(1)H NMR-based metabolite profiling of planktonic and biofilm cells in Acinetobacter baumannii 1656-2

Abstract

Acinetobacter baumannii is an aerobic and gram-negative pathogenic bacterium that is resistant to most antibiotics. Recently, A. baumannii 1656-2 exhibited the ability to form biofilms under clinical conditions. In this study, global metabolite profiling of both planktonic and biofilm forms of A. baumannii 1656-2 was performed using high-resolution nuclear magnetic resonance (NMR) spectroscopy and multivariate statistical analysis to investigate the metabolic patterns leading to biofilm formation. Principal components analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA) score plots showed a distinct separation between planktonic and biofilm cells. Metabolites including acetates, pyruvate, succinate, UDP-glucose, AMP, glutamate, and lysine were increasingly involved in the energy metabolism of biofilm formation. In particular, the ratio of N-acetyl-D-glucosamine (GlcNAc) to D-glucosamine (GlcNH2) was significantly higher during biofilm formation than under the planktonic condition. This study demonstrates that NMR-based global metabolite profiling of bacterial cells can provide valuable insight into the metabolic changes in multidrug resistant and biofilm-forming bacteria such as A. baumannii 1656-2.

Figure 1. Representative 600-MHz ¹H NMR spectra of planktonic (PMI) and biofilm (BMI) cells.

UDPG, UDP-glucose; Asp, aspartate; Glu, glutamate; Pry, pyruvate; Suc, succinate; Lys, lysine; GlcNAc, N-acetylglucosamine; GlcNH₂, glucosmine.

Figure 2. PCA score plots derived from the ¹H NMR spectra of planktonic and biofilm cells.

PCA score plots of planktonic cells demonstrated clear metabolic differences (IM/PMI, A; IM/PMII, B; PMI/PMII, C). PCA score plots of biofilm cells also indicated clear metabolic differences (IM/BMI, D; IM/BMII, E; BMI/BMII, F). Each PCA model was generated with principal components (PC). IM, initial metabolites; PMI, planktonic metabolites in the early stage; PMII, planktonic metabolites in the mature growth stage; BMI, biofilm metabolites in the early stage; BMII, biofilm metabolites in the mature growth stage.

Figure 3. PCA and OPLS-DA score/loading plots derived from the ¹H NMR spectra depend on growth stage.

PCA and OPLS-DA score plots derived from the ¹H NMR spectra obtained from early (A and B) and mature (D and E) stages of planktonic and biofilm cells, respectively. OPLS-DA loading plots of early (C) and mature (F) stage cells show the metabolites responsible for discrimination in the score plots. Enhanced metabolites in biofilm were reperented in the OPLS-DA loading plote. Each PCA model was generated with principal components (PC) and each OPLS-DA model was generated with predictive components (T) and orthogonal components (TO) to discriminate between groups. The PCA models for distinguishing early stage (A) and mature (D) stage cells were established using each four components.

Figure 4. Quantification of the identified metabolites as a function growth stage in planktonic and biofilm cells.

The patterns of the identified metabolites were determined as a function of growth stages (A–G). The acetylation of glucosamine was determined by a t-test (p = 0.026) in planktonic and biofilm cells (H).