Combing Immunoinformatics with Pangenome Analysis To Design a Multiepitope Subunit Vaccine against Klebsiella pneumoniae K1, K2, K47, and K64

Abstract

Klebsiella pneumoniae is an opportunistic Gram-negative bacterium that has become a leading causative agent of nosocomial infections, mainly infecting patients with immunosuppressive diseases. Capsular (K) serotypes K1, K2, K47, and K64 are commonly associated with higher virulence (hypervirulent Klebsiella pneumoniae), and more threateningly, isolates belonging to the last two K serotypes are also frequently associated with resistance to carbapenem (hypervirulent carbapenem-resistant Klebsiella pneumoniae). The prevalence of these isolates has posed significant threats to human health, and there are no appropriate therapies available against them. Therefore, in this study, a method combining immunoinformatics and pangenome analysis was applied for contriving a multiepitope subunit vaccine against these four threatening serotypes. To obtain cross-protection, 12 predicted conserved antigens were screened from the core genome of 274 complete Klebsiella pneumoniae genomes (KL1, KL2, KL47, and KL64), from which the epitopes of T and B cells were extracted for vaccine construction. In addition, the immunological properties, the interaction with Toll-like receptors, and the stability in a simulative humoral environment were evaluated by immunoinformatics methods, molecular docking, and molecular dynamics simulation. All of these evaluations indicated the potency of this constructed vaccine to be an effective therapeutic agent. Lastly, the cDNA of the designed vaccine was optimized and ligated to pET-28a(+) for expression vector construction. Overall, our research provides a newly cross-protective control strategy against these troublesome pathogens and paves the way for the development of a safe and effective vaccine. IMPORTANCE Klebsiella pneumoniae is an opportunistic Gram-negative bacterium that has become a leading causative agent of nosocomial infections. Among the numerous capsular serotypes, K1, K2, K47, and K64 are commonly associated with higher virulence (hypervirulent K. pneumoniae). More threateningly, the last two serotypes are frequently associated with resistance to carbapenem (hypervirulent carbapenem-resistant K. pneumoniae). However, there is currently no therapeutic agent or vaccine specifically against these isolates. Therefore, development of a vaccine against these pathogens is very essential. In this study, for the first time, a method combining pangenome analysis, reverse vaccinology, and immunoinformatics was applied for contriving a multiepitope subunit vaccine against K. pneumoniae isolates of K1, K2, K47, and K64. Also, the immunological properties of the constructed vaccine were evaluated and its high potency was revealed. Overall, our research will pave the way for the vaccine development against these four threatening capsular serotypes of K. pneumoniae.

Keywords: Klebsiella pneumoniae; carbapenem resistant; hypervirulent; immunoinformatics; multiepitope subunit vaccine; pangenome analysis.

FIG 1

Pangenome analysis of 274 K. pneumoniae isolates. (a) The number of genes in the pangenome of K. pneumoniae increases with the number of isolates. (b) The number of conserved genes is reduced to stabilize as the number of isolates increases. (c) Whole-genome phylogenetic tree and a matrix with gene presence and absence.

FIG 2

Location of proteins extracted form the core genome predicted using PSORTb.

FIG 3

Immune simulation in silico. (a) Antigen and antibody levels in response to vaccine injection. (b) B-lymphocyte population after three injections of the vaccine. (c) B-lymphocyte population at each stage. (d) T-lymphocyte population after two injections of the vaccine. (e) T-lymphocyte population at each stage. (f) The population of CD8 T-cytotoxic lymphocytes at each state. (g) The population of dendritic cells at each state. The bars in the legend indicate “INTERNALIZED,” “PRESENTING-1,” “PRESENTING-2,” “Total,” “ACTIVE,” and “RESTING,” respectively. (h) Macrophage population at each state. The bars in the legend indicate “INTERNALIZED,” “PRESENTING-2,” “Total,” “ACTIVE,” and “RESTING,” respectively. (i) Concentration of cytokines and interleukins, “D” in the inset plot indicates the danger signal.

FIG 4

Three-dimensional structure prediction and quality assessment. (a) The 3D structure of the developed vaccine predicted using the Phyre 2 server. (b) Ramachandran plot of the final structure of the developed vaccine. (c) Z-score plot for the 3D structure of the final vaccine.

FIG 5

Molecular dynamics simulation. (a) The final vaccine protein in the cubic water box for molecular dynamics simulation; green balls represent sodium ions. (b) The process of system energy minimization. (c) NVT ensemble. (d) NPT ensemble. (e) Root mean square deviation (RMSD) plot reflect the stability of the final vaccine. (f) Root mean square fluctuation (RMSF) reflecting the flexibility and fluctuation of the certain regions of the final vaccine.

FIG 6

Molecular docking of designed vaccine with Toll-like receptors. (a) Vaccine-TLR2 docked complex. (b) Residue interaction between vaccine and TLR2. (c) Vaccine-TLR4 docked complex. (d) Residue interaction between vaccine and TLR4.

FIG 7

Expression vector of designed multiepitope K. pneumoniae vaccine constructed using pET28a(+). The vaccine is marked with red color, while the rest, in black, represents the pET28a(+) expression vector.

FIG 8

Workflow of the multiepitope KP vaccine construction based on immunoinformatics.