Evidence map›Paper›PMID 41704464›Full record

ReviewSynthetic and systems biotechnology2026

High-throughput editing boosts the construction of next-generation microbial cell factories.

Xi Sun, Yangyang Zheng, Jiancheng Luo, Zhouxiao Geng, Ziyao Wang, Jianxiao Zhao, Tao Chen, Zhiwen Wang

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Synthetic and systems biotechnology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Xi SunSchool of Life Sciences, Key Laboratory of Ministry of Education for Protection and Utilization of Special Biological Resources in Western China, Ningxia University, Yinchuan, 750021, China.
Yangyang ZhengState Key Laboratory of Synthetic Biology, Frontier Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Synthetic Biology and Biomanufacturing, Tianjin University, Tianjin, 300072, China.
Jiancheng LuoState Key Laboratory of Synthetic Biology, Frontier Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Synthetic Biology and Biomanufacturing, Tianjin University, Tianjin, 300072, China.
Zhouxiao GengState Key Laboratory of Synthetic Biology, Frontier Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Synthetic Biology and Biomanufacturing, Tianjin University, Tianjin, 300072, China.
Ziyao WangState Key Laboratory of Synthetic Biology, Frontier Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Synthetic Biology and Biomanufacturing, Tianjin University, Tianjin, 300072, China.
Jianxiao ZhaoState Key Laboratory of Synthetic Biology, Frontier Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Synthetic Biology and Biomanufacturing, Tianjin University, Tianjin, 300072, China.
Tao ChenState Key Laboratory of Synthetic Biology, Frontier Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Synthetic Biology and Biomanufacturing, Tianjin University, Tianjin, 300072, China.
Zhiwen WangSchool of Life Sciences, Key Laboratory of Ministry of Education for Protection and Utilization of Special Biological Resources in Western China, Ningxia University, Yinchuan, 750021, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Traditional metabolic engineering faces numerous challenges in constructing microbial cell factories, such as inefficient gene editing, time-consuming and labor-intensive screening processes, and the complexity of multi-gene optimization. High-throughput (HTP) genome editing technology accelerates the optimization of microbial metabolic pathways by precisely and efficiently modifying multiple genes. Furthermore, HTP genome editing technology enables the rapid screening and modification of key enzymes or regulatory factors across multiple metabolic pathways, facilitating the analysis of complex regulatory mechanisms. These advantages make it a key enabling tool for both top-down analysis and bottom-up assembly of metabolic pathways. This review summarizes the mechanisms and applications of various HTP genome editing and discusses the development of HTP strategies that accelerate the design-build phase for microbial cell factories (MCFs). These advancements promised to significantly enhance the performance of MCFs and drive the next generation of sustainable, bio-based production technologies.

Indexed as

Genome editingHigh-throughputMicrobial cell factoriesNucleasesRecombinationTransposition

Identifiers

PMID41704464
PMCPMC12907669

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.