Quorum Sensing Loss-of-Function Mutations Impose Polymicrobial Fitness Costs: A Computational Hypothesis for Combined QS-Inhibitor and QS-Dependent Antibiotic Therapy
Abstract
Antibiotic resistance mediated through quorum sensing (QS) loss-of-function creates a strategic vulnerability: QS-deficient mutants escape QS-dependent antibiotic action but simultaneously lose access to cooperative extracellular public goods, making them exploitable as cheaters in polymicrobial environments. We formalize the hypothesis that combined QS-inhibitor therapy with QS-dependent antibiotics creates a doubly unfavorable evolutionary landscape for resistant mutants — simultaneously imposing antibiotic selection pressure and ecological disadvantage. Using Lotka-Volterra public-goods competition models, we derive conditions under which selection coefficients against QS-deficient mutants are strongly negative under combined therapy but near-zero under antibiotic monotherapy. The hypothesis predicts selection coefficients s ≤ −0.05 per passage in ≥5 of 6 replicates under combined therapy, versus positive selection under monotherapy. We present a complete experimental validation package comprising a 160-day, three-phase study using isogenic QS-deficient mutants (ΔlasR, ΔrhlR, Δagr) in polymicrobial competition assays.
0/3 confirmed · 3 awaiting experimental validation
In polymicrobial competition assays, QS loss-of-function mutants of Gram-negative ESKAPE pathogens that demonstrate reduced QS-dependent antibiotic susceptibility will exhibit mean selection coefficients s ≤ −0.05 per serial passage cycle under combined QS-inhibitor plus QS-dependent antibiotic therapy, compared to selection coefficients s > 0 under antibiotic monotherapy alone.
The magnitude of fitness cost (|s|) will be proportional to the fraction of competitive fitness attributable to QS-regulated public goods in the test environment, as measured by comparative growth of ΔQS strains in conditioned vs. unconditioned media.
The combined therapy effect will be demonstrable in ≥2 of 3 canonical QS systems tested (LasR/RhlR in P. aeruginosa; Agr in S. aureus; LuxR/LuxI in V. fischeri), confirming generalizability beyond a single pathogen.
Key Findings
- 1The Lotka-Volterra public-goods model does NOT produce the hypothesis (25 August 2026). Sweeping 400,000 parameter sets, the two arms of H₁ are satisfied together in 0.0% of the space, and the reason is structural rather than statistical: combined therapy is what drives public-goods availability toward zero, s decreases in that availability, so s under monotherapy is at most s under combined therapy for every possible parameter draw — while H₁ needs the reverse
- 2In the stated fitnesses the cheater's advantage is the avoided production cost, which does not depend on public-goods availability at all, so removing the goods leaves that advantage untouched. No values of b, c_G or the antibiotic terms can satisfy both arms
- 3The s ≤ −0.05 threshold is not a model output. It is borrowed from measured cheater costs in Sandoz et al. 2007 and chosen to clear a drift threshold; §3.1 of the paper states the model 'does not yield a precise numerical prediction because b and c_G are uncharacterised'
- 4160-day, 3-phase experimental design proposed using ΔlasR, ΔrhlR (P. aeruginosa), Δagr (S. aureus) isogenic mutants — the design is intact, but its primary success criterion is unreachable under the paper's own model and must be restated before anyone runs it
- 5The biology is not refuted: measured cheater costs are real and QS-deficient mutants may well pay one. What fails is the derivation offered here for a specific combined-versus-mono contrast
Source Discoveries
Hypotheses in this paper were sourced from the following AegisMind discoveries on solver.press.
- 63%
160 days
Timeline
320
GPU hours
2,400
CPU hours
512 GB
Memory
$87k
Budget (min)
$340k
Budget (full)
Required Datasets
- Isogenic mutants: P. aeruginosa PA14 ΔlasR, ΔrhlR (mCherry/GFP labelled); S. aureus Newman Δagr
- Media: Artificial Sputum Medium (ASM) for CF-relevant P. aeruginosa; Todd-Hewitt broth for S. aureus
- Flow cytometer (mCherry:GFP ratio quantification)
- RNA-seq at passages 0, 10, 30
- LC-MS/MS proteomics: elastase, pyocyanin, rhamnolipids
- Allele-specific qPCR for compensatory mutation tracking
Experimental Protocol
Phase 1 (Days 1–50): Four-arm competition assays — Arm A (antibiotic monotherapy), Arm B (QS-inhibitor only), Arm C (combined), Arm D (no treatment). 30 serial passages at 24-hour intervals × 5 replicates per arm. Flow cytometry at each passage; MIC determination at passages 0, 5, 10, 20, 30.
Phase 2 (Days 51–110): Replicate in second QS system (Agr in S. aureus). Additional ≥5 independent passage cycles. RNA-seq at passages 0, 10, 30 to confirm QS regulon suppression.
Phase 3 (Days 111–160): LC-MS/MS proteomics for public-goods quantification. Allele-specific qPCR to confirm absence of compensatory mutations. Statistical analysis: logistic growth fit for selection coefficient s per passage; Mann-Whitney U for between-arm comparisons.
Success Criteria
Primary:
- s ≤ −0.05 per passage under Arm C in ≥5/6 replicates; Arm A shows s ≥ 0
- QS-deficient frequency ≤30% at passage 10 under Arm C vs. ≥70% under Arm A
- Result replicates in ≥2/3 QS systems (LasR/RhlR, Agr)
Secondary:
- Allele-specific qPCR: no compensatory mutations in >80% of tracked lineages
- RNA-seq: QS regulon suppression ≥2-fold reduction (FDR < 0.05)
- Public-goods proteins reduced ≥50% under Arms B and C
Failure Criteria
- s > −0.02 with 95% CI overlapping zero across all replicates in Arm C
- QS-deficient fixation > 50% in Arm C at passage 10
- Compensatory mutations conferring QS-independent resistance in > 20% of lineages
- Public-goods production equivalence between QS-proficient and QS-deficient strains
Abort Checkpoints
- Day 15: Abort if no measurable fitness difference between Arm C and control after 10 passages
- Day 30: Abort if QS-deficient frequency in Arm C ≥ Arm A (no selection pressure evident)
- Day 50: Abort Phase 2 if Arm C replicates show s > −0.01 across all 5 replicates
Commercial ROI
Rational combination therapy design pairing QS-inhibitors with QS-dependent antibiotics as an evolutionary trap. Applicable to P. aeruginosa (cystic fibrosis, ventilator-associated pneumonia) and S. aureus (wound infection, bacteremia) — combined market >$2B annually. Biomarker development: QS-deficient allele frequency as real-time resistance evolution tracker.
Research ROI
Provides the first formal experimental test of the dual evolutionary trap mechanism. If confirmed, establishes a rational basis for treatment sequencing protocols that exploit the ecological disadvantage window before compensatory mutations accumulate. Generalizable to any QS-dependent pathogen with extracellular public goods.
Aggregated EVP Package
This paper is part of the AMR Evolutionary Trap EVP cluster. The aggregated EVP combines evidence from multiple papers targeting related mechanisms, enabling shared experimental infrastructure and compounded validation.
View aggregated EVP →