Journal: Molecular Cell

Article Title: A cooperative PNPase-Hfq-RNA carrier complex facilitates bacterial riboregulation

doi: 10.1016/j.molcel.2021.05.032

Figure Lengend Snippet: The KH-S1 portal is crucial for PNPase-sRNA-Hfq complex formation (A–C) Electrophoretic mobility shift assays (EMSAs) of wild-type PNPase and KH-S1 mutants with 400 nM RyhB (A), 3ʹETS leuZ (B), and CyaR (C) in the absence and presence of 400 nM Hfq hexamer. Ternary complexes are highlighted with a red dot. Two different PNPase concentrations were used for every PNPase construct (1:1 and 1:3 RNA:PNPase trimer molar ratio), represented by a concentration bar. (D and E) RNA half-life experiments to determine RyhB and CyaR sRNA stabilities in an E. coli strain expressing a 3X-FLAG tagged construct of PNPase WT and mutants. RyhB and CyaR signal intensities were quantified using northern blots and normalized to their corresponding loading controls (SsrA). sRNA decay curves were generated by fitting the normalized signal intensities for each time point. Points and error bars in the curves represent the means and the standard errors (SEM) of at least three independent experiments. Northern blots for RyhB and CyaR half-life measurements corresponding to RNA stability curves are shown and values tabulated in . (F and G) Cell extracts prepared from late exponential phase cultures of E. coli strains expressing WT PNPase, or FLAG-tagged PNPase WT and mutants were used to assess coprecipitation of sRNAs, which were analyzed using northern blot. (G) Fold enrichment of a given RNA upon immunoprecipitation was determined by first calculating the signal intensity per microgram of RNA for the input and the elution from the northern blots in (F). The normalized elution signal was then divided by the input signal. An untagged wild-type strain (WT) was used as a control for data presented in (F) and (G). S1x2: PNPase K657A, R658A; KHx2: PNPase K566A, K571A; S1x4: PNPase R681A, Q682A, R684A, R686A.

Article Snippet: All strains used in this study are derivatives of E. coli K12 strain MG1655 (RRID:Addgene_61440) or BL21DE3.

Techniques: Electrophoretic Mobility Shift Assay, Construct, Concentration Assay, Expressing, Northern Blot, Generated, Immunoprecipitation, Control

Journal: Molecular Cell

Article Title: A cooperative PNPase-Hfq-RNA carrier complex facilitates bacterial riboregulation

doi: 10.1016/j.molcel.2021.05.032

Figure Lengend Snippet:

Article Snippet: All strains used in this study are derivatives of E. coli K12 strain MG1655 (RRID:Addgene_61440) or BL21DE3.

Techniques: Virus, Recombinant, Software

Journal: Nucleic Acids Research

Article Title: Detecting a wide range of epitranscriptomic modifications using a nanopore-sequencing-based computational approach with 1D score-clustering

doi: 10.1093/nar/gkae1168

Figure Lengend Snippet: Average F1-scores of the five algorithms compared in this study on the E. coli and S. cerevisiae rRNA test dataset ( NC : Nanocompore; DRM : Drummer; E - DSE: Epinano Delta-Sum-Error; E - LR : Epinano Linear Regression). The E. coli and S. cerevisiae rRNA datasets comprise 10 independent samples. Each sample contains eight subsamples with coverage-depths ranging from 10 to 2000. Different coverage-depths were used since algorithm performance depends on the coverage-depth, as indicated by recent studies ( , ) and also confirmed by our results. Note that all positions are treated as either positive or negative since unsupervised algorithms, do not distinguish between different modification types. In line with this, we do not compute separate F1-scores for each modification type separately, but rather only one F1-score for the whole dataset (for the given coverage-depth). As shown, Modena outperformed other algorithms across all coverage-depths; in some cases by a large margin (e.g. at coverage-depths of 50, 75, 100 and 200). The performance of all algorithms was very stable across the 10 independent samples . Thus, although the figure above shows average F1-scores, the results are highly consistent across all Samples 1–10.

Article Snippet: The E. coli K12 MG1655 strain was grown in 1× LB Broth Miller (1st Base, Singapore) without antibiotics at 37°C at 160 rpm shaking.

Techniques: Modification

Journal: Nucleic Acids Research

Article Title: Detecting a wide range of epitranscriptomic modifications using a nanopore-sequencing-based computational approach with 1D score-clustering

doi: 10.1093/nar/gkae1168

Figure Lengend Snippet: Precision–Recall curves (PR curves) for Sample 1 ( E. coli and S. cerevisiae rRNA dataset) for different coverage-depths. As shown, resampling increases the area under the PR curves (i.e. AUPRC scores) across all coverage-depths. Kuiper test further improves AUPRC scores across all coverage-depths, although to a lesser extent.

Article Snippet: The E. coli K12 MG1655 strain was grown in 1× LB Broth Miller (1st Base, Singapore) without antibiotics at 37°C at 160 rpm shaking.

Techniques:

Journal: Nucleic Acids Research

Article Title: Detecting a wide range of epitranscriptomic modifications using a nanopore-sequencing-based computational approach with 1D score-clustering

doi: 10.1093/nar/gkae1168

Figure Lengend Snippet: Violin plots of Modena score distributions for positive and negative test cases across different coverage-depths for Sample 1 of the E. coli / S. cerevisiae benchmark dataset are shown. Two well-separated clusters can be seen for all coverage-depths. The final Step 5 of our algorithm (1D score-clustering) leverages this separation to determine the classification threshold. Note that this represents a different paradigm from the standardly used P -value based thresholds. As shown in our study, this approach is not limited to Modena and can, in principle, be applied to any threshold-based unsupervised algorithm.

Article Snippet: The E. coli K12 MG1655 strain was grown in 1× LB Broth Miller (1st Base, Singapore) without antibiotics at 37°C at 160 rpm shaking.

Techniques:

Journal: Nucleic Acids Research

Article Title: Detecting a wide range of epitranscriptomic modifications using a nanopore-sequencing-based computational approach with 1D score-clustering

doi: 10.1093/nar/gkae1168

Figure Lengend Snippet: Average F1-scores (for Samples 1 through 10, E.coli / S. cerevisiae dataset) with coverage-depths ranging from 10 to 2000 are shown. Drummer : original Drummer algorithm with P -value and odds ratio-based threshold; Drummer + 1D clustering : Drummer algorithm (i.e. G-test statistic) with 1D score-clustering step (see Figure ). For detailed results across all samples, see and .

Article Snippet: The E. coli K12 MG1655 strain was grown in 1× LB Broth Miller (1st Base, Singapore) without antibiotics at 37°C at 160 rpm shaking.

Techniques:

Journal: Nucleic Acids Research

Article Title: Detecting a wide range of epitranscriptomic modifications using a nanopore-sequencing-based computational approach with 1D score-clustering

doi: 10.1093/nar/gkae1168

Figure Lengend Snippet: Average F1-scores (for Samples 1 through 10, E.coli / S. cerevisiae dataset) with coverage-depths ranging from 10 to 2000 are depicted. Epinano: Epinano-DSE algorithm with z-score based threshold; Epinano + 1D clustering : Epinano-DSE algorithm with 1D score-clustering step (see Figure ). For detailed results across all samples, see and .

Article Snippet: The E. coli K12 MG1655 strain was grown in 1× LB Broth Miller (1st Base, Singapore) without antibiotics at 37°C at 160 rpm shaking.

Techniques: