Evidence map›Paper›PMID 25405149›Full record

ArticleFrontiers in bioengineering and biotechnology2014

Protein Network Signatures Associated with Exogenous Biofuels Treatments in Cyanobacterium Synechocystis sp. PCC 6803.

Guangsheng Pei, Lei Chen, Jiangxin Wang, Jianjun Qiao, Weiwen Zhang

Abstract read
In one paragraph

Article in Frontiers in bioengineering and biotechnology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Four New Genes of CyanobacteriumMicroorganisms · 2020
    Article
  6. 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

5 authors.

Guangsheng PeiLaboratory of Synthetic Microbiology, School of Chemical Engineering and Technology, Tianjin University , Tianjin , China ; Key Laboratory of Systems Bioengineering, Ministry of Education of China , Tianjin , China ; SynBio Research Platform, Collaborative Innovation Center of Chemical Science and Engineering , Tianjin , China.
Lei ChenLaboratory of Synthetic Microbiology, School of Chemical Engineering and Technology, Tianjin University , Tianjin , China ; Key Laboratory of Systems Bioengineering, Ministry of Education of China , Tianjin , China ; SynBio Research Platform, Collaborative Innovation Center of Chemical Science and Engineering , Tianjin , China.
Jiangxin WangLaboratory of Synthetic Microbiology, School of Chemical Engineering and Technology, Tianjin University , Tianjin , China ; Key Laboratory of Systems Bioengineering, Ministry of Education of China , Tianjin , China ; SynBio Research Platform, Collaborative Innovation Center of Chemical Science and Engineering , Tianjin , China.
Jianjun QiaoLaboratory of Synthetic Microbiology, School of Chemical Engineering and Technology, Tianjin University , Tianjin , China ; Key Laboratory of Systems Bioengineering, Ministry of Education of China , Tianjin , China ; SynBio Research Platform, Collaborative Innovation Center of Chemical Science and Engineering , Tianjin , China.
Weiwen ZhangLaboratory of Synthetic Microbiology, School of Chemical Engineering and Technology, Tianjin University , Tianjin , China ; Key Laboratory of Systems Bioengineering, Ministry of Education of China , Tianjin , China ; SynBio Research Platform, Collaborative Innovation Center of Chemical Science and Engineering , Tianjin , China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Although recognized as a promising microbial cell factory for producing biofuels, current productivity in cyanobacterial systems is low. To make the processes economically feasible, one of the hurdles, which need to be overcome is the low tolerance of hosts to toxic biofuels. Meanwhile, little information is available regarding the cellular responses to biofuels stress in cyanobacteria, which makes it challenging for tolerance engineering. Using large proteomic datasets of Synechocystis under various biofuels stress and environmental perturbation, a protein co-expression network was first constructed and then combined with the experimentally determined protein-protein interaction network. Proteins with statistically higher topological overlap in the integrated network were identified as common responsive proteins to both biofuels stress and environmental perturbations. In addition, a weighted gene co-expression network analysis was performed to distinguish unique responses to biofuels from those to environmental perturbations and to uncover metabolic modules and proteins uniquely associated with biofuels stress. The results showed that biofuel-specific proteins and modules were enriched in several functional categories, including photosynthesis, carbon fixation, and amino acid metabolism, which may represent potential key signatures for biofuels stress responses in Synechocystis. Network-based analysis allowed determination of the responses specifically related to biofuels stress, and the results constituted an important knowledge foundation for tolerance engineering against biofuels in Synechocystis.

Indexed as

biofuelsnetworkSynechocystistoleranceWGCNA

Identifiers

PMID25405149
PMCPMC4217553

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Registered trials

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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.