mRNA stability 54 (Sedlyarova et al., 2016; Updegrove et al., 2016; Vogel and Luisi, 2011; Waters and Storz, 55 2009). 56During growth in rich media, E. coli are exposed to continuously changing conditions, 57 such as fluctuations in nutrient availability, pH and osmolarity. Consequently, E. coli elicit 58 complex responses that result in physiological and behavioural changes such as envelope 59 composition remodelling, quorum sensing, nutrient scavenging, swarming and biofilm 60 formation. Even subtle changes in the growth conditions can trigg...

Granneman (2020): The mRNA derived MalH sRNA contributes to alternative carbon source utilization by tuning maltoporin expression in E. coli, RNA Biology,