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Large-scale mapping of sequence-function relations in small regulatory RNAs reveals plasticity and modularity.

Peterman N, Lavi-Itzkovitz A, Levine E - Nucleic Acids Res. (2014)

Bottom Line: Two decades into the genomics era the question of mapping sequence to function has evolved from identifying functional elements to characterizing their quantitative properties including, in particular, their specificity and efficiency.Our approach generalizes the sort-seq method, introduced recently to analyze promoter sequences, in order to accurately quantify the efficiency of a large library of sequence variants.In addition to precisely identifying functional elements in the sRNAs, our data establish quantitative relationships between structural and energetic features of the sRNAs and their regulatory activity, and characterize a large set of direct and indirect interactions between nucleotides.

View Article: PubMed Central - PubMed

Affiliation: Department of Physics and FAS Center for Systems Biology, Harvard University, Cambridge, MA 02138, USA.

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Mutation interactions for RyhB-sodB. Interactions are defined by deviations from the additive model for variants with two mutations compared to the wild-type. (A and B) Interaction maps for RyhB-sodB. The 150 most significant interactions (out of 11 910 pairs measured) are color-coded based on the strength and the ‘sign’ of the interaction, synergistic (green) or antagonistic (blue). RyhB sequence and the seed for sodB are annotated, and the secondary structure is marked by arrows indicating downstream and upstream pairing. The two single point mutations with the most interactions, A30G (*) and T55A (**), are marked. For clarity, compensatory interactions which maintain a potential Watson–Crick pairing lost by each mutation alone are plotted separately (A) from all other interactions (B). Eleven of the 21 compensatory interactions correspond to known stem-loop pairings in SL1 and SL3. (C) Histogram of IS, with mapped interactions highlighted in green (synergistic) and blue (antagonistic). (D) Seed-specificity assay: Fold-repression (log-scale) of two sodB variants, by wild-type RyhB and three sRNA variants, estimated from bulk GFP fluorescence measurements. ***P < 0.001 (two-tailed t-test).
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Figure 6: Mutation interactions for RyhB-sodB. Interactions are defined by deviations from the additive model for variants with two mutations compared to the wild-type. (A and B) Interaction maps for RyhB-sodB. The 150 most significant interactions (out of 11 910 pairs measured) are color-coded based on the strength and the ‘sign’ of the interaction, synergistic (green) or antagonistic (blue). RyhB sequence and the seed for sodB are annotated, and the secondary structure is marked by arrows indicating downstream and upstream pairing. The two single point mutations with the most interactions, A30G (*) and T55A (**), are marked. For clarity, compensatory interactions which maintain a potential Watson–Crick pairing lost by each mutation alone are plotted separately (A) from all other interactions (B). Eleven of the 21 compensatory interactions correspond to known stem-loop pairings in SL1 and SL3. (C) Histogram of IS, with mapped interactions highlighted in green (synergistic) and blue (antagonistic). (D) Seed-specificity assay: Fold-repression (log-scale) of two sodB variants, by wild-type RyhB and three sRNA variants, estimated from bulk GFP fluorescence measurements. ***P < 0.001 (two-tailed t-test).

Mentions: For RyhB-sodB, our sRNA mutant library included data for 11 910 pairs of mutations, presented in Figure 6. Because the additive model provided a good approximation of repression efficiency for the vast majority of variants with two mutations (98% of measurements fall within 2-fold of the additive prediction), most pairs of mutations interacted weakly if at all. However, a small fraction of measurements showed strong interactions. Interactions that we consider both strong and statistically significant are presented in Figure 6A and B, and the distribution of all measured IS is plotted in Figure 6C.


Large-scale mapping of sequence-function relations in small regulatory RNAs reveals plasticity and modularity.

Peterman N, Lavi-Itzkovitz A, Levine E - Nucleic Acids Res. (2014)

Mutation interactions for RyhB-sodB. Interactions are defined by deviations from the additive model for variants with two mutations compared to the wild-type. (A and B) Interaction maps for RyhB-sodB. The 150 most significant interactions (out of 11 910 pairs measured) are color-coded based on the strength and the ‘sign’ of the interaction, synergistic (green) or antagonistic (blue). RyhB sequence and the seed for sodB are annotated, and the secondary structure is marked by arrows indicating downstream and upstream pairing. The two single point mutations with the most interactions, A30G (*) and T55A (**), are marked. For clarity, compensatory interactions which maintain a potential Watson–Crick pairing lost by each mutation alone are plotted separately (A) from all other interactions (B). Eleven of the 21 compensatory interactions correspond to known stem-loop pairings in SL1 and SL3. (C) Histogram of IS, with mapped interactions highlighted in green (synergistic) and blue (antagonistic). (D) Seed-specificity assay: Fold-repression (log-scale) of two sodB variants, by wild-type RyhB and three sRNA variants, estimated from bulk GFP fluorescence measurements. ***P < 0.001 (two-tailed t-test).
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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Figure 6: Mutation interactions for RyhB-sodB. Interactions are defined by deviations from the additive model for variants with two mutations compared to the wild-type. (A and B) Interaction maps for RyhB-sodB. The 150 most significant interactions (out of 11 910 pairs measured) are color-coded based on the strength and the ‘sign’ of the interaction, synergistic (green) or antagonistic (blue). RyhB sequence and the seed for sodB are annotated, and the secondary structure is marked by arrows indicating downstream and upstream pairing. The two single point mutations with the most interactions, A30G (*) and T55A (**), are marked. For clarity, compensatory interactions which maintain a potential Watson–Crick pairing lost by each mutation alone are plotted separately (A) from all other interactions (B). Eleven of the 21 compensatory interactions correspond to known stem-loop pairings in SL1 and SL3. (C) Histogram of IS, with mapped interactions highlighted in green (synergistic) and blue (antagonistic). (D) Seed-specificity assay: Fold-repression (log-scale) of two sodB variants, by wild-type RyhB and three sRNA variants, estimated from bulk GFP fluorescence measurements. ***P < 0.001 (two-tailed t-test).
Mentions: For RyhB-sodB, our sRNA mutant library included data for 11 910 pairs of mutations, presented in Figure 6. Because the additive model provided a good approximation of repression efficiency for the vast majority of variants with two mutations (98% of measurements fall within 2-fold of the additive prediction), most pairs of mutations interacted weakly if at all. However, a small fraction of measurements showed strong interactions. Interactions that we consider both strong and statistically significant are presented in Figure 6A and B, and the distribution of all measured IS is plotted in Figure 6C.

Bottom Line: Two decades into the genomics era the question of mapping sequence to function has evolved from identifying functional elements to characterizing their quantitative properties including, in particular, their specificity and efficiency.Our approach generalizes the sort-seq method, introduced recently to analyze promoter sequences, in order to accurately quantify the efficiency of a large library of sequence variants.In addition to precisely identifying functional elements in the sRNAs, our data establish quantitative relationships between structural and energetic features of the sRNAs and their regulatory activity, and characterize a large set of direct and indirect interactions between nucleotides.

View Article: PubMed Central - PubMed

Affiliation: Department of Physics and FAS Center for Systems Biology, Harvard University, Cambridge, MA 02138, USA.

Show MeSH
Related in: MedlinePlus