Back

Insights into the Klebsiella pneumoniae adaptive response mechanisms to colistin exposure using a label-free quantitative proteomics approach

Dwibedy, S. K.; Padhy, I.; Pathak, S. K.; Mohapatra, S. S.

2026-03-26 microbiology
10.64898/2026.03.26.714365 bioRxiv
Show abstract

The rise of MDR Klebsiella pneumoniae and its resistance to the last-resort antibiotic colistin poses a significant threat to global healthcare. While genomic studies have identified several resistance mutations, the transient proteomic shifts that occur during the initial exposure of sensitive strains to lethal antibiotic doses remain poorly characterised. In this study, we employed a label-free quantitative proteomics approach to investigate the protein expression profile of K. pneumoniae strain ATCC 13883 treated with colistin at its MIC. Membrane proteins were extracted at critical growth stages, and differentially abundant proteins (DAPs) were analysed using Gene Ontology and KEGG pathway enrichment analysis. Our proteomic analysis identified 718 DAPs (339 upregulated and 379 downregulated). The cellular response was characterised primarily by outer membrane remodelling and a significant upregulation of the capsule-associated kinase Wzc and the ArnBCADTEF operon, which facilitates lipid A modification with L-Ara4N moiety. Paradoxically, while RND-family efflux pumps (AcrAB) were significantly induced, the global activator RamA and major porins (OmpA, OmpX, LamB) were downregulated, possibly to minimise antibiotic entry. KEGG pathway enrichment analysis further revealed a synchronised metabolic shift, characterised by an intensified TCA cycle flux to fuel high-energy resistance processes despite a general slowdown in carbohydrate metabolism. Our findings demonstrate that K. pneumoniae responds to colistin stress through a rapid, multifaceted proteomic reorganisation involving charge neutralisation, structural reinforcement of the cell envelope, and metabolic re-routing. These results provide a molecular blueprint of the early adaptive response, identifying several proteins as potential therapeutic targets.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
Nature Communications
4913 papers in training set
Top 5%
18.9%
2
mSystems
361 papers in training set
Top 0.5%
12.5%
3
ACS Infectious Diseases
74 papers in training set
Top 0.1%
9.3%
4
mBio
750 papers in training set
Top 2%
7.3%
5
Molecular & Cellular Proteomics
158 papers in training set
Top 0.5%
4.4%
50% of probability mass above
6
npj Biofilms and Microbiomes
56 papers in training set
Top 0.6%
2.8%
7
iScience
1063 papers in training set
Top 8%
2.6%
8
Scientific Reports
3102 papers in training set
Top 45%
2.6%
9
The ISME Journal
194 papers in training set
Top 1%
2.1%
10
Frontiers in Microbiology
375 papers in training set
Top 4%
1.9%
11
Journal of Proteome Research
215 papers in training set
Top 1%
1.7%
12
ISME Communications
103 papers in training set
Top 1%
1.7%
13
PLOS Pathogens
721 papers in training set
Top 7%
1.3%
14
Microbiology Spectrum
435 papers in training set
Top 3%
1.3%
15
PLOS Biology
408 papers in training set
Top 12%
1.3%
16
Cell Reports
1338 papers in training set
Top 28%
1.2%
17
Proceedings of the National Academy of Sciences
2130 papers in training set
Top 37%
1.2%
18
Communications Biology
886 papers in training set
Top 16%
1.1%
19
Journal of Proteomics
27 papers in training set
Top 0.4%
0.8%
20
Emerging Microbes & Infections
74 papers in training set
Top 2%
0.8%
21
Science Signaling
55 papers in training set
Top 0.4%
0.8%
22
Gut Microbes
70 papers in training set
Top 0.9%
0.8%
23
mSphere
281 papers in training set
Top 6%
0.8%
24
Frontiers in Molecular Biosciences
100 papers in training set
Top 5%
0.8%
25
PLOS Computational Biology
1633 papers in training set
Top 24%
0.8%
26
PLOS ONE
4510 papers in training set
Top 69%
0.7%
27
Science Advances
1098 papers in training set
Top 31%
0.7%
28
EMBO Molecular Medicine
85 papers in training set
Top 5%
0.7%
29
Frontiers in Cellular and Infection Microbiology
98 papers in training set
Top 7%
0.7%
30
Metallomics
11 papers in training set
Top 0.2%
0.7%