A conserved inhibitory interdomain interaction regulates DNA-binding activities of hybrid two-component systems in Bacteroides

ABSTRACT Hybrid two-component systems (HTCSs) comprise a major class of transcription regulators of polysaccharide utilization genes in Bacteroides. Distinct from classical two-component systems in which signal transduction is carried out by intermolecular phosphotransfer between a histidine kinase (HK) and a cognate response regulator (RR), HTCSs contain the membrane sensor HK and the RR transcriptional regulator within a single polypeptide chain. Tethering the DNA-binding domain (DBD) of the RR with the dimeric HK domain in an HTCS could potentially promote dimerization of the DBDs and would thus require a mechanism to suppress DNA-binding activity in the absence of stimulus. Analysis of phosphorylation and DNA-binding activities of several HTCSs from Bacteroides thetaiotaomicron revealed a DBD suppression mechanism in which an inhibitory interaction between the DBD and the phosphoryl group-accepting receiver domain (REC) decreases autophosphorylation rates of HTCS-RECs and represses DNA-binding activities in the absence of phosphorylation. Sequence analyses and structure predictions identified a highly conserved sequence motif correlated with a conserved inhibitory domain arrangement of REC and DBD. The presence of the motif, as in most HTCSs, or its absence, in a small subset of HTCSs, is likely predictive of two distinct regulatory mechanisms evolved for different glycans. Substitutions within the conserved motif relieve the inhibitory interaction and result in elevated DNA-binding activities in the absence of phosphorylation. Our data suggest a fundamental regulatory mechanism shared by most HTCSs to suppress DBD activities using a conserved inhibitory interdomain arrangement to overcome the challenge of the fused HK and RR components. IMPORTANCE Different dietary and host-derived complex carbohydrates shape the gut microbial community and impact human health. In Bacteroides, the prevalent gut bacteria genus, utilization of these diverse carbohydrates relies on different gene clusters that are under sophisticated control by various signaling systems, including the hybrid two-component systems (HTCSs). We have uncovered a highly conserved regulatory mechanism in which the output DNA-binding activity of HTCSs is suppressed by interdomain interactions in the absence of stimulating phosphorylation. A consensus amino acid motif is found to correlate with the inhibitory interaction surface while deviations from the consensus can lead to constitutive activation. Understanding of such conserved HTCS features will be important to make regulatory predictions for individual systems as well as to engineer novel systems with substitutions in the consensus to explore the glycan regulation landscape in Bacteroides.


Supplemental Fig. S1
Supplemental Fig. S8 Supplemental Tables Table S1 in which N is the total number of data points, K is the number of estimated parameters and RSS is the residual sum of squares for the fitting.

Supplemental Text Kinetic Analyses of Autophosphorylation
As shown below, a simple phosphorylation scheme involves multiple reactions: the binding equilibrium of small-molecule phosphodonor, e.g., phosphoramidate (PAM), the conformational equilibrium to switch RR to the active conformation, autophosphorylation and autodephosphorylation reactions.The exact order or mechanism of the binding and conformational equilibriums is unknown, but can be considered as one overall equilibrium leading to a PAM-bound phosphorylation-competent state.The subsequent autophosphorylation and autodephosphorylation reactions determine the phosphorylation level of RR proteins.

RR + PAM
RR autophosphorylation kinetics have been characterized for several proteins, such as CheY, PhoB and NtrX (1)(2)(3)(4).In these proteins, phosphorylation causes conformational changes that lead to alteration of the tryptophan fluorescence.Tracking the time course of fluorescence quenching provides sufficient data points for a robust fitting to derive the kinetic parameters.In contrast, HTCS-RRs analyzed here do not contain tryptophan.Phosphorylation reactions were followed by Phos-tag gels with a limited number of time points, which may lead to large uncertainty in parameter estimation, especially coupled with large variations in Phos-tag gel results.Rather than analyzing the entire time course, focusing on the early stage of phosphorylation and fitting initial rates with a simple Michaelis-Menten kinetic model were used for quantitative kinetic analyses.
Full time courses were only used for qualitative assessment of kinetics for a limited set of phosphorylation data at 20 mM PAM.An exponential fit of the fractions of phosphorylated protein illustrates the reaction progression curve (Fig. 2B, S1B and S2A).The plateau reflects the steady state in which autophosphorylation is balanced by autodephosphorylation.Considering that RR and REC of the same HTCS likely have similar dephosphorylation rates kdp, faster phosphorylation for REC leads to a higher steady-state level of phosphorylation than that of RR.
For other PAM concentrations, Phos-tag analyses were usually performed for the first 3-4 time points and 1 later time point (see example in Fig. S2D).Increasing PAM concentrations also result in faster phosphorylation, thus higher steady-state phosphorylation levels (Fig. S2D, 30 min, quantification not shown).
Initial rates obtained from the linear regression of phosphorylation data were fitted with the Michaelis-Menten kinetic equation.Differences in Km and kcat (Table S1) for HTCS-RRs and HTCS-RECs reflect effects of the inhibitory interaction on different reactions shown in Scheme 1. Decreased Km values are consistent with an enhanced conformation activation equilibrium (Kact) for HTCS-RECs or HTCS-RRs with substitutions in contact residues.Surprisingly, HTCS-RECs and HTCS-RR AG/AD variants also show higher kcat values than HTCS-RRs, suggesting that the phosphorylation catalysis is also affected by interdomain-interactions, leading to higher k2 values in Scheme 1.The exact mechanism is not clear.It may involve a more complex reaction scheme with additional reactions.For example, dimerization may promote cooperativity in either the catalysis or the conformation activation equilibrium.The limited number of Phos-tag data points is not adequate for a further exploration of the phosphorylation mechanism and the detailed mechanism is not the focus of this study.We chose to derive the kcat/Km value, a parameter that has been suggested to reflect the energy barrier of the reaction transition state (5), to qualitatively evaluate the inhibitory interdomain interaction for HTCS proteins.
Phosphorylation promotes dimerization of some RRs and a positive cooperativity of autophosphorylation has been observed for some RRs (3,4).Dimerization can impact the equilibria for PAM binding and conformation activation as well as catalysis.The classic Michaelis-Menten equation may require modification as below to account for cooperativity: However, fitting an additional parameter of the cooperativity with limited data points can result in overfitting and large uncertainty associated with the estimated parameters.Table S3 shows All phosphorylation experiments were initially performed without correction for ionic strength changes caused by PAM.As pointed out by a reviewer, addition of PAM could increase the ionic strength, decrease the phosphorylation rate, and impact the kinetic quantification of autophosphorylation (1,2).We evaluated whether ionic strength has a significant impact on HTCS autophosphorylation.Phosphorylation experiments were performed in parallel, one without ionic strength correction and the other with salt added to keep a constant ionic strength.
We chose a constant ionic strength corresponding to 50 mM PAM in the reaction buffer for the convenience of experimental set-up.At pH 7.5, ~14% of ammonium phosphoramidate will be double-charged and 86% will be mono-charged, based on its pK2 of 8.3 (6), thus the ionic strength will be 1.2 times the PAM concentration.To correct for ionic strength, appropriate volumes of 500 mM PAM were mixed with 600 mM NaCl (1.2x PAM concentration) to reach one tenth of the total reaction volume, and the mixture was added to the reaction to achieve desired PAM concentrations and a constant ionic strength at 0.22 M. As shown in Fig. S1, BT4124 phosphorylation data with or without ionic strength correction displayed nearly identical phosphorylation time courses (Fig. S1A and S1B), phosphorylation rates at 10 mM and 20 mM PAM (Fig. S1C) and kinetic profiles across different PAM concentrations (Fig. S1D).For other HTCS proteins, we focused on the intermediate PAM concentrations of 10 mM and 20 mM where a rate decrease could greatly impact the data fitting.All proteins showed comparable phosphorylation rates for experiments with or without ionic strength correction (Fig. S1E-G).
None of the data has a p value smaller than 0.05 for comparison of the two ionic strength conditions.Therefore, ionic strength appears not to impact phosphorylation kinetics of HTCS proteins significantly at experimental conditions with PAM below 50 mM.The difference in ionic strength effects for CheY and HTCS proteins may result from differences in the local protein surface despite the highly conserved active site or differences in phosphorylation detection methods.Quenching of tryptophan fluorescence in CheY depends on the phosphorylation-induced change in the local environment of tryptophan, which may be sensitive to ionic strength.Given no significant effect of ionic strength on phosphorylation, kinetic analyses of HTCSs were performed with all data without ionic strength correction.

Sequences of BT-HTCS proteins
FIG S1.Autophosphorylation of HTCS proteins is not significantly affected by ionic strength.Phosphorylation kinetics of BT4124 (A-D), BT4663 (E), BT3334 (F) and BT1754 (G) with no salt addition (-) or with added NaCl to reach a constant ionic strength at 0.22 M (+).One representative example of Phos-tag gels at the indicated PAM concentration is shown for each protein.Quantification of Phos-tag gels yielded the phosphorylation time course (B) and the early stage of the time course (B inset) was used to calculate the initial rates (C, D, mid and right panels in E, F and G).

FIG S2 .FIG S3 .
FIG S2.Autophosphorylation kinetics of HTCS-RR and HTCS-REC proteins.(A-C) Phos-tag gel analyses of protein phosphorylation at 20 mM PAM for BT4663, BT3334 and BT1754.One representative example is shown for each protein.The lower panel of (A) shows an example of phosphorylation time course of BT4663 based on Phos-tag gel quantification.(D) Phosphorylation of BT4663 at different PAM concentrations.One representative example is shown for each condition.Longer times were used for BT4663-RR to allow sufficient phosphorylation to be observed and quantified for calculating the initial rates.(E-G) Dependence of phosphorylation initial rates on PAM concentrations.Data are shown as mean ± SD from at least three independent experiments.Lines with corresponding shaded ranges indicate the fitted curves and the 95% confidence intervals.The shaded ranges for BT4633-RR and BT1754-RR are barely noticeable due to a narrow confidence interval.

FIG S4 .FIG
FIG S4.Sequence conservation of HTCS-DBD domains.Sequence logos were obtained from the profile hidden Markov model of 6908 HTCS proteins in Bacteroides.Secondary structural elements are shown above the logos.DBD domains of HTCSs belong to the HTH18 family (PFAM id, PF12833).Bar graphs compare the normalized information contents (ICs) between HTCS-DBDs (gray) and the entire HTH18 family (pink).HMM of HTH18 only contains residues starting from helix α7 (vertical dashed line), thus ICs were not calculated for HTH18 preceding α7.Residues involved in the predicted REC-DBD interfaces (red box) appear highly conserved in HTCS-DBDs, but not in the entire HTH18 family.
FIG S8.Autophosphorylation kinetics of BT4663-RR and corresponding interface variants.Phos-tag gels (A) were quantified to track the fraction of phosphorylated proteins (B) at indicated times after addition of 20 mM PAM. Lines in (B) represent the global exponential fit to illustrate the kinetic trendline of phosphorylation.Initial rates of phosphorylation were measured from early stages of the reaction to derive the kinetic curves in (C).Similar to that observed for BT4124, the interface variants, BT4663-RR AG and BT4663-RR AD , showed faster phosphorylation kinetics than BT4663-RR, suggesting relief of inhibitory interactions.
the kinetic parameter values obtained from fitting with the cooperative model shown above.Some proteins, such as BT4124-REC, BT4663-REC, BT3334-REC and BT3334-RR, do show a high cooperativity value, suggesting that dimerization of these proteins may play a role in promoting positive cooperativity in autophosphorylation.The corrected Akaike information criterion (AICc) values, a statistical score evaluating the relative quality of models, were computed to compare the cooperative and the Michaelis-Menten model.Lower AICc values, or negative ΔAICc values between the cooperative and the Michaelis-Menten models, reflect better model quality to fit the current dataset.As shown in TableS3, the cooperative model has lower AICc values only for two proteins, BT4124-REC and BT3334-REC.Higher AICc scores for the cooperativity model likely result from the penalty of the additional cooperativity parameter and overfitting with a limited dataset.The Michaelis-Menten model without cooperativity appears to be a better model for most proteins.More data points, especially at the intermediate rate range to define the sigmoidal curve, are needed for a robust fitting using the cooperative model.Nevertheless, the kcat/Km values from the two models are similar and the conclusion for the inhibitory role of the DBD on autophosphorylation remains valid.For simplicity, data presented in main figures are from kinetic analyses using the Michaelis-Menten model without considering the cooperativity.
b.The number of data points indicates the number of initial rates used for the fitting to derive kcat and Km.Initial rates were measured at different PAM concentrations from multiple independent experiments.

Table S2 .
Strains and plasmids used in this study.

Table S3 .
Autophosphorylation parameters derived from the cooperative model.
a. ΔAICc indicates the difference between the corrected Akaike information criterion (AICc) values from the Michaelis-Menten and the cooperative model, AICc coop -AICc MM.Negative value of ΔAICc indicates a smaller value of AICc coop than AICc MM, suggesting a relative better quality for the cooperative model.Positive value suggests a better quality for the Michaelis-Menten model.AICc is calculated with the following equation: