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Leveraging Multi-Level Biomarkers Using Machine Learning: Identifying Physiological and Skin Microbial Dynamics in

Jun-Kyu Park1, Ji-Eun Lee1, Yuno Do1

  • 1Department of Biological Sciences, Kongju National University, Chungcheongnam-do, Republic of Korea.

Integrative Zoology
|July 21, 2025
PubMed
Summary
This summary is machine-generated.

Amphibian populations face threats from chytridiomycosis (Bd). This study used machine learning to analyze physiological and microbiome data in Pelophylax nigromaculatus, revealing corticosterone levels and bacterial killing capacity as key infection indicators.

Keywords:
amphibianendocrine disruptioninnate immunitymachine learningskin microbiota

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Area of Science:

  • Amphibian ecology and disease dynamics
  • Microbiology and immunology
  • Machine learning applications in wildlife health

Background:

  • Global amphibian declines are driven by stressors, including chytridiomycosis caused by Batrachochytrium dendrobatidis (Bd).
  • South Korean amphibian species like Pelophylax nigromaculatus exhibit resistance to Bd, complicating the assessment of its precise impacts.
  • Understanding Bd infection dynamics in resistant species is crucial for amphibian conservation efforts.

Purpose of the Study:

  • To investigate the dynamics of Batrachochytrium dendrobatidis (Bd) infection in Pelophylax nigromaculatus.
  • To integrate physiological, microbiological, and morphological data with machine learning for infection analysis.
  • To identify reliable biomarkers for Bd infection status in a Bd-resistant amphibian population.

Main Methods:

  • Collected data on Bd prevalence, body size, weight, corticosterone (CORT) levels, innate immune function (bacterial killing assays), and skin microbiome composition.
  • Applied machine learning methodologies, including Light Gradient Boosting Machine and generative adversarial networks for data augmentation.
  • Analyzed physiological and microbiome data to differentiate between infected and non-infected individuals.

Main Results:

  • Significant physiological differences were observed between infected and non-infected P. nigromaculatus, notably elevated CORT levels and altered bacterial killing capacity.
  • Skin microbiome analysis revealed subtle variations but no significant differences in alpha or beta diversity between infected and non-infected groups.
  • Machine learning models, particularly Light Gradient Boosting Machine with data augmentation, achieved high predictive performance for classifying infection status.

Conclusions:

  • Corticosterone levels and bacterial killing ability are effective predictors of Bd infection status in Pelophylax nigromaculatus.
  • Machine learning can effectively integrate multi-level biomarkers to assess amphibian health, even with variations in disease resistance and infection loads.
  • This integrated approach is vital for understanding and mitigating the impacts of multiple threats on amphibian populations worldwide.