Multiplexed CRISPR-Cas9 mutagenesis of rice <i>PSBS1</i> noncoding sequences for transgene-free overexpression

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Abstract

Understanding CRISPR-Cas9’s capacity to produce native overexpression (OX) alleles would accelerate agronomic gains achievable by gene editing. To generate OX alleles with increased RNA and protein abundance, we leveraged multiplexed CRISPR-Cas9 mutagenesis of noncoding sequences upstream of the rice PSBS1 gene. We isolated 120 gene-edited alleles with varying non-photochemical quenching (NPQ) capacity in vivo—from knockout to overexpression—using a high-throughput screening pipeline. Overexpression increased OsPsbS1 protein abundance two- to threefold, matching fold changes obtained by transgenesis. Increased PsbS protein abundance enhanced NPQ capacity and water-use efficiency. Across our resolved genetic variation, we identify the role of 5′UTR indels and inversions in driving knockout/knockdown and overexpression phenotypes, respectively. Complex structural variants, such as the 252-kb duplication/inversion generated here, evidence the potential of CRISPR-Cas9 to facilitate significant genomic changes with negligible off-target transcriptomic perturbations. Our results may inform future gene-editing strategies for hypermorphic alleles and have advanced the pursuit of gene-edited, non-transgenic rice plants with accelerated relaxation of photoprotection.

Abstract

Complex structural variants generated by CRISPR-Cas9 editing significantly increase native rice PSBS expression and activity.

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Most cited references 82

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Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

Michael Love, Wolfgang Huber, Simon Anders (2014)

In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.

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KEGG: kyoto encyclopedia of genes and genomes.

M Kanehisa (2000)

KEGG (Kyoto Encyclopedia of Genes and Genomes) is a knowledge base for systematic analysis of gene functions, linking genomic information with higher order functional information. The genomic information is stored in the GENES database, which is a collection of gene catalogs for all the completely sequenced genomes and some partial genomes with up-to-date annotation of gene functions. The higher order functional information is stored in the PATHWAY database, which contains graphical representations of cellular processes, such as metabolism, membrane transport, signal transduction and cell cycle. The PATHWAY database is supplemented by a set of ortholog group tables for the information about conserved subpathways (pathway motifs), which are often encoded by positionally coupled genes on the chromosome and which are especially useful in predicting gene functions. A third database in KEGG is LIGAND for the information about chemical compounds, enzyme molecules and enzymatic reactions. KEGG provides Java graphics tools for browsing genome maps, comparing two genome maps and manipulating expression maps, as well as computational tools for sequence comparison, graph comparison and path computation. The KEGG databases are daily updated and made freely available (http://www. genome.ad.jp/kegg/).

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featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.

Y. Liao, G K Smyth, W Shi (2014)

Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.

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Author and article information

Contributors

Dhruv Patel-Tupper:

ORCID: https://orcid.org/0000-0002-0642-1485

Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: ValidationRole: VisualizationRole: Writing - original draftRole: Writing - review & editing

Armen Kelikian:

ORCID: https://orcid.org/0009-0009-6021-8822

Role: Data curationRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: ValidationRole: VisualizationRole: Writing - review & editing

Anna Leipertz: Role: Investigation

Nina Maryn:

ORCID: https://orcid.org/0000-0002-0975-3354

Role: Formal analysisRole: InvestigationRole: SoftwareRole: Visualization

Michelle Tjahjadi: Role: InvestigationRole: Validation

Nicholas G. Karavolias: Role: Formal analysisRole: InvestigationRole: MethodologyRole: Writing - review & editing

Myeong-Je Cho:

ORCID: https://orcid.org/0000-0002-7638-7235

Role: InvestigationRole: ResourcesRole: Writing - review & editing

Krishna K. Niyogi:

ORCID: https://orcid.org/0000-0001-7229-2071

Role: ConceptualizationRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: SupervisionRole: ValidationRole: Writing - review & editing

Journal

Journal ID (nlm-ta): Sci Adv

Journal ID (iso-abbrev): Sci Adv

Journal ID (publisher-id): sciadv

Journal ID (hwp): advances

Title: Science Advances

Publisher: American Association for the Advancement of Science

ISSN (Electronic): 2375-2548

Publication date Collection: 07 June 2024

Publication date (Electronic, pub): 07 June 2024

Volume: 10

Issue: 23

Electronic Location Identifier: eadm7452

Affiliations

[ ¹ ]Department of Plant and Microbial Biology, University of California, Berkeley, CA 94720, USA.

[ ² ]Howard Hughes Medical Institute, University of California, Berkeley, CA 94720, USA.

[ ³ ]Innovative Genomics Institute, University of California, Berkeley, CA 94720, USA.

[ ⁴ ]Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.

Author notes

[* ]Corresponding author. Email: niyogi@ 123456berkeley.edu

Author information

Dhruv Patel-Tupper https://orcid.org/0000-0002-0642-1485

Armen Kelikian https://orcid.org/0009-0009-6021-8822

Nina Maryn https://orcid.org/0000-0002-0975-3354

Myeong-Je Cho https://orcid.org/0000-0002-7638-7235

Krishna K. Niyogi https://orcid.org/0000-0001-7229-2071

Article

Publisher ID: adm7452

DOI: 10.1126/sciadv.adm7452

PMC ID: 11160471

PubMed ID: 38848363

SO-VID: c2e527e6-9971-4432-9456-70fe9ea4d53b

Copyright © Copyright © 2024 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY).

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

History

Date received : 04 November 2023

Date accepted : 03 May 2024

Funding

Funded by: FundRef http://dx.doi.org/10.13039/100000011, Howard Hughes Medical Institute;

Funded by: FundRef http://dx.doi.org/10.13039/100000865, Bill and Melinda Gates Foundation;

Award ID: OPP1172157

Funded by: FundRef http://dx.doi.org/10.13039/100011929, Foundation for Food and Agriculture Research;

Award ID: OPP1172157

Funded by: FundRef http://dx.doi.org/10.13039/100023581, National Science Foundation Graduate Research Fellowship Program;

Award ID: 1752814

Funded by: FundRef http://dx.doi.org/10.13039/501100020171, Foreign, Commonwealth and Development Office;

Award ID: OPP1172157

Funded by: Berkeley Fellowship;

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Copyeditor Lou Notario

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Multiplexed CRISPR-Cas9 mutagenesis of rice PSBS1 noncoding sequences for transgene-free overexpression

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CRISPR/Cas9 editing in human blood

Most cited references 82

Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

KEGG: kyoto encyclopedia of genes and genomes.

featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.

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