Chaperones Skp and SurA dynamically expand unfolded OmpX and synergistically disassemble oligomeric aggregates

Abstract

Periplasmic chaperones 17-kilodalton protein (Skp) and survival factor A (SurA) are essential players in outer membrane protein (OMP) biogenesis. They prevent unfolded OMPs from misfolding during their passage through the periplasmic space and aid in the disassembly of OMP aggregates under cellular stress conditions. However, functionally important links between interaction mechanisms, structural dynamics, and energetics that underpin both Skp and SurA associations with OMPs have remained largely unresolved. Here, using single-molecule fluorescence spectroscopy, we dissect the conformational dynamics and thermodynamics of Skp and SurA binding to unfolded OmpX and explore their disaggregase activities. We show that both chaperones expand unfolded OmpX distinctly and induce microsecond chain reconfigurations in the client OMP structure. We further reveal that Skp and SurA bind their substrate in a fine-tuned thermodynamic process via enthalpy-entropy compensation. Finally, we observed synergistic activity of both chaperones in the disaggregation of oligomeric OmpX aggregates. Our findings provide an intimate view into the multifaceted functionalities of Skp and SurA and the fine-tuned balance between conformational flexibility and underlying energetics in aiding chaperone action during OMP biogenesis.

Keywords: chaperones; disaggregation; outer membrane protein biogenesis; protein folding; single-molecule FRET.

Fig. 1.

Probing structural dynamics of uOmpX under native-like conditions by smFRET. (A) Dilution scheme used for smFRET experiments to study unfolded and chaperone-bound uOmpX. The positions of donor (green sphere) and acceptor (red sphere) fluorophores at the N- and C-terminal ends of OmpX_1,149 are indicated. (B) FRET efficiency (E) histograms of uOmpX in aqueous buffer conditions without denaturant (uOmpX_aq) obtained during the first 2 h of measurement at 25 °C, 31 °C, and 37 °C. Shot noise limited distributions are shown as red cityscapes. (C) Recurrence analysis with recurrence time intervals ΔT = {0, 100} ms and varying initial ΔE windows of 0.2 to 0.3, 0.4 to 0.5, and 0.6 to 0.7 at 25 °C (Top, black dashed lines) and 0.5 to 0.6, 0.6 to 0.7, and 0.7 to 0.8 at 37 °C (Bottom, black dashed lines). The probability of observing recurrence of the same molecule in the interval of 0 to 100 ms is 81% and 78% for uOmpX_aq measurements performed at 25 °C and 37 °C, respectively. Recurrence histograms are shown in orange. Complete FRET efficiency histograms created from all detected bursts are shown in gray. (D) 2D scatter plot of relative fluorescence lifetime (τ_D(A)/τ_D(0)) vs. E for uOmpX_aq at 25 °C, 31 °C, and 37 °C for 0 to 2 h of measurement. Lines represent the static FRET line (solid black line) and the expected correlation for a Gaussian chain (dashed black line). The red dot and the red dashed circle denote the center position and 68% area of the uOmpX_aq population. The scatter plot density is color coded (gray to yellow).

Fig. 2.

Conformations and structural dynamics of Skp- and SurA-bound uOmpX. (A) FRET efficiency (E) histogram of uOmpX in the absence of chaperones. (B and C) E histograms of uOmpX in the presence of different concentrations of Skp₃ (yellow) and SurA (blue), respectively. Concentrations are indicated. The three underlying Gaussian distributions are highlighted by black lines as follows: Skp₃- or SurA-bound uOmpX (Skp₃–uOmpX or SurA–uOmpX, solid line), unbound uOmpX (uOmpX_aq, dashed line), and compact uOmpX (uOmpX_compact, dotted line). The sum of the three Gaussian distributions is shown in red. FRET state peak positions (Ê) of (D) Skp₃–uOmpX and (E) SurA–uOmpX at three different temperatures for measurements performed with [Skp₃] > 100 nM and [SurA] > 1,160 nM, respectively. (F) Relative fluorescence lifetime (τ_D(A)/τ_D(0)) and E position of Skp₃–uOmpX and SurA–uOmpX (yellow and blue, respectively) populations for different temperatures. Here, we modeled the interdye distance with a log-normal distribution. The black line depicts the static FRET line. The inset shows the average interdye distance ⟨R_interdye⟩ of uOmpX_aq (gray dashed line) and the empirical log-normal interdye distance distributions of uOmpX complexed with 2.5 µM Skp₃ (yellow curve) and 11.6 µM SurA (blue curve), respectively.

Fig. 3.

Conformational dynamics of the Skp₃- and SurA-bound uOmpX. (A) Representation of species-filtered 2D FLCS using SurA–uOmpX complexes as an example. Here, t is the microtime of the emitted photons, ΔT is the time interval, and ΔΔT is the window size. Emission-delay histograms corresponding to uOmpX_aq (B), Skp₃-uOmpX (C), and SurA-uOmpX (D). The legend indicates the short and long initial microtimes.

Fig. 4.

Thermodynamics of Skp₃ and SurA interaction with uOmpX. (A) Schematic of the global χ² minimization routine to obtain the enthalpic change (ΔH) and entropic change (ΔS) for Skp₃ and SurA interaction with uOmpX. ΔH, ΔS, f_c(T) (i.e., fraction of compact uOmpX at each temperature), and f_b,max(T) (i.e., maximum fraction of Skp₃–uOmpX or SurA–uOmpX) were the varying fitting parameters. ln(K_a) is the natural logarithm of the association constant, T is the temperature in Kelvin (K), and [SurA] is the initial SurA concentration in nM. Protein states were modeled with three Gaussian distributions (black dashed lines) and their sum (red) compared to the experimental data. (B) ΔH and ΔS values for the Skp₃ and SurA interaction with uOmpX as obtained by a bootstrapping algorithm. The best estimators for ΔH and ΔS are indicated as a black circle with SD. (C) Comparison of the best estimators for ΔH, TΔS, and ΔG for the Skp₃ and SurA interaction with uOmpX. ΔG is the free energy change of interaction at 37 °C.

Fig. 5.

Disaggregation of OmpX aggregates by Skp and SurA. (A) Dilution and complex formation scheme to investigate OmpX disaggregation by Skp and SurA. (B) FCS curves corresponding to measurements containing 10 pM uOmpX (gray) and 1 µM uOmpX + 10 pM uOmpX (green) in aqueous buffer conditions without chaperones yield two species, namely, uOmpX_aq and/or OmpX_Agg, with diffusion times as indicated. Here, G_RR is the normalized autocorrelation function of the acceptor dye and τ is the diffusion time. (C) Upon addition of Skp (yellow) or SurA (blue) or both (purple) to the aggregate mixture, more species appear corresponding to the Skp₃–uOmpX and/or SurA–uOmpX complex with diffusion times as indicated. (D) The probability of OmpX_Agg (green bar) in different measurement conditions, as folllows: at a micromolar concentration of OmpX in aqueous buffer without chaperones, at a micromolar concentration of OmpX in presence of Skp (+Skp), at a micromolar concentration of OmpX in presence of SurA (+SurA), and at a micromolar concentration of OmpX in presence of both the chaperones (+ SurA and Skp).

Fig. 6.

Model of Skp₃ and SurA chaperone action on uOmpX and OmpX_Agg. (A) Skp₃–uOmpX is expanded due to numerous intermolecular interactions between the chaperone and its substrate, while the substrate itself undergoes fast chain reconfiguration on timescales <5 μs. (B) The increased expansion of uOmpX upon incrementing [SurA] indicates that more than one molecule of SurA binds to uOmpX. Similar to Skp₃, SurA expands its substrate and induces uOmpX chain reconfiguration on a timescale of <10 μs. Interactions of both chaperones with uOmpX are energetically calibrated through an exquisite entropy–enthalpy compensation, as indicated by the values of entropy and enthalpy change (ΔH and ΔS, respectively), along with ΔG. (C) uOmpX_aq exhibits both submillisecond chain reconfiguration dynamics and ≥100 ms timescale conformational changes in aqueous buffer. (D) Both chaperones can disassemble aggregated OmpX, which can emerge under stress conditions.