Evidence map›Paper›PMID 42726309›Full record

ReviewBiotechnology letters2026

Research progress on metabolic engineering for efficient squalene synthesis in yeast cell factories.

Chen Yang, Chuanbo Zhang, Wenyu Lu

Abstract readReview
PubMed Publisher
In one paragraph

Review in Biotechnology letters, 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

3 authors.

Chen YangSchool of Synthetic Biology and Biomanufacturing, Tianjin University, Tianjin, 300072, China.
Chuanbo ZhangSchool of Synthetic Biology and Biomanufacturing, Tianjin University, Tianjin, 300072, China.
Wenyu LuSchool of Synthetic Biology and Biomanufacturing, Tianjin University, Tianjin, 300072, China. wenyulu@tju.edu.cn.

Funding

National Key R&D Program of China 2023YFF0713805National Natural Science Foundation of China No. 22578324
6 · The paper itself

Abstract

Squalene, a naturally occurring triterpenoid, is widely utilized in the food, pharmaceutical, and vaccine adjuvant industries. Conventional production methods, which rely on extraction from shark liver oil or plant sources, are limited by ecological sustainability concerns and low extraction efficiency. Recent progress in synthetic biology and metabolic engineering has enabled the development of sustainable and efficient microbial platforms for squalene biosynthesis. This review provides a comprehensive overview of the biosynthetic pathways of squalene in yeast, summarizes key metabolic engineering strategies, and highlights recent advancements in the field, with an emphasis on multi-organelle synergistic engineering and critical distinction of evidence sources. We focus on approaches such as enhancing the mevalonate pathway, modulating competing metabolic routes, applying subcellular compartmentalization engineering, optimizing cofactor balance, and implementing product efflux strategies. Notably, integrated strategies, particularly organelle engineering in Yarrowia lipolytica and Saccharomyces cerevisiae, have increased squalene titers to high levels (up to 55.8 g/L). These advances provide a useful basis for future metabolic engineering efforts aimed at improving squalene production and facilitating the synthesis of related terpenoids.

Indexed as

Metabolic EngineeringSaccharomyces cerevisiaeSqualeneYarrowiaBiosynthetic PathwaysMevalonic AcidMevalonic AcidSqualeneMetabolic engineeringSaccharomyces cerevisiaeSqualeneSubcellular compartmentalizationTerpenoidsYarrowia lipolytica

Identifiers

What OpenQuestion holds

Textmetadata
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.