Cryo-EM structure of antibacterial efflux transporter QacA from Staphylococcus aureus reveals a novel extracellular loop with allosteric role

Efflux of antibacterial compounds is a major mechanism for developing antimicrobial resistance. In the Gram-positive pathogen Staphylococcus aureus, QacA, a 14 transmembrane helix containing major facilitator superfamily antiporter, mediates proton-coupled efflux of mono and divalent cationic antibacterial compounds. In this study, we report the cryo-EM structure of QacA, with a single mutation D411N that improves homogeneity and retains efflux activity against divalent cationic compounds like dequalinium and chlorhexidine. The structure of substrate-free QacA, complexed to two single-domain camelid antibodies, was elucidated to a resolution of 3.6 Å. The structure displays an outward-open conformation with an extracellular helical hairpin loop (EL7) between transmembrane helices 13 and 14, which is conserved in a subset of DHA2 transporters. Removal of the EL7 hairpin loop or disrupting the interface formed between EL7 and EL1 compromises efflux activity. Chimeric constructs of QacA with a helical hairpin and EL1 grafted from other DHA2 members, LfrA and SmvA, restore activity in the EL7 deleted QacA revealing the allosteric and vital role of EL7 hairpin in antibacterial efflux in QacA and related members.

Cryo-EM structure of GABA transporter 1 reveals substrate recognition and transport mechanism

The inhibitory neurotransmitter γ-aminobutyric acid (GABA) is cleared from the synaptic cleft by the sodium- and chloride-coupled GABA transporter GAT1. Inhibition of GAT1 prolongs the GABAergic signaling at the synapse and is a strategy to treat certain forms of epilepsy. In this study, we present the cryo-electron microscopy structure of Rattus norvegicus GABA transporter 1 (rGAT1) at a resolution of 3.1 Å. The structure elucidation was facilitated by epitope transfer of a fragment-antigen binding (Fab) interaction site from the Drosophila dopamine transporter (dDAT) to rGAT1. The structure reveals rGAT1 in a cytosol-facing conformation, with a linear density in the primary binding site that accommodates a molecule of GABA, a displaced ion density proximal to Na site 1 and a bound chloride ion. A unique insertion in TM10 aids the formation of a compact, closed extracellular gate. Besides yielding mechanistic insights into ion and substrate recognition, our study will enable the rational design of specific antiepileptics.

POS0514 PREDICTION OF RESPONSE TO METHOTREXATE IN PATIENTS WITH RHEUMATOID ARTHRITIS: A MACHINE LEARNING APPROACH USING CLINICAL TRIAL DATA

Background

Methotrexate (MTX) is the preferred initial disease-modifying drug (DMARD) for rheumatoid arthritis (RA). However, up to 50% of patients respond inadequately to MTX (1). Clinically useful predictors that effectively identify patients with RA who are likely to respond to MTX are lacking. Whether machine learning (ML) can provide robust and clinically useful prediction of response to MTX monotherapy in the first months of treatment in patients with early RA using uniformly collected baseline demographics and clinical data has not been investigated in large patient populations.

Objectives

We aimed to identify clinical predictors of response to MTX as the first DMARD among patients with RA using ML methods.

Methods

Randomized clinical trials (RCT) of patients with RA who were DMARD-naïve and randomized to placebo plus MTX were identified and accessed through the Clinical Study Data Request Consortium and Vivli Center for Global Clinical Research Data. Studies with available Disease Activity Score with 28-joint count and erythrocyte sedimentation rate (DAS28-ESR) at baseline, 12 and 24 weeks were included. Latent class modeling of MTX response was performed. Least absolute shrinkage and selection operator (LASSO) and random forest were used to identify predictors of response.

Results

A total of 775 patients from 4 RCTs were included (mean age 50 years, 80% female). Two distinct classes of patients were identified based on DAS28-ESR change over 24 weeks: “good responders” and “poor responders” to MTX treatment (Figure 1). Baseline DAS28-ESR, anti-citrullinated protein antibody (ACPA) and health assessment questionnaire (HAQ) score were the top predictors of good response to MTX using LASSO (Area Under the Curve [AUC] 0.79) and Random Forest models (AUC 0.68) in the external validation set. DAS28-ESR≤7.4, ACPA positive and HAQ≤2 provided the highest likelihood of response (Table 1). Among patients with 12-week DAS28-ESR>3.2, at least 1 point improvement in DAS28-ESR baseline-to-12-week was predictive of achieving DAS28-ESR≤3.2 at 24 weeks.

Table 1.

Matrix prediction model: Probability of achieving a good response to methotrexate at 24 weeks

DAS28ESR≤7.480.1 (76.4, 83.8)77.3 (70.6, 84)PositiveACPA Status77.1 (68.6, 85.6)74.1 (63.3, 84.9)Negative>7.440.3 (32.1, 48.5)36.5 (29.3, 43.6)Positive36.2 (23.3, 49.1)32.5 (20.9, 44.1)Negative≤2>2HAQ

Footnote: The number in each cell represents the percentage and 95% CI of achieving the outcome, based on the combination of predictors at baseline. DAS28-ESR: Disease Activity Score with 28-joint count with erythrocyte sedimentation rate; HAQ: Health assessment questionnaire score; ACPA: Anti-citrullinated protein antibody.

Figure 1.

Two patient class trajectories identified with latent class modeling of DAS28-ESR (N=775)

Conclusion

We have developed and externally validated a prediction model for response to MTX within 24 weeks in DMARD-naïve patients with RA, providing variably weighted clinical features and defined cut-offs for clinical decision-making. Trajectory of DAS28-ESR change over 24 weeks in patients with moderate-to-high RA disease activity at baseline who are starting MTX can be predicted by baseline DAS28-ESR, ACPA status and HAQ-score. Patients with at least 1 unit decline in DAS28-ESR within the first 12 weeks of treatment who have not achieved low disease activity by week 12, may be more likely to achieve low disease activity at 24 weeks. These parameters should be considered as part of the clinical decision-making process when initiating MTX in DMARD-naïve patients with RA.

References

[1]Aletaha D, Smolen JS. Effectiveness profiles and dose dependent retention of traditional disease modifying antirheumatic drugs for rheumatoid arthritis. An observational study. J Rheumatol. 2002;29(8):1631-8.

Acknowledgements

This abstract is based on research using data from data contributors UCB and Roche that has been made available through Vivli, Inc. Vivli has not contributed to or approved, and is not in any way responsible for, the contents of this publication.

Disclosure of Interests

Stephanie Duong: None declared, Cynthia S. Crowson: None declared, Arjun Athreya: None declared, Elizabeth Atkinson: None declared, John M Davis III Grant/research support from: Pfizer, Kenneth J Warrington Speakers bureau: Chemocentryx, Consultant of: Roche/Genentech, Eric Matteson: None declared, Richard Weinshilboum Shareholder of: OneOme, Liewei Wang Shareholder of: OneOme, Elena Myasoedova: None declared.

Structural basis of inhibition of a transporter from Staphylococcus aureus, NorC, through a single-domain camelid antibody

Transporters play vital roles in acquiring antimicrobial resistance among pathogenic bacteria. In this study, we report the X-ray structure of NorC, a 14-transmembrane major facilitator superfamily member that is implicated in fluoroquinolone resistance in drug-resistant Staphylococcus aureus strains, at a resolution of 3.6 Å. The NorC structure was determined in complex with a single-domain camelid antibody that interacts at the extracellular face of the transporter and stabilizes it in an outward-open conformation. The complementarity determining regions of the antibody enter and block solvent access to the interior of the vestibule, thereby inhibiting alternating-access. NorC specifically interacts with an organic cation, tetraphenylphosphonium, although it does not demonstrate an ability to transport it. The interaction is compromised in the presence of NorC-antibody complex, consequently establishing a strategy to detect and block NorC and related transporters through the use of single-domain camelid antibodies.

FRI0046 PHARMACOGENOMICS-DRIVEN INDIVIDUALIZED PREDICTION OF TREATMENT RESPONSE TO METHOTREXATE IN PATIENTS WITH RHEUMATOID ARTHRITIS: A MACHINE LEARNING APPROACH

Background:

Methotrexate (MTX) is the most common anchor drug for rheumatoid arthritis (RA), but the risk of missing the opportunity for early effective treatment with alternative medications is substantial given the delayed onset of MTX action and 30-40% inadequate response rate. There is a compelling need to accurately predicting MTX response prior to treatment initiation, which allows for effectively identifying patients at RA onset who are likely to respond to MTX.

Objectives:

To test the ability of machine learning approaches with clinical and genomic biomarkers to predict MTX response with replications in independent samples.

Methods:

Age, sex, clinical, serological and genome-wide association study (GWAS) data on patients with early RA of European ancestry from 647 patients (336 recruited in United Kingdom [UK]; 307 recruited across Europe; 70% female; 72% rheumatoid factor [RF] positive; mean age 54 years; mean baseline Disease Activity Score with 28-joint count [DAS28] 5.65) of the PhArmacogenetics of Methotrexate in RA (PAMERA) consortium was used in this study. The genomics data comprised 160 genome-wide significant single nucleotide polymorphisms (SNPs) with p<1×10-5 associated with risk of RA and MTX metabolism. DAS28 score was available at baseline and 3-month follow-up visit. Response to MTX monotherapy at the dose of ≥15 mg/week was defined as good or moderate by the EULAR response criteria at 3 months’ follow up visit. Supervised machine-learning methods were trained with 5-repeats and 10-fold cross-validation using data from PAMERA’s 336 UK patients. Class imbalance (higher % of MTX responders) in training was accounted by using simulated minority oversampling technique. Prediction performance was validated in PAMERA’s 307 European patients (not used in training).

Results:

Age, sex, RF positivity and baseline DAS28 data predicted MTX response with 58% accuracy of UK and European patients (p = 0.7). However, supervised machine-learning methods that combined demographics, RF positivity, baseline DAS28 and genomic SNPs predicted EULAR response at 3 months with area under the receiver operating curve (AUC) of 0.83 (p = 0.051) in UK patients, and achieved prediction accuracies (fraction of correctly predicted outcomes) of 76.2% (p = 0.054) in the European patients, with sensitivity of 72% and specificity of 77%. The addition of genomic data improved the predictive accuracies of MTX response by 19% and achieved cross-site replication. Baseline DAS28 scores and following SNPs rs12446816, rs13385025, rs113798271, and rs2372536 were among the top predictors of MTX response.

Conclusion:

Pharmacogenomic biomarkers combined with DAS28 scores predicted MTX response in patients with early RA more reliably than using demographics and DAS28 scores alone. Using pharmacogenomics biomarkers for identification of MTX responders at early stages of RA may help to guide effective RA treatment choices, including timely escalation of RA therapies. Further studies on personalized prediction of response to MTX and other anti-rheumatic treatments are warranted to optimize control of RA disease and improve outcomes in patients with RA.

Disclosure of Interests:

Elena Myasoedova: None declared, Arjun Athreya: None declared, Cynthia S. Crowson Grant/research support from: Pfizer research grant, Richard Weinshilboum Shareholder of: co-founder and stockholder in OneOme, Liewei Wang: None declared, Eric Matteson Grant/research support from: Pfizer, Consultant of: Boehringer Ingelheim, Gilead, TympoBio, Arena Pharmaceuticals, Speakers bureau: Simply Speaking

Isolation and structural characterization of a Zn2+-bound single-domain antibody against NorC, a putative multidrug efflux transporter in bacteria

Single-chain antibodies from camelids have served as powerful tools ranging from diagnostics and therapeutics to crystallization chaperones meant to study protein structure and function. In this study, we isolated a single-chain antibody from an Indian dromedary camel (ICab) immunized against a bacterial 14TM helix transporter, NorC, from Staphylococcus aureus We identified this antibody in a yeast display screen built from mononuclear cells isolated from the immunized camel and purified the antibody from Escherichia coli after refolding it from inclusion bodies. The X-ray structure of the antibody at 2.15 Å resolution revealed a unique feature within its CDR3 loop, which harbors a Zn2+-binding site that substitutes for a loop-stabilizing disulfide bond. We performed mutagenesis to compromise the Zn2+-binding site and observed that this change severely hampered antibody stability and its ability to interact with the antigen. The lack of bound Zn2+ also made the CDR3 loop highly flexible, as observed in all-atom simulations. Using confocal imaging of NorC-expressing E. coli spheroplasts, we found that the ICab interacts with the extracellular surface of NorC. This suggests that the ICab could be a valuable tool for detecting methicillin-resistant S. aureus strains that express efflux transporters such as NorC in hospital and community settings.

Dissection of Protonation Sites for Antibacterial Recognition and Transport in QacA, a Multi-Drug Efflux Transporter

QacA is a drug:H⁺ antiporter with 14 transmembrane helices that confers antibacterial resistance to methicillin-resistant Staphylococcus aureus strains, with homologs in other pathogenic organisms. It is a highly promiscuous antiporter, capable of H⁺-driven efflux of a wide array of cationic antibacterial compounds and dyes. Our study, using a homology model of QacA, reveals a group of six protonatable residues in its vestibule. Systematic mutagenesis resulted in the identification of D34 (TM1), and a cluster of acidic residues in TM13 including E407 and D411 and D323 in TM10, as being crucial for substrate recognition and transport of monovalent and divalent cationic antibacterial compounds. The transport and binding properties of QacA and its mutants were explored using whole cells, inside-out vesicles, substrate-induced H⁺ release and microscale thermophoresis-based assays. The activity of purified QacA was also observed using proteoliposome-based substrate-induced H⁺ transport assay. Our results identify two sites, D34 and D411 as vital players in substrate recognition, while E407 facilitates substrate efflux as a protonation site. We also observe that E407 plays an additional role as a substrate recognition site for the transport of dequalinium, a divalent quaternary ammonium compound. These observations rationalize the promiscuity of QacA for diverse substrates. The study unravels the role of acidic residues in QacA with implications for substrate recognition, promiscuity and processive transport in multidrug efflux transporters, related to QacA.