Conventional management of S. rolfsii has relied on deep tillage, crop rotations, soil solarization, and broad-spectrum fungicides such as metham sodium, fluazinam, tebuconazole, and boscalid. Long-term efficacy, however, is undermined by the sclerotial melanized rind that limits fungicide penetration, by selection for reduced-sensitivity isolates, and by suppression of beneficial microbial guilds, including antibiotic-producing Pseudomonas species, mycoparasitic Trichoderma species, and plant growth-promoting rhizobacteria that constitute the first line of biological defense. This creates a negative feedback loop requiring ever-greater chemical intervention and accelerating the collapse of soil ecology. Combined with stricter regulatory constraints and consumer demand for residue-free produce, these failures have catalyzed a paradigm shift toward microbiome-centered disease management.

The rhizosphere, the narrow biologically hyperactive zone surrounding plant roots, harbors microbial densities 10 to 100 times higher than bulk soil, driven by root exudation of organic acids, sugars, amino acids, and secondary metabolites that can constitute up to 40 percent of net photosynthetic carbon. This rhizosphere effect shapes a distinctive microbiome performing ecosystem services including phosphate solubilization, nitrogen fixation, phytohormone production, and direct pathogen suppression through competitive exclusion, antibiosis, and mycoparasitism. Disease-suppressive soils, where pathogen populations decline despite susceptible hosts, are now understood to be primarily microbiome-mediated, and landmark studies on Rhizoctonia solani, Fusarium species, and Pythium species provide proof-of-concept for analogous suppression of S. rolfsii. Plants under pathogen attack also actively modify root exudate chemistry to recruit protective microorganisms, a phenomenon known as the cry for help response, suggesting these interactions are dynamic, inducible, and amenable to biotechnological enhancement.

Culture-dependent approaches capture fewer than 1 percent of soil microorganisms, but the genomic revolution has produced culture-independent multi-omics platforms enabling comprehensive characterization of community composition, genomic potential, transcriptional activity, and metabolic output. Amplicon sequencing of the 16S rRNA gene and ITS regions provides high-throughput taxonomic inventories at operational taxonomic unit or amplicon sequence variant resolution. Shotgun metagenomics enables functional gene annotation and assembly of metagenome-assembled genomes from uncultured lineages, revealing biosynthetic gene clusters linked to the production of novel antifungal metabolites. Metatranscriptomics captures active community gene expression under pathogen challenge, identifying marker bacteria positively associated with disease resistance that show increased expression of nitrogen fixation, siderophore production, and ACC deaminase genes. Metaproteomics and metabolomics bridge the gap between genetic potential and ecological function, profiling antifungal metabolites, volatile organic compounds, and root exudate shifts, while ionomics links microbial recruitment to plant nutritional status. Integrated multi-omics analyses reveal emergent insights, including novel biosynthetic gene clusters, unrecognized mycoparasitic interactions, and cross-kingdom communication networks inaccessible to any single platform.

The pathobiological complexity of S. rolfsii underpins the challenge. The fungus employs a brute force infection strategy centered on oxalic acid and cell-wall-degrading enzymes including cellulase, hemicellulase, and polygalacturonase. Oxalic acid serves as a multipurpose weapon: it creates an acidic environment with pH between 3.5 and 5.0 that promotes cell-wall-degrading enzyme activity, chelates calcium ions in plant pectins, decomposing the middle lamella that glues plant cells together, and suppresses host defense responses. During the initial infection phase, oxalic acid inhibits stomatal closure despite abscisic acid signaling, opening the gate for colonization without activating the plant’s drought defense system. The combined action of acidification and enzymatic maceration produces rapid tissue decay, damping-off of seedlings, and crown rot of mature plants, after which the fungus produces new sclerotia inside dead host tissues. High genetic and morphological variation within S. rolfsii populations, with studies on around 200 isolates showing sclerotia numbers per isolate ranging from 0 to 543, growth rates from 1.1 to 3.6 centimeters per day, and disease severity indices in common bean from 10.1 to 93.3 percent, further casts doubt on the efficiency of generalized biocontrol strategies.

The review categorizes soil suppressiveness into two mechanistically distinct types. Nonspecific suppression results indirectly from the cumulative metabolic activities of the entire soil microbial community, driven largely by resource competition for limiting nutrients such as carbon and iron. Specific suppressiveness is a higher-order phenomenon mediated by targeted activities of defined microbial taxa that directly interfere with the pathogen’s life cycle. The defining operational criterion for specific suppressiveness is transferability: mixing as little as 1 to 10 percent of suppressive soil into a disease-conducive soil confers measurable resistance to the recipient soil. This transferability assay distinguishes biologically mediated suppression from physicochemical soil properties and suggests that the suppressome, the collective genetic and metabolic repertoire of the suppressive community, can be decoded and potentially engineered. In soils suppressive to sclerotial pathogens, microbial groups such as Pseudomonas, Streptomyces, and Trichoderma are repeatedly identified as key antagonists, and a diverse array of antagonistic fungi including Trichoderma reesei, Trichoderma yunnanense, Talaromyces purpureogenus, Talaromyces calidicanius, Penicillium citrinum, and Clonostachys swieteniae have been characterized for their capacity to inhibit S. rolfsii through mycoparasitism, antifungal enzymes such as chitinase and beta-1,3-glucanase, volatile organic compounds, and induced systemic resistance.

The data volume generated by these platforms, tens of thousands of taxa and millions of functional genes per sample, exceeds the interpretive capacity of classical statistics, making artificial intelligence and machine learning indispensable. Supervised machine learning algorithms predict disease outcomes from microbiome data with accuracies exceeding 80 to 90 percent. Random Forest and XGBoost models excel at supervised classification and biomarker ranking, identifying key microbial taxa and metabolites for suppressiveness, while L2-regularized support vector machines are widely used for their computational efficiency. Convolutional neural networks achieve accuracy exceeding 98 percent in leaf and stem photos for image-based disease detection, and hypothetically could utilize satellite or drone-based spectral data together with historical climate data and soil characteristics such as humidity, temperature, and pH to predict outbreak likelihood, though such forecasting for S. rolfsii specifically remains hypothetical. Variational autoencoders learn joint embeddings across multiple data modalities, dimensionality reduction through PCA, t-SNE, and K-means clustering reduces hundreds of microbial taxa to ecologically meaningful axes, and graph neural networks decipher complex microbial co-occurrence and metabolic networks. Generative adversarial networks have generated thousands of novel antifungal peptides effective against Fusarium and Candida with no significant toxic effects on human cells, though applicability to agricultural plant pathogens such as S. rolfsii requires further validation. Transformer-based large language models pre-trained on genomic data, including ESM-2, Nucleotide Transformer, and HyenaDNA, enable zero-shot annotation of novel microbial genes.

Engineering the rhizosphere represents the end game of decoding it. Synthetic microbial communities, or SynComs, transition from single magic bullet strains to complex communities that imitate the stability and functionality of natural suppressive soils. Two design philosophies dominate: the top-down approach reduces a naturally occurring suppressive community to identify the core microorganisms needed to retain suppressive traits, guaranteeing functional redundancy as originally found in nature, while the bottom-up approach constructs communities from well-characterized isolate strains using modeling tools such as the generalized Lotka-Volterra model to predict interactions of competition, mutualism, and commensalism. Strains are ranked by AI algorithms based on gene function and metabolic sharing capacity. Effective consortia should incorporate functionally distinct strains, including broad-spectrum antibiotic producers, mycoparasites targeting sclerotia, and ISR-eliciting strains, to enable multi-tiered suppression. SynComs validated through isolate-based greenhouse studies can function as integrated immune modulators, activating salicylic acid, jasmonic acid, and ethylene pathways to help plants respond quickly without growth penalties. CRISPR-based genome editing further enables researchers to tune the metabolic output of community members, for example by upregulating siderophore production or deleting genes leading to unwanted metabolic competition, integrated with AI simulations to predict how genetic changes map into emergent community behaviors. Separately, mycovirus-mediated hypovirulence offers a unique strategy: the BLH-1 strain of S. rolfsii, infected with three novel mycoviruses including one partially similar to Sclerotinia sclerotiorum hypovirus 2, exhibits slow growth, inability to produce sclerotia, and decreased pathogenicity, suggesting a virus-based system capable of inhibiting sclerotia formation could break the disease life cycle.

Despite these advances, significant challenges remain. Data integration must contend with high dimensionality, the large p small n problem, zero-inflation, collinearity, batch effects, and compositionality, while the lack of standardized preprocessing and normalization processes makes comparison across laboratories difficult. AI model limitations include risks of data leakage, overfitting, class imbalance, site-specific confounding, insufficient interpretability, and the absence of causal validation frameworks. Field efficacy and formulation stability pose further constraints: several SynComs cannot survive their usefulness once removed from controlled laboratory settings, and environmental stressors like drought, heat, and UV radiation can disrupt the delicate balance of an engineered community. Single-strain biocontrol agents face bottlenecks related to narrow host specificity and inconsistent colonization, while SynComs encounter inter-strain competition and community collapse under environmental perturbation. Biosafety concerns, including horizontal gene transfer of engineered genetic elements to non-target organisms, regulatory hurdles, and the need for robust pre-release ecological risk assessments, further complicate field deployment. The review concludes that the convergence of AI-driven and multi-omics analytics offers a powerful strategy for advancing sustainable, microbiome-mediated management of S. rolfsii, but future research must prioritize mechanistic models that go beyond simple correlations, pathogen-specific multi-omics datasets, controlled suppressive versus conducive soil comparisons, longitudinal infection studies, benchmark AI datasets, and rigorous field validation to move from descriptive ecology toward programmable ecotechnology.

Subject of Research: Multi-omics and AI-driven analysis of the rhizosphere microbiome for biological suppression of the soil-borne pathogen Sclerotium rolfsii

Article Title: Decoding the rhizosphere microbiome against Sclerotium rolfsii: integrating multi-omics and AI-driven predictive models

Article References: Das, A., Boddana, P., Paul, P., Banerjee, P., & Das, S. (2026). Decoding the rhizosphere microbiome against Sclerotium rolfsii: integrating multi-omics and AI-driven predictive models. Frontiers in Microbiomes, 5, Article 1884781. https://doi.org/10.3389/frmbi.2026.1884781

Image Credits: AI Generated

DOI: 10.3389/frmbi.2026.1884781

Keywords: rhizosphere microbiome, Sclerotium rolfsii, multi-omics, machine learning, deep learning, soil suppressiveness, synthetic microbial consortia, biocontrol, metagenomics, metabolomics, oxalic acid, mycoviruses