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A protein-RNA specificity code enables targeted activation of an endogenous human transcript.

Campbell ZT, Valley CT, Wickens M - Nat. Struct. Mol. Biol. (2014)

Bottom Line: PUF proteins are an attractive platform for that purpose because they bind specific single-stranded RNA sequences by using short repeated modules, each contributing three amino acids that contact an RNA base.The resulting specificity code reveals the RNA binding preferences of natural proteins and enables the design of new specificities.Our study provides a guide for rational design of engineered mRNA control, including translational stimulation.

View Article: PubMed Central - PubMed

Affiliation: Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA.

ABSTRACT
Programmable protein scaffolds that target DNA are invaluable tools for genome engineering and designer control of transcription. RNA manipulation provides broad new opportunities for control, including changes in translation. PUF proteins are an attractive platform for that purpose because they bind specific single-stranded RNA sequences by using short repeated modules, each contributing three amino acids that contact an RNA base. Here, we identified the specificities of natural and designed combinations of those three amino acids, using a large randomized RNA library. The resulting specificity code reveals the RNA binding preferences of natural proteins and enables the design of new specificities. Using the code and a translational activation domain, we designed a protein that targets endogenous cyclin B1 mRNA in human cells, increasing sensitivity to chemotherapeutic drugs. Our study provides a guide for rational design of engineered mRNA control, including translational stimulation.

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A quantitative TRM recognition code(A) Experimental overview. TRM substitutions are introduced in PUF repeat 7 of FBF-2. The predicted site of variation is base +2 in the RNA sequence. TRM mutants are analyzed using the SEQRS technique (see text). (B) Hierarchical clustering reveals three classes of TRM binding specificity. Left, highly enriched 10-mer sequences for each TRM were identified (Y-axis) and the enrichment values were used to cluster similar binding profiles for each mutant (X-axis). For each TRM, the data were normalized to the maximum enrichment value. Right, three clusters were identified empirically and a representative motif was generated. (C) Sequence logos for members of cluster A reveal a common specificity consistent with the results from clustering (Top). The innermost ring contains sequences perfectly matched to a given seed motif, while subsequent rings contain increasing numbers of mismatches from that seed motif. SSLs for three representative TRMs reveal non-equivalent differences in overall specificity (Bottom). (D) Enrichment at position +2 of the PUF binding element. The relative enrichment for G (yellow), U (red), A (green), and C (blue). TRMs previously described as preferential C-binders, SR–Y, AR–Y, and CR–Y, are italicized16,17. The remaining synthetic combinations are underlined.
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Figure 2: A quantitative TRM recognition code(A) Experimental overview. TRM substitutions are introduced in PUF repeat 7 of FBF-2. The predicted site of variation is base +2 in the RNA sequence. TRM mutants are analyzed using the SEQRS technique (see text). (B) Hierarchical clustering reveals three classes of TRM binding specificity. Left, highly enriched 10-mer sequences for each TRM were identified (Y-axis) and the enrichment values were used to cluster similar binding profiles for each mutant (X-axis). For each TRM, the data were normalized to the maximum enrichment value. Right, three clusters were identified empirically and a representative motif was generated. (C) Sequence logos for members of cluster A reveal a common specificity consistent with the results from clustering (Top). The innermost ring contains sequences perfectly matched to a given seed motif, while subsequent rings contain increasing numbers of mismatches from that seed motif. SSLs for three representative TRMs reveal non-equivalent differences in overall specificity (Bottom). (D) Enrichment at position +2 of the PUF binding element. The relative enrichment for G (yellow), U (red), A (green), and C (blue). TRMs previously described as preferential C-binders, SR–Y, AR–Y, and CR–Y, are italicized16,17. The remaining synthetic combinations are underlined.

Mentions: To analyze TRM specificities, mutations were introduced into the seventh repeat of FBF-2 which binds the +2 RNA base. We determined the specificity of 25 TRMs using an unbiased approach, termed SEQRS, that combines in vitroselection, high-throughput sequencing of RNA, and Sequence Specificity Landscapes (SSLs) 4 (Fig. 2A). SEQRS yields a proxy for binding affinity, in which the number of reads for a specific sequence is correlated with its affinity measured in vitro4. In our experiments, a DNA library encoding a random 20-mer region was transcribed to generate a random pool of RNAs. A sufficient quantity of RNA to cover all possible 20-mer sequences is incubated with recombinant proteins. The pool was then incubated with purified GST-tagged recombinant protein immobilized on magnetic resin to enable capture of the RNA protein complex. After repeated washing, bound RNAs were thermally eluted, and converted into double-stranded DNA using reverse transcription followed by PCR. The RNA was reverse transcribed into DNA using a primer complementary to the constant region. The single stranded DNA was amplified using a primer set that re-introduces the T7 promoter. This enrichment procedure, analogous to SELEX, was repeated for five cycles prior to multiplexed deep sequencing 22,23.


A protein-RNA specificity code enables targeted activation of an endogenous human transcript.

Campbell ZT, Valley CT, Wickens M - Nat. Struct. Mol. Biol. (2014)

A quantitative TRM recognition code(A) Experimental overview. TRM substitutions are introduced in PUF repeat 7 of FBF-2. The predicted site of variation is base +2 in the RNA sequence. TRM mutants are analyzed using the SEQRS technique (see text). (B) Hierarchical clustering reveals three classes of TRM binding specificity. Left, highly enriched 10-mer sequences for each TRM were identified (Y-axis) and the enrichment values were used to cluster similar binding profiles for each mutant (X-axis). For each TRM, the data were normalized to the maximum enrichment value. Right, three clusters were identified empirically and a representative motif was generated. (C) Sequence logos for members of cluster A reveal a common specificity consistent with the results from clustering (Top). The innermost ring contains sequences perfectly matched to a given seed motif, while subsequent rings contain increasing numbers of mismatches from that seed motif. SSLs for three representative TRMs reveal non-equivalent differences in overall specificity (Bottom). (D) Enrichment at position +2 of the PUF binding element. The relative enrichment for G (yellow), U (red), A (green), and C (blue). TRMs previously described as preferential C-binders, SR–Y, AR–Y, and CR–Y, are italicized16,17. The remaining synthetic combinations are underlined.
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Related In: Results  -  Collection

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Figure 2: A quantitative TRM recognition code(A) Experimental overview. TRM substitutions are introduced in PUF repeat 7 of FBF-2. The predicted site of variation is base +2 in the RNA sequence. TRM mutants are analyzed using the SEQRS technique (see text). (B) Hierarchical clustering reveals three classes of TRM binding specificity. Left, highly enriched 10-mer sequences for each TRM were identified (Y-axis) and the enrichment values were used to cluster similar binding profiles for each mutant (X-axis). For each TRM, the data were normalized to the maximum enrichment value. Right, three clusters were identified empirically and a representative motif was generated. (C) Sequence logos for members of cluster A reveal a common specificity consistent with the results from clustering (Top). The innermost ring contains sequences perfectly matched to a given seed motif, while subsequent rings contain increasing numbers of mismatches from that seed motif. SSLs for three representative TRMs reveal non-equivalent differences in overall specificity (Bottom). (D) Enrichment at position +2 of the PUF binding element. The relative enrichment for G (yellow), U (red), A (green), and C (blue). TRMs previously described as preferential C-binders, SR–Y, AR–Y, and CR–Y, are italicized16,17. The remaining synthetic combinations are underlined.
Mentions: To analyze TRM specificities, mutations were introduced into the seventh repeat of FBF-2 which binds the +2 RNA base. We determined the specificity of 25 TRMs using an unbiased approach, termed SEQRS, that combines in vitroselection, high-throughput sequencing of RNA, and Sequence Specificity Landscapes (SSLs) 4 (Fig. 2A). SEQRS yields a proxy for binding affinity, in which the number of reads for a specific sequence is correlated with its affinity measured in vitro4. In our experiments, a DNA library encoding a random 20-mer region was transcribed to generate a random pool of RNAs. A sufficient quantity of RNA to cover all possible 20-mer sequences is incubated with recombinant proteins. The pool was then incubated with purified GST-tagged recombinant protein immobilized on magnetic resin to enable capture of the RNA protein complex. After repeated washing, bound RNAs were thermally eluted, and converted into double-stranded DNA using reverse transcription followed by PCR. The RNA was reverse transcribed into DNA using a primer complementary to the constant region. The single stranded DNA was amplified using a primer set that re-introduces the T7 promoter. This enrichment procedure, analogous to SELEX, was repeated for five cycles prior to multiplexed deep sequencing 22,23.

Bottom Line: PUF proteins are an attractive platform for that purpose because they bind specific single-stranded RNA sequences by using short repeated modules, each contributing three amino acids that contact an RNA base.The resulting specificity code reveals the RNA binding preferences of natural proteins and enables the design of new specificities.Our study provides a guide for rational design of engineered mRNA control, including translational stimulation.

View Article: PubMed Central - PubMed

Affiliation: Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA.

ABSTRACT
Programmable protein scaffolds that target DNA are invaluable tools for genome engineering and designer control of transcription. RNA manipulation provides broad new opportunities for control, including changes in translation. PUF proteins are an attractive platform for that purpose because they bind specific single-stranded RNA sequences by using short repeated modules, each contributing three amino acids that contact an RNA base. Here, we identified the specificities of natural and designed combinations of those three amino acids, using a large randomized RNA library. The resulting specificity code reveals the RNA binding preferences of natural proteins and enables the design of new specificities. Using the code and a translational activation domain, we designed a protein that targets endogenous cyclin B1 mRNA in human cells, increasing sensitivity to chemotherapeutic drugs. Our study provides a guide for rational design of engineered mRNA control, including translational stimulation.

Show MeSH