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Author: Samantha Maragh
A revolution is underway to reengineer the blueprint for life: the genetic code, whose sequence determines identity and function for every living organism. The genome (expressed in DNA base pairs) is the entire complement of an organism’s genetic code and is housed in the basic functional unit of life, the cell.
Genome engineering involves tools and techniques to target a specific sequence in a genome and alter the genetic code (genome editing) or to alter the chemical signatures associated with the genetic code (epigenetic engineering). The technology operates by biochemical principles generally applicable to every kind of cell (Carroll 2014; Kim and Kim 2014).
What Is Genome Editing?
Genome editing aims to generate edited cells that have permanently changed genetic codes and are functional (Doudna and Charpentier 2014; Gaj et al. 2016). The process typically involves the following:
Applications of Genome Editing
Genome editing is being pursued globally by government, academic, and private sectors to transform medicine and bioscience to enable previously impossible advances in areas such as basic biology research, gene therapy, synthetic biology, novel antimicrobials and antivirals, biomanufacturing, agriculture, and food production (Barrangou and Doudna 2016). In some human diseases, just a handful of incorrect letters in the genetic code or as little as a single incorrect letter at a specific position (e.g., sickle cell disease) of the approximately 6.6 billion-letter human genetic code can cause a -serious and/or deadly disease.
Genome editing ushers in the first era of technology where the medical field isn’t limited to solely managing symptoms and treating illness episodes, but where the cells of a patient may be edited to “fix” a disease at the genetic code level. The biomedical field is daring to think and even speak the word “cure” for diseases that once had few or no treatment options (Fellmann et al. 2017; Porteus 2015; Salsman et al. 2017).
The agriculture industry has also begun targeted editing of crop genomes to have more favorable traits, higher yield, and better disease resistance (Mao et al. 2013; Yin et al. 2017b). And there are large efforts in the environmental science community to engineer microbes with new abilities to produce biofuels and act as biosensors (Ng et al. 2017).
The Edit’s in the Details: Challenges and Opportunities for Standards
Concurrent with the global pursuit to leverage existing genome editing systems, there is significant technological innovation taking place to expand capabilities for the high-precision targeting of any genomic sequence to make any intended change in any cell. But crucial measurement challenges must be addressed to facilitate the transfer of these technologies into trusted data and products.
An editing process is carried out on many cells at a time—often thousands to millions—but there is little technical control over the efficiency of editing and which sequence changes result. Each cell is a closed system where the resulting edits are independent events.
Due to technical limitations on the ability to measure the sequence of individual cells at high throughput, edited sequence confirmation involves a bulk measurement sampling of the genomes from a heterogeneously edited cell pool that may contain both intended and unintended edits even at the target site. In addition, due to biological limitations, particularly for human therapeutics, this heterogeneous pool of cells may be the final product.
It is also technically challenging to accurately parse sequencing data from bulk analyses because the number of edits detected can range into the tens or even hundreds for a single genomic location. Bioinformatic pipelines to parse these data were benchmarked on what has been observed in nature: only a handful (if that many) of variants are expected to occur at any one location. It is therefore unclear how accurately sequencing analysis pipelines report what is biologically present in a sample.
The type of edit also contributes to detection difficulty. In general, small edits (e.g., one to tens of bases) are less challenging to sequence verify; large edits (e.g., hundreds of bases or more) are technically challenging to reliably detect and sequence verify.
A prominent measurement challenge for human -therapeutic genome editing is the occurrence of -unintended, or off-target, editing, when a sequence change occurs at a site or sites other than the target site (Fu et al. 2013; Tsai et al. 2015). Even if the intended edit was effected at the target site, off-target editing may change the cell in a way that makes the therapy or product unsuitable or unsafe. To assess off-target editing, the entire genome, or at least sites where there is reason to think off-target edits could occur, is sequenced after an editing process.
There are technical limitations to sequencing the entirety of large genomes like the human genome with sufficient sensitivity to report any off-target edit at any location in the heterogeneous sample. Means of limiting where to sequence for off-target edits are being developed, but there is very limited understanding of their accuracy. Even with reliable sequence data in hand, there’s still the challenge of interpreting the significance of unintended edits and understanding whether edited cells are fit for the intended purpose.
Finally, a genome-edited product must be manufactured. Traditional manufacturing approaches don’t directly translate to this field where the input is live cells, which must be manipulated precisely and still be live and functional at the end of the process (Harrison et al. 2017, 2018). Moreover, the product may be a personalized therapy for a patient, involving a short storage life and requiring small batch manufacturing with rapid distribution and use.
Standards play an essential role in the translation and durable adoption of technology (figure 1) (Plant et al. 2014, 2018). Standards for genome editing will support and enhance innovation and technology adoption as well as evidence that new biological understanding is based on sound data and that products generated with these technologies are suitable and safe.
Standards in the form of traceable materials can help increase confidence that a process reliably reports where editing occurred, what edit(s) resulted, and the relative abundance of each edit. For this purpose, traceable material or control samples (a well-qualified series of cells or genomes containing a variety of edits at known relative abundance across the genome) can serve as “ground truth samples” for assessing sequencing methods. These control materials would enable comparability among operators at the same site, operators at different sites or organizations, and sequencing methods.
Standards for datasets and metadata can help enhance the accuracy of sequencing data analysis pipelines, transfer and reproducibility of data, analysis, and data interpretation within and between labs. Shared standard datasets, along with associated metadata detailing the editing and data handling process, will provide a means to benchmark data analysis pipelines, to compare the performance between iterations of both a single pipeline and different pipelines (figure 2). Supporting these technical standards is the need for standardized definitions of key terms in genome editing to enable clear communication of results both within the field and to regulatory agencies.
The US National Institute of Standards and Technology has launched the NIST Genome Editing Consortium to work across the genome editing community to develop standard and norms toward filling the needs stated above. Standards for the safe and efficient manufacture of engineered cell products will likely require a paradigm shift and disruptive technologies to address the particular challenges of manufacturing living cells or manufacturing genome editing delivery systems for in vivo editing at large scale.
Translating the promise of genome editing into production and medical practice requires robust quantitative assays, accurate data tools, and associated standards and benchmarks to enable high confidence in the characterization of engineered genomes and cells. Steps are being taken to address some of these needs through standards such as physical controls, standard datasets, and a standard lexicon for this field.
Genome engineering technology that is more controlled or not reliant on damaging the genome or changing the sequence at all (e.g., a genome’s chemical signature might be changed) is rapidly progressing (-Anzalone et al. 2019; Liao et al. 2017; Thakore et al. 2016). Further progress can be made through the development of a suite of tools and technology employing a multidisciplinary approach to address unmet measurement needs such as single cell editing detection and in vivo tracking and monitoring of edited cells once introduced into the environment or a live organism (e.g., in a human for therapeutic treatment).
As genome engineering matures, there will be a need for continuous evaluation of new standards and norms that can support rapid innovation and expansion of this field.
Anzalone AV, Randolph PB, Davis JR, Sousa AA, Koblan LW, Levy JM, Chen PJ, Wilson C, Newby GA, Raguram A, Liu DR. 2019. Search-and-replace genome editing without double-strand breaks or donor DNA. Nature, doi:10.1038/s41586-019-1711-4.
Barrangou R, Doudna JA. 2016. Applications of CRISPR technologies in research and beyond. Nature -Biotechnology 34(9):933–41.
Carroll D. 2014. Genome engineering with targetable -nucleases. Annual Review of Biochemistry 83:409–39.
Doudna JA, Charpentier E. 2014. Genome editing: The new frontier of genome engineering with CRISPR-Cas9. -Science 346(6213):1258096.
Fellmann C, Gowen BG, Lin PC, Doudna JA, Corn JE. 2017. Cornerstones of CRISPR-Cas in drug discovery and -therapy. Nature Reviews Drug Discovery 16(2):89–100.
Fu Y, Foden JA, Khayter C, Maeder ML, Reyon D, Joung JK, Sander JD. 2013. High-frequency off-target mutagenesis induced by CRISPR-Cas nucleases in human cells. Nature Biotechnology 31(9):822–26.
Gaj T, Sirk SJ, Shui SL, Liu J. 2016. Genome-editing technologies: Principles and applications. Cold Spring Harbor Perspectives in Biology 8(12).
Harrison RP, Ruck S, Medcalf N, Rafiq QA. 2017. Decentralized manufacturing of cell and gene therapies: Over-coming challenges and identifying opportunities. Cytotherapy 19(10):1140–51.
Harrison RP, Ruck S, Rafiq QA, Medcalf N. 2018. Decentralised manufacturing of cell and gene therapy products: Learning from other healthcare sectors. Biotechnology Advances 36(2):345–57.
Kim H, Kim JS. 2014. A guide to genome engineering with programmable nucleases. Nature Reviews Genetics 15(5):321–34.
Komor AC, Badran AH, Liu DR. 2017. CRISPR-based technologies for the manipulation of eukaryotic genomes. Cell 168(1-2):20–36.
Liao HK, Hatanaka F, Araoka T, Reddy P, Wu MZ, Sui Y, Yamauchi T, Sakurai M, O’Keefe DD, Núñez-Delicado E, and 6 others. 2017. In vivo target gene activation via CRISPR/Cas9-mediated trans-epigenetic modulation. Cell 171(7):1495–507.
Maeder ML, Gersbach CA. 2016. Genome-editing technologies for gene and cell therapy. Molecular Therapy 24(3):430–46.
Mao Y, Zhang H, Xu N, Zhang B, Gou F, Zhu JK. 2013. Application of the CRISPR-Cas system for efficient genome engineering in plants. Molecular Plant 6(6):2008–11.
Ng IS, Tan SI, Kao PH, Chang YK, Chang JS. 2017. Recent developments on genetic engineering of microalgae for biofuels and bio-based chemicals. Biotechnology 12(10).
Plant AL, Locascio LE, May WE, Gallagher PD. 2014. Improved reproducibility by assuring confidence in measurements in biomedical research. Nature Methods 11(9):895–98.
Plant AL, Becker CA, Hanisch RJ, Boisvert RF, Possolo AM, Elliott JT. 2018. How measurement science can improve confidence in research results. PLoS Biology 16(4):e2004299.
Porteus MH. 2015. Towards a new era in medicine: Thera-peutic genome editing. Genome Biology 16:286.
Salsman J, Masson JY, Orthwein A, Dellaire G. 2017. -CRISPR/Cas9 gene editing: From basic mechanisms to improved strategies for enhanced genome engineering in vivo. -Current Gene Therapy 17(4):263–74.
Sander JD, Joung JK. 2014. CRISPR-Cas systems for editing, regulating and targeting genomes. Nature Biotechnology 32(4):347–55.
Thakore PI, Black JB, Hilton IB, Gersbach CA. 2016. Editing the epigenome: Technologies for programmable -transcription and epigenetic modulation. Nature Methods 13(2):127–37.
Tsai SQ, Zheng Z, Nguyen NT, Liebers M, Topkar VV, Thapar V, Wyvekens N, Khayter C, Iafrate AJ, Le LP, and 2 -others. 2015. GUIDE-seq enables genome-wide profiling of off-target cleavage by CRISPR-Cas nucleases. Nature -Biotechnology 33(2):187–97.
Urnov FD. 2018. Genome editing BC (before CRISPR): Lasting lessons from the Old Testament. CRISPR Journal 1:34–46.
Yin H, Kauffman KJ, Anderson DG. 2017a. Delivery technologies for genome editing. Nature Reviews Drug Discovery 16(6):387–99.
Yin K, Gao C, Qiu JL. 2017b. Progress and prospects in plant genome editing. Nature Plants 3:17107.