A phenotypic state variable for antimicrobial resistance: MIC-distribution entropy exposes population heterogeneity and phenotypically quiet genomic reservoirs

Antimicrobial-resistance surveillance reports an isolate as susceptible, intermediate, or resistant, and a genome as a list of resistance genes; neither shows whether a population is uniformly wild-type or harbours a resistant minority. Both discard the shape of the minimum inhibitory concentration (MIC) distribution, which encodes within-population resistance heterogeneity. We treated the Miller-Madow-corrected Shannon entropy of that distribution as a phenotypic state variable and asked whether it carries information beyond the resistance genome. Entropy was computed on a fixed 19-bin MIC ladder for 1,612 species-drug distributions (European Committee on Antimicrobial Susceptibility Testing [EUCAST], 252 species) and 11,259 species-drug-year series (Antimicrobial Testing Leadership and Surveillance [ATLAS], 241 species, 2004–2024), paired with resistance-gene annotations (Comprehensive Antibiotic Resistance Database) for 64 species (6,382 genomes). Entropy rose with the non-wild-type fraction defined by epidemiological cut-offs (Spearman ρ = 0.72), indexing coexisting subpopulations. Across 50 paired species, resistance-gene content explained less than 2% of the variance in phenotypic distributional complexity (ordinary least squares R 2 = 0.014, p = 0.406). This decoupling was the most reproducible feature, recovered under phylogenetic correction (Pagel-λ phylogenetic generalised least squares p = 0.891), a mechanism-matched comparison (pooled R 2 = 0.002), and a 145-specification multiverse, although only | r | ≥ 0.39 could be excluded. In 33,862 Bacterial and Viral Bioinformatics Resource Center genomes with MICs, catalogued genes explained a minority of isolate-level entropy (median 20.7%, 95% confidence interval [CI] 18.5–25.2%). Population MIC complexity is therefore not reconstructable from the genome, and the residual is descriptive but stable: 14% of species expressed more complexity than their gene content predicts, 16% less, and 70% remained aligned, including all six ESKAPE species ( Enterococcus , Staphylococcus , Klebsiella , Acinetobacter , Pseudomonas , Enterobacter ). Entropy was reproducible across estimators (Spearman ρ ≥ 0.92) and discriminated later resistant-subpopulation emergence in walk-forward testing (area under the curve [AUC] = 0.695, 95% CI 0.629–0.756), similar to a momentum baseline (0.665), but did not forecast escalation in a hold-out split at the record’s midpoint (AUC = 0.535, 95% CI 0.472–0.594). MIC-distribution entropy therefore resolves a phenotypic dimension of resistance, heterogeneity consistent with heteroresistance, and silent genomic reservoirs, that is invisible to breakpoint categories and gene counts.

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Journal
Frontiers in Microbiology
Published
2026-11-04
DOI
https://doi.org/10.3389/fmicb.2026.1976579
Primary Topic
Antibiotic Resistance in Bacteria
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article
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article

A phenotypic state variable for antimicrobial resistance: MIC-distribution entropy exposes population heterogeneity and phenotypically quiet genomic reservoirs

Karthikeyan Sivashanmugam, Siva Bharathi Ulaganathan, C. Shreya
Frontiers in Microbiology
Antibiotic Resistance in Bacteria
article

A phenotypic state variable for antimicrobial resistance: MIC-distribution entropy exposes population heterogeneity and phenotypically quiet genomic reservoirs

Karthikeyan Sivashanmugam, Siva Bharathi Ulaganathan, C. Shreya
article en

Abstract

Antimicrobial-resistance surveillance reports an isolate as susceptible, intermediate, or resistant, and a genome as a list of resistance genes; neither shows whether a population is uniformly wild-type or harbours a resistant minority. Both discard the shape of the minimum inhibitory concentration (MIC) distribution, which encodes within-population resistance heterogeneity. We treated the Miller-Madow-corrected Shannon entropy of that distribution as a phenotypic state variable and asked whether it carries information beyond the resistance genome. Entropy was computed on a fixed 19-bin MIC ladder for 1,612 species-drug distributions (European Committee on Antimicrobial Susceptibility Testing [EUCAST], 252 species) and 11,259 species-drug-year series (Antimicrobial Testing Leadership and Surveillance [ATLAS], 241 species, 2004–2024), paired with resistance-gene annotations (Comprehensive Antibiotic Resistance Database) for 64 species (6,382 genomes). Entropy rose with the non-wild-type fraction defined by epidemiological cut-offs (Spearman ρ = 0.72), indexing coexisting subpopulations. Across 50 paired species, resistance-gene content explained less than 2% of the variance in phenotypic distributional complexity (ordinary least squares R 2 = 0.014, p = 0.406). This decoupling was the most reproducible feature, recovered under phylogenetic correction (Pagel-λ phylogenetic generalised least squares p = 0.891), a mechanism-matched comparison (pooled R 2 = 0.002), and a 145-specification multiverse, although only | r | ≥ 0.39 could be excluded. In 33,862 Bacterial and Viral Bioinformatics Resource Center genomes with MICs, catalogued genes explained a minority of isolate-level entropy (median 20.7%, 95% confidence interval [CI] 18.5–25.2%). Population MIC complexity is therefore not reconstructable from the genome, and the residual is descriptive but stable: 14% of species expressed more complexity than their gene content predicts, 16% less, and 70% remained aligned, including all six ESKAPE species ( Enterococcus , Staphylococcus , Klebsiella , Acinetobacter , Pseudomonas , Enterobacter ). Entropy was reproducible across estimators (Spearman ρ ≥ 0.92) and discriminated later resistant-subpopulation emergence in walk-forward testing (area under the curve [AUC] = 0.695, 95% CI 0.629–0.756), similar to a momentum baseline (0.665), but did not forecast escalation in a hold-out split at the record’s midpoint (AUC = 0.535, 95% CI 0.472–0.594). MIC-distribution entropy therefore resolves a phenotypic dimension of resistance, heterogeneity consistent with heteroresistance, and silent genomic reservoirs, that is invisible to breakpoint categories and gene counts.

Frontiers in MicrobiologyVol. 17
Vellore Institute of Technology University (IN)
Openalex Percentile: Top 24%
Antibiotic Resistance in Bacteria
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