Nanoparticle-Assisted Metabolomics

Understanding and harnessing the interactions between nanoparticles and biological molecules is at the forefront of applications of nanotechnology to modern biology. Metabolomics has emerged as a prominent player in systems biology as a complement to genomics, transcriptomics and proteomics. Its focus is the systematic study of metabolite identities and concentration changes in living systems. Despite significant progress over the recent past, important challenges in metabolomics remain, such as the deconvolution of the spectra of complex mixtures with strong overlaps, the sensitive detection of metabolites at low abundance, unambiguous identification of known metabolites, structure determination of unknown metabolites and standardized sample preparation for quantitative comparisons. Recent research has demonstrated that some of these challenges can be substantially alleviated with the help of nanoscience. Nanoparticles in particular have found applications in various areas of bioanalytical chemistry and metabolomics. Their chemical surface properties and increased surface-to-volume ratio endows them with a broad range of binding affinities to biomacromolecules and metabolites. The specific interactions of nanoparticles with metabolites or biomacromolecules help, for example, simplify metabolomics spectra, improve the ionization efficiency for mass spectrometry or reveal relationships between spectral signals that belong to the same molecule. Lessons learned from nanoparticle-assisted metabolomics may also benefit other emerging areas, such as nanotoxicity and nanopharmaceutics

Differential metabolism between biofilm and suspended Pseudomonas aeruginosa cultures in bovine synovial fluid by 2D NMR-based metabolomics

Total joint arthroplasty is a common surgical procedure resulting in improved quality of life; however, a leading cause of surgery failure is infection. Periprosthetic joint infections often involve biofilms, making treatment challenging. The metabolic state of pathogens in the joint space and mechanism of their tolerance to antibiotics and host defenses are not well understood. Thus, there is a critical need for increased understanding of the physiological state of pathogens in the joint space for development of improved treatment strategies toward better patient outcomes. Here, we present a quantitative, untargeted NMR-based metabolomics strategy for Pseudomonas aeruginosa suspended culture and biofilm phenotypes grown in bovine synovial fluid as a model system. Significant differences in metabolic pathways were found between the suspended culture and biofilm phenotypes including creatine, glutathione, alanine, and choline metabolism and the tricarboxylic acid cycle. We also identified 21 unique metabolites with the presence of P. aeruginosa in synovial fluid and one uniquely present with the biofilm phenotype in synovial fluid. If translatable in vivo, these unique metabolite and pathway differences have the potential for further to development to serve as targets for P. aeruginosa and biofilm control in synovial fluid

Cadaverine is a switch in the lysine degradation pathway in Pseudomonas aeruginosa biofilm identified by untargeted metabolomics

There is a critical need to accurately diagnose, prevent, and treat biofilms in humans. The biofilm forming P. aeruginosa bacteria can cause acute and chronic infections, which are difficult to treat due to their ability to evade host defenses along with an inherent antibiotic-tolerance. Using an untargeted NMR-based metabolomics approach, we identified statistically significant differences in 54 metabolites between P. aeruginosa grown in the planktonic and lawn biofilm states. Among them, the metabolites of the cadaverine branch of the lysine degradation pathway were systematically decreased in biofilm. Exogenous supplementation of cadaverine caused significantly increased planktonic growth, decreased biofilm accumulation by 49% and led to altered biofilm morphology, converting to a pellicle biofilm at the air-liquid interface. Our findings show how metabolic pathway differences directly affect the growth mode in P. aeruginosa and could support interventional strategies to control biofilm formation

Isoleucine side chain solution NMR spectra along the GCK reaction coordinate.

<p>Crystal structures of GCK in (A) unliganded (PDB 1V4T), (B) glucose-bound (PDB 3IDH), and (C) glucose and AMP-PNP-bound forms (PDB 3FGU) depicting the location of isotopically labeled side chains used in this study. Ile Cα positions and tryptophan Cα side chains are depicted as red and yellow spheres, respectively. The large and the small domains are represented in gray and blue, respectively. β-hairpin/loop 151–179 is colored in magenta, glucose is in green, and AMP–PNP in orange. 2D ¹H-¹³C HMQC NMR spectra of Cδ1 methyl groups of (D) unliganded, (E) glucose-bound, and (F) glucose and AMP–PNP-bound forms of GCK. Assignments of Ile residues in the large domain are in gray with label “L,” Ile residues in the small domain are in blue with label “S,” and Ile residues in the 151–179 loop are in magenta. The average Ile Cδ1 chemical shift of intrinsically disordered Ile side chains deposited in the BMRB database is displayed as an open red circle in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001452#pbio-1001452-g001" target="_blank">Figure 1D</a> (for more details, see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001452#pbio.1001452.s004" target="_blank">Figure S4</a>).</p

Structural, dynamic, and kinetic descriptions of GCK activating states.

<p>(A) Crystal structure of the GCK–activator complex depicting the spatial vicinity of the regions involved in allosteric communication. The 151–179 β-hairpin is shown in magenta, the α13-helix in blue, and the Cαs of Ile-159, Ile-163, and Trp-167 residues are colored in red (Ile) and yellow (Trp) spheres. Glucose is colored in green, and the synthetic allosteric activator in cyan. The side chain of Lys-169, which is represented by magenta sticks, is hydrogen-bonded to O6 of glucose. Val-452 and Ile-159 side chains are located within 5 Å of each other and of the activator thiazole ring. (B) Kinetic response of the α13-helix variant (green), and wild-type GCK in the absence (blue) and presence (red) of saturating activator (see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001452#pbio.1001452.s010" target="_blank">Table S1</a>). 2D ¹H-¹³C HMQC spectra of (C) unliganded GCK, (D) activator-bound complex, and (E) unliganded α13-helix variant, specifically labeled with Ile ¹³C(δ1) methyl groups. 2D ¹H-¹⁵N HSQC spectra of (F) unliganded GCK, (G) glucose-bound complex, and (H) unliganded α13-helix variant, with Trp-¹⁵Nε cross-peaks assigned by black labels. The yellow box highlights the position of the W167 cross-peak. The asterisk indicates that W167 is the main contributor to the intensity of the unliganded cross-peak (for more details, see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001452#pbio.1001452.s003" target="_blank">Figure S3</a>).</p

Mechanism of GCK kinetic cooperativity.

<p>(A) The small domain of unliganded GCK is intrinsically disordered, giving rise to a broad conformational ensemble. (B) Glucose binding, activator binding, or an activating PHHI-associated mutation promotes folding of the disordered regions in the small domain, narrowing the conformational distribution. Upon formation of the GCK–glucose binary complex, ATP binds and catalysis proceeds with little additional reorganization. (C) Following product release, ordered unliganded GCK persists until the small domain undergoes an order–disorder transition on the millisecond time scale, allowing access to the “time delay loop” (red): Under low glucose concentrations, the delay loop is operational, leading to slow turnover and kinetic cooperativity. Under high glucose concentrations (or when GCK is activated), the delay loop is effectively bypassed, turnover is fast, and cooperativity is eliminated (green).</p