Decoding pathogen ecological memory from chemical-stress phenotypes

Abstract

Can standardized chemical stress reveal a reproducible, taxonomy-agnostic stress-response fingerprint that is predictive of sample-source labels (crop-of-isolation) in fungal isolates? Six coffee{square}associated Colletotrichum isolates were profiled using four phenolic{square}branched compounds and compared them with a previously characterized cacao panel. Quantitative morphology, hyperspectral imaging (HSI), and supervised machine learning (ML) yielded panel-specific fingerprints under uniform, isotropic in vitro conditions. Circularity, a measure of edge symmetry, was the most informative morphological feature, and ML classified the crop-of-isolation label (coffee vs cacao, in this panel) with 86.7% accuracy in within-panel cross-validation. HSI detected dose-dependent spectral shifts in a targeted subset of isolates and compounds, including changes near 1930 nm in the short-wave infrared, a moisture-sensitive region that warrants robustness checks (e.g., band masking or preprocessing sensitivity) prior to biochemical attribution. Multi{square}locus phylogeny showed the coffee isolates are polyphyletic, so the predictive signal should be interpreted conservatively as a taxonomy-agnostic phenotype fingerprint associated with crop background in this mixed-lineage panel, acknowledging that crop labels are partially confounded with phylogenetic structure. We propose a "chemical priors" framework as a working hypothesis, in which long-term environmental exposure may imprint stress-response pathways that become legible under simple, standardized probes. This integrative workflow supports scalable screening of eco-friendly antifungals and sensor-driven decision support for high-throughput phenotype-based screening workflows. HighlightsO_LITaxonomy-agnostic workflow integrates hyperspectral imaging and morphology. C_LIO_LIMachine learning predicts crop-source labels with 86.7% accuracy in mixed lineages. C_LIO_LISystem exhibits robustness, maintaining >86% accuracy even after feature ablation. C_LIO_LINon-linear ML captures structure missed by linear stats (1.7% variance). C_LIO_LIEnables rapid, sensor-driven antifungal screening without prior DNA sequencing. C_LI