Definition and research guide
This page combines a MeSH definition, publication statistics and selected research abstracts. It is a starting point for reading the literature, not a medical textbook or a systematic evidence review.
Content available: 3 complete abstracts (610 words), 9 references. Full article text is not stored or reproduced here; follow the PMID, DOI or PMC links to read the original sources and their access terms.
Definition
Directed modification of the gene complement of a living organism by such techniques as altering the DNA, substituting genetic material by means of a virus, transplanting whole nuclei, transplanting cell hybrids, etc.
Definition: NLM Medical Subject Headings (MeSH) scope note (Public domain (U.S. National Library of Medicine); attribution requested). View the MeSH record.
Also known as: Engineering, Genetic; Genetic Intervention; Intervention, Genetic; Genetic Interventions; Interventions, Genetic.
Where it fits in the MeSH classification
- Broader topics
- Genetic Techniques
- Narrower topics
- Directed Molecular Evolution; DNA Shuffling; Epigenome Editing; Gene Editing; Genetic Enhancement; Genetic Therapy; Metabolic Engineering; Protein Engineering; Sex Preselection
- See also
- DNA, Recombinant; Fertilization in Vitro; Mice, Transgenic; Protein Engineering; Mice, Knockout; Organisms, Genetically Modified; Animals, Genetically Modified; Plants, Genetically Modified
Research landscape
PubMed indexes 248,402 records tagged with the MeSH heading “Genetic Engineering” (counts retrieved 2026-10-29). 41,608 of them (16.8%) were published from 2020 onward, and the busiest period so far is 2000-2009 with 82,265 records.
Reviews account for 40,200 records (16.2%) and meta-analyses for 401. Randomized controlled trials number 356 (0.1%) as a share of all records on the topic. Randomized trials make up only a small share of this literature, so much of what is known comes from laboratory, observational or descriptive work.
Show the data
| Period | Records |
|---|---|
| through 1999 | 44,161 |
| 2000-2009 | 82,265 |
| 2010-2019 | 80,368 |
| 2020 onward | 41,608 |
Publication-type chart data
| Type | Records | Share |
|---|---|---|
| Reviews | 40,200 | 16.2% |
| Meta-analyses | 401 | 0.2% |
| Randomized controlled trials | 356 | 0.1% |
Method: PubMed query "Genetic Engineering"[MeSH Terms] with publication-type and date filters, retrieved 2026-10-29.
Reading the evidence in genetics and genomics
Genetic findings vary widely in strength. A variant linked to a condition in one study may not replicate in other ancestries, and most common variants have small effects that are meaningful only in aggregate. Studies of rare, high-impact variants can support strong causal claims about a gene, while association studies describe statistical links that need functional and independent replication. Check the sample size, the ancestry of participants, whether results were replicated, and whether a laboratory or clinical outcome was actually measured.
Evidence from open-access studies
These open-access studies come from PubMed’s best-match results for this heading (papers whose main topic it is), ranked here by citation count, with one recent addition. Each abstract is reproduced in full under its recorded open licence so that you can read the authors’ own words. They illustrate the literature; they are not a systematic summary of it. Citation counts measure attention, not reliability or clinical benefit.
| Study / authors | Year | Source label / design | Europe PMC citations | Licence / source |
|---|---|---|---|---|
| AI-redesigned starting points and outcomes enhance protein evolution | 2026 | Research article Design not recorded in structured metadata | 5 | cc by PMID 42486980 · DOI · PMC13477572 |
| Optimised genome editing for precise DNA insertion and substitution using prime editors in zebrafish | 2026 | Research article Design not recorded in structured metadata | 4 | cc by PMID 42411453 · DOI · PMC13341114 |
| EvoMut: A computational framework for engineering oxidative stability in proteins | 2026 | Research article Design not recorded in structured metadata | 1 | cc by PMID 42741978 · DOI · PMC13575976 |
AI-redesigned starting points and outcomes enhance protein evolution [1]
Abstract reading summary — source excerpts
- Opening excerpt
- Engineered or laboratory-evolved proteins often have suboptimal stability, activity or specificity.
- Closing excerpt
- This study establishes a practical workflow using AI-redesigned starting points to evolve enzymes with improved properties compared with those evolved from natural proteins, with broad implications for protein science.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (244 words)
Engineered or laboratory-evolved proteins often have suboptimal stability, activity or specificity. Here we applied artificial intelligence (AI)-based protein sequence design to address challenges in experimental enzyme evolution. Using the model ProteinMPNN, we redesigned three distinct botulinum neurotoxin (BoNT) proteases, generating variants with improved stability and full catalytic efficiency1. We hypothesized that redesigned enzymes may be more mutationally robust than their wild-type (WT) counterparts, and therefore may serve as better starting points to evolve new function. We performed side-by-side phage-assisted continuous evolution campaigns initiated with AI-redesigned proteases or with the corresponding WT proteases2. Evolving three distinct redesigned enzymes as starting points consistently yielded proteases with higher activity than evolving WT proteases in the same selection. Across four evolution campaigns, redesign conferred robustness that unlocked access to otherwise inaccessible highly functional sequences, confirmed by the inability of redesign-evolved mutations to function in WT enzyme backgrounds. When redesign raises fitness in sequence space local to the starting point, redesigned starting points adapt at a faster rate. Finally, we evolved both WT and AI-redesigned BoNT/E protease to selectively cleave the therapeutically relevant protein ataxin-2. Proteases evolved from the redesigned starting point reached higher catalytic efficiency and stability while minimizing native substrate cleavage, achieving more than 79-fold greater selected specificity for ataxin-2 than the best-performing variant evolved from WT BoNT/E. This study establishes a practical workflow using AI-redesigned starting points to evolve enzymes with improved properties compared with those evolved from natural proteins, with broad implications for protein science.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 42486980 · DOI · PMC13477572
Optimised genome editing for precise DNA insertion and substitution using prime editors in zebrafish [2]
Abstract reading summary — source excerpts
- Opening excerpt
- CRISPR/Cas9-mediated genome editing has rapidly become a popular tool for studying gene functions and generating genetically modified organisms.
- Closing excerpt
- We show that PE-mediated prime editing can efficiently manipulate genome information in zebrafish without using exogenous donor DNA.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (171 words)
CRISPR/Cas9-mediated genome editing has rapidly become a popular tool for studying gene functions and generating genetically modified organisms. However, using this system, stochastic integration of random insertions and deletions restricts precise genome manipulation. Advanced CRISPR/Cas9 technologies using Prime Editors (PEs), Cas9 proteins fused with reverse transcriptase, enable programmed integration of short DNA modifications into the genome. However, its application in precise genome editing in animal models is challenging. Here, we utilise a nickase- and a nuclease-based PE to perform programmed short DNA substitutions and insertions at various loci in the zebrafish genome. Whereas nickase-based PE2 mediated a higher ratio of precise prime edits to the total edits, nuclease-based PEn was more efficient for short DNA modifications, achieving up to 27.3% precise insertion. To further evaluate our approach, we inserted a nuclear localisation signal into a reporter transgene to incorporate longer fragments by prime editing. These gene modifications were transmitted to the next generation. We show that PE-mediated prime editing can efficiently manipulate genome information in zebrafish without using exogenous donor DNA.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 42411453 · DOI · PMC13341114
EvoMut: A computational framework for engineering oxidative stability in proteins [3]
Abstract reading summary — source excerpts
- Opening excerpt
- Amino acid oxidation is a major cause of protein instability and loss of function in therapeutic and industrial settings.
- Closing excerpt
- edu/evomut.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (195 words)
Amino acid oxidation is a major cause of protein instability and loss of function in therapeutic and industrial settings. Although methionine, cysteine, tryptophan, tyrosine, histidine, lysine, and arginine residues are widely recognized as oxidation-prone, only a subset of such residues is dominant functional hotspots, and not all are suitable targets for mutation. Identifying these vulnerable, yet engineerable, sites remains a major challenge. Here, we present EvoMut, a residue-level analytical framework for evaluating both oxidative vulnerability and mutation feasibility. EvoMut estimates oxidation risk by integrating structural features, local functional context, intrinsic chemical susceptibility, and evolutionary conservation. A central feature of the framework is the explicit separation of oxidation risk from mutation feasibility. Specifically, candidate substitutions are evaluated only after high-risk residues are identified and ranked by evolutionary substitution patterns. Application of EvoMut to multiple proteins, and evaluation with experimental data, showed that oxidation-prone residues differ markedly in their engineering potential. EvoMut distinguishes residues that are both oxidation-sensitive and evolutionarily permissive from those that are chemically vulnerable but functionally constrained. By providing residue-level mechanistic insight, EvoMut offers a practical framework for the rational design of oxidation-resistant proteins. EvoMut is freely available as a web server at https://proteus.cmm.uga.edu/evomut.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 42741978 · DOI · PMC13575976
Key reviews and meta-analyses
The most cited review-type papers among PubMed’s best-match results for this heading, listed with bibliographic details only. Follow the links for the full text and access terms.
- Research article Meta-analysis of gross insertions causing human genetic disease: novel mutational mechanisms and the role of replication slippage [4]
- Research article Mesenchymal Stem Cell Therapy for Inflammatory Bowel Disease: A Systematic Review and Meta-analysis [5]
- Research article An evolutionary perspective on single-nucleotide polymorphism screening in molecular cancer epidemiology [6]
- Research article An overview and metanalysis of machine and deep learning-based CRISPR gRNA design tools [7]
- Research article Climatic factors and species range position predict sexually antagonistic selection across taxa [8]
- Research article Natural family planning and sex selection: fact or fiction? [9]
References
- Krasnow NA, Xu JA, Zhang E, Mahadeshwar GK, Tao YA, McCreary J, et al.. AI-redesigned starting points and outcomes enhance protein evolution. . 2026. PMID 42486980 · DOI · PMC13477572
- Ono Y, Peterka M, Love M, Khan A, Bowers F, Bhandari A, et al.. Optimised genome editing for precise DNA insertion and substitution using prime editors in zebrafish. . 2026. PMID 42411453 · DOI · PMC13341114
- Arab SS, Hsieh C, Lewis NE. EvoMut: A computational framework for engineering oxidative stability in proteins. . 2026. PMID 42741978 · DOI · PMC13575976
- Chen JM, Chuzhanova N, Stenson PD, Férec C, Cooper DN. Meta-analysis of gross insertions causing human genetic disease: novel mutational mechanisms and the role of replication slippage. Hum Mutat. 2005. PMID 15643617 · DOI
- Dave M, Mehta K, Luther J, Baruah A, Dietz AB, Faubion WA. Mesenchymal Stem Cell Therapy for Inflammatory Bowel Disease: A Systematic Review and Meta-analysis. Inflamm Bowel Dis. 2015. PMID 26230863 · DOI · PMC4615553
- Zhu Y, Spitz MR, Amos CI, Lin J, Schabath MB, Wu X. An evolutionary perspective on single-nucleotide polymorphism screening in molecular cancer epidemiology. Cancer Res. 2004. PMID 15026370 · DOI
- Wang J, Zhang X, Cheng L, Luo Y. An overview and metanalysis of machine and deep learning-based CRISPR gRNA design tools. RNA Biol. 2020. PMID 31533522 · DOI · PMC6948960
- De Lisle SP, Goedert D, Reedy AM, Svensson EI. Climatic factors and species range position predict sexually antagonistic selection across taxa. Philos Trans R Soc Lond B Biol Sci. 2018. PMID 30150216 · DOI · PMC6125731
- Gray RH. Natural family planning and sex selection: fact or fiction?. Am J Obstet Gynecol. 1991. PMID 1836712 · DOI
About this article
- Sources
- NLM Medical Subject Headings (definition, synonyms); NCBI PubMed (counts, identifiers); Europe PMC (open-access abstracts and citation counts).
- Verification
- Every reference was resolved against PubMed on 2026-10-31; title, year and DOI matched.
- Review state
- automated source verified; no human expert review has been recorded yet.
- Version
- 1, last updated 2026-10-29.
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Evidence strength
EvidenceHelix's computed view of the study designs behind this topic: how common meta-analyses, randomized trials and reviews are, how recent the literature is, and a plain-language reading. It describes the design mix of the literature. It is not a quality appraisal of any study, and it is not medical advice.