Biology subjects

Meinhardt, L.

Publications and source records attributed to Meinhardt, L..

4 recordsLinked to original sources

bioRxiv2025

From minutes to bounds: A probabilistic UV-C control and a shape-only morphological fingerprint for postharvest Colletotrichum

Postharvest losses in high-value horticultural crops such as cacao and coffee are often driven by Colletotrichum spp. and other latent fruit pathogens. Ultraviolet-C (UV-C) is increasingly deployed as a chemical-free postharvest technology, yet prescriptions remain framed in minutes rather than in probabilistic guarantees of disease control and safety. We present a dual framework that (1) establishes conservative confidence bounds on survival and (2) validates a "shape-only" morphological fingerprint. This approach addresses the biological complexity of host-pathogen interactions by quantifying isolate-specific heterogeneity, rather than averaging it away. We utilized a large Colletotrichum dataset (n = 5,363) from cacao and coffee, spanning diverse treatments including UV-C, UV-B, and sonication. First, focusing on the Coffee UV-C cohort ([~]10 min), we quantified this heterogeneity; the most conservative Clopper-Pearson upper 95% bound on survival reached 1.000, highlighting partial survival events (e.g., isolate P24-88) under otherwise high-efficacy conditions. This probabilistic framework captures the "tail-risk" of biological resilience instead of assuming complete kill. Second, we trained machine learning models on the full dataset using only geometric features (e.g., aspect ratio, asymmetry), explicitly excluding all primary size metrics. Serving as a rapid physiological indicator of UV-induced stress, these "shape-only" models successfully predicted pathogen host-origin (Accuracy {approx} 0.93) and post-treatment survival (R{superscript 2} {approx} 0.74). The signals ability to generalize across UV-B and sonication confirms that geometry, not just growth reduction, carries a robust and transferable physiological stress signature. This work provides a device-agnostic, probabilistic control platform, replacing time-based heuristics with quantitative guarantees and a generalizable, shape-based diagnostic. HighlightsO_LIA conservative upper 95 % confidence bound (on survival = 1.000) was observed for the Coffee UV-C ([~]10 min) cohort, revealing strong isolate-specific heterogeneity rather than a universal kill guarantee C_LIO_LIIsolate-specific heterogeneity (e.g., localized P24-88 survival) was quantified rather than averaged away. C_LIO_LIA size-free morphological fingerprint predicted host-origin (Accuracy {approx} 0.93) and survival (R{superscript 2} {approx} 0.74). C_LIO_LIThe shape-only signal generalized across diverse stressors, including UV-C, UV-B, and sonication. C_LIO_LIA probabilistic, device-agnostic control framework replaces traditional time-based heuristics. C_LI

microbiology↗

bioRxiv2025

Decoding pathogen ecological memory from chemical-stress phenotypes

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

microbiology↗

bioRxiv2025

An Eligibility-Aware Pipeline for Robust ITS Diagnostics in Fungi: A Cacao Case Study with Generalizable Rules

Accurate fungal ITS diagnostics rely on public sequence archives, but heterogeneous record lengths, especially frequent truncation before the LSU/28S segment, cause naive in silico benchmarking to conflate primer performance with database incompleteness. To resolve this persistent "denominator error," we present an open and fully reproducible "eligibility-aware" framework. Our pipeline first establishes eligibility by confirming both primer sites are present before applying bench-realistic performance rules, including a strict penalty for 3-terminal mismatches. It further provides mechanistic insights by analyzing binding-site conservation and uses a rarefaction-based approach to guide efficient quality control as databases grow. We demonstrate the frameworks utility using the cacao pathosystem, a context where rapid differentiation of the fungal pathogen Moniliophthora from symptomatically similar oomycetes is critical. The result is a robust, field-ready diagnostic decision tree operable under a single touchdown (TD) PCR program. By providing a transparent and barcode-agnostic template, our eligibility-aware approach offers a significant methodological advance for designing and validating molecular assays in mycology and beyond.

microbiology↗

bioRxiv2025

Unveiling differential responses to UVB (305 nm) and UVC (275 nm) in cacao-infecting Colletotrichum gloeosporioides and Pestalotiopsis sp.

Sustainable control of microbial pathogens requires alternatives to chemicals, but physical methods like Ultraviolet-C (UVC) show variable efficacy linked to poorly understood pathogen-specific responses. Here, we investigate differential UVB/UVC responses in plant pathogenic fungi (Colletotrichum gloeosporioides, Pestalotiopsis sp.). Using hyperspectral imaging and machine learning, we dissect the physiological underpinnings of UV sensitivity. UVC proves more potent than UVB, with Pestalotiopsis sp. showing significantly higher resistance than C. gloeosporioides isolates. Crucially, hyperspectral signatures correlated with resistance, revealing UVC-induced photopigment changes, biochemical disruption, and oxidative stress markers in sensitive isolates, contrasting with minimal perturbation in the resistant isolate. Machine learning accurately decoded these complex phenotypes for classification. This understanding enabled enhanced inactivation via optimized pulsed UVC and synergistic sonication. We link distinct physiological states, non-invasively detected via hyperspectral imaging, to fungal UV resistance, demonstrating how integrating advanced phenotyping and machine learning provides a mechanistic basis for optimizing physical pathogen controls.

microbiology↗