Molecular mechanisms of heavy metals resistance of Stenotrophomonas rhizophila JC1 by whole genome sequencing.
Shang-Chen SunJi-Xiang ChenYong-Gang WangFei-Fan LengJian ZhaoKai ChenQing-Chun ZhangPublished in: Archives of microbiology (2021)
In this study, a higher metal ions-resistant bacterium, Stenotrophomonas rhizophila JC1 was isolated from contaminated soil in Jinchang city, Gansu Province, China. The Pb2+ (120 mg/L) and Cu2+ (80 mg/L) removal rate of the strain reached at 76.9% and 83.4%, respectively. The genome comprises 4268161 bp in a circular chromosome with 67.52% G + C content and encodes 3719 proteins. The genome function analysis showed czc operon, mer operon, cop operon, arsenic detoxification system in strain JC1 were contributed to the removal of heavy metals. Three efflux systems (i.e., RND, CDF, and P-ATPase) on strain JC1 genome could trigger the removal of divalent cations from cells. cAMP pathway and ABC transporter pathway might be involved in the transport and metabolism of heavy metals. The homology analysis exhibited multi-gene families such as ABC transporters, heavy metal-associated domain, copper resistance protein, carbohydrate-binding domain were distributed across 410 orthologous groups. In addition, heavy metal-responsive transcription regulator, thioredoxin, heavy metal transport/detoxification protein, divalent-cation resistance protein CutA, arsenate reductase also played important roles in the heavy metals adsorption and detoxification process. The complete genome data provides insight into the exploration of the interaction mechanism between microorganisms and heavy metals.
Keyphrases
- heavy metals
- risk assessment
- health risk assessment
- health risk
- genome wide
- binding protein
- sewage sludge
- protein protein
- transcription factor
- amino acid
- induced apoptosis
- copy number
- dna methylation
- aqueous solution
- artificial intelligence
- electronic health record
- south africa
- cancer therapy
- machine learning
- small molecule
- cell cycle arrest
- drug delivery
- cell proliferation
- big data
- endoplasmic reticulum stress
- signaling pathway
- deep learning
- protein kinase