Evidence map›Paper›PMID 37421473›Full record

ArticleApplied microbiology and biotechnology2023

Biosensor-based high-throughput screening enabled efficient adipic acid production.

Rui Zhi, Nan Cheng, Guohui Li, Yu Deng

Abstract read
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In one paragraph

Article in Applied microbiology and biotechnology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Advancing lignocellulosic conversion though biosensor-enabled metabolic engineering.Green chemistry : an international journal and green chemistry resource : GC · 2025
    Review
  3. Review
  4. Article
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

4 authors.

Rui ZhiNational Engineering Research Center of Cereal Fermentation and Food Biomanufacturing, Jiangnan University, Wuxi, 214122, Jiangsu, China.
Nan ChengNational Engineering Research Center of Cereal Fermentation and Food Biomanufacturing, Jiangnan University, Wuxi, 214122, Jiangsu, China.
Guohui LiNational Engineering Research Center of Cereal Fermentation and Food Biomanufacturing, Jiangnan University, Wuxi, 214122, Jiangsu, China. guohuili@jiangnan.edu.cn.
Yu DengNational Engineering Research Center of Cereal Fermentation and Food Biomanufacturing, Jiangnan University, Wuxi, 214122, Jiangsu, China. dengyu@jiangnan.edu.cn.ORCID http://orcid.org/0000-0002-1909-7223

Funding

National Natural Science Foundation of China 21877053National Natural Science Foundation of China 22008088
6 · The paper itself

Abstract

Adipic acid is an industrially important chemical, but the current approach to synthesize it can be of serious pollution to the environment. Rencently, bio-based production of adipic acid has significantly advanced with the development of metabolic engineering and synthetic biology. However, genetic heterogeneity-caused decrease of product titer has largely limited the industrialization of chemicals like adipic acid. Therefore, in the attempt to overcome this challenge, we constitutively expressed the reverse adipate degradation pathway, designed and optimized an adipic acid biosensor, and established a high-throughput screening platform to screen for high-performance strains based on the optimized biosensor. Using this platform, we successfully screened a strain with an adipic acid titer of 188.08 mg·L

Indexed as

AdipatesBiosensing TechniquesHigh-Throughput Screening AssaysBioreactorsFermentationMetabolic EngineeringAdipatesadipic acidAdipic acidBiosensorGenetic heterogeneityHigh-throughput screeningReverse adipate degradation pathway

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.