Building Functional Evidence in Plant Microbiome Research: From Multi-omics Discovery to Experimental Validation

Plant microbiome studies have linked community composition and microbial gene content with plant development, nutrition, and responses to biotic and abiotic stress. These associations generate valuable hypotheses, but taxonomic profiles and gene inventories rarely identify which organisms, genes, metabolites, or interactions contribute to a measured plant process. Genome-resolved metagenomics, metatranscriptomics, metabolomics, spatial methods, and long-read sequencing now resolve candidate functions with increasing precision. In parallel, culturomics, defined communities, gnotobiotic systems, microbial genetics, and in planta assays provide complementary routes for testing those candidates. Important gaps remain: many plant-associated organisms are difficult to culture, molecular tools are unevenly distributed across taxa, and effects observed in simplified systems may depend on host genotype, resident communities, soil, or environment. Progress therefore depends on connecting discovery and experimentation while matching the strength of each claim to the evidence available.This Research Topic will examine how signals detected in plant-associated microbiomes can be converted into testable and reproducible knowledge of microbial and plant function. Its organizing framework is Discovery → Functional prioritization → Experimental validation → Context and application. The stages describe complementary contributions across the collection rather than a requirement that each study complete the full sequence.The collection will include multi-omics studies that identify candidate functions; culturomics studies that recover organisms and create strain resources; computational approaches that prioritize genes, metabolites, taxa, or community properties; and experimental work using isolates, synthetic communities, gnotobiotic systems, in vitro assays, plant tissues, greenhouse studies, or field experiments. Mechanistic, causal, or field evidence should be reported when supported by the design, but will not be required for discovery-stage work. Conversely, association alone should not be described as mechanism.The topic fills a timely and practical gap between broad surveys of microbiome diversity and collections centered on microbiome engineering or deployment. It gives early-stage studies a clear role while asking each contribution to improve functional interpretation, experimental tractability, or reproducibility. Both beneficial and disease-associated microbiomes are relevant, as are biotic and abiotic stresses. Studies may focus on bacteria, archaea, fungi, protists, viruses, phages, or multi-kingdom communities.Discovery and prioritization studies may use multi-omics, genome-resolved or long-read metagenomics, genome mining, spatial or temporal designs, ecological networks, or machine learning to identify testable functions. Cultivation studies may develop isolation methods, plant-based media, culture collections, or genome-guided recovery of target organisms. Experimental studies may use in vitro assays, microbial genetics, metabolite tests, gnotobiotic systems, defined communities, or in planta and greenhouse models to examine colonization, nutrient exchange, signaling, antagonism, host responses, community interactions, or stress phenotypes. Context and application studies may test reproducibility, environmental dependence, inoculum establishment, or field performance. Field validation is welcomed but not required. Studies should state the level of inference supported by their design and distinguish association, prediction, and experimental evidence.The following early-stage work is explicitly eligible:1. Hypothesis-driven amplicon or metagenomic studies with adequate design and a clear functional or ecological question.2. Genome-resolved, long-read, transcriptomic, proteomic, metabolomic, or spatial studies that prioritize candidate functions, even when the relevant organisms cannot yet be cultured.3. Culturomics, culture-media development, genome-guided isolation, strain collections, and protocols that expand experimental access to plant-associated microorganisms.4. In vitro assays that establish a defined biochemical, nutritional, antagonistic, or interaction phenotype without claiming an untested plant outcome.5. Gnotobiotic, defined-community, or in planta studies that test a specific candidate under controlled conditions without requiring field confirmation.6. Data, code, standards, and benchmark resources that improve reproducibility or connect culture-independent and culture-dependent evidence.Routine taxonomic catalogues with insufficient replication, limited contextual metadata, no functional interpretation, and no reusable resource would fall outside the topic. The same applies to generic plant-growth-promotion screens that are disconnected from a microbiome-derived question, and to predictive models lacking a clear biological target or adequate safeguards against overfitting. These boundaries protect scientific coherence without excluding studies at an early stage. Topic editors: Wen Chen, Adeline Picot, Xiang Sean Li Source: https://www.frontiersin.org/research-topics/84002/building-functional-evidence-in-plant-microbiome-research-from-multi-omics-discovery-to-experimental-validation