Evidence map›Paper›PMID 38064000›Full record

ArticleMycotoxin research2024

Degradation of deoxynivalenol by a microbial consortia C1 from duck intestine.

Yunduo Zheng, Boquan Gao, Jianwen Wu, Xiumin Wang, Bing Han, Hui Tao, Jie Liu, Zhenlong Wang, Jinquan Wang

Abstract read
PubMed Publisher
In one paragraph

Article in Mycotoxin research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.2field-weighted citation impact, top 15% of its field
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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Review
  2. 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

9 authors at 1 institution in 1 country.

Yunduo ZhengKey Laboratory of Feed Biotechnology, Ministry of Agriculture and Rural Affairs, Institute of Feed Research, Chinese Academy of Agricultural Sciences, No. 12 Zhongguancun South Street, Beijing, 100081, China.
Boquan GaoKey Laboratory of Feed Biotechnology, Ministry of Agriculture and Rural Affairs, Institute of Feed Research, Chinese Academy of Agricultural Sciences, No. 12 Zhongguancun South Street, Beijing, 100081, China.
Jianwen WuKey Laboratory of Feed Biotechnology, Ministry of Agriculture and Rural Affairs, Institute of Feed Research, Chinese Academy of Agricultural Sciences, No. 12 Zhongguancun South Street, Beijing, 100081, China.
Xiumin WangKey Laboratory of Feed Biotechnology, Ministry of Agriculture and Rural Affairs, Institute of Feed Research, Chinese Academy of Agricultural Sciences, No. 12 Zhongguancun South Street, Beijing, 100081, China.
Bing HanKey Laboratory of Feed Biotechnology, Ministry of Agriculture and Rural Affairs, Institute of Feed Research, Chinese Academy of Agricultural Sciences, No. 12 Zhongguancun South Street, Beijing, 100081, China.
Hui TaoKey Laboratory of Feed Biotechnology, Ministry of Agriculture and Rural Affairs, Institute of Feed Research, Chinese Academy of Agricultural Sciences, No. 12 Zhongguancun South Street, Beijing, 100081, China.
Jie LiuKey Laboratory of Feed Biotechnology, Ministry of Agriculture and Rural Affairs, Institute of Feed Research, Chinese Academy of Agricultural Sciences, No. 12 Zhongguancun South Street, Beijing, 100081, China.
Zhenlong WangKey Laboratory of Feed Biotechnology, Ministry of Agriculture and Rural Affairs, Institute of Feed Research, Chinese Academy of Agricultural Sciences, No. 12 Zhongguancun South Street, Beijing, 100081, China. wangzhenlong02@caas.cn.ORCID http://orcid.org/0009-0001-9889-2021
Jinquan WangKey Laboratory of Feed Biotechnology, Ministry of Agriculture and Rural Affairs, Institute of Feed Research, Chinese Academy of Agricultural Sciences, No. 12 Zhongguancun South Street, Beijing, 100081, China. wangjinquan@caas.cn.
Chinese Academy of Agricultural Sciences · CN

Funding

Central Public-interest Scientific Institution Basal Research Fund No. 1610382022007European Union's Horizon 2020 research and innovation program No. 678012National Key Research and Development Program of China No.2016YFE0113300
6 · The paper itself

Abstract

Deoxynivalenol (DON), one of the most widespread mycotoxins in food and feed, poses a persistent health threat to humans and farm animals, and is difficult to eliminate. The utilization of the biotransformation mechanism by microorganisms to detoxify DON is a promising strategy. Although individual strains are capable of DON degradation, their isolation and purification are challenging and time-consuming. Recently, the microbial consortia concept has been proposed, owing to their ability to perform more complex tasks and are more tolerant to environmental changes than individual strains or species. In this study, the novel microbial consortia C1 that could efficiently convert DON to de-epoxy DON (DOM-1) was screened from the cecum contents of ducks. After 24 h anaerobic incubation, 100 μg/ml DON was completely degraded by C1. In vitro, C1 can effectively degrade DON in corn steep liquor (CSL) with an efficiency of 49.44% within 14 days. Furthermore, C1 effectively alleviated the DON poisoning in mice. After C1 treatment, the serum DON level decreased by 40.39%, and the reduction in serum total protein and albumin levels were mitigated. Additionally, C1 is effective in protecting the mouse liver against 5 mg/kg DON. These findings suggest that C1 could be a promising DON biological detoxifier and provide novel microbial resources for preventing DON contamination.

Indexed as

DucksMycotoxinsTrichothecenesAnimal FeedAnimalsFood ContaminationHumansIntestinesMiceMicrobial ConsortiadeoxynivalenolMycotoxinsTrichothecenesCorn steep liquorDeoxynivalenolDOM-1DONMicrobial consortia

Identifiers

PMID38064000
OpenAlexW4389486773

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.