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SMRT-Cappable-seq reveals complex operon variants in bacteria. Government and, as regards Drs. Chen and Gottesman and the U. Government, is not subject to copyright protection in the United States. Foreign and other copyrights may apply. The use, distribution or reproduction in other forums is permitted, provided the original author s and the copyright owner s are credited and that the original publication in this journal is cited, in accordance with accepted academic practice.
Unlike the prokaryotic RNA polymerase that can bind to a DNA template on its own, eukaryotes require several other proteins, called transcription factors, to first bind to the promoter region and then help recruit the appropriate polymerase. The completed assembly of transcription factors and RNA polymerase bind to the promoter, forming a transcription pre-initiation complex PIC.
The most-extensively studied core promoter element in eukaryotes is a short DNA sequence known as a TATA box, found base pairs upstream from the start site of transcription. However, only a low, or basal, rate of transcription is driven by the pre-initiation complex alone. Other proteins known as activators and repressors, along with any associated coactivators or corepressors, are responsible for modulating transcription rate.
Activator proteins increase the transcription rate, and repressor proteins decrease the transcription rate. Transcription factors recognize the promoter, RNA polymerase II then binds and forms the transcription initiation complex. The features of eukaryotic mRNA synthesis are markedly more complex those of prokaryotes. Instead of a single polymerase comprising five subunits, the eukaryotes have three polymerases that are each made up of 10 subunits or more.
Each eukaryotic polymerase also requires a distinct set of transcription factors to bring it to the DNA template. The rRNA molecules are considered structural RNAs because they have a cellular role but are not translated into protein. At an OD of 0. Peak calling from ChIP-seq data was performed as previously described Fitzgerald et al. Overlapping regions were merged and the central position was used as a reference point for downstream analysis.
Spacers were only assigned to a ChIP-seq peak if they had a unique match to a spacer sequence. This yielded uniquely assigned peak-spacer combinations from the ChIP-seq peaks. We then summed the relative sequence read coverage values on both strands for each peak center position to give peak center coverage values Supplementary file 2. We calculated ratio of peak center coverage values in the first replicates of AMDAMD data, and repeated this for the second replicates, generating two ratio values.
Thus, we were able to uniquely assign spacers to an additional 32 peak centers that had previously been assigned multiple spacers. For data plotted in Figures 4 and 5 , S2, S3, and S4, values for peak center coverage were normalized in one replicate two biological replicates were performed for all ChIP-seq experiments by summing the values at all peak centers to be analyzed i.
Cells were lysed by brief vortexing. The duration of the reaction and OD readings were recorded. Protospacer plasmids and an empty pKS control were transformed into E. All E. The plasmid pool was transferred to V. The next day, colonies were scraped from the plates. In the second replicate, scraped cells were resuspended in 10 mL M9 minimal medium and pelleted by centrifugation.
In the first replicate, scraped cells were resuspended in LB to an OD of 0. From each cell resuspension, 1. Sequence reads were assigned to each protospacer-containing plasmid by searching for an exact match to a 10 nt sequence within the protospacer using a custom Python script Supplementary file 8.
We then normalized these values to the value for the control plasmid that lacks a protospacer. These sequences were then used to search a local collection of bacterial genome sequences using tBLASTn Altschul et al. We then selected the sequences with perfect matches to full-length Cas2 sequences.
We further refined this set of sequences by arbitrarily selecting only one sequence per genus. We then extracted bp downstream of each cas2 gene. We used MEME v5. Conservation of nusB across the bacterial kingdom was assessed using the Aquerium tool Adebali and Zhulin, Raw sequencing data for conjugation experiments involving V.
In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses. Here Stringer et al.
Longer CRISPR transcripts result in more guides and may provide broad spectrum resistance, but short transcripts may result in higher concentrations of certain guides, providing higher levels of protection from fewer pathogens. Your 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 Gisela Storz as the Senior Editor.
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Specifically, we are asking editors to accept manuscripts that they judge can stand as eLife papers without additional data, even if they feel that additional experiments would make the manuscript stronger. While the reviewers have identified additional experiments that would improve this work, and you are welcome to add these to the revised manuscript if conditions allow, the only required experiments are computational in nature. The paper is well written, the results are convincing, and the work is of interest of a broad audience.
However, it is not clear why the competing activities of Rho and Nus are important for regulation of these systems. Are expression levels of Rho and Nus controlled in response to phage infection or plasmid conjugation?
Please include data for the H-NS knockout or explain why this is not included. Explain where the natural transcriptional start sites are located relative to the engineered promoter.
Leader sequences are typically AT-rich and contain the promotor. What is the relevance of a GC-rich "Rut" if it is upstream of the natural promoter? Typhimurium Figure 1 , and from that observation conclude that Nus factors are also involved. SuhB is a relatively new addition to the Nus complex and only recently found to play a role in rRNA expression Singh et al.
It is unclear whether SuhB is always associated with Nus antitermination complexes, or if it might work alone, or with other factors at different promoters. Typhimurium, or revise the text to be more clear about the role of SuhB and the inferred role of Nus.
The authors show the high conservation of NusB in bacteria Figure 7—figure supplement 1 to support the notion that Nus-mediated antitermination is a general mechanism employed in diverse CRISPR loci.
The phylogenetic analysis should be performed on SuhB. Observations made about one locus were assumed to apply to the other. The boxA -dependent stimulation of promoter-distal spacer activity is assumed to be through a mechanism of antitermination. Alternatively, temper the conclusion that the mechanism is antitermination SuhB dependent antitermination. Clarify the statistical methods used in the ChIP-seq experiments. For example, in Figure 4A, it appears as though the purple data points indeed cluster away from the orange, but in Figure 4—figure supplement 2A, it is less clear which of the blue data points cluster away from the orange data points in a statistically significant manner.
Throughout the manuscript, it is implied that the proposed antitermination mechanism occurs in all CRISPR-Cas types, while experimental data was collected for only two Type I systems. It is important to explicitly state which CRISPR Type s were found to harbor boxA sequences Figure 7A to support the possibility that a general mechanism has been discovered and to clarify how this conclusion relates to the work presented by Lin et al. This approach is anticipated to provide more reliable evidence to support the general prevalence of boxA sequences upstream of CRISPR arrays.
The authors speculate that "Rho termination acts as a selective pressure to limit adaptation in species that lack an antitermination mechanism". However, the possible role of Rho in limiting adaption seems indirect at best.
If Rho-dependent termination limits the number of different spacers that can be expressed from a single locus then this will limit selective pressures that maintain "older spacers", but the advantage this afford the host is unclear. We have reviewed the rebuttal and the revised manuscript. The revision sufficiently addresses the reviewer's concerns, with one important exception.
One of the reviewers raised this concern during the review. They pointed to the following statement: "Rho termination acts as a selective pressure to limit adaptation in species that lack an antitermination mechanism". However, as the reviewer pointed out, "the possible role of Rho in limiting adaption seems indirect at best.
Data presented by the authors, clearly demonstrates that Rho limits the length of CRISPR transcripts and Nus antagonizes Rho-dependent termination, but as the reviewer points out, "the possible role of Rho in limiting adaption i. Speculation should be omitted form the Abstract. I suspect that the context of this statement is important, but I have read this several times and it still seems to me like the authors are suggestion that type-II systems have a cas3.
Please clarify. We have expanded the Discussion to speculate on the significance of Rho termination and antitermination for crRNA expression. We believe that Rho termination, a highly conserved and often essential activity in bacteria, inevitably poses a barrier to the expression of longer CRISPR arrays, necessitating an antitermination mechanism.
We chose to introduce promoters in the chromosome. Thus, these promoters mimic transcription read-through from the cas operon and queE mRNAs, respectively. Hence, in situations where the cas genes are transcribed, we would expect considerable read-through into the CRISPR array. We have expanded the description of the first supplementary figure to highlight the significance of this result.
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