In silico analysis enabling informed design for genome editing in medicinal cannabis; gene families and variant characterisation.

<h4>Background</h4>Cannabis has been used worldwide for centuries for industrial, recreational and medicinal use, however, to date no successful attempts at editing genes involved in cannabinoid biosynthesis have been reported. This study proposes and develops an in silico best practices...

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Autores principales:	L Matchett-Oates, S Braich, G C Spangenberg, S Rochfort, N O I Cogan
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/45014fb6c8154d2d9be294274616c30b
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spelling	oai:doaj.org-article:45014fb6c8154d2d9be294274616c30b2021-12-02T20:14:16ZIn silico analysis enabling informed design for genome editing in medicinal cannabis; gene families and variant characterisation.1932-620310.1371/journal.pone.0257413https://doaj.org/article/45014fb6c8154d2d9be294274616c30b2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0257413https://doaj.org/toc/1932-6203<h4>Background</h4>Cannabis has been used worldwide for centuries for industrial, recreational and medicinal use, however, to date no successful attempts at editing genes involved in cannabinoid biosynthesis have been reported. This study proposes and develops an in silico best practices approach for the design and implementation of genome editing technologies in cannabis to target all genes involved in cannabinoid biosynthesis.<h4>Results</h4>A large dataset of reference genomes was accessed and mined to determine copy number variation and associated SNP variants for optimum target edit sites for genotype independent editing. Copy number variance and highly polymorphic gene sequences exist in the genome making genome editing using CRISPR, Zinc Fingers and TALENs technically difficult. Evaluation of allele or additional gene copies was determined through nucleotide and amino acid alignments with comparative sequence analysis performed. From determined gene copy number and presence of SNPs, multiple online CRISPR design tools were used to design sgRNA targeting every gene, accompanying allele and homologs throughout all involved pathways to create knockouts for further investigation. Universal sgRNA were designed for highly homologous sequences using MultiTargeter and visualised using Sequencher, creating unique sgRNA avoiding SNP and shared nucleotide locations targeting optimal edit sites.<h4>Conclusions</h4>Using this framework, the approach has wider applications to all plant species regardless of ploidy number or highly homologous gene sequences.<h4>Significance statement</h4>Using this framework, a best-practice approach to genome editing is possible in all plant species, including cannabis, delivering a comprehensive in silico evaluation of the cannabinoid pathway diversity from a large set of whole genome sequences. Identification of SNP variants across all genes could improve genome editing potentially leading to novel applications across multiple disciplines, including agriculture and medicine.L Matchett-OatesS BraichG C SpangenbergS RochfortN O I CoganPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 9, p e0257413 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q L Matchett-Oates S Braich G C Spangenberg S Rochfort N O I Cogan In silico analysis enabling informed design for genome editing in medicinal cannabis; gene families and variant characterisation.
description	<h4>Background</h4>Cannabis has been used worldwide for centuries for industrial, recreational and medicinal use, however, to date no successful attempts at editing genes involved in cannabinoid biosynthesis have been reported. This study proposes and develops an in silico best practices approach for the design and implementation of genome editing technologies in cannabis to target all genes involved in cannabinoid biosynthesis.<h4>Results</h4>A large dataset of reference genomes was accessed and mined to determine copy number variation and associated SNP variants for optimum target edit sites for genotype independent editing. Copy number variance and highly polymorphic gene sequences exist in the genome making genome editing using CRISPR, Zinc Fingers and TALENs technically difficult. Evaluation of allele or additional gene copies was determined through nucleotide and amino acid alignments with comparative sequence analysis performed. From determined gene copy number and presence of SNPs, multiple online CRISPR design tools were used to design sgRNA targeting every gene, accompanying allele and homologs throughout all involved pathways to create knockouts for further investigation. Universal sgRNA were designed for highly homologous sequences using MultiTargeter and visualised using Sequencher, creating unique sgRNA avoiding SNP and shared nucleotide locations targeting optimal edit sites.<h4>Conclusions</h4>Using this framework, the approach has wider applications to all plant species regardless of ploidy number or highly homologous gene sequences.<h4>Significance statement</h4>Using this framework, a best-practice approach to genome editing is possible in all plant species, including cannabis, delivering a comprehensive in silico evaluation of the cannabinoid pathway diversity from a large set of whole genome sequences. Identification of SNP variants across all genes could improve genome editing potentially leading to novel applications across multiple disciplines, including agriculture and medicine.
format	article
author	L Matchett-Oates S Braich G C Spangenberg S Rochfort N O I Cogan
author_facet	L Matchett-Oates S Braich G C Spangenberg S Rochfort N O I Cogan
author_sort	L Matchett-Oates
title	In silico analysis enabling informed design for genome editing in medicinal cannabis; gene families and variant characterisation.
title_short	In silico analysis enabling informed design for genome editing in medicinal cannabis; gene families and variant characterisation.
title_full	In silico analysis enabling informed design for genome editing in medicinal cannabis; gene families and variant characterisation.
title_fullStr	In silico analysis enabling informed design for genome editing in medicinal cannabis; gene families and variant characterisation.
title_full_unstemmed	In silico analysis enabling informed design for genome editing in medicinal cannabis; gene families and variant characterisation.
title_sort	in silico analysis enabling informed design for genome editing in medicinal cannabis; gene families and variant characterisation.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/45014fb6c8154d2d9be294274616c30b
work_keys_str_mv	AT lmatchettoates insilicoanalysisenablinginformeddesignforgenomeeditinginmedicinalcannabisgenefamiliesandvariantcharacterisation AT sbraich insilicoanalysisenablinginformeddesignforgenomeeditinginmedicinalcannabisgenefamiliesandvariantcharacterisation AT gcspangenberg insilicoanalysisenablinginformeddesignforgenomeeditinginmedicinalcannabisgenefamiliesandvariantcharacterisation AT srochfort insilicoanalysisenablinginformeddesignforgenomeeditinginmedicinalcannabisgenefamiliesandvariantcharacterisation AT noicogan insilicoanalysisenablinginformeddesignforgenomeeditinginmedicinalcannabisgenefamiliesandvariantcharacterisation
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In silico analysis enabling informed design for genome editing in medicinal cannabis; gene families and variant characterisation.

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