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<p>This study investigates the fungal pathogens associated with wheat leaf blight in the Indo-Gangetic plains of India, emphasizing their identification, pathogenicity, and interactions. A total of 106 fungal isolates from symptomatic wheat samples across 24 sites were analyzed. Morphological and molecular identification revealed that <i>Bipolaris sorokiniana</i> was the most prevalent species (41.50%), followed by <i>Alternaria alternata</i> (26.42%) and <i>Curvularia lunata</i> (22.64%). Lower prevalence was observed for <i>Alternaria triticina</i> (5.66<i>%)</i>,<i> Curvularia spicifera</i> (1.89%), and <i>Bipolaris drechsleri</i> (1.89%). Pathogenicity assessments revealed that co-infection treatments, particularly those involving multiple pathogens (<i>Bipolaris</i> spp. + <i>Alternaria</i> spp. + <i>Curvularia</i> spp.), resulted in the highest disease severity, with increased infection frequency, lesion size, and necrosis. Co-infection raised the average PDI to 48.2% compared to infection by a single pathogen. This suggests a shift from the traditional one pathogen–one disease paradigm to a more complex pathobiome approach integrating the interaction of multiple symbionts and host in a new understanding of disease etiology. Additionally, an AI-based image analysis system utilizing deep learning models was developed for early detection of wheat leaf blight. The system, trained on 1,000 digital images, achieved high accuracy (&gt; 90%) in classifying leaf blight symptoms using Convolutional Neural Networks (CNNs) like GoogleNet and Inception V3. This study supports the one pathogen–one disease paradigm is shifting towards the pathobiome concept.</p>

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Unravelling the wheat leaf blight pathobiome: a shift beyond the one pathogen–one disease paradigm

  • 期刊论文

  • 发表日期：

    2025年11月4日

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  • Abhijeet Shankar Kashyap,
  • Nazia Manzar,
  • Pawan K. Sharma

摘要

This study investigates the fungal pathogens associated with wheat leaf blight in the Indo-Gangetic plains of India, emphasizing their identification, pathogenicity, and interactions. A total of 106 fungal isolates from symptomatic wheat samples across 24 sites were analyzed. Morphological and molecular identification revealed that Bipolaris sorokiniana was the most prevalent species (41.50%), followed by Alternaria alternata (26.42%) and Curvularia lunata (22.64%). Lower prevalence was observed for Alternaria triticina (5.66%), Curvularia spicifera (1.89%), and Bipolaris drechsleri (1.89%). Pathogenicity assessments revealed that co-infection treatments, particularly those involving multiple pathogens (Bipolaris spp. + Alternaria spp. + Curvularia spp.), resulted in the highest disease severity, with increased infection frequency, lesion size, and necrosis. Co-infection raised the average PDI to 48.2% compared to infection by a single pathogen. This suggests a shift from the traditional one pathogen–one disease paradigm to a more complex pathobiome approach integrating the interaction of multiple symbionts and host in a new understanding of disease etiology. Additionally, an AI-based image analysis system utilizing deep learning models was developed for early detection of wheat leaf blight. The system, trained on 1,000 digital images, achieved high accuracy (> 90%) in classifying leaf blight symptoms using Convolutional Neural Networks (CNNs) like GoogleNet and Inception V3. This study supports the one pathogen–one disease paradigm is shifting towards the pathobiome concept.