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.By shaping gene expression profiles, small RNAs (sRNAs) enable bacteria to efficiently adapt to changes in their environment. To better understand how  Escherichia coli. Post-transcriptional regulation is key for controlling adaptive responses. By using riboregulators and RNA-binding proteins (RBPs), cells can efficiently integrate multiple pathways and incorporate additional signals into regulatory circuits. E. coli employs many post-transcriptional regulators, including small regulatory RNAs , cis-acing RNAs , and RNAing RNAs . The sRNing RNAs . By baseing RNAs .Pseudomonas aeruginosa, Hfq directly binds to mRNAs to repress translation in response to changes in nutrient availability, which relies on a protein co-factor Crc that acts cooperatively with Hfq to inhibit translation   to E. co E CLASH  and RIL- E CLASH . Additio E CLASH . The sRN E CLASH .E. coli, we performed CLASH . These data imply that a large fraction of the chimeras represent genuine base-pairing interactions and not random ligation events.To provide additional evidence that the low abundant interactions identified with CLASH represent genuine interactions and not weak or stochastic interactions, we calculated the base-pairing potential between the two halves of the chimeras. For this purpose, we used RNAduplex  to compuIf the recovered interactions indeed represent bona fide interactions, then it may be expected that the putative mRNA targets found in CLASH chimeras are enriched for sequence motifs complementary to the sRNA seed sequences. To test this, we performed motif analyses on targets of 38 sRNAs that showed at least five unique interactions with different mRNAs . Some sRWe also reasoned that genuine interactions should be enriched in RNA-RNA interaction data generated by alternative experimental approaches. To test this, we compared our data to recent GcvB and CyaR MS2 Affinity Purification coupled with RNA Sequencing (MAPS) datasets . The Cyabona fide sRNA-mRNA interactions and less likely to be formed by random/stochastic events.Collectively, these analyses strongly suggest that the predicted interactions found in our CLASH data, even those supported by a relatively low number of chimeras, are highly enriched for E. coli microarray datasets  by more than 50%, but also that of CyaR (nadE and yqaE (nadE) by ~40% but it did not significantly alter the level of ArcZ or ArcZ mRNA targets  of which were also found in the RIL-Seq data  datasets , suggestE 3’UTRs . We predgadE 3’UTR was also detected in the RIL-seq data and experimentally confirmed and annotated as GadF (CCAGGGG and CUGGUG) were identified in mRNA fragments of these chimeras, the former of which was not previously detected , the in silico predicted interactions between the two halves of these chimeras are fully consistent with previously published work  two promoters, one of which is located near the 3’ end of the gene that we predict is the TSS for YgaN  to map transcription start sites (TSS) of coding and non-coding RNAs in E. coli . TEX degfor YgaN . Furtherfor YgaN  and the for YgaN .malG mRNA  was expressed very transiently and peaked at an OD600 of 1.8  and their associated RNA-binding proteins play a key role in this process. By controlling translation and degradation rates of mRNAs in response to stress , they caS. cerevisiae Cross-linking and cDNA analysis data . These stringent purification steps can significantly reduce noise by strongly enriching for RNAs covalently cross-linked to the bait protein . Indeed,The very stringent purification conditions used in CLASH could, in some cases, also be a disadvantage as it completely relies on UV cross-linking to isolate directly bound RNAs. In cases where the efficiencies of protein-RNA cross-linking are low , RIL-seq may be a better approach as it does not completely rely on UV cross-linking .A large number of interactions were unique to both RIL-seq and Hfq CLASH datasets, which we believe can be explained by a number of technical and experimental factors. The denaturing purification conditions used with CLASH completely disrupt the Hfq hexamer , whilst For the RIL-seq experiments, the authors harvested the cells at 4°C and resuspended them in ice-cold PBS prior to UV irradiation . This prSalmonella revealed widespread changes in gene expression, presumably as a result of redistribution of Hfq over the transcriptome  by inverse PCR using Q5 DNA Polymerase (NEB). The sRNA genes and seed mutants were synthesized as ultramers . Small RNA over-expression constructs derived from the pZA21MCS (Expressys) were generated identically, using the indicated ultramers in The plasmids used in this study are listed in the Key Resources Table. The gene fragments and primers used for cloning procedures in this work are provided in ers IDT;  which seers IDT;  bears a E. coli expressing the chromosomal Hfq-HTF were grown overnight in LB at 37°C with shaking (200 rpm), diluted to starter OD600 0.05 in fresh LB, and re-grown with shaking at 37°C in 750 ml LB. A volume of culture equivalent to 80 OD600 per ml was removed at the following cell-densities (OD600): 0.4, 0.8, 1.2, 1.8, 2.4, 3.0 and 4.0, and immediately subjected to UV (254 nm) irradiation for 22 s (~500 mJ/cm2) in the Vari-X-linker (https://www.vari-x-link.com). Cells were harvested using a rapid filtration device (https://www.vari-x-link.com) onto 0.45 μM nitrocellulose filters  and flash-frozen on the membrane in liquid nitrogen. Membranes were washed with ~15 ml ice-cold phosphate-buffered saline (PBS), and cells were harvested by centrifugation. Cell pellets were lysed by bead-beating in 1 vol per weight TN150 buffer  in the presence of protease inhibitors , and 3 volumes 0.1 mm Zirconia beads , by performing five cycles of 1 min vortexing followed by 1 min incubation on ice. One additional volume of TN150 buffer was added. To reduce the viscosity of the lysate and remove contaminating DNA, the lysate was incubated with RQ1 DNase I  for 30 min on ice. Two-additional volumes of TN150 were added and mixed with the lysates by vortexing. The lysates were centrifuged for 20 min at 4000 rpm at 4°C and subsequently clarified by a second centrifugation step at 13.4 krpm, for 20 min at 4°C. Purification of the UV cross-linked Hfq-HTF-RNA complexes and cDNA library preparation was performed as described . The washes were followed by on-column TSAP incubation  treatment for 1 hr at 37°C with 8 U of phosphatase in 60 μl of 1xPNK, in the presence of 80U RNasin . The beads were washed once with 500 μl wash buffer 1 and three times with 500 μl 1xPNK buffer. To add 3’-linkers (App-PE – Key Resources Table), the Nickel-NTA beads were incubated in 80 μl 3’-linker ligation mix with , 60U RNasin). The samples were incubated for 4 hr at 25°C. The 5’ ends of bound RNAs were radiolabelled with 30U T4 PNK  and 3 μl 32P-γATP  in 1xPNK buffer for 40 min at 37°C, after which ATP  was added to a final concentration of 1 mM, and the incubation prolonged for another 20 min to complete 5’ end phosphorylation. The resin was washed three times with 500 μl wash buffer one and three times with equal volume of 1xPNK buffer. For on-bead 5’-linker ligation, the beads were incubated 16 hr at 16°C in 1xPNK buffer with 40U T4 RNA ligase I , and 1 μl 100 μM L5 adapter (Key Resources Table), in the presence of 1 mM AtP and 60U RNasin. The Nickel-NTA beads were washed three times with wash buffer one and three times with buffer 2 . The protein-RNA complexes were eluted in two steps in new tubes with 200 μl of elution buffer (wash buffer 2 with 250 mM imidazole). The protein-RNA complexes were precipitated on ice by adding TCA (T0699-100ML) to a final concentration of 20%, followed by a 20 min centrifugation at 4°C at 13.4 krpm. Pellets were washed with 800 μl acetone, and air dried for a few minutes in the hood. The protein pellet was resuspended and incubated at 65°C in 20 μl 1x NuPage loading buffer , resolved on 4–12% NuPAGE gels  and visualised by autoradiography. The cross-linked proteins-RNA were cut directly from the gel and incubated with 160 μg of Proteinase K  in 600 μl wash buffer 2 supplemented with 1% SDS and 5 mM EDTA at 55°C for 2–3 hr with mixing. The RNA was subsequently extracted by phenol-chloroform extraction and ethanol precipitated. The RNA pellet was directly resuspended in RT buffer and was transcribed in a single reaction with the SuperScript IV system  according to manufacturer’s instructions using the PE_reverse oligo as primer. The cDNA was purified with the DNA Clean and Concentrator 5 kit (Zymo Research) and eluted in 11 μl DEPC water. Half of the cDNA (5 μl) was amplified by PCR using Pfu Polymerase  with the cycling conditions . The PCR primers are listed in the Key Resources Table. PCR products were treated with 40U Exonuclease 1  for 1 hr at 37°C to remove free oligonucleotide and purified by ethanol precipitation/or the DNA Clean and Concentrator 5 kit . Libraries were resolved on a 2% MetaPhor agarose  gel and 175–300 bp fragments were gel-extracted with the MinElute kit  according to manufacturer’s instructions. All libraries were quantified on a 2100 Bionalyzer using the High-Sensitivity DNA assay and a Qubit 4 . Individual libraries were pooled based on concentration and barcode sequence identity. Paired-end sequencing (75 bp) was performed by Edinburgh Genomics on an Illumina HiSeq 4000 platform.CLASH was performed essentially as described , with a X-linker  . Sequencing libraries from two biological replicates were prepared by NovoGene using the TruSeq library preparation protocol and 150 bp paired-end sequencing was performed on an Illumina NovaSeq 6000 system. This yielded ~7–8 million paired-end reads per sample.600 0.1 and expression of sRNAs was induced with IPTG and anhydrotetracycline hydrochloride  for 1 hr. Cells were collected by centrifugation for 30 s at 14,000 rpm, flash-frozen in liquid nitrogen and total RNA was isolated as above. Gene expression was quantified by RT-qPCR (see below) using 10 ng total RNA as template, and expressed as fold change relative to the reference sample containing pJV300  were cultured to ODg pJV300  or empty-ddCp method was employed, using 5S rRNA (rrfD) as the reference gene. Experiments were performed for three biological replicates, and the mean fold-change and standard error of the mean were computed. Unless otherwise stated, significance of the fold-change difference compared to the reference sample control (for which fold-change = 1 by definition) was tested with a one-sample t-test.Total RNA (10 µg) was treated with 2 U of Turbo DNase  for 1 hr at 37°C in a 10 μl reaction in the presence of 2 U superaseIn RNase inhibitor . The RNA was purified with RNAClean XP beads . Quantitative PCR was performed on 10 ng of DNAse I-treated total RNA using the Luna Universal One-Step RT-qPCR Kit  according to manufacturer’s instructions. The qPCRs were run on a LightCycler 480 (Roche), and the specificity of the product was assessed by generating melt curves, as follows: 65 °C-60s, 95°C . The data analyses were performed with the IDEAS2.0 software, at default settings: Absolute Quantification/Fit Points for Cp determination and Melt Curve Genotyping. The RT-qPCR for all samples was performed in technical triplicate. Outliers from the samples with technical triplicate standard deviations of Cp >0.3 were discarded from the analyses. To calculate the fold-change relative to the control, the 232P-labeled DNA oligo at 42°C for 12–18 hr in a hybridization oven. The sequences of the probes used for Northern blot detection are detailed in Total RNA was extracted from cell lysates by GTC-Phenol extraction. 10 μg total RNA was separated on an 8% polyacrylamide TBE-Urea gel and transferred to a nylon membrane  by electroblotting for 4 hr at 50 V. Membranes were pre-hybridised in 10 ml of UltraHyb Oligo Hyb  for 1 hr and probed with E. coli MG1655 Hfq::htf lysates using strains cultured, cross-linked, harvested and lysed in identical conditions as the CLASH experiments containing 40 µg protein were resolved on PAGE gels and transferred to a nitrocellulose membrane. The membranes were blocked for 1 hr in blocking solution . To detect Hfq-HTF protein, the membrane was probed overnight at 4°C with the Rabbit anti-TAP polyclonal primary antibody , which recognizes an epitope at the region between the TEV-cleavage site and His6. For the loading control we used a rabbit polyclonal to GroEL primary antibody , for 2 hr at room temperature. After 3 × 10 min PBST washes, the membranes were blotted for one hour with a Goat anti-rabbit IgG H and L (IRDye 800) secondary antibody  at room temperature. Finally, after three 10 min PBST washes, the blot was rinsed in PBS, and the proteins were visualised with a LI-COR (Odyssey CLx) using the 800 nm channel and scan intensity 4. Image acquisition and quantifications were performed with the Image Studio Software.https://bitbucket.org/sgrann/). The CRAC_pipeline_PE.py pipeline first demultiplexes the data using pyBarcodeFilter.py and the in-read barcode sequences found in the L5 5’ adapters. Flexbar then trims the reads to remove 3’-adapter sequences and poor-quality nucleotides (Phred score <23). Using the random nucleotide information present in the L5 5’ adaptor sequences, the reads are then collapsed to remove potential PCR duplicates. The reads were then mapped to the E. coli MG1655 genome using Novoalign (www.novocraft.com). To determine to which genes the reads mapped to, we generated an annotation file in the Gene Transfer Format (GTF). This file contains the start and end positions of each gene on the chromosome as well as what genomic features  it belongs to. To generate this file, we used the Rockhopper software , a minimal GTF file obtained from ENSEMBL (without UTR information). The resulting GTF file contained information not only on the coding sequences, but also complete 5’ and 3’ UTR coordinates. We then used pyReadCounters.py with Novoalign output files as input and the GTF annotation file to count the total number of unique cDNAs that mapped to each gene.Raw sequencing reads in fastq files were processed using a pipeline developed by Sander Granneman, which uses tools from the pyCRAC package . The entsoftware  on E. co600 studied were then log2-normalized.To normalize the read count data generated with pyReadCounters.py and to correct for differences in library depth between time-points, we calculated Transcripts Per Million reads (TPM) for each gene. Briefly, for each time-point the raw counts for each gene was first divided by the gene length and then divided by the sum of all the values for the genes in that time-point to normalize for differences in library depth. The TPM values for each ODhttp://kplogo.wi.mit.edu/).For the analysis of the Hfq binding sites the pyCRAC package  was usedhttps://github.com/dstreett/FLASH2)  were used in subsequent analyses. To visualise the hybrids in the genome browser, the. ua.hyb output files were converted to the GTF format. To generate distribution plots for the genes to which the chimeric reads mapped, the parts of the chimeras were clustered with pyClusterReads.py and BEDtools (Chimeric reads were identified using the hyb package using default settings  and furt/FLASH2) , which mBEDtools   to avoid NaN for nucleotide positions with 0 hits when log-transforming the data).To visualize the nucleotide read density of sRNA-target pairs for a given sRNA, the hit counts at each nucleotide position for all statistically filtered chimeras were summed. The count data was logTo make distributions of the chimeric reads around known sRNA and mRNA seeds, we manually retrieved the experimentally validated sRNA and mRNA seed sequences from sRNATarbase 3.0  and lite2-transformed and visualized with the igraph Python package.Only the sRNA-sRNA chimeric reads representing statistically filtered chimeras in the merged CLASH dataset were considered. For each such interaction, chimera counts corresponding in either orientation were summed, loghttps://bitbucket.org/sgrann (pyCRAC up to version 1.4.3), https://git.ecdf.ed.ac.uk/sgrannem/ and pypi (https://pypi.org/user/g_ronimo/). The hyb pipeline for identifying chimeric reads is available from https://github.com/gkudla/hyb. The scripts for statistical analysis of hyb data is available from https://bitbucket.org/jaitree/hyb_stats/. The FLASH algorithm for merging paired reads is available from https://github.com/dstreett/FLASH2. Bedgraph and Gene Transfer Format (GTF) generated from the analysis of the Hfq CLASH, RNA-seq and TEX RNA-seq data (https://datashare.is.ed.ac.uk/handle/10283/2915).The next generation sequencing data have been deposited on the NCBI Gene Expression Omnibus (GEO) with accession number GSE123050. The python pyCRAC , kineticseq data  are avai In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Acceptance summary:Escherichia coli. The interacting RNA pairs identified in this work represent a valuable resource for groups studying RNA-based regulation in bacteria. Moreover, the data reveal many interacting pairs of small, regulatory RNAs (sRNAs), suggesting complex regulatory cross-talk among sRNAs.This study uses a genome-scale approach, CLASH, to identify many RNA-RNA interactions in Decision letter after peer review:[Editors’ note: the authors submitted for reconsideration following the decision after peer review. What follows is the decision letter after the first round of review.]eLife. Your article has been reviewed by two peer reviewers, and the evaluation has been overseen by a Reviewing Editor and a Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Ben F Luisi (Reviewer #2).Thank you for submitting your work entitled "Hfq CLASH uncovers sRNA-target interaction networks involved in adaptation to nutrient availability" for consideration by eLife.Our decision has been reached after consultation between the reviewers and the Reviewing Editor. Based on these discussions and the individual reviews below, we regret to inform you that your work cannot be considered further for publication in eLife, without the need for much in the way of new experimental data. Nonetheless, the required analyses will likely take some time. We encourage you to resubmit if you can make a more compelling case that the CLASH data represent physiological RNA-RNA interactions.While the reviewers are enthusiastic about the potential of the resource that the CLASH data represent, concerns were raised about the validation of these data. Additionally, the reviewers felt that the follow-up studies are interesting, but that some of the conclusions need to be softened. With additional validation of the CLASH data, the manuscript would likely be suitable for publication in The major concern is that there is currently insufficient evidence to conclude that the RNA-RNA pairs identified by CLASH represent bona fide RNA-RNA pairs inside cells. Many of the reported RNA-RNA pairs appear to have been identified only once, and many include highly abundant RNAs . Moreover, overlap with RIL-seq data is fairly limited. While the discussion clearly lays out why overlap with RIL-seq data might be low, this also raises the bar for validating the RNA-RNA pairs not found by RIL-seq. It should be possible to use bioinformatic analyses to further test whether the novel RNA-RNA pairs are genuine. For example, are novel sRNA targets enriched for sequences complementary to the sRNA seeds? Are known sRNA-regulated genes enriched for sRNA-mRNA pairs, and vice versa? These analyses are most important for the novel RNA-RNA pairs identified by CLASH (i.e. not found by RIL-seq).Reviewer #1:Overall, this is very interesting work, and the manuscript obviously represents a great deal of effort. My major criticisms of the work are two-fold. First, the manuscript seems very diffuse – touching on too many topics at a rather surface level. Second, the biological implications of the experimental results are overstated. I hope my comments will be useful to the authors as they consider how to revise their manuscript.1) The title of the manuscript is misleading. The main functional characterization of MdoR and its targets is intriguing and hints at a physiological function related to carbon source adaptation, but there is a long way to go to say that this is truly the function of this sRNA.2) ArcZ-CyaR experiment in the middle is not well connected to the rest of the manuscript. The inclusion in the model figure doesn't really help shed light on the biological role for this interaction.3) Subsection “Hfq CLASH predicts sRNA-sRNA interactions as a widespread layer of post transcriptional regulation”, third paragraph. Figure 5D, the wild-type ArcZ still affects mutant CyaR levels. The authors provide a hand-waving explanation that could be tested. Moreover, the authors state that ArcZ promotes CyaR degradation, but there is no direct evidence for this. It could be tested. Not sure it's the highest priority for this manuscript, given that this experiment in general is not well integrated. But at least the authors should modulate their statement to reflect the actual data.4) Abstract – there is no direct evidence that MdoR enhances maltose uptake.5) I did not understand the logic behind the analyses in Figure 2. The authors state that it was "logical to assume that changes in Hfq binding would also be reflected in changes in sRNA steady state levels." However, there are numerous studies showing that different sRNAs bind Hfq via different modes, and that there is a great deal of variability regarding the role of Hfq in stabilizing sRNAs. Moreover, the competition among RNAs for binding to a limiting pool of Hfq will certainly change over time, and be influenced by the total sRNA abundance and any given sRNA's proportion of the total RNA pool. There seems to be no overall conclusion from the figure, and no follow up, so I would recommend deleting it.6) Subsection “MdoR directly regulates the expression of major outer membrane porins and represses the envelope stress response pathway”, fourth paragraph: The authors state hypotheses in this section that are not further tested, and are not supported by data shown. These are more appropriate for modest speculation in the Discussion.7) Figure 7F: Is the effect of MdoR SM on MicA significant?rpoE?) and the differences in MicA/lamB more clear.8) The only MdoR-target interaction that was definitively demonstrated was MdoR-ompC, and indeed, the authors went above and beyond with evidence here. It is interesting that ompC levels are reduced in maltose , but this is clearly NOT MdoR-dependent . The differences in MicA and lamB RNA levels in the mdoR mutant grown in maltose are intriguing, but these effects can't be linked to a specific MdoR-target regulation. Minimally, the authors should try to make the link between molecular interaction of MdoR and a target (9) Subsection “MdoR enhances maltoporin expression during maltose fermentation”, last paragraph: It would be very exciting if the data directly supported this statement. However, the experiments presented fall short. More physiological evidence is needed – growth phenotypes, maltose uptake assays, etc. In the absence of these, the authors must tone down their claims.E. coli growing in LB. What becomes limiting? The authors say very generically "the most favorable nutrients" become limiting. The finding that malEFG and MdoR are specifically expressed during a very narrow window of time in LB grown cells is very interesting. There must be more to the story of their regulation than malT-dependent maltose-inducible expression given this expression pattern in LB given that the main carbon source in LB is peptides/amino acids. The authors should work on improving the quality of the discussion of these issues, and be up front about the limitations of their study in this regard.10) Because there is so little investigation of the physiology, the discussion of the physiological relevance of these findings is very superficial. The transition from exponential to stationary phase growth has been studied in 11) One key issue that should be addressed in the Discussion is the fact that these global approaches have so little overlap. I did appreciate the thorough description of the relative advantages provided by the Hfq-CLASH method as compared to RIL-seq. However, I think the field as a whole needs to find a way to discern direct, physiologically-relevant interactions from those that may be transient, weaker, and stochastic. I don't expect the authors to solve this issue, but it should be acknowledged. The sensitivity and accuracy of various methods needs a thorough investigation. At least, the authors could consider their Hfq-CLASH results in light of their total expression profiles (RNA-seq) of well characterized sRNAs and their regulons. What's the false negative rate for known interactions?Reviewer #2:mal operon. The experimental work is extensive and there are many interesting new findings reported. There are several comments listed below that will hopefully be useful for the authors to consider:This manuscript analyses the RNAs associated with the RNA chaperone Hfq in Escherchia coli at different growth stages, and in particular during the transition between stages. There has been other work published in this topic, but the new aspect of the work presented here is the depth of analysis of the transitions and the in depth characterisation of the associated RNAs. One important finding from this study is that sRNA expression does not correlate strongly with Hfq binding profile – suggesting that there must be context dependent binding of the RNA to Hfq. Another is the model for the regulatory network involving the processed transcript from the 1) Hfq for CLASH has two large tags on C-terminus. As the C-terminus has been proposed to participate in RNA/protein partners binding and Hfq autoinhibition (work from the Woodson group), have the authors done any controls to make sure this does not interfere with RNA banding/introduce false results?2) "Hfq binds to sRNA-target RNA duplexes" – are RNA duplexes the only Hfq targets? For example, sRNAs were shown to cycle on Hfq, therefore one can imagine a situation in which one sRNA is not fully displaced and the second one already bound. Could some of the sRNA hybrids represent such state?3) 'tRNA-tRNA and rRNA-rRNA chimeras originating from different coding regions were removed' why?4) Can the authors please comment on other chimeras isolated, others than sRNA-mRNA and mRNA-mRNA? Would these represent Hfq targets in the cell?5) Figure 3 – it is not clear what the enriched motifs are showing, 5' end of the chimera? 5' end of both RNAs in the chimera? Only mRNAs?6) Explain in more detail what is meant by scrambled RNA.7) Figure 4 – as only mutations in ArcZ cause disruption of the regulation, can it be an indirect effect, not the result of direct sRNA-sRNA regulation?8) Figure 5B – It is difficult to see expression of ygaM . Where is MdoR on the blot? If it is labelled malG it is somewhat confusing, as the blot presumably shows the sRNA fragments, not the whole mRNAs?9) The signal for RyhB on Figure 5B is quite strong for OD 1.2 and 1.8, however on Figure 6C it is very weak. Can the authors explain? MdoR intensities seem to match, so presumably the RNAs quantities used are similar?10) Is there an evidence that RyhB is in the cell as 5'PPP RNA? Perhaps it is not processed, but has the possibility of it harbouring a different 5' end has been excluded?11) Figure 7C – It is confusing that the authors label 5' and 3' ends which are not real ends, and are different for each panel for MdoR. Could they mark, e.g. with dots, that these are not real ends of RNAs? Or indicate positions of the nucleotides shown? It would make analysing the results much easier.12) Figure 7D- If an empty plasmid is used as a control and the blot probed for MdoR, can the authors explain what is being expressed in their control after 20 minutes? There may be a typo in the legend as it states that the samples were harvested 15 minutes after induction, but the blot shows the results for 20. What is the meaning of the red rectangle over the 15 minutes into MdoR expression?13) Figure 7F – explain MdoR SM, it also isn't introduced in the text. Why does the seed mutation cause higher target levels? For RyeA it doesn't seem like the seed mutation has abolished regulation. Is RybB regulating MicA as well?14) Have the authors tested how their substantial MdoR seed mutation influences RNA structure? Is it possible that, as the seed seems internal, the overall structure of the sRNA is disrupted and therefore the regulation lost? Can the mutant still bind to Hfq? The structure change would also explain problems with RNase E processing.15) 'Notably, the fully-processed mutant MdoR sRNA is less abundant than the wild-type  and longer (unprocessed) fragments that contain upstream malG regions could be readily detected  '- should be 8C and 8E16) 'We conclude that the dynamics of sRNA expression and binding to Hfq are not always highly correlated.' Any thoughts why?17) Polysome preps used cyclohexamide, but this acts but blocking the peptide exit channel in the ribosome and may not trap polysomes except by blocking the last ribosome on the assembly. Another antibiotic or non-hydrolysable GTP might be better.18) These references have related information that may be useful to comment on in the manuscript: de Mets, van Melderen and Gottesman, 2018; Miyakoshi et al., 2018.Pseudomonas aeruginosa, where it inhibits translation of certain mRNAs depending on which nutrients are available. Pei et al., 2019, have solved high resolution structures of Hfq in complex with a target mRNA and other effector molecules to show how this Hfq based regulatory complex works. This research may be related to the theme of the report here and it might be helpful to comment on these findings.Also, Hfq has been known to be involved in nutrient uptake regulation in eLife. Your revised article has been reviewed by three peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by James Manley as the Senior Editor.Thank you for resubmitting your work entitled "Hfq CLASH uncovers sRNA-target interaction networks linked to nutrient availability adaptation" for further consideration by E. coli, and reveal important insights such as the abundance of sRNA-sRNA interactions. To better focus the manuscript, we recommend removing the section on MdoR. While the reviewers found this work to be of interest, they also felt that it was peripheral to the main theme of the study, and would be better suited to an independent publication in a more specialized journal. This would free up some space in the paper to move some of the supplementary figure panels into the main figures, improving readability. Reviewer 3 has some specific suggestions for supplementary figure panels that could be moved into the main set of figures. The detailed reviews are listed below:All the reviewers were enthusiastic about the manuscript and recommend acceptance pending some edits to the text. In particular, the reviewers felt that the new analyses of the CLASH data make a strong case that the identified RNA-RNA interactions are real, and thus greatly expand the known set of interactions for Reviewer #1:The authors have provided further experimental data and analysis and made compelling response to most of the points raised in the review. The manuscript has been improved and the support for the conclusions strengthened considerably.One minor issue is the Figure 2E legend does not explain the figure very clearly.Reviewer #2:This is a much improved revised version of a manuscript describing a global method for characterization of RNA-RNA interactions. The authors have nicely addressed my previous concerns and I have no additional major issues.Reviewer #3 – :The new analyses of the CLASH data make a very convincing case that the novel RNA-RNA pairs reflect real in vivo interactions. My preference would be to remove the MdoR story, which is interesting but peripheral to the main theme of the paper, and does not look at a novel sRNA (MdoR was identified previously by RIL-seq). Moreover, I suggest moving some of the more important supplementary figure panels into the main part of the paper.Figure 2—figure supplement 1C. The "distance from sRNA seed" numbers appear to be similar to the length of the sRNAs. The authors should indicate the sRNA lengths.Figure 2—figure supplement 6 (predicted base-pairing strength for identified interactions). This is an important analysis and should be moved to the main figures.Figure 2—figure supplement 7 (number of enriched sequence motifs from mRNA targets that match the paired sRNA) also belongs in the main figures. I suggest combining this with a couple of the most interesting examples of newly found motifs (i.e. unique to this study).Figure 2—figure supplement 7. The criteria used to make the yes/no calls should be described in the legend.Figure 3—figure supplement 3. This could be moved to the main figures. The legend needs to be expanded for panel C.E. coli and Salmonella for this analysis to be informative. I suggest removing this figure.Figure 4—figure supplement 4. I would not expect to see sufficient overlap in regulation between Figure 4—figure supplement 8. Panel labels are wrong in the legend. While the reviewers are enthusiastic about the potential of the resource that the CLASH data represent, concerns were raised about the validation of these data. Additionally, the reviewers felt that the follow-up studies are interesting, but that some of the conclusions need to be softened. With additional validation of the CLASH data, the manuscript would likely be suitable for publication in eLife, without the need for much in the way of new experimental data. Nonetheless, the required analyses will likely take some time. We encourage you to resubmit if you can make a more compelling case that the CLASH data represent physiological RNA-RNA interactions.We were extremely pleased with the opportunity to submit a revised version of the manuscript. We have performed a large number of additional bioinformatics analyses to demonstrate the robustness of the CLASH data, focussing also on chimeras that uniquely identified in our CLASH data and those that are supported by a relatively low number of reads.https://github.com/dstreett/FLASH2) that merges overlapping paired-reads together into one single contig, which are then sent to the hyb pipeline to detect chimeric reads. Those paired reads that FLASH did not consider to be overlapping were subsequently concatenated by our pipeline to make sure that we would also be able to recover chimeras from non-overlapping reads. However, we recently discovered that FLASH is less sensitive in detecting overlapping reads than we expected. As a result, the pipeline concatenated many overlapping paired reads, which hyb then subsequently identified as chimeras. As a result, 10% of the chimeras called by hyb were false positives as they contained almost identical sequences that were frequently annotated as intramolecular interactions. Therefore, we removed these false-positive chimeras and reanalysed the data. This did not change the interpretation of the data. On the contrary, it improved the results substantially. We have included these additional filtering steps in the Materials and methods section of the revised manuscript.The requested analyses took longer than expected as we discovered an annoying bug in our bioinformatics pipeline that resulted in some of the reads being incorrectly assigned as chimeras. In brief, we used a software package called FLASH . Moreover, overlap with RIL-seq data is fairly limited. While the discussion clearly lays out why overlap with RIL-seq data might be low, this also raises the bar for validating the RNA-RNA pairs not found by RIL-seq. It should be possible to use bioinformatic analyses to further test whether the novel RNA-RNA pairs are genuine. For example, are novel sRNA targets enriched for sequences complementary to the sRNA seeds? Are known sRNA-regulated genes enriched for sRNA-mRNA pairs, and vice versa? These analyses are most important for the novel RNA-RNA pairs identified by CLASH (i.e. not found by RIL-seq).again recovered the known sRNA seed sequences. The number of chimeras containing rRNA fragments is slightly higher in this group (12-13%), suggesting higher background. However, considering the sheer abundance of rRNAs in cells (up to 80%) and the fact that we do not do any rRNA depletion step before library preparation, we would argue that the background is remarkably low. Finally, to test whether the novel sRNA targets are enriched for complementary sequences, we folded the sRNA-mRNA chimeras in silico using RNADuplex from the Vienna package and compared it to chimeric reads in which the fragments were randomly shuffled over the same gene or the same class of genes . These analyses revealed that the vast majority of chimeras, even those supported by only a few reads , had a significantly higher propensity to form stable duplexes compared to randomly generated chimeric reads .To address these points, we did a number of additional bioinformatics analyses. These analyses as well as the new results are described in the Results section and presented in Figure 2—figure supplements 1 to 5. In the first supplementary Figure we show that for almost all the sRNA and mRNAs identified in our data that have experimentally verified seed regions we indeed recovered the known seed sequences in the chimera fragments. Moreover, we show that the data has low background levels, as judged by the percentage of rRNA chimeras in the dataset. We obtained very similar results when we repeated these analyses for all the chimeras  and the chimeras uniquely identified in the CLASH data . The group of chimeras supported by a low number of reads (<4) also largely consisted of sRNA-mRNA fragments as well as sRNA-sRNA and mRNA-mRNA fragments. Moreover, for the vast majority of sRNA chimeras with low read counts we Collectively, these data strongly suggest that the vast majority of chimeras that we recovered, including the new interactions and the less abundant interactions, represent genuine base-pairing interactions rather than random ligations.Reviewer #1:Overall, this is very interesting work, and the manuscript obviously represents a great deal of effort. My major criticisms of the work are two-fold. First, the manuscript seems very diffuse – touching on too many topics at a rather surface level. Second, the biological implications of the experimental results are overstated. I hope my comments will be useful to the authors as they consider how to revise their manuscript.1) The title of the manuscript is misleading. The main functional characterization of MdoR and its targets is intriguing and hints at a physiological function related to carbon source adaptation, but there is a long way to go to say that this is truly the function of this sRNA.”. We hope that this title now better reflects the experimental data.We have now changed the title to “Hfq CLASH uncovers sRNA-target interaction networks linked to nutrient availability adaptation2) ArcZ-CyaR experiment in the middle is not well connected to the rest of the manuscript. The inclusion in the model figure doesn't really help shed light on the biological role for this interaction.We agree that this experiment does not blend in well with the rest of the manuscript, but we felt we had to do some validation of the identified sRNA-sRNA interactions to demonstrate that the sRNA-sRNA interactions that we have identified are biologically relevant. We have moved the results describing the validation of the ArcZ-CyaR interaction to the Supplementary data.3) Subsection “Hfq CLASH predicts sRNA-sRNA interactions as a widespread layer of post transcriptional regulation”, third paragraph. Figure 5D, the wild-type ArcZ still affects mutant CyaR levels. The authors provide a hand-waving explanation that could be tested.We agree that it is strange that the wild-type ArcZ can still affect mutant CyaR levels, but we believe this is because we did not sufficiently disrupt the base-pairing interaction potential. However, the observation that the compensatory mutations in CyaR restore the regulatory activity of the ArcZ seed mutant does support the idea that the two sRNA physically interact in vivo. As stated above, we have now moved these data to the Supplementary data.Moreover, the authors state that ArcZ promotes CyaR degradation, but there is no direct evidence for this. It could be tested. Not sure it's the highest priority for this manuscript, given that this experiment in general is not well integrated. But at least the authors should modulate their statement to reflect the actual data.We have removed the text where we state that ArcZ promotes CyaR degradation. We now statein the Results section: “These results, together with the CLASH data, imply that ArcZ and CyaR base-pair in vivo, and that this interaction could lead to a reduction in CyaR levels but not vice versa.” .4) Abstract – there is no direct evidence that MdoR enhances maltose uptake.We now state in the Abstract that: “We hypothesize that MdoR contributes to the rearrangements in the outer membrane necessary for efficient uptake of maltose/maltodextrins”.5) I did not understand the logic behind the analyses in Figure 2. The authors state that it was "logical to assume that changes in Hfq binding would also be reflected in changes in sRNA steady state levels." However, there are numerous studies showing that different sRNAs bind Hfq via different modes, and that there is a great deal of variability regarding the role of Hfq in stabilizing sRNAs. Moreover, the competition among RNAs for binding to a limiting pool of Hfq will certainly change over time, and be influenced by the total sRNA abundance and any given sRNA's proportion of the total RNA pool. There seems to be no overall conclusion from the figure, and no follow up, so I would recommend deleting it.As requested by the reviewer, we have removed these analyses from the manuscript.6) Subsection “MdoR directly regulates the expression of major outer membrane porins and represses the envelope stress response pathway”, fourth paragraph: The authors state hypotheses in this section that are not further tested, and are not supported by data shown. These are more appropriate for modest speculation in the Discussion.As requested by the reviewer, we have moved this to the Discussion section.7) Figure 7F: Is the effect of MdoR SM on MicA significant?rpoE and RyeA data were noisier than the other samples. We have now also added p-values to all the bar plots. We feel that the new results more convincingly show that MdoR suppression of MicA relies on the MdoR seed sequence. We do note that the MdoR seed mutation still results in down-regulation of RyeA, suggesting that the regulation is indirect or relies on other sequences within MdoR. We now discuss this in the subsection “MdoR directly regulates the expression of major outer membrane porins and represses the envelope stress response pathway”.In this dataset the effect on MdoR SM on MicA was not statistically significant . However, to generate more convincing results, we repeated the qPCRs and included an additional biological replicate experiment. We also repeated the other qPCRs as the 8) The only MdoR-target interaction that was definitively demonstrated was MdoR-ompC, and indeed, the authors went above and beyond with evidence here. It is interesting that ompC levels are reduced in maltose , but this is clearly NOT MdoR-dependent . The differences in MicA and lamB RNA levels in the mdoR mutant grown in maltose are intriguing, but these effects can't be linked to a specific MdoR-target regulation. Minimally, the authors should try to make the link between molecular interaction of MdoR and a target (rpoE?) and the differences in MicA/lamB more clear.rpoE, but we did not find any evidence for direct interactions in our data or the RIL-seq data. However, to make the link between MdoR, rpoE and MicA clearer, we repeated the qPCRs of the data shown in Figure 6F and included a third biological replicate so that we could get more reliable statistics. These data show that, consistent with the DESeq analyses, rpoE levels do go down upon over-expression of MdoR, however, the changes in rpoE levels are not statistically significant. Secondly, we also performed qPCR on rpoE levels in RNA samples extracted from the strain that has seed mutations in chromosomal copy of MdoR. We assumed that if the increase of MicA in the MdoR seed mutant strain was directly linked to rpoE, we would also see also higher rpoE mRNA levels in this strain. This was not the case . Therefore, our current model is that MdoR enhances LamB expression by suppressing MicA independently of rpoE. This then begs the question whether MdoR directly targets MicA. Unfortunately, MdoR-MicA chimeras were not found in our Hfq CLASH data and the base-pairing interactions predicted by RNA co-fold and RNADuplex from the Vienna package were not at all convincing, so we have not yet been able to address this question. We now discuss these new findings in the Discussion section.Our initial hypothesis was that MdoR would help to suppress MicA levels by directly targeting 9) Subsection “MdoR enhances maltoporin expression during maltose fermentation”, last paragraph: It would be very exciting if the data directly supported this statement. However, the experiments presented fall short. More physiological evidence is needed – growth phenotypes, maltose uptake assays, etc. In the absence of these, the authors must tone down their claims.rpoE. This in turn enhances the production of the LamB maltoporin ”. We believe that, based on the data, this is a reasonable hypothesis. With respect to physiological evidence, we now acknowledge in the Discussion section that this is indeed lacking and present an idea on how to pursue this.We have removed this sentence from the manuscript and now present a hypothesis about the role of MdoR in nutrient adaptation: “Based on these results, we hypothesise that when cells decide to use maltose as a main carbon source, subsequent MdoR expression enhances the uptake of maltose by suppressing MicA expression, independently of E. coli growing in LB. What becomes limiting? The authors say very generically "the most favorable nutrients" become limiting. The finding that malEFG and MdoR are specifically expressed during a very narrow window of time in LB grown cells is very interesting. There must be more to the story of their regulation than malT-dependent maltose-inducible expression given this expression pattern in LB given that the main carbon source in LB is peptides/amino acids. The authors should work on improving the quality of the discussion of these issues, and be up front about the limitations of their study in this regard.10) Because there is so little investigation of the physiology, the discussion of the physiological relevance of these findings is very superficial. The transition from exponential to stationary phase growth has been studied in E. coli physiology in LB  and that this is worthwhile investigating. We initially prioritised the study of nutrient-dependent aspect of MdoR function because MdoR accumulates after glucose depletion and the few remaining carbohydrates in LB are utilised sequentially – and during this process MalT is induced. We now discuss in detail which nutrients become limiting in the Discussion section. Moreover, the MdoR expression pattern is similar to that of the other MalT-regulated genes – thus MalT control is key for understanding MdoR physiology. Thus, we considered it important to dissect separately the role of MdoR when maltose is the sole carbon source. It is also known that induction of the mal operon is not as effective in a medium that contains other carbon sources .We acknowledge that we have not presented evidence demonstrating the physiological relevance of MdoRWe agree that the transient MdoR expression in LB may not be exclusively dependent on the availability of maltodextrins – given the complexity of 11) One key issue that should be addressed in the Discussion is the fact that these global approaches have so little overlap. I did appreciate the thorough description of the relative advantages provided by the Hfq-CLASH method as compared to RIL-seq. However, I think the field as a whole needs to find a way to discern direct, physiologically-relevant interactions from those that may be transient, weaker, and stochastic. I don't expect the authors to solve this issue, but it should be acknowledged. The sensitivity and accuracy of various methods needs a thorough investigation.We completely agree that a thorough comparison between the various methods should be done to test the sensitivity and accuracy of each method. In our opinion, this should ideally be done with multiple identical samples that are then analysed in several labs that are using these methods. We would be very keen to contribute to such a study so that the field as a whole can come to a consensus on best practices for performing these type of studies.It is not possible to quantify the number of interactions that are the result of transient/stochastic/weak interactions, however, we acknowledge that they will certainly be present in our data. We do predict that the frequency of such interactions in our data will be low. This is now discussed in detail in the Discussion section, but we would like to point out here that the CLASH data is highly enriched for chimeras that form stable duplexes, even those chimeras supported by only a few reads . Therefore, we would predict that the majority of interactions that we recover represent genuine base-pairing interactions but to what extent these are functional is of course not possible to deduce from the data. We now also discuss ways that would enable us to systematically test which sRNA-mRNA interactions could be biologically relevant.At least, the authors could consider their Hfq-CLASH results in light of their total expression profiles (RNA-seq) of well characterized sRNAs and their regulons.This is an excellent point and we had looked at the correlation of CLASH hits and target expression in detail see . For thesstT interaction), we recover the known sRNA and mRNA seeds . Therefore, the false-negative rate, which we define as the number of incorrect wrong seed sequences identified for known interactions in our data, seems to be low. We now discuss these data in the Results section.What's the false negative rate for known interactions?In response to the comments, we have done a significant amount of additional bioinformatics analyses to evaluate in more detail the quality of our data . Here, we also focussed on experimentally verified sRNA-mRNA interactions identified in our data . These results show that in all but one case  Hfq for CLASH has two large tags on C-terminus. As the C-terminus has been proposed to participate in RNA/protein partners binding and Hfq autoinhibition (work from the Woodson group), have the authors done any controls to make sure this does not interfere with RNA banding/introduce false results?This is an important point and we addressed this in a previous paper . The data indicated that HTF tagged Hfq is functional and facilitates MicF repression of OmpF.2) "Hfq binds to sRNA-target RNA duplexes" – are RNA duplexes the only Hfq targets? For example, sRNAs were shown to cycle on Hfq, therefore one can imagine a situation in which one sRNA is not fully displaced and the second one already bound. Could some of the sRNA hybrids represent such state?monomer was UV cross-linked to both sRNAs simultaneously and if the available 5’ end 3’ ends are in close proximity. Considering the low efficiency of UV cross-linking, the likelihood of this happening is very low. Secondly, we show that our chimeras, including those chimeras that are supported by only a few reads, are highly enriched for stable duplexes as well as sRNA seed sequences. It seems therefore unlikely that the CLASH protocol would efficiently recover weak and or transient interactions. We now discuss this in the Discussion section.It is certainly possible that such chimeras can be formed during the ligation step. Although it is not possible to quantify the number of such interactions, based on the following we would argue that these interactions are probably not very abundant in our data. Firstly, we purify Hfq and cross-linked RNAs under very stringent and completely denaturing conditions before we do the intermolecular ligation reactions. We would predict that most of such interactions, including weak and stochastic interactions, would dissociate under these conditions. Furthermore, because our purification conditions completely disrupt the Hfq hexamer ), such transient interactions would only be detected if an Hfq 3) 'tRNA-tRNA and rRNA-rRNA chimeras originating from different coding regions were removed' why?There are many copies of tRNA and rRNA genes in the genome that are very similar, and the sequence aligner cannot always determine from which copy the two halves of a chimera came from. So, in many cases our software will label these rRNA-rRNA and tRNA-tRNA chimeras as “intermolecular” because each half was mapped to a different copy. But we cannot rule out the possibility that these fragments originated from the same gene. Therefore, we consider these to be false positives as they are very likely intramolecular interactions. We now mention this in the legend of Figure 2A.4) Can the authors please comment on other chimeras isolated, others than sRNA-mRNA and mRNA-mRNA? Would these represent Hfq targets in the cell?E. coli external transcribed spacers of tRNAs can base-pair with sRNAs to absorb transcriptional noise . Moreover, the predicted base-pairing interactions between the tRNA and sRNA halves in chimeras are in many cases quite extensive (Supplementary file 2). Between 4-5% of the chimeras represented sRNA-rRNA interactions. Binding of Hfq to rRNA has also been demonstrated , however, this interaction appears ot be independent of sRNAs. Therefore we predict that sRNA-rRNA interactions likely represent noise. However, considering the sheer abundance of ribosomal RNA in a cell (80%) we would argue the noise is quite low.A few % of the chimeras appear to represent sRNA-tRNA interactions. Although it is unclear whether these are biologically relevant, it is worth noting here that in We now discuss this in more detail in the Results section and we have included more extensive analyses of all the types of interactions in Figure 2A and Figure 2—figures supplements 1-5.5) Figure 3 – it is not clear what the enriched motifs are showing, 5' end of the chimera? 5' end of both RNAs in the chimera? Only mRNAs?We apologise for the confusion. Only the mRNA fragments in chimeras were considered for the motif analyses. The left panel shows motifs of the 5’UTRs of mRNAs found in chimeras, the right panel, the motifs of the 3’UTRs of mRNAs found in chimeras. We have now added a sentence to the figure as well as the figure legend to make this clearer.6) Explain in more detail what is meant by scrambled RNA.rrnB terminator region of the backbone plasmid.This an sRNA that has a random nucleotide sequence. We have now made this clearer in the figure legend. The control plasmid is pJV300, the standard control plasmid for pL-driven sRNA expression. It expresses a ~50 nucleotides long nonsense RNA derived from 7) Figure 4 – as only mutations in ArcZ cause disruption of the regulation, can it be an indirect effect, not the result of direct sRNA-sRNA regulation?We cannot rule out that the regulation is indirect, however, the fact that the compensatory mutations in CyaR restore the regulation of the ArcZ mutant is strong evidence that the regulation is direct. Note that we have now moved these analyses to the supplementary information, as requested by reviewer #18) Figure 5B – It is difficult to see expression of ygaM . We apologize for the fact that YgaN is poorly detectable. It is clearly a very low abundant fragment and even after exposing the blot for two weeks with several probes, we were not able to get a stronger signal. This is why we added a larger scan of the same blot in Figure 4—figure-supplement 1A, where ygaN is more visible.Where is MdoR on the blot? If it is labelled malG it is somewhat confusing, as the blot presumably shows the sRNA fragments, not the whole mRNAs?We apologise for the confusion. What is shown in Figure 5B  are indeed the sRNAs, but we had labelled those sRNAs derived from 3’UTRs with the names of the host mRNA. We now included the sRNA names.9) The signal for RyhB on Figure 5B is quite strong for OD 1.2 and 1.8, however on Figure 6C it is very weak. Can the authors explain? MdoR intensities seem to match, so presumably the RNAs quantities used are similar?32P label was already about two weeks old. This explains why the signal is a bit weaker. CpxQ and 5S rRNA probing was performed with fresh label once the signals on the blot had sufficiently decayed.We had to expose the blot for about 4-5 days to get a good MdoR signal and by the time we hybridized the blot with the RybB probe the 10) Is there an evidence that RyhB is in the cell as 5'PPP RNA? Perhaps it is not processed, but has the possibility of it harbouring a different 5' end has been excluded?RybB was not reported to be processed by RNase E for maturation  and it does not have a 5’NAD modification . RybB is also TEX insensitive . In this study, the 5’PPP ends were converted using TAP to 5’P after the TEX step. RybB was also detected in the TEX untreated sample, so we can infer it originally harboured a 5’PPP.11) Figure 7C – It is confusing that the authors label 5' and 3' ends which are not real ends, and are different for each panel for MdoR. Could they mark, e.g. with dots, that these are not real ends of RNAs? Or indicate positions of the nucleotides shown? It would make analysing the results much easier.As suggested by the reviewer we have now marked the ends with dashed lines to indicate that these are not the beginning and ends of the RNAs. Note that the original Figure 7 is now Figure 6.12) Figure 7D- If an empty plasmid is used as a control and the blot probed for MdoR, can the authors explain what is being expressed in their control after 20 minutes? There may be a typo in the legend as it states that the samples were harvested 15 minutes after induction, but the blot shows the results for 20. What is the meaning of the red rectangle over the 15 minutes into MdoR expression?600 0.8, the OD600 at which MdoR starts being expressed). The plasmid that we used to over-express MdoR has two transcriptional terminators, one from the operon itself and one already present in the plasmid. We believe that the longer band represents MdoR terminated at the second terminator. The red rectangle indicates the time-point for which RNAseq and differential expression analyses were performed. Made this clearer in the main text and the figure legend.The RNA expressed in the control after induction is the endogenous MdoR  Figure 7F – explain MdoR SM, it also isn't introduced in the text. Why does the seed mutation cause higher target levels? For RyeA it doesn't seem like the seed mutation has abolished regulation. Is RybB regulating MicA as well?rseA; Figure 6F) but that the decrease in their RNA is the result of a homeostatic negative feedback loop, as proposed by the Gottesman lab . A recent paper from Sarah Ades lab  suggests that the RybB levels need to be carefully controlled as too high expression can be toxic under some circumstances. One way to respond to RybB over-expression would be to reduce transcription of the sigmaE operon, which includes rpoE and RseA.MdoR SM indicates the MdoR seed mutant. We apologise for not including this in the text. This has now been fixed. As stated above (# reviewer 1 point 7), we have repeated the qPCRs presented in this figure and added an additional biological replicates to make the data more robust. The new data confirm that the MdoR seed mutant does not abolish RyeA regulation. We now discuss this in the subsection “MdoR directly regulates the expression of major outer membrane porins and represses the envelope stress response pathway”. We do not believe RybB directly regulates MicA (or 14) Have the authors tested how their substantial MdoR seed mutation influences RNA structure? Is it possible that, as the seed seems internal, the overall structure of the sRNA is disrupted and therefore the regulation lost? Can the mutant still bind to Hfq? The structure change would also explain problems with RNase E processing.This is an interesting point and we have not tested it. Our initial idea was to make a knock-out of MdoR, but we were worried that this would also impair MalG expression. Therefore, we decided to make a mutant version that we predicted would completely disrupt the base-pairing interaction of MdoR with its targets but would not affect accumulation of MalG. The fact that it is no longer cleaved by RNase E was actually a bonus is because we wanted to remove the sRNA entirely. We therefore did not test whether the transcript was still bound by Hfq or its secondary structure.15) 'Notably, the fully-processed mutant MdoR sRNA is less abundant than the wild-type  and longer (unprocessed) fragments that contain upstream malG regions could be readily detected  '- should be 8C and 8EWe thank the reviewer for pointing this out. This has been corrected. Note that the data is now presented in Figure 7.16) 'We conclude that the dynamics of sRNA expression and binding to Hfq are not always highly correlated.' Any thoughts why?E. coli sRNA IsrA/McaS has been shown to associate with a large number of different proteins, including Hfq, ProQ and CsrA; RNA chaperones that are known to bind and stabilize sRNAs and regulate sRNA-target interactions. It is tempting to speculate that the composition of this RNP may be growth phase dependent. Therefore, Hfq may not be essential to stabilize all sRNAs at low cell densities and their stability may vary at different growth stages. A plausible model is that some sRNAs are sequestered and sufficiently stabilized by other RBPs (such as ProQ and CsrA) during early growth stages and that Hfq can only stably associate with these RNPs once expression levels are sufficiently high. However, reviewer #1 recommended that we remove these results from the manuscript and therefore these data are no longer included.We hypothesized that this may be linked to the availability of Hfq, as the protein is ~15 times less abundant at exponential phase as compared to stationary. Similar changes in Hfq expression at different growth phases have also been observed in pathogenic bacteria. It is conceivable that at different growth stages sRNAs are packaged in different RNPs and that the composition of these complexes is dynamic. The 17) Polysome preps used cyclohexamide, but this acts but blocking the peptide exit channel in the ribosome and may not trap polysomes except by blocking the last ribosome on the assembly. Another antibiotic or non-hydrolysable GTP might be better.E. coli samples and pulverize them under liquid nitrogen. The latter procedure is known to be most conservative of polysomes without introducing biases or artefacts. Indeed, the use of the eukaryotic elongation inhibitor cycloheximide (CHX) is under debate for ribosome profiling , as it may introduce artefacts in ribosome positioning along the transcripts . In our manuscript we performed polysomal profiling and not ribosome profiling. Moreover, we analysed the data as differential uploading of transcripts between two conditions, making it is reasonable to assume that any possible bias induced by the drug are negligible.We trapped ribosomes on mRNAs using two combined approaches. One is to add cycloheximide, the other was to flash freeze 18) These references have related information that may be useful to comment on in the manuscript: de Mets, van Melderen and Gottesman, 2018; Miyakoshi et al.,.We now discuss the 3’UTR-derived SdhX sRNA in the subsection “katG and ackA) and we show that the predicted secondary structure of the chimeric reads matches the known interaction reported in these papers.Hfq CLASH identifies novel sRNAs in untranslated regions” where we describe chimeras supported by a low number of reads. We found 2-3 chimeras with SdhX and known interactions .We have now removed all the data referring to MdoR and will include this in a different manuscript. Because of this, we had to make a number of changes to the text, including rewriting the Abstract and the last paragraphs of the Introduction. In these sections we now focus more on our findings that interactions that are more reproducibly recovered are more likely to have a regulatory outcome and that base-pairing potential is important but it does not have the strongest predictive power. Note that we have now renamed MdoR MalH in the main text and all of the figures as this is more in line with the nomenclature in the field.Moreover, I suggest moving some of the more important supplementary figure panels into the main part of the paper.Figure 2—figure supplement 1C. The "distance from sRNA seed" numbers appear to be similar to the length of the sRNAs. The authors should indicate the sRNA lengths.We have added the lengths of the sRNAs to the heat maps shown in the supplementary figures associated with Figure 2.Figure 2—figure supplement 6 (predicted base-pairing strength for identified interactions). This is an important analysis and should be moved to the main figures.We have now included this in the main figures as Figure 3.Figure 2—figure supplement 7 (number of enriched sequence motifs from mRNA targets that match the paired sRNA) also belongs in the main figures. I suggest combining this with a couple of the most interesting examples of newly found motifs (i.e. unique to this study).This figure, as well as two examples of complementary sequence motifs identified specifically in our study is now included in the main figures as Figure 4.Figure 2—figure supplement 7. The criteria used to make the yes/no calls should be described in the legend.We have now added this to the legend of Figure 4 where these data are described. An sRNA was considered to have an enriched motif if a motif identified by MEME had an E-value <= 0.1 and/or the MAST p-value of the motif, which indicates the overall match between the identified motifs and the sRNA sequence, was <= 0.001.Figure 3—figure supplement 3. This could be moved to the main figures. The legend needs to be expanded for panel C.This is now Figure 7 in the main figures.E. coli and Salmonella for this analysis to be informative. I suggest removing this figure.Figure 4—figure supplement 4. I would not expect to see sufficient overlap in regulation between We have removed the figure from the manuscript.Figure 4—figure supplement 8. Panel labels are wrong in the legend.We have corrected the legend.


Zoonotic infections are an imminent threat to human health. Pangolins were recently identified as carriers and intermediate hosts of coronaviruses. Previous research has shown that infection with coronaviruses activates an innate immune response upon sensing of viral RNA by interferon-induced with helicase C domain 1 (IFIH1), also known as MDA5. Here, we performed a comparative genomics study of RNA sensor genes in three species of pangolins. DDX58/RIG-I, a sensor of cytoplasmic viral RNA and toll-like receptors (TLR) 3, 7, and 8, which bind RNA in endosomes, are conserved in pangolins. By contrast, IFIH1 a sensor of intracellular double-stranded RNA, has been inactivated by mutations in pangolins. Likewise, Z-DNA-binding protein (ZBP1), which senses both Z-DNA and Z-RNA, has been lost during the evolution of pangolins. These results suggest that the innate immune response to viruses differs significantly between pangolins and other mammals, including humans. We put forward the hypothesis that loss of IFIH1 and ZBP1 provided an evolutionary advantage by reducing inflammation-induced damage to host tissues and thereby contributed to a switch from resistance to tolerance of viral infections in pangolins. Emerging infectious diseases represent a major challenge to public health. The transmission of pathogens from other vertebrate animals to humans is of particular concern because the resulting diseases, known as zoonoses, have caused major epidemics in the past and continue to pose enormous threats to the human population, as exemplified by the recent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak , 2. In aTo cope with viral infections, vertebrate species have evolved response strategies which can be classified into resistance and tolerance . ResistaPangolins have been identified, besides bats, as a possible source of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of coronavirus disease 2019 (COVID-19) –14. EighAntiviral defense of vertebrates is initiated by sensors of viral nucleic acids. Infections with RNA viruses, such as coronaviruses, influenza viruses and Ebolavirus activate sensors of extracellular or endosomal RNA, such as TLR3, TLR7, and TLR8 , and senHere we report a unique degeneration of the innate immune response against RNA viruses in pangolins.Manis javanica), Assembly: ManJav1.0 (GCA_001685135.1), submitted by The International Pangolin Research Consortium (Manis pentadactyla), Assembly: M_pentadactyla-1.1.1 (GCA_000738955.1), submitted by Washington University; Tree pangolin (Manis tricuspis), Assembly: ManTri_v1_BIUU (GCA_004765945.1), submitted by Broad Institute. Gene annotations were available in GenBank only for M. javanica (NCBI Manis javanica Annotation Release 100).The following genome sequences of pangolin species were analyzed: Malayan pangolin , cat, dog, cattle, mouse, and human was assessed by comparison of gene loci that were downloaded from GenBank at https://www.ncbi.nlm.nih.gov/gene/ . In addition, Basic Local Alignment Search Tool (BLAST) was used to find regions of local similarity between sequences (www.timetree.org) (Shared order of gene arrangement (synteny) in the Malayan pangolin (equences . Amino aequences . Divergeree.org) .IFIH1 gene locus showed conservation of the arrangement of IFIH1 relative to the neighboring genes in mammals (IFIH1 is inactivated by more than 10 frameshift and in-frame stop mutations. In silico translation of the pangolin IFIH1 pseudogene (GenBank gene ID: 108398082) and alignment of the resulting amino acid sequence to that of human IFIH1 showed numerous disruptive mutations , binds to double-stranded RNA in the cytosol and signals through mitochondrial antiviral-signaling protein (MAVS) to activate expression of interferons and to induce inflammation . IFIH1 s mammals . In the utations . An openutations . Detaileangolins . One of angolins .ZBP1 gene locus showed conservation of the arrangement of ZBP1 relative to the neighboring genes in mammals (ZBP1 is inactivated by multiple in-frame stop codons. In silico translation of the pangolin ZBP1 pseudogene (GenBank gene ID: 108390931) and alignment of the resulting amino acid sequence to that of human ZBP1 showed premature termination of the translation product and lack of the carboxy-terminal half of the protein  (ZBP1 binds to left-handed double helix structures of DNA and RNA (Z-DNA and Z-RNA) and thereupon triggers necroptosis and inflammation through interactions with receptor-interacting serine/threonine-protein kinase 3 (RIPK3) . Influen mammals . In the  protein . Mutatioicuspis) .IFIH1 and ZBP1, the genes encoding the intracellular RNA sensor RIG-I, i.e., DExD/H-box helicase 58 (DDX58), and TLR3, TLR7, and TLR8 which control the sensing of RNA in endosomes and a series of other genes involved in antiviral signaling and defense, such as MAVS, RIPK3, MLKL, SKIV2L, OAS2, RNASEL, and EIF2AK2 (PKR) do not contain disruptive mutations and therefore appear to be intact in the Malayan pangolin (M. javanica) (DDX58 contains in-frame stop codons and frameshift mutations in the tree pangolin (M. tricuspis) but not in the Chinese pangolin (M. pentadactyla) (In contrast to avanica) . DDX58 cdactyla) , suggestZBP1, IFIH1 and other RNA sensor genes. Mapping the presence or absence of these genes onto the phylogenetic tree suggested that loss of both ZBP1 and IFIH1 occurred in the pangolin lineages soon after divergence from the lineage leading to Carnivora  of ZBP1 and IFIH1 suggests that the response to Z-RNA and long double-stranded RNA is diminished in pangolins. Accordingly, the resistance to RNA viruses that depend on cytoplasmic Z-RNA and long double-stranded RNA for replication has likely decreased in the evolution of pangolins. We put forward the hypothesis that strong antiviral defense was harmful and loss of ZBP1 and IFIH1 provided an evolutionary advantage by increasing tolerance to infections by certain RNA viruses, including coronaviruses.Based on the known target specificities of mammalian RNA sensors , the losViruses are potent drivers of evolutionary adaptations in their hosts. Both insufficient and overshooting responses to viral infections have deleterious effects, leading to strong selection for resistant or tolerant host genotypes , 38. BatAll datasets presented in this study are included in the article/HF and LE designed the study and performed bioinformatic analyses. HF, ET, and LE wrote the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Escherichia coli. Here, we report that PNPase contributes to the degradation of specific short mRNA fragments, the majority of which bind Hfq and are derived from targets of sRNAs. Specifically, we found that these mRNA-derived fragments accumulate in the absence of PNPase or its exoribonuclease activity and interact with PNPase. Additionally, we show that mutations in hfq or in the seed pairing region of some sRNAs eliminated the requirement of PNPase for their stability. Altogether, our results are consistent with a model that PNPase degrades mRNA-derived fragments that could otherwise deplete cells of Hfq-binding sRNAs through pairing-mediated decay.In many Gram-negative and some Gram-positive bacteria, small regulatory RNAs (sRNAs) that bind the RNA chaperone Hfq have a pivotal role in modulating virulence, stress responses, metabolism and biofilm formation. These sRNAs recognize transcripts through base-pairing, and sRNA–mRNA annealing consequently alters the translation and/or stability of transcripts leading to changes in gene expression. We have previously found that the highly conserved 3′-to-5′ exoribonuclease polynucleotide phosphorylase (PNPase) has an indispensable role in paradoxically stabilizing Hfq-bound sRNAs and promoting their function in gene regulation in  Small regulatory RNAs (sRNAs) are crucial modulators of cellular function and homeostasis in bacteria. Most sRNAs range in size from 50 to 250 nucleotides and can regulate the expression of hundreds of genes in response to specific signals . TypicalEscherichia coli and other Gram-negative bacteria, one of the largest and most well-studied classes of sRNAs are those dependent on the sRNA chaperone protein Hfq. In addition to binding and protecting unpaired sRNAs from degradation, Hfq binds to mRNA targets and facilitates annealing between sRNA and mRNA  of the target mRNA is more easily accessible to the ribosome, resulting in positive regulation . Howeverpnp) paradoxically destabilizes sRNAs and reduces sRNA-mediated gene regulation, but the exact mechanism of this role has not yet been determined  and are listed in the 600).All strains and plasmids used in this study are derivatives of 600 before collecting samples. Where indicated, RyhB or CyaR was first induced for 15 min prior to initial collection; RyhB was induced by addition of 2,2′-dipyridyl (Sigma) to cultures at a final concentration of 250 μM and CyaR was induced by addition of cAMP (Sigma) to a final concentration of 5 mM. For RNA stability curves, rifampicin (Fisher Scientific) was added at 16 min post-induction to a final concentration of 250 μg/ml, then additional samples were collected at either 1, 2, 4 and 6 min or 2, 5, 10 and 15 min after rifampicin addition.Overnight cultures were diluted into fresh LB and grown with shaking to the indicated OD2O, 160 μl 3M NaOAc, 1200 μl 10% SDS, 48 μl 0.5 M EDTA) and 800 μl of 5:1 phenol:chloroform  pre-warmed to 65°C. Samples were processed at 65°C for five min with intermittent shaking, then centrifuged at 4°C. The aqueous layer was transferred to a new Eppendorf tube and extracted once or twice with 25:24:1 phenol:chloroform:isoamyl alcohol  or with pure chloroform (Fisher Scientific). Total RNA was alcohol-precipitated from the final aqueous fraction and suspended in Tris-EDTA (TE) buffer or DEPC-treated water. The final RNA concentration was quantified using a Nano Drop 2000 spectrophotometer (Thermo Fisher Scientific).Samples of 700 μl were withdrawn directly from cultures and collected into 2 ml tubes containing 100 μl of 8× lysis buffer . Pellets were flash-frozen, then suspended in 500 μl of TBS containing 5 μl of HALT protease inhibitor (Thermo Fisher Scientific) and 2 μl of Superase RNase inhibitor (Ambion) and mixed with an equal volume of 0.1 mm glass beads. Samples were vortexed for 10 min, alternating between vortexing and ice incubation every 30 s. After addition of 500 μl of TBS, each sample was vortexed for an additional 30 s followed by centrifugation for 30 min at 18 000 × g. The soluble fraction was transferred to a new tube, and TBS plus 5 μl of Superase was added to bring the volume to 1.0 ml. Of this, 50 μl was retained as the input fraction. The remaining sample was incubated with 75 μl of anti-FLAG M2 agarose resin for 2 h with mixing. Resin was washed three times with 1.5 ml of TBS, and bound proteins were subsequently eluted by incubation with 150 μg/ml of 3xFLAG peptide (Millipore Sigma) in 250 μl of TBS for 30 min with mixing. Samples were pelleted after the addition of 250 μl of TBS, and the supernatant was retained as the output fraction. RNA was isolated from input and output fractions by hot phenol/chloroform extraction as described above.For Hfq immunoprecipitations, overnight cultures were diluted into 25 ml of fresh LB medium and grown to an ODeviously . Immunopeviously  using anN-morpholino)propanesulfonic (MOPS) gels. Gels were electrophoresed at 65 V and transferred to Zeta-Probe GT membrane by overnight capillary transfer. After transfer, membranes were UV-crosslinked and hybridized overnight with 100 ng/ml of 5′ biotinylated DNA probes -urea gels and electrophoresed at 60–85 V unless noted otherwise. The RNA was then transferred to a Zeta-Probe GT membrane (Bio-Rad) using a Trans-Blot SD semidry transfer apparatus (Bio-rad) following the manufacturer's guidelines. For agarose gels, 10 μg of each sample was prepared with formaldehyde and formamide loading buffer and loaded on 1.2% agarose 3-(A probes  in ULTRAhttps://www.R-project.org) and visualized using the ggplot2 package (https://ggplot2.tidyverse.org). To plot decay curves for each sample, the average intensity of each replicate time course was normalized, and the individual time points were each plotted as a fraction of the average normalized T0 value. Half-lives and standard error were derived from the best-fit exponential decay curves of the combined replicate data.Blots were imaged with a ChemiDoc MP imager (Bio-Rad) and quantified using Image Lab (Bio-Rad). Data analysis and half-life calculations of RNA stability time course experiments were conducted using R  following the manufacturer's protocol. Sample mixtures  were incubated for 1 h at 37°C and the reaction was stopped by addition of 100 μl of DEPC-treated water and 200 μl of neutral phenol–chloroform–isoamyl alcohol (Fisher Scientific). DNase-treated RNA samples were phenol extracted, alcohol precipitated, and RNA concentration was measured. 5 μg of DNase-treated RNA was subjected to ribosomal RNA removal . For mRNA sequencing, libraries were constructed by Macrogen  from the rRNA-depleted samples using the Illumina TruSeq Stranded Total RNA library kit. For short RNA sequencing of total RNA and RNA immunoprecipitated with Hfq, rRNA-depleted samples were subjected to RNA fragmentation using the Ambion RNA fragmentation kit (AM8740) followed by RNA 5′ polyphosphatase treatment (Epicenter), which was performed to facilitate 5′ adapter ligation. Libraries were generated by Macrogen  using the TruSeq Small RNA library kit (Illumina). All high throughput RNA sequencing was performed using 100 bp paired end read sequencing with an Illumina HiSeq2000 sequencer.Total RNA samples collected from cultures at an ODE. coli MG1655 using BWA-MEM (https://github.com/ta-cameron/RCoverage). These analyses were performed in part using high-performance computing resources of the Texas Advanced Computing Center (TACC) at The University of Texas at Austin.High throughput RNA sequencing data were preprocessed for alignment with Cutadapt  to remov BWA-MEM , and the BWA-MEM . Aligned BWA-MEM , and dif BWA-MEM . Read copnp replicates was first calculated genome-wide over 5 nt intervals, then analyzed by DESeq2 to identify up-regulated 5 nt segments with adjusted P-values <0.05. All contiguous significant segments totaling 60 nt or greater were combined, resulting in 463 significantly up-regulated fragments. Z-scores were calculated for each fragment by comparing in the Δpnp strain expression of the fragment versus expression within flanking 350 nt regions extending 150–500 nt from the fragment on each side. Z-scores were also calculated for any overlapping genes, if present. To reduce the initial fragment hits to those most likely to be biologically relevant, the initial fragment hits were further filtered for a minimum log2 fold change of 1.5, average Δpnp coverage of at least 300, and the smallest expression Z-score >1, resulting in 106 distinctly expressed fragments. Fragments were annotated relative to genes and the furthest known operon boundaries  or a derived Δpnp strain (NRD999) grown to late log phase (OD600 of 1.0). Comparison of the transcriptome of the wild-type and the derived Δpnp strain identified 76 genes that were differentially expressed between these strains, i.e. 15 genes were significantly down-regulated and 61 genes were significantly up-regulated in the Δpnp strain relative to the parental strain  adjusted P-value <0.05. Approximately 25% of those genes that are significantly altered in expression upon deletion of pnp encode mRNAs that have previously been shown to be regulated by Hfq-dependent sRNAs.To elucidate the mechanism by which PNPase stabilizes sRNAs, we first examined the impact of deletion of n Figure  S3; genepnp deletion on the levels of smaller transcripts including mRNA fragments and most sRNAs involved in Hfq-mediated gene regulation, an alternative short RNA-seq library preparation was employed. In this protocol, rRNA-depleted RNA samples were fragmented to a relatively uniform size by metal ion hydrolysis prior to library construction using the Illumina TruSeq Small RNA library kit. With this approach, subsequently referred to as ‘short RNA-seq’, comparison of the transcriptomes of the wild-type and the derived Δpnp strains identified 358 genes that were significantly differentially expressed (194 up-regulated and 164 down-regulated transcripts), most of which had not been identified in the initial mRNA-seq analysis  and cfa 5′ UTR , the gene encoding RNase PH , which removes portions of the protein that interact with PNPase and Hfq, did not impact the generation of most fragments . Previously we demonstrated a synergistic decrease in sRNA stability by the RNase PH . AlthougRNase PH . We examragments . Howeverlization .pnp deletion impacts the population of RNA that interacts with Hfq, short RNA-seq was also performed on Hfq co-immunoprecipitation (co-IP) fractions collected in parallel with the total RNA samples. Comparisons of the short RNA-seq total RNA and RNA that co-IPed with Hfq between the wild-type and Δpnp strains indicated that most genes enriched in the total RNA samples for the Δpnp strain were also highly enriched in the Hfq immunoprecipitant  were enriched by approximately 14- to 34-fold in the Hfq co-IP fraction. In total, 49 fragments were present at similar or increased levels in the Hfq co-IP, and 26 fragments were notably enriched (log2 fold change > 1.5) in the Hfq fraction in comparison to the input transcriptome. Moreover, the median Hfq co-IP enrichment of fragments whose sequence directly overlapped the target sequences obtained by RIL-seq was ∼4.3-fold over the input, far higher than for fragments not identified by RIL-seq either because the transcript was not identified as a target of an sRNA or the fragment was not within the precise boundaries of the sRNA-target sequence obtained by RIL-seq  were generated in the presence of the PNPase catalytic mutant but not the wild-type protein indicating that catalytic activity of PNPase is required for the degradation of these RNAs. Next, we tested if these fragments interact with PNPase in vivo by assessing their ability to co-IP with the wild-type PNPase and the S438A catalytic mutant. Northern blots confirmed that the PNPase catalytic mutant pulled down each of these fragments  or R17A (hfqR17A) substitution into a Δpnp strain would suppress the stability defect of Class I or Class II sRNAs, respectively. We first examined steady-state levels of seven sRNAs at early and late exponential phase , the hfqY25D allele was not able to eliminate the negative impact of the pnp deletion on steady state levels  compared to WT and were not rescued in the pnp deletion strain (Δpnp hfqQ8A); the Q8A substitution disrupts binding of both Class I and Class II sRNAs to Hfq.Protection of sRNAs by PNPase is limited to Hfq-dependent sRNAs ,42,43 sue Figure . In the s Figure . Converse Figure . However0 Figure , D, F, HhfqY25D and hfqR17A alleles and the pnp deletion on the turnover rates of Class I and Class II sRNAs. At an OD600 of 0.3, the half-lives of RyhB and GcvB increased from 3.1 and 4.1 min in the Δpnp strain to 13.5 and 8.7 min in hfqY25D Δpnp strain, respectively  indicating that the Y25D substitution in Hfq can overcome the negative impact on stability caused by absence of PNPase. For Class II sRNAs such as MgrR and CyaR, instability in Δpnp could not be completely suppressed by introduction of hfqR17A  compared to RyhB (3.7 min) in the Δpnp strain background . MgrR showed a decrease in stability in the pnp deletion strain ; however, the four nucleotide substitution in the seed region of MgrR led to a modest increase in the MgrR half-life (27.6 min) in the pnp deletion background (Δpnp mgrRmut), which may indicate a partial suppression of the pnp deletion phenotype.Since RyhB has greater than one hundred known targets, we therefore sought to mutate other sRNAs with fewer validated targets such as MgrR and CyaR. Four nucleotides in  targets  were rep targets , Table 1cyaR genomic locus by replacing four nucleotides within the first hairpin loop of CyaR previously identified as a pairing region for several CyaR targets  that were identified by short RNA-seq as up-regulated in the Δpnp strain compared to the wild-type strain revealed a significant increase in the corresponding RNA fragments in the Δpnp strain by northern blot analysis  accumulated in an E. coli strain expressing the active site mutant form of PNPase  into the Δpnp strain completely suppressed the stability defect of RyhB and GcvB  into the Δpnp strain reduced MgrR turnover, albeit not to wild-type levels  some sRNAs such as CyaR or MicA are not destabilized by the loss of PNPase  under accession number GSE125368 and will be released upon publication of this manuscript.gkz616_Supplemental_FilesClick here for additional data file.


The notion that epigenetic information can be transmitted across generations is supported by mounting waves of data, but the underlying mechanisms remain elusive. Here, a model is proposed which combines different lines of experimental evidence. First, it has been shown that somatic tissues exposed to stressing stimuli release circulating RNA-containing extracellular vesicles; second, epididymal spermatozoa can take up, internalize and deliver the RNA-containing extracellular vesicles to oocytes at fertilization; third, early embryos can process RNA-based information. These elements constitute the building blocks upon which the model is built. The model proposes that a continuous stream of epigenetic information flows from parental somatic tissues to the developing embryos. The flow can cross the Weismann barrier, is mediated by circulating vesicles and epididymal spermatozoa, and has the potential to generate epigenetic traits that are then stably acquired in the offspring. In a broader perspective, it emerges that a natural ‘assembly line’ operates continuously, aiming at passing the parental epigenetic blueprint in growing embryos. Living organisms are endowed with an amazing potential to generate phenotypic novelties, not necessarily caused only by random genetic mutations but also driven by epigenetic processes triggered in response to a variety of environmental stimuli [More specifically, an increasing body of evidence shows that the exposure of cells and organisms to various stimuli, including environmental toxics , 4, dietClassical epigenetic variations are incorporated as genome-shaping marks, such as DNA methylation  and histEpigenetic alterations can be transmitted to the progeny in one or across multiple generations. It is important to distinguish between ‘intergenerational’ effects, when the environmental stimulus is germline-independent and directly affects gestating embryos, and ‘transgenerational’ effects, when the latter are inherited from one generation to the next through the germline, even without exposure of the next generation to the original triggering stimulus . That diConverging evidence suggests that extrachromosomal information provides the molecular tools that trigger the transgenerational emergence of altered traits in the developing progeny. The molecular mechanism through which such variations are acquired in the embryos, however, remains elusive. Based on published experimental data, what follows is a hypothetical mechanistic model illustrating the molecular machinery that enables the decoding of the delivered extrachromosomal information in the permissive context offered by early embryos, and hence the emergence of altered phenotypic features.The model proposes a stepwise process in which novel epigenetic information, under the form of small regulatory RNAs, is sequentially generated in somatic tissues, packaged in extracellular vesicles (ECVs), and delivered to early embryos, where they ultimately drive a global reprogramming of genome expression. The model is, therefore, built on three sequential phases, as sketched out below.Phase 1: RNA-based information, predominantly constituted by small regulatory tRNA-derived small RNAs and miRNAs, is in tissues in response to stressing environmental stimuli and is packed in ECVs, that can then be released from tissues in different districts of the body to the blood stream.Phase 2: RNA-containing ECVs circulating in the blood stream are eventually taken up by epididymal spermatozoa and internalized in nuclei. The uptake of this extracellular RNA generates an RNA ‘storage’ in the sperm. As said above, these RNAs have a heterogeneous composition and include a large population of small regulatory RNAs that vary with both the original cellular sources and the nature of the stressing stimulus. For example, sperm cells from obese animals contain elements of specific coregulatory networks that include RNAs, chromatin modifiers, as well as a large number of sequences associated with obesity-related inflammation, adipogenesis and cellular stress .Phase 3: At fertilization spermatozoa deliver to oocytes their RNA cargo, which propagates further in zygotic pronuclei and in two- and four-cell embryos, as indicated by the evidence that sperm RNAs persist in early embryos . The remECVs, including exosomes, are heterogeneous membrane-bound particles released from tissues and can carry heterogeneous DNA, RNA, proteins and metabolites (extensively reviewed in Ref. ) deliverRemarkably, ECVs released from tumor cells contain high levels of transcripts from LINE-1 and Alu retrotransposons and are also endowed with reverse transcriptase (RT) activity . Strikinin vitro fertilization assays in mice, artificial insemination in large farm animals and external fertilization in fish and amphibia, indicate that seminal fluid-free spermatozoa are permeable to exogenous RNA and DNA molecules which they carry through and deliver to oocytes at fertilization. The foreign molecules propagate as non-integrated structures in developing embryos and are eventually mosaic-inherited, with variable efficacy, in tissues of born animals, where they are expressed as novel traits , i.e. large DNA-lamina interacting regions that contribute to the spatial distribution of chromosomes , are estlization . At firslization ). Interelization .Another fundamental contribution towards forming novel genomic circuits is provided by targeted retrotransposal insertions, represented by colored symbols placed on the wake of the progressing green ball along the newly ‘canalized pathways’ . The insertions involve LINE-1, HERV and Alu/SINEs, members of large retrotransposon families that use a ‘copy-and-paste’ mechanism for their amplification and mobilization: briefly, full-length genomic elements are first transcribed into RNA, followed by reverse-transcription of the RNA in cDNA copies that finally integrate stably in the host genome . GermlinRecent data show that families of repetitive elements are tightly associated with the expression of particular classes of genes during embryo development and in embryonic stem cells: Alu/SINE sequences tendentially associate with housekeeping genes, while LINE-1-enriched genes have specialized functions . These dThe sperm-delivered regulatory RNAs, albeit having low abundance, can effectively reshape the embryonic epigenetic landscape because zygotes and two-cell embryos represent highly responsive and variation-prone contexts. A key aspect in this process is the transient global abrogation of miRNA-dependent control in preimplantation embryos , which iIn this framework, LINE-1 retroelements play another important role in addition to that associated with targeted retrotranspositions. LINE-1 expression is activated in preimplantation embryos , 91 and ’-bromodeoxyuridine (BrdU) cultured even in the presence of aphidicolin, a strong inhibitor of DNA replication, but this incorporation is abolished in the presence of the nucleoside RT inhibitor abacavir [LINE-1 retroelements encode their RT enzyme, that is active in preimplantation embryos , 96 and abacavir : thus, rabacavir  suggestsUnrelated evidence comes from studies in cancer cells, which, similar to embryos, are also endowed with high levels of LINE-1-derived RT activity , 99. EleEvidence at this point suggest roles of LINE-1-encoded RT in retrotransposition events and in the inhibition of miRNA-dependent control in early embryos. A third role is also emerging in replication and maintenance of the sperm-delivered RNA information.Unlike lower organisms, mammals lack an RNA-dependent RNA polymerase for RNA replication. That function can be replaced by the interplay between RT and DNA-dependent RNA polymerase activities in a two-step process: RT first reverse-transcribes RNA molecules in cDNA copies, which are then transcribed back in multiple RNA copies. Mimicking a sort of ‘natural’ RT-PCR/PCR amplification cycle, this process contributes to amplify the RNA-based information in high copy numbers. Sperm cells are endowed with both RT and RNA polymerase activities  and can The exponential growth of epigenetic studies in recent years has evoked Lamarckism from its historical and scientific dismissal and has resumed Pangenesis to some extent, which was a rejected aspect of Darwinism , 102. BoOn the one hand, this highlights the striking potential of living organisms to collect information and evolve accordingly, generating non-random genomic and phenotypic novelties; on the other hand, it must be pointed out that, at the same time, the remarkable stability of organismal genomes is even more striking. In order to maintain their stability in a continuously changing environment the genome must undergo continuous small adjustments that keep substantially invariant their global state, i.e. they reside in a ‘stable critical state’. In conclusion, genomes are complex networks self-controlling their global expression fluctuations , yet at