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Specificity of RNAi, LNA and CRISPRi as loss-of-function methods in transcriptional analysis
Loss-of-function (LOF) methods such as RNA interference (RNAi), antisense oligonucleotides or CRISPR-based genome editing provide unparalleled power for studying the biological function of genes of interest. However, a major concern is non-specific targeting, which involves depletion of transcripts...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6093183/ https://www.ncbi.nlm.nih.gov/pubmed/29860520 http://dx.doi.org/10.1093/nar/gky437 |
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author | Stojic, Lovorka Lun, Aaron T L Mangei, Jasmin Mascalchi, Patrice Quarantotti, Valentina Barr, Alexis R Bakal, Chris Marioni, John C Gergely, Fanni Odom, Duncan T |
author_facet | Stojic, Lovorka Lun, Aaron T L Mangei, Jasmin Mascalchi, Patrice Quarantotti, Valentina Barr, Alexis R Bakal, Chris Marioni, John C Gergely, Fanni Odom, Duncan T |
author_sort | Stojic, Lovorka |
collection | PubMed |
description | Loss-of-function (LOF) methods such as RNA interference (RNAi), antisense oligonucleotides or CRISPR-based genome editing provide unparalleled power for studying the biological function of genes of interest. However, a major concern is non-specific targeting, which involves depletion of transcripts other than those intended. Little work has been performed to characterize the off-target effects of these common LOF methods at the whole-transcriptome level. Here, we experimentally compared the non-specific activity of RNAi, antisense oligonucleotides and CRISPR interference (CRISPRi). All three methods yielded non-negligible off-target effects in gene expression, with CRISPRi also exhibiting strong clonal effects. As an illustrative example, we evaluated the performance of each method for determining the role of an uncharacterized long noncoding RNA (lncRNA). Several LOF methods successfully depleted the candidate lncRNA but yielded different sets of differentially expressed genes as well as a different cellular phenotype upon depletion. Similar discrepancies between methods were observed with a protein-coding gene (Ch-TOG/CKAP5) and another lncRNA (MALAT1). We suggest that the differences between methods arise due to method-specific off-target effects and provide guidelines for mitigating such effects in functional studies. Our recommendations provide a framework with which off-target effects can be managed to improve functional characterization of genes of interest. |
format | Online Article Text |
id | pubmed-6093183 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-60931832018-08-22 Specificity of RNAi, LNA and CRISPRi as loss-of-function methods in transcriptional analysis Stojic, Lovorka Lun, Aaron T L Mangei, Jasmin Mascalchi, Patrice Quarantotti, Valentina Barr, Alexis R Bakal, Chris Marioni, John C Gergely, Fanni Odom, Duncan T Nucleic Acids Res Computational Biology Loss-of-function (LOF) methods such as RNA interference (RNAi), antisense oligonucleotides or CRISPR-based genome editing provide unparalleled power for studying the biological function of genes of interest. However, a major concern is non-specific targeting, which involves depletion of transcripts other than those intended. Little work has been performed to characterize the off-target effects of these common LOF methods at the whole-transcriptome level. Here, we experimentally compared the non-specific activity of RNAi, antisense oligonucleotides and CRISPR interference (CRISPRi). All three methods yielded non-negligible off-target effects in gene expression, with CRISPRi also exhibiting strong clonal effects. As an illustrative example, we evaluated the performance of each method for determining the role of an uncharacterized long noncoding RNA (lncRNA). Several LOF methods successfully depleted the candidate lncRNA but yielded different sets of differentially expressed genes as well as a different cellular phenotype upon depletion. Similar discrepancies between methods were observed with a protein-coding gene (Ch-TOG/CKAP5) and another lncRNA (MALAT1). We suggest that the differences between methods arise due to method-specific off-target effects and provide guidelines for mitigating such effects in functional studies. Our recommendations provide a framework with which off-target effects can be managed to improve functional characterization of genes of interest. Oxford University Press 2018-07-06 2018-06-01 /pmc/articles/PMC6093183/ /pubmed/29860520 http://dx.doi.org/10.1093/nar/gky437 Text en © The Author(s) 2018. Published by Oxford University Press on behalf of Nucleic Acids Research. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Computational Biology Stojic, Lovorka Lun, Aaron T L Mangei, Jasmin Mascalchi, Patrice Quarantotti, Valentina Barr, Alexis R Bakal, Chris Marioni, John C Gergely, Fanni Odom, Duncan T Specificity of RNAi, LNA and CRISPRi as loss-of-function methods in transcriptional analysis |
title | Specificity of RNAi, LNA and CRISPRi as loss-of-function methods in transcriptional analysis |
title_full | Specificity of RNAi, LNA and CRISPRi as loss-of-function methods in transcriptional analysis |
title_fullStr | Specificity of RNAi, LNA and CRISPRi as loss-of-function methods in transcriptional analysis |
title_full_unstemmed | Specificity of RNAi, LNA and CRISPRi as loss-of-function methods in transcriptional analysis |
title_short | Specificity of RNAi, LNA and CRISPRi as loss-of-function methods in transcriptional analysis |
title_sort | specificity of rnai, lna and crispri as loss-of-function methods in transcriptional analysis |
topic | Computational Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6093183/ https://www.ncbi.nlm.nih.gov/pubmed/29860520 http://dx.doi.org/10.1093/nar/gky437 |
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