Machine Learning and DIA Proteomics Reveal New Insights into Carbapenem Resistance Mechanisms in Klebsiella pneumoniae

The emergence
of Carbapenem-resistant Klebsiella pneumoniae (CRKP) represents a major public health concern, primarily driven
by its ability to evade a wide range of antibiotics. Despite extensive
genomic studies, proteomic insights into antibiotic resistance mechanisms
remain scarce. Here, we employed a Data-Independent Acquisition (DIA)-based
quantitative proteomics approach to investigate proteomic differences
between 78 CRKP and 18 Carbapenem-sensitive K. pneumoniae (CSKP) clinical isolates. A total of 3380 proteins were identified,
with 946 showing significant differential expression. CRKP isolates
exhibited increased expression of efflux pumps, beta-lactamases, and
transcriptional regulators, while proteins associated with transport
were enriched in CSKP isolates. To validate our findings, a quantitative
proteomics analysis in an independent cohort of 10 CRKP and 11 CSKP
isolates was performed. The key biomarkers identified via machine
learning in the discovery cohort, including aldehyde dehydrogenase
(KPN_03361), acyltransferase (KPN_02072), uncharacterized protein
(YjeJ), plasmid partition protein B (ParaB), HTH-type transcriptional
activator (RhaR), and beta-lactamase (Bla), were evaluated. They collectively
achieved AUC > 0.7 in the validation cohort, confirming their discriminatory
capacity as diagnostic markers. These findings provide novel insights
into the molecular mechanisms of antibiotic resistance and identify
promising biomarkers for diagnosing carbapenem-resistant K. pneumoniae, offering potential avenues for therapeutic
intervention.