| Literature DB >> 36078090 |
Xun Wei1,2, Aqing Pu1, Qianqian Liu1, Quancan Hou1,2, Yong Zhang2, Xueli An1,2, Yan Long1,2, Yilin Jiang1, Zhenying Dong2, Suowei Wu1,2, Xiangyuan Wan1,2.
Abstract
Gene editing (GE) has become one of the mainstream bioengineering technologies over the past two decades, mainly fueled by the rapid development of the CRISPR/Cas system since 2012. To date, plenty of articles related to the progress and applications of GE have been published globally, but the objective, quantitative and comprehensive investigations of them are relatively few. Here, 13,980 research articles and reviews published since 1999 were collected by using GE-related queries in the Web of Science. We used bibliometric analysis to investigate the competitiveness and cooperation of leading countries, influential affiliations, and prolific authors. Text clustering methods were used to assess technical trends and research hotspots dynamically. The global application status and regulatory framework were also summarized. This analysis illustrates the bottleneck of the GE innovation and provides insights into the future trajectory of development and application of the technology in various fields, which will be helpful for the popularization of gene editing technology.Entities:
Keywords: CRISPR; bibliometrics analysis; gene editing technology; regulation; text clustering
Mesh:
Year: 2022 PMID: 36078090 PMCID: PMC9454589 DOI: 10.3390/cells11172682
Source DB: PubMed Journal: Cells ISSN: 2073-4409 Impact factor: 7.666
Figure 1The development history of general genetics, modern molecular biology, GMO and GE.
Figure 2GE-related publications from 1999 to 2021. The number of publications in three sectors (medicine, agriculture and industry) and four different GE technologies.
The research status and application fields of GE technologies (mainly CRISPR/Cas9).
| GE Technologies | Research Status | Typical Application Cases | ||||||
|---|---|---|---|---|---|---|---|---|
| Creators | Institutions and Countries | Years | Refs | Application Fields | Research Objects | Target Genes | Refs | |
| Meganuclease | Colleaux. L | CNRS Laboratoire and Université Pierre et Marie Curie (France) | 1986 | [ | Medicine and Health | Human xeroderma pigmentosum |
| [ |
| Agriculture | Cotton insect resistant | [ | ||||||
| Industry | UDP-glucose pyrophosphorylase gene | [ | ||||||
| ZFNs | Srinivasan Chandrasegaran | Johns Hopkins University (JHU, USA) | 1996 | [ | Medicine and Health | K562, CD4+ T cells (X-linked severe combined immune deficiency (SCID)) |
| [ |
| Agriculture | Maize herbicide tolerance | [ | ||||||
| TALENs | Daniel F. Voytas | University of Minnesota (USA) | 2010 | [ | Medicine and Health | Rat immunoglobulinM (rat model) |
| [ |
| Agriculture | Rice disease-resistant ( |
| [ | |||||
| Industry |
| [ | ||||||
| CRISPR/Cas9 | Jennifer A. Doudna and Emmanuelle Charpentier | Howard Hughes Medical Institute (HHMI, USA) and The Laboratory for Molecular Infection Medicine Sweden (MIMS, Sweden) | 2012 | [ | Medicine and Health | Human 293FT and mouse cells (first mammalian model) | [ | |
| Cynomolgus monkey (mammalian model) |
| [ | ||||||
| Duchenne muscular dystrophy(mouse model) |
| [ | ||||||
| Human hereditary tyrosinemia (mouse model) |
| [ | ||||||
| Human hemophilia (mouse model) |
| [ | ||||||
| Human intestinal neoplasia (mouse model) | [ | |||||||
| Human lung adenocarcinoma (mouse model) |
| [ | ||||||
| Cataracts (mouse model) |
| [ | ||||||
| Human obesity (mouse model) |
| [ | ||||||
| Resistance to | Whole genome | [ | ||||||
| Functional genomics in human cells | Anthrax and diphtheria toxin host genes | [ | ||||||
| Human autologous CD34+ cells |
| [ | ||||||
| Human hepatocytes |
| [ | ||||||
| Human fibroblast |
| [ | ||||||
| Mouse acute myeloid leukemia cell | [ | |||||||
| 204 human cancer cell lines |
| [ | ||||||
| SARS-CoV-2 | [ | |||||||
| Human leukemic |
| [ | ||||||
| Human HeLa cells | Telomerase gene | [ | ||||||
| Agriculture | Rice and Wheat (first applied to plants) | [ | ||||||
| Rice herbicide tolerance | [ | |||||||
| Tomato fruit size, quantity and nutritional value |
| [ | ||||||
| Tomato storability |
| [ | ||||||
| Wheat grain weight enhancement |
| [ | ||||||
| Sativa oleic acid content |
| [ | ||||||
|
| [ | |||||||
| Oil content of rapeseed |
| [ | ||||||
| Rice grain quality |
| [ | ||||||
| Tomato fruit nutrition |
| [ | ||||||
| Rice amylose |
| [ | ||||||
| Tomato high quality seedless fruit |
| [ | ||||||
| Mitochondrial function and fruit ripening in tomato |
| [ | ||||||
| Tomato ripening regulation |
| [ | ||||||
| Grape resistance to |
| [ | ||||||
| Citrus (improvement of citrus canker resistance) |
| [ | ||||||
| Wheat resistance to mildew |
| [ | ||||||
| Rice resistance to salinity tolerance |
| [ | ||||||
| Rice herbicide-tolerant |
| [ | ||||||
| Potato resistance to the herbicide |
| [ | ||||||
| Rice disease resistance |
| [ | ||||||
| Rice broad-spectrum disease resistance |
| [ | ||||||
| Duncan grapefruit (disease resistant citrus varieties) |
| [ | ||||||
| Rice resistance to plant hoppers and stem borers |
| [ | ||||||
| Rice tolerance to abiotic stresses |
| [ | ||||||
| Rice seed setting rate, the total number of grains, number of full grains per panicle and 1000-grain weight |
| [ | ||||||
| Rice grain size, width and weight | [ | |||||||
| Rice ear length, grain size, cold tolerance | [ | |||||||
| Rice grain number, ear structure, particle size | [ | |||||||
| Soybean NSPP and leaflet shape |
| [ | ||||||
| Maize grain yield | [ | |||||||
| Wheat grain shape and weight |
| [ | ||||||
| Industry | Yeast biosynthesizing monoterpenes |
| [ | |||||
|
| [ | |||||||
|
|
| [ | ||||||
| CRISPR /Cas12a(Cpf1) | Feng Zhang | Broad Institute of MIT and Harvard (USA) | 2015 | [ | Medicine and Health | First mammalian model (HEK 293T cell) | [ | |
| SARS-CoV-2 | SARS-CoV-2 N gene, E gene | [ | ||||||
| Agriculture | Soybean fatty acid desaturases |
| [ | |||||
| Industry | [ | |||||||
| Base Editor (BE) | David R. Liu | Harvard University (USA) | 2016 | [ | Medicine and Health | Albinism (mouse model) | [ | |
| Agriculture | Rice, Wheat and Maize genome | [ | ||||||
| Industry |
| [ | ||||||
| Adenine Base Editors (ABEs) | David R. Liu | Harvard University (USA) | 2017 | [ | Medicine and Health | Duchenne muscular dystrophy |
| [ |
| Prime Editors (PEs) | David R. Liu | Harvard University (USA) | 2019 | [ | Agriculture | Rice and Wheat genome | [ | |
| DddA-derived cytosine Base Editors (DdCBEs) | David R. Liu | Harvard University (USA) | 2020 | [ | Medicine and Health | Human mitochondrial (HEK 293T cell) | [ | |
| Target-AID | Akihiko Kondo | Kobe University (Japan) | 2016 | [ | Agriculture | Tomato hormone |
| [ |
| Industry | [ | |||||||
| dCas9-AIDx | Yan Song and Xing Chang | Chinese Academy of Sciences and Shanghai Jiao Tong University School of Medicine (China) | 2016 | [ | Medicine and Health | Chronic myeloid leukemia cells |
| [ |
| CRISPR /Cas13a (C2c2) | Feng Zhang | Broad Institute of MIT and Harvard (USA) | 2016 | [ | Medicine and Health | Dengue and Zika virus | the | [ |
| Human monocytic cell |
| [ | ||||||
| CRISPR /Cas13b (C2c6) | Feng Zhang | Broad Institute of MIT and Harvard (USA) | 2017 | [ | Medicine and Health | SARS-CoV-1 | RNA genome | [ |
| CRISPR /Cas13d | Feng Zhang | Broad Institute of MIT and Harvard (USA) | 2018 | [ | Medicine and Health | SARS-CoV-2 | RNA genome | [ |
| Agriculture |
| [ | ||||||
| Industry |
| [ | ||||||
Figure 3Schematic diagram of four types of gene editing technologies. (A) Schematic diagram of Meganuclease editing genomic DNA. (B) Schematic diagram of zinc-finger nuclease (ZFN) editing genomic DNA. (C) Schematic diagram of transcription activator-like effector nuclease (TALEN) editing genomic DNA. (D) Schematic diagram of clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 editing genomic DNA. (E) Schematic diagram of two DNA repair pathways (NHEJ and HDR) when all four enzymes edit and produce DNA double strand breaks (DSBs).
Figure 4The academic performance of the top 20 countries. (A) Bubble chart of publications and average citations in the 20 countries. (B) National cooperation network among the 20 countries.
The top 20 institutions in gene editing with the publication volume as the first rank.
| Rank | Organizations | Number of Records | Average |
|---|---|---|---|
| 1 | Chinese Academy of Sciences | 599 | 37.53 |
| 2 | University of Chinese Academy of Sciences | 311 | 30.90 |
| 3 | Harvard Medical School | 291 | 40.30 |
| 4 | Stanford University | 261 | 46.75 |
| 5 | Harvard University | 251 | 280.71 |
| 6 | University of California, Berkeley | 220 | 118.13 |
| 7 | Chinese Academy of Agricultural Sciences | 192 | 22.41 |
| 8 | Massachusetts Institute of Technology | 173 | 298.36 |
| 9 | University of California, San Diego | 160 | 39.74 |
| 10 | University of California, San Francisco | 155 | 54.07 |
| 11 | Seoul National University | 153 | 70.65 |
| 12 | The University of Tokyo | 152 | 36.21 |
| 13 | University of Oxford | 151 | 22.07 |
| 14 | University of Minnesota | 148 | 46.14 |
| 15 | University of Pennsylvania | 147 | 56.56 |
| 16 | Zhejiang University | 146 | 20.82 |
| 17 | University of Washington | 144 | 40.56 |
| 18 | Kyoto University | 138 | 24.96 |
| 19 | Massachusetts Gen Hospital | 137 | 173.14 |
| 20 | University of California, Davis | 136 | 27.55 |
The top 20 prolific authors in gene editing with the publication volume as the first rank. The recent 3-years rate is the percentage of publications published in the recent three years as a proportion of the author’s total publications.
| Author | Number of | Total | Average Citations | Year Range | Recent 3-Year Rate | Organizations | Top Research Fields (Number of Records) |
|---|---|---|---|---|---|---|---|
| Takashi Yamamoto | 69 | 1866 | 27.04 | 2012–2021 | 20% | Hiroshima University | Cell Biology (24); Multidisciplinary Sciences (21); Developmental Biology (13) |
| Tetsushi Sakuma | 62 | 1763 | 28.44 | 2012–2021 | 19% | Hiroshima University | Cell Biology (22); Multidisciplinary Sciences (19); Developmental Biology (11) |
| Feng Zhang | 62 | 36,524 | 589.1 | 2012–2021 | 25% | Massachusetts Institute of Technology | Biochemistry Molecular Biology (22); Cell Biology (21); Multidisciplinary Sciences (15) |
| Jin-Soo Kim | 61 | 9354 | 153.34 | 2009–2021 | 24% | Seoul National University | Biotechnology Applied Microbiology (24); Biochemistry Molecular Biology (15); Multidisciplinary Sciences (14) |
| Jennifer A. Doudna | 52 | 14,594 | 280.65 | 2012–2021 | 44% | University of California, Berkeley | Multidisciplinary Sciences (23); Biochemistry Molecular Biology (14); Cell Biology (7) |
| Caixia Gao | 45 | 3965 | 88.11 | 2014–2021 | 58% | Chinese Academy of Sciences | Biotechnology Applied Microbiology (15); Plant Sciences (11); Biochemistry Molecular Biology (9) |
| J. Keith Joung | 42 | 16,411 | 390.74 | 2010–2021 | 20% | Harvard University | Biotechnology Applied Microbiology (15); Biochemistry Molecular Biology (9); Multidisciplinary Sciences (8) |
| Matthew H. Porteus | 40 | 2762 | 69.05 | 2013–2021 | 44% | Stanford University | Medicine Research Experimental (11); Multidisciplinary Sciences (9); Biotechnology Applied Microbiology (8) |
| Toni Cathomen | 40 | 2033 | 50.83 | 2009–2021 | 28% | University of Freiburg | Medicine Research Experimental (15); Genetics Heredity (12); Biotechnology Applied Microbiology (11) |
| Gang Bao | 39 | 5242 | 134.41 | 2013–2021 | 39% | Rice University | Medicine Research Experimental (12); Biotechnology Applied Microbiology (8); Genetics Heredity (7) |
| Daniel F. Voytas | 39 | 4877 | 125.05 | 2011–2021 | 24% | University Minnesota Crookston | Plant Sciences (18); Biotechnology Applied Microbiology (10); Multidisciplinary Sciences (7) |
| Michael C. Holmes | 39 | 9602 | 246.21 | 2007–2021 | 18% | Sangamo Therapeutics | Biotechnology Applied Microbiology (14); Medicine Research Experimental (13); Genetics Heredity (9) |
| David R. Liu | 35 | 8268 | 236.23 | 2013–2021 | 38% | Harvard University | Multidisciplinary Sciences (17); Biochemistry Molecular Biology (8); Biotechnology Applied Microbiology (6); |
| Rodolphe Barrangou | 35 | 1996 | 57.03 | 2013–2021 | 49% | North Carolina State University | Microbiology (10); Biochemistry Molecular Biology (8); Biotechnology Applied Microbiology (7) |
| Philip D. Gregory | 34 | 10,762 | 316.53 | 2007–2018 | 31% | Sangamo Therapeutics | Biotechnology Applied Microbiology (9); Multidisciplinary Sciences (7); Biochemical Research Methods (5) |
| Yong Zhang | 33 | 1654 | 50.12 | 2015–2021 | 60% | University of Electronic Science Technology of China | Plant Sciences (14); Biochemistry Molecular Biology (11); Biotechnology Applied Microbiology (8) |
| Bing Yang | 33 | 2103 | 63.73 | 2011–2021 | 44% | Iowa State University | Plant Sciences (19); Biotechnology Applied Microbiology (12); Biochemistry Molecular Biology (10) |
| YiPing Qi | 32 | 1682 | 52.56 | 2015–2021 | 69% | University of Maryland, College Park | Plant Sciences (25); Biotechnology Applied Microbiology (6); Biochemistry Molecular Biology (5) |
| Xingxu Huang | 28 | 1447 | 51.68 | 2014–2021 | 32% | Shanghai Tech University | Biochemistry Molecular Biology (9); Cell Biology (9); Multidisciplinary Sciences (7) |
| Huimin Zhao | 28 | 1618 | 57.79 | 2012–2021 | 31% | University of Illinois, Urbana-Champaign | Biotechnology Applied Microbiology (14); Biochemical Research Methods (6); Biochemistry Molecular Biology (3) |
Figure 5The academic performance of key institutes and researchers. (A) Cooperation network of the top 20 institutes. (B) Map of the collaborative patterns among the top 20 prolific scientists.
Figure 6The hotspots of GE research. (A) The word cloud map of keywords according to the frequency of occurrence. (B) The emergence graph of the top 20 keywords.
Figure 7The keyword cluster timing diagram with the change of keywords from 2005 to 2022.3.5 The Worldwide Regulatory Framework.
Figure 8Regulation-related publications of GE from 1999 to 2021.
Figure 9Keyword co-occurrence network diagram of GE regulation-related research.
Figure 10The co-occurrence network analysis of national cooperation in GE regulatory research.