Back

Genome analysis of the steroid-degrading denitrifying Denitratisoma oestradiolicum DSM 16959 and Denitratisoma sp. strain DHT3

Chen, Y.-L.; Wei, S.; Chiang, Y.-R.

2019-07-22 microbiology
10.1101/710707 bioRxiv
Show abstract

Steroid hormones (androgens and estrogens) are crucial for development, reproduction, and communication of multicellular eukaryotes. The ubiquitous distribution and persistence of steroid hormones in our ecosystems have become an environmental issue due to the adverse effects on wildlife and humans upon long-term exposure. Microbial degradation is critical for the removal of steroid hormones from ecosystems. The aerobic degradation pathways for androgens and estrogens and the anaerobic degradation pathway for androgen have been studied into some details; however, the mechanism for anaerobic estrogen degradation remains completely unknown. Here, we presented the circular genomes of D. oestradiolicum DSM 16959 and Denitratisoma sp. strain DHT3, two betaproteobacteria capable of anaerobic estrogen degradation. We identified the genes involved in steroid transformation and in the anaerobic 2,3-seco pathway in both genomes. Additionally, the comparative genomic analysis revealed that genes exclusively represented in estrogen-degrading anaerobes might play a role in anaerobic estrogen catabolism.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

1
Frontiers in Microbiology
○ 427 papers in training set
Top 0.1%
22.3%
Similar papers in this journal
2
Archives of Microbiology
○ 11 papers in training set
Top 0.1%
6.4%
Similar papers in this journal
3
Microorganisms
○ 106 papers in training set
Top 0.1%
6.4%
Similar papers in this journal
4
Applied and Environmental Microbiology
● 339 papers in training set
Top 1%
5.6%
Similar papers in this journal
5
Applied Microbiology and Biotechnology
○ 32 papers in training set
Top 0.1%
4.9%
Similar papers in this journal
6
Scientific Reports
○ 3612 papers in training set
Top 21%
4.4%
7
Environmental Microbiology
○ 133 papers in training set
Top 0.8%
4.1%
50% of probability mass above
8
mSystems
● 394 papers in training set
Top 3%
2.8%
9
PLOS ONE
● 5266 papers in training set
Top 39%
2.7%
10
Microbiology Spectrum
● 469 papers in training set
Top 5%
2.5%
11
eLife
● 5828 papers in training set
Top 45%
2.2%
12
Current Microbiology
○ 18 papers in training set
Top 0.2%
2.2%
13
Microbial Biotechnology
○ 34 papers in training set
Top 0.4%
1.8%
14
BMC Genomics
○ 406 papers in training set
Top 4%
1.7%
15
PeerJ
◐ 308 papers in training set
Top 5%
1.7%
16
Microbiology Resource Announcements
● 25 papers in training set
Top 0.2%
1.5%
17
Microbiological Research
○ 22 papers in training set
Top 0.3%
1.5%
18
International Journal of Molecular Sciences
○ 494 papers in training set
Top 11%
1.1%
19
Journal of Bacteriology
● 212 papers in training set
Top 2%
1.1%
20
Environmental Pollution
○ 37 papers in training set
Top 0.8%
1.1%
21
BMC Microbiology
○ 49 papers in training set
Top 1%
1.1%
22
International Journal of Systematic and Evolutionary Microbiology
● 14 papers in training set
Top 0.2%
0.9%
23
Bioresource Technology
○ 12 papers in training set
Top 0.3%
0.9%
24
Environmental Microbiology Reports
○ 31 papers in training set
Top 1.0%
0.6%
25
Algal Research
○ 21 papers in training set
Top 0.4%
0.6%
26
Journal of Agricultural and Food Chemistry
● 15 papers in training set
Top 0.7%
0.6%
27
Frontiers in Plant Science
○ 256 papers in training set
Top 4%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.