BacDive Phenotype Signatures of Metal Tolerance

The Metal Fitness Atlas scored 27,702 pangenome species for metal tolerance using gene functional signatures validated against RB-TnSeq fitness data. BacDive provides standardized phenotypic measurements (Gram stain, oxygen tolerance, metabolite utilization, enzyme activities) for 97K bacterial strains. This project tests whether these readily measurable phenotypes predict metal tolerance, connecting classical microbiology (culture-based phenotyping) to genomic metal tolerance predictions. A two-scale design validates associations both directly (12 FB organisms matching BacDive) and at pangenome scale (~3,000-5,000 species linked via genome accessions).

Scale of the Analysis

Coverage Waterfall

Univariate Association Results

Model Comparison (5-Fold Phylogenetic-Blocked CV)

Hypothesis Outcomes

Direct FB-BacDive Validation (n = 12)

Twelve Fitness Browser organisms match BacDive by taxonomy ID (6 unique species: Cupriavidus basilensis, Methanococcus maripaludis, Ralstonia solanacearum, Pseudomonas simiae, Azospirillum brasilense, Pseudomonas fluorescens). All Gram-typed organisms are Gram-negative, precluding within-set testing of H1a. All urease-typed organisms are urease-negative yet are routinely tested against nickel, consistent with the pangenome-scale finding that urease status does not predict metal tolerance. The single anaerobe (Methanococcus) has only 1 metal tested versus 4–5 for aerobes, but n = 1 is not interpretable.

(Notebook: 05_fb_bacdive_case_studies.ipynb)

The Phylogenetic Confounding Wall

The central result is a cautionary tale for phenotype-based prediction in microbial ecology: classical microbiology phenotypes (Gram stain, oxygen tolerance, enzyme activities) capture real biological differences in metal tolerance, but these differences are entirely explained by phylogenetic structure. Adding phenotype features to a taxonomy-based model actually decreases predictive power slightly (delta R² = -0.009), indicating that phenotypes are noisier proxies for taxonomy, not independent predictors.

This is consistent with Goberna & Verdú (2016), who showed through meta-analysis that bacterial trait-environment associations routinely reflect shared ancestry rather than direct ecological adaptation. Van Assche & Álvarez (2017) found phylogenetic signal values up to λ = 0.98 for chemical sensitivity traits in Acinetobacter, directly analogous to our findings.

Why Phenotypes Fail as Metal Tolerance Predictors

1. Gram stain is taxonomy, not a trait: The Gram-negative advantage in metal tolerance is real (outer membrane barrier; Paulsen et al. 1997) but indistinguishable from "being a Proteobacterium" in a predictive model. There are no Gram-positive Proteobacteria and no Gram-negative Actinomycetes in our data.

2. Urease reversal reflects lineage composition: The surprising urease-negative → higher metal tolerance finding is entirely driven by Actinomycetes, where urease-positive species are a phylogenetically distinct subgroup with low metal scores. Within other classes, the urease effect is zero.

3. Catalase exhibits Simpson's paradox: The overall catalase effect is d = +0.10 (catalase-positive species have slightly higher metal scores), marginally supporting H1d. But class-stratified analysis reveals the opposite within every major class: catalase-negative species score higher within Actinomycetes (d = -0.62, p < 1e-5), Gammaproteobacteria (d = -0.49, p = 0.004), and Betaproteobacteria (d = -0.51, p = 0.006). The overall positive effect is an artifact of between-class composition — catalase-positive classes (Proteobacteria) happen to have higher metal scores than catalase-negative classes. This is the same Simpson's paradox pattern as urease, reinforcing the phylogenetic confounding narrative.

4. Metabolic breadth is not a metal tolerance proxy: The hypothesis that metabolically versatile organisms harbor more resistance genes (Martin-Moldes et al. 2015) was not supported. Metabolite utilization breadth shows no correlation with metal score (rho = -0.01), likely because metabolic versatility operates in different genomic neighborhoods than metal resistance.

What Does Predict Metal Tolerance

The genome's metal resistance gene content (n_metal_clusters from the Metal Fitness Atlas) explains more variance than all phenotype features combined, and substantially more than taxonomy alone. The full model (taxonomy + phenotype + gene count) achieves R² = 0.63, with gene count contributing the lion's share of the improvement over taxonomy (delta R² = +0.28 over taxonomy alone). This aligns with Schwan et al. (2023), who found strong genotype-phenotype concordance for metal resistance gene presence in Salmonella and E. coli.

The Urease-Nickel Paradox

Urease-positive bacteria require nickel import and handling machinery (Maier & Benoit 2019), so they should tolerate nickel — yet they show lower overall metal tolerance. This is not a true paradox: urease positivity correlates with Actinomycetes and other lineages that have fewer metal resistance genes overall. The nickel-handling machinery associated with urease is narrowly specific to nickel and does not confer general metal tolerance. Furthermore, the tight coupling between urease activity and nickel efflux (Stähler et al. 2006) means that nickel homeostasis in urease producers is a balancing act, not a broad tolerance mechanism.

Novel Contribution

1. First large-scale test of BacDive phenotypes as metal tolerance predictors — 5,647 species, 10 phenotype features, cross-referenced against genome-based metal tolerance scores from the Metal Fitness Atlas.
2. Definitive demonstration of phylogenetic confounding — phenotype features capture real signal (R² = 0.16) but add nothing beyond what taxonomy already provides (delta R² = -0.009).
3. Urease reversal — urease-positive bacteria have lower metal tolerance, driven by lineage composition, not nickel biology.
4. Gene count as the true predictor — the number of metal resistance gene clusters outperforms all phenotype features combined.

Limitations

• Metal Fitness Atlas scores are genome-based predictions, not direct metal tolerance measurements. Circular reasoning is mitigated by controlling for n_metal_clusters in partial correlations, but the phenotype-score associations are ultimately phenotype-to-genome correlations, not phenotype-to-phenotype.
• BacDive species name matching achieves only 38.4% (5,647/27,702 GTDB species). GCA accession matching could recover additional links but was not implemented.
• 12-organism direct validation is underpowered — all Gram-typed organisms are Gram-negative, preventing within-set testing of the strongest association.
• BacDive testing bias: well-studied organisms (Pseudomonas, E. coli) have many phenotype tests; poorly-studied species have sparse data.
• H₂S production is underpowered: only 8 H₂S-negative species in the matched set, making the large effect size (d = -0.87) unreliable.

1. Per-metal phenotype analysis: Test whether specific phenotypes predict tolerance to specific metals (e.g., catalase → copper, urease → nickel) rather than composite metal scores. This would require per-metal scores for the 27K species.
2. GCA accession matching: The current bridge uses species name matching (38.4%). Adding GCA accession matching could recover 10–30% more species.
3. Phylogenetic comparative methods: Use PGLS or phylogenetic PCA to formally remove phylogenetic signal before testing phenotype-metal associations.
4. BacDive AI-predicted phenotypes: The current analysis uses only measured values. Including BacDive's ML-predicted Gram stain, motility, and oxygen tolerance could increase coverage substantially while introducing model-dependent bias.
5. Experimental validation: The H₂S → chalcophilic metal hypothesis (d = -0.87, underpowered) could be tested experimentally with sulfate-reducing bacteria and zinc/copper challenge.

Sources

Generated Data

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• Maier RJ, Benoit SL (2019). "Role of Nickel in Microbial Pathogenesis." Inorganics 7:80. DOI: 10.3390/inorganics7070080
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• Schwan CL et al. (2023). "Genotypic and phenotypic characterization of antimicrobial resistance and metal tolerance in Salmonella and E. coli." J Food Prot 86:100113. PMID: 37290750
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• Van Assche A, Álvarez-Pérez S (2017). "Phylogenetic signal in carbon source assimilation profiles and tolerance to chemical agents." Appl Microbiol Biotechnol 101:3049-3061. DOI: 10.1007/s00253-016-7866-0

Testing whether urease+ organisms are specifically nickel-tolerant (rather than generally metal-tolerant) requires per-metal fitness profiling of urease+ vs urease- bacteria from the same taxonomic class, ideally Gammaproteobacteria (where the pangenome-scale urease effect is near zero). RB-TnSeq with matched urease+/- strains under nickel vs other metals would directly test whether urease enables nickel-specific tolerance.