Automated identification of sequence-tailored Cas9 proteins using massive metagenomic data

Matteo Ciciani, Michele Demozzi, Eleonora Pedrazzoli, Elisabetta Visentin, Laura Pezzè, Lorenzo Federico Signorini, Aitor Blanco-Miguez, Moreno Zolfo, Francesco Asnicar, Antonio Casini, Anna Cereseto () and Nicola Segata ()
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Matteo Ciciani: Cellular and Integrative Biology, University of Trento
Michele Demozzi: Cellular and Integrative Biology, University of Trento
Eleonora Pedrazzoli: Cellular and Integrative Biology, University of Trento
Elisabetta Visentin: Cellular and Integrative Biology, University of Trento
Laura Pezzè: Alia Therapeutics
Lorenzo Federico Signorini: Cellular and Integrative Biology, University of Trento
Aitor Blanco-Miguez: Cellular and Integrative Biology, University of Trento
Moreno Zolfo: Cellular and Integrative Biology, University of Trento
Francesco Asnicar: Cellular and Integrative Biology, University of Trento
Antonio Casini: Alia Therapeutics
Anna Cereseto: Cellular and Integrative Biology, University of Trento
Nicola Segata: Cellular and Integrative Biology, University of Trento

Nature Communications, 2022, vol. 13, issue 1, 1-8

Abstract: Abstract The identification of the protospacer adjacent motif (PAM) sequences of Cas9 nucleases is crucial for their exploitation in genome editing. Here we develop a computational pipeline that was used to interrogate a massively expanded dataset of metagenome and virome assemblies for accurate and comprehensive PAM predictions. This procedure allows the identification and isolation of sequence-tailored Cas9 nucleases by using the target sequence as bait. As proof of concept, starting from the disease-causing mutation P23H in the RHO gene, we find, isolate and experimentally validate a Cas9 which uses the mutated sequence as PAM. Our PAM prediction pipeline will be instrumental to generate a Cas9 nuclease repertoire responding to any PAM requirement.

Date: 2022
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DOI: 10.1038/s41467-022-34213-9

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