铜绿假单胞菌
生物膜
抗菌剂
微生物学
生物信息学
生物
细菌
基因
生物化学
遗传学
作者
Qi Yin,Siwen Wu,Lei Wu,Zhenling Wang,Yandong Mu,Rui Zhang,Chunyan Dong,Bailing Zhou,Binyan Zhao,Jiajun Zheng,Ying Sun,Xiao Cheng
摘要
Abstract Background Antimicrobial peptides are promising alternative antimicrobial agents to combat MDR. DP7, an antimicrobial peptide designed in silico, possesses broad-spectrum antimicrobial activities and immunomodulatory effects. However, the effects of DP7 against Pseudomonas aeruginosa and biofilm infection remain largely unexplored. Objectives To assess (i) the antimicrobial activity of DP7 against MDR P. aeruginosa; and (ii) the antibiofilm activity against biofilm infection. Also, to preliminarily investigate the possible antimicrobial mode of action. Methods The MICs of DP7 for 104 clinical P. aeruginosa strains (including 57 MDR strains) and the antibiofilm activity were determined. RNA-Seq, genome sequencing and cell morphology were conducted. Both acute and chronic biofilm infection mouse models were established. Two mutants, resulting from point mutations associated with LPS and biofilms, were constructed to investigate the potential mode of action. Results DP7, at 8–32 mg/L, inhibited the growth of clinical P. aeruginosa strains and, at 64 mg/L, reduced biofilm formation by 43% to 68% in vitro. In acute lung infection, 0.5 mg/kg DP7 exhibited a 70% protection rate and reduced bacterial colonization by 50% in chronic infection. DP7 mainly suppressed gene expression involving LPS and outer membrane proteins and disrupted cell wall structure. Genome sequencing of the DP7-resistant strain DP7R revealed four SNPs controlling LPS and biofilm production. gshA44 and wbpJ139 mutants displayed LPS reduction and motility deficiency, conferring the reduction of LPS and biofilm biomass of strain DP7R and indicating that LPS was a potential target of DP7. Conclusions These results demonstrate that DP7 may hold potential as an effective antimicrobial agent against MDR P. aeruginosa and related infections.
科研通智能强力驱动
Strongly Powered by AbleSci AI