The pH Robustness of Bacterial Sensing

Abstract

Bacteria have evolved many different signal transduction systems to sense and respond to changing environmental conditions. Signal integration is mainly achieved by signal recognition at extracytosolic ligand-binding domains (LBDs) of receptors. Hundreds of different LBDs have been reported, and our understanding of their sensing properties is growing. Receptors must function over a range of environmental pH values, but there is little information available on the robustness of sensing as a function of pH. Here, we have used isothermal titration calorimetry to determine the pH dependence of ligand recognition by nine LBDs that cover all major LBD superfamilies, of periplasmic solute-binding proteins, and cytosolic LBDs. We show that periplasmic LBDs recognize ligands over a very broad pH range, frequently stretching over eight pH units. This wide pH range contrasts with a much narrower pH response range of the cytosolic LBDs analyzed. Many LBDs must be dimeric to bind ligands, and analytical ultracentrifugation studies showed that the LBD of the Tar chemoreceptor forms dimers over the entire pH range tested. The pH dependences of Pseudomonas aeruginosa motility and chemotaxis were bell-shaped and centered at pH 7.0. Evidence for pH robustness of signaling in vivo was obtained by Förster Resonance Energy Transfer (FRET) measurements of the chemotaxis pathway responses in Escherichia coli. Bacteria have evolved several strategies to cope with extreme pH, such as periplasmic chaperones for protein refolding. The intrinsic pH resistance of periplasmic LBDs appears to be another strategy that permits bacteria to survive under adverse conditions. IMPORTANCE Demonstration of the pH robustness of extracytoplasmic sensing reveals a previously undescribed evolutionary mechanism that enables bacteria to monitor environmental changes under changing conditions. This mechanism includes the maintenance of the dimeric state of four-helixbundle ligand-binding domains (LBDs). The construction of biosensors is a rapidly growing field of research, and their use to monitor the progression of the COVID-19 pandemic has impressively demonstrated their usefulness. LBDs represent an enormous reservoir of binding modules that can be used to create novel biosensors. Among ligands recognized by LBDs are neurotransmitters, hormones, and quorum-sensing signals. The demonstration that extracytosolic LBDs bind their signals over a wide range of pH values will facilitate the design of biosensors that function under highly variable conditions of acidity and alkalinity.

Keywords: bacterial adaptation; pH; pH robustness; receptors; sensing; sensor domains; signal transduction.

FIG 1

The 4 major structural superfamilies of bacterial ligand-binding domains. Examples of sequence-based LBD superfamilies are shown, and the corresponding Pfam name is provided in orange: the chemoreceptors PscD in complex with acetate (sCache_2) (23), PctA in complex with L-Trp (dCache_1) (24), Tar in complex with L-Asp (TarH) (27) and McpS in complex with malate and acetate (HBM) (28). Bound signal molecules are shown in space-filling mode. Monomers of dimeric LBDs are shown in different colors.

FIG 2

Microcalorimetric titrations of P. aeruginosa PctA-LBD with L-Ala in buffers of different pH. The protein concentration was between 20 and 31 μM and the concentration of L-Ala was 1 mM. Upper panel: raw titration data. The pH and corresponding dissociation constant (K_D) are shown. Lower panel: dilution-heat-corrected and concentration-normalized integrated raw data. Data were fitted with the “One-Binding Site Model” of the MicroCal (Northampton, MA, USA) version of ORIGIN. The derived thermodynamic parameters are shown in Table S2. The dissociation constant pH profile was flat and showed very little variation over the pH range 3.0 to 11.0, for which an average dissociation constant (K_D) of 1.26 ± 0.7 μM was derived. The high affinities at the extreme pH values of 3.0 and 11.0, with K_D values of 1.3 and 3.9 μM, respectively, are noteworthy (Fig. 2 and Fig. 3A).

FIG 3

The pH dependence of ligand-binding for different proteins. The dissociation constants (K_D) at different pH values are shown for periplasmic LBDs with α/β folds (A) four-helixbundle folds (B), periplasmic solute-binding proteins (C), and cytosolic LBDs (D). The titration curves and the derived thermodynamic parameters are shown in Fig. S1 to S3 and Table S2. The following ligands were used for the binding studies: CtpM: L-malate, McpV: propionate, PctA: l-alanine, McpH: adenine, McpU: putrescine, Tar: L-aspartate, PcaY_PP: quinate, McpS: L-malate, McpQ: citrate, MBP: D-maltose, E6B08_RS28125: l-ornithine, TodS: toluene, AdmX: indole-3-pyruvic acid, TtgV: benzonitrile. The asterisks indicate high-affinity binding, but data analysis with different models failed.

FIG 4

Microcalorimetric titrations of the E. coli Tar-LBD with L-Asp in buffers with different pH values. The protein concentration was between 32 and 37 μM, and the concentration of L-Asp was 300 μM. Upper panel: raw titration data. The pH and the corresponding K_D are shown. Lower panel: dilution-heat-corrected and concentration-normalized integrated raw data. Data were fitted with the “One-Binding Site Model” of the MicroCal (Northampton, MA, USA) version of ORIGIN. The derived dissociation constants are shown in Table S2.

FIG 5

Analysis of the pH dependence of Tar-LBD by biophysical methods. (A) Sedimentation-velocity analytical ultracentrifugation studies at different pH values in the presence and absence of 1 mM L-Asp. The sedimentation-coefficient profiles of ligand-free Tar-LBD in buffers at pH 3.5, 5.0, 7.0, and 10.0 and of Tar-LBD in the presence of L-Asp at pH 5.0. Dashed lines indicate the theoretical sedimentation coefficient estimated from the hydrodynamic models of the Tar-LBD monomer and dimer. (B) and (C) Differential Scanning Calorimetry studies at different pH in the absence (B) and presence (C) of 1 mM L-Asp.

FIG 6

The effect of pH on cell viability, motility, and chemotaxis of P. aeruginosa PAO1. (A) Cell viability following a 1-h incubation in buffers at different pH, (B) Estimation of cell motility following a 1-h incubation in buffers of different pH. The percentage of motile cells is shown with respect to the total cell number. (C) Quantitative capillary chemotaxis assays toward 1 mM l-alanine (PctA ligand) using the chemotaxis buffers at different pH values. The number of bacteria that swam into buffer-containing capillaries was subtracted from the number of bacteria that migrated into chemoeffector containing capillaries. The number of bacteria that swam into buffer-containing capillaries were: pH 4.0: 354 ± 126; pH 5.0: 3,765 ± 1,040; pH 6.0: 6,609 ± 1,744; pH 7.0: 8,352 ± 2,574; pH 8.0: 4,337 ± 1,088; pH 9.0: 11,291 ± 2,415; pH 10.0: 1,873 ± 295. Data are the means and the standard deviations from 3 biological replicates conducted in triplicate.

FIG 7

Chemotaxis pathway responses mediated by Tar and McpS-Tar at different pH. (A) FRET measurements of the responses of E. coli strain VS181 expressing CheY-YFP/CheZ-CFP FRET pair and Tar as sole chemoreceptor, to a stepwise addition and subsequent removal of 100 μM L-Asp (indicated by down and up arrows, respectively) at different values of ambient pH (indicated with different colors). The ratio of YFP to CFP fluorescence represents the FRET signal and thus the activity of the chemotaxis pathway. (B) Response amplitudes of the VS181 strain carrying Tar (red) or McpS-Tar (purple) to their specific ligands, 100 μM L-Asp or 100 μM L-malate, respectively, as a function of pH, normalized to the response at pH 7.0. Dose dependence of responses mediated by Tar (C) and by McpS-Tar (D) at different values of ambient pH. The amplitudes of the initial FRET response were calculated from changes in the ratio of YFP/CFP fluorescence after stimulation with the indicated ligand concentrations and normalized to the saturated response. Error bars indicate the standard errors of three independent experiments; wherever they are invisible, error bars are smaller than the symbol size. Data were fitted using Hill equation.