Back

Isolation and characterization of three lytic podo-bacteriophages with two receptor recognition modules against multidrug-resistant Klebsiella pneumoniae

Xiang, Y.; Huang, L.; Huang, X.; Zhang, J.; Zhao, T.

2024-05-20 microbiology
10.1101/2024.05.19.594906 bioRxiv
Show abstract

The multidrug-resistant nosocomial pathogen Klebsiella pneumoniae is considered as one of the major threats to public health. Recent studies showed that bacteriophages could be used as an alternative to antibiotics for treating infections caused by multidrug-resistant K. pneumoniae. Here we isolated and characterized three lytic bacteriophages Kp7, Kp9 and Kp11 of K. pneumoniae. Transmission electron microscopy analysis showed that all three phages have an isometric head of approximately 60 nm in diameter and a short noncontractile tail. Host specificity, one-step growth curve, tolerance to pH and temperature changes were characterized for Kp7, Kp9, and Kp11. Mass-spectrum and bioinformatical analysis identified two different types of tail fibers in each phage. The tail fiber proteins gp52 of Kp7, gp43 of Kp9 and gp46 of Kp11 are homologous and have robust enzymatic activity in digesting purified serotype K2 capsule of K. pneumoniae. However, tail fiber proteins gp51 of Kp7, gp42 of Kp9 and gp45 of Kp11 have diverse sequences and show weak or no enzyme activity in digesting purified serotype K2 capsule. Genetic knockout and biochemical assays indicated that the capsule is essential for the infection of Kp7, Kp9 and Kp11. Spot tests showed that 15 clinical isolates of drug-resistant K. pneumoniae with the serotype K2 capsule can all be infected by Kp7, Kp9 and Kp11 but with different susceptibility. Further mechanistic studies showed that transcriptional inhibition at the early stage of phage infection determines the susceptibility of different K. pneumoniae isolates.

Matching journals

The top 10 journals account for 50% of the predicted probability mass.

50% of probability mass above

"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.