The mechanisms by which bacterial endosymbionts (bacteria living within fungal cells) enhance the fitness and virulence of fungal pathogens remain poorly understood. Here, we report that the tomato Fusarium wilt pathogen Fusarium oxysporum f. sp. lycopersici (FOL) hosts Achromobacter spp. endosymbionts that enhance fungal virulence. This virulence potentiation is partially dependent on interactions with the native rhizosphere microbiota. We show that bacterial endosymbiont-harboring FOL reshapes the rhizosphere bacterial community during pathogen infection and decreases the abundance of disease-suppressive bacteria, including Streptomyces spp. taxa. This inhibitory effect is mediated by bacterial endosymbiont-stimulated production of beauvericin (an antibacterial cyclic hexadepsipeptide) by FOL. Together, our findings reveal a tripartite interaction in which a fungal pathogen leverages its bacterial endosymbiont to weaken rhizosphere microbiome-based disease suppression by inhibiting plant-protective bacterial taxa. This work highlights how cross-kingdom symbioses can modulate pathogen ecology and virulence in soil environments.

Abstract Designing microbial communities to generate target products is crucial for biotechnology, agriculture, and disease treatment. However, rationally designing such communities from large seed pools has become a major challenge, as the rapidly expanding number of complete microbial genomes greatly expands the search space and sharply increases the required screening time and computational cost. Here, we introduce eBiota, a platform for ab initio design of microbial communities from a pool of 21,514 strains to generate target products. eBiota not only identifies optimal strain combinations but also simulates community behaviors, including microbial interactions and relative abundances. eBiota integrates three modules: CoreBFS, a graph-based search algorithm that rapidly screens for bacteria with complete metabolic pathways related to the target product; ProdFBA, an extended flux balance analysis that identifies microbial consortia with maximal production efficiency; and DeepCooc, a deep learning model trained on 23,323 microbiome samples across various environments to infer co-occurrence patterns. We validated eBiota’s capabilities in microbial community design and production efficiency calculation using public microbiome datasets, ranging from single strains to six-member consortia. Further in vitro experiments involving 94 strains confirmed eBiota’s ability to identify species that inhibit pathogen growth and to accurately model the relative abundances within complex microbial communities. As an initial digital twin, eBiota provides a powerful platform for the rational design of functional microbial communities, offering new opportunities for metabolic engineering and synthetic biology.

Microbiome research is rapidly evolving, accompanied by a growing variety of analytical techniques and visualization formats. However, existing R-based tools remain fragmented, differ in data structures, and often require extensive package-specific configuration. This increases command-line complexity and limits both interoperability and reproducibility across studies. Here, we present LorMe (Lightweight One-line Resolving Microbial Ecology), an R package that provides a unified, interoperable, and user-friendly framework for microbiome data analysis. LorMe integrates a standardized S4 object system with full bidirectional compatibility with phyloseq and microeco, allowing seamless data exchange across major microbial analysis packages. A modular architecture facilitates both full end-to-end workflows and flexible execution of individual analytical components, while a global configuration system guarantees uniform parameter control and visualization standards. Based on these, LorMe further offers a one-command pipeline that performs the full spectrum of microbial community analyses, including alpha and beta diversity, differential abundance testing, co-occurrence network inference, and meta-network construction, while archiving all intermediate objects and source data to ensure complete reproducibility. Demonstrations with sample datasets show that LorMe performs comprehensive analyses through a single command, produces publication-ready outputs, and maintains methodological transparency. Application to a real rhizosphere data demonstrates biologically coherent patterns across diversity metrics, differential taxa, and network modules, highlighting the ability of LorMe to support robust ecological interpretation. LorMe provides a lightweight, extensible, and flexible solution that reduces technical barriers and enhances reproducibility in microbiome research. The LorMe package is freely accessible from both CRAN ( https://cran.r-project.org/web/packages/LorMe) and GitHub (https://github.com/wangnq111/LorMe).

Abstract Amplicon sequencing protocol targeting the 16S rRNA gene is a widely used and cost-effective method for exploring bacterial communities. However, its performance is often limited by primer bias arising from the arbitrary use of universal primers across diverse microbial communities and habitats. We propose KuafuPrimer to design the optimal 16S rRNA gene primers toward minimal bias for targeted bacterial communities, using few-shot machine learning to guide the primer design procedure based on a small number of samples. Simulations on 809 samples across 26 representative environments and habitats showed that KuafuPrimer-designed primers outperformed the universal primers in taxonomic accuracy, achieving an averaged 16.31% relative reduction in primer bias, with reductions up to 46.08% in plant samples. Notably, KuafuPrimer detected 29 rare and key taxa undetectable by the universal primers. Validation with 317 longitudinal gut microbiota samples demonstrated that KuafuPrimer-designed primers consistently outperformed the universal primers across temporal, individual, and cohort levels, with relative bias reductions of 5.03%, 3.53%, and 3.10%, respectively. Finally, in real PCR experiments on human gut samples from Clostridioides difficile -infected and healthy groups showed that polymerase chain reaction products using KuafuPrimer-designed primers correlated better with metagenomic data compared to the universal primers. More importantly, KuafuPrimer successfully detected Clostridioides difficile , the key pathogen missed by the universal primers, highlighting its potential for improving clinical diagnostics. In summary, KuafuPrimer provides a machine learning-based primer design strategy for targeted bacterial communities, with demonstrated utility in large-scale microbiome initiatives, longitudinal surveys and clinical diagnostics.

Strigolactones (SLs) are carotenoid-derived hormones that regulate plant development and abiotic stress responses, but their role in regulating plant-microbe interactions remains unclear. Here, we show that Arabidopsis thaliana loss-of-function mutants of two SL biosynthetic genes, MORE AXILLARY GROWTH 3 (MAX3) and MAX4, exhibit opposite responses to soil-borne pathogen Ralstonia solanacearum, with max3 mutants displaying enhanced resistance, whereas max4 mutants are hypersusceptible. Exogenous SL analog rac-GR24 restores resistance in max4 mutants supporting a role for canonical SL-dependent immunity, while max3 mutants mediated resistance is SL-independent. Multi-omics analyses suggest that MAX3 deficiency is associated with enhanced abscisic acid (ABA) and flavonoid pathways under natural conditions, coinciding with the enrichment of beneficial Pseudomonas in rhizosphere. Both in vitro and in planta validations suggest that the ABA-flavonoid axis cooperatively enhances Pseudomonas-mediated niche competition and antibiotic biosynthesis, thereby potentially contributing to pathogen suppression. Our findings support a model in which MAX3 is associated with the modulation of rhizosphere-mediated defense, linking hormone signaling, secondary metabolism, and microbiome assembly in the context of soil-borne disease resistance.

Wastewater forms a reservoir for diverse microbial pathogens, posing significant risks to public health and aquatic ecosystems. They further contain several pharmaceutical residues that can induce profound shifts in the wastewater microbiome. In this study, we assessed whether common non-antimicrobial pharmaceuticals such as pain killers and blood pressure regulators impact wastewater pathogen profiles. We spiked wastewater with caffeine (stimulant), atenolol (beta-blocker), paracetamol (analgesic), ibuprofen (anti-inflammatory) and enalapril (ACE-inhibitor), and monitored microbiome 16S rRNA gene profiles. We then matched species composition to a comprehensive database of human, animal, and plant pathogens. All pharmaceuticals significatively increased abundance and biodiversity for zoonotic, plant, and animal pathogens. These findings highlight that pharmaceutical contamination poses a biohazard risk by fostering pathogen growth. We call for a paired, continuous monitoring of chemical and biological pollutants and a more stringent removal of pharmaceutical residues from wastewater.

Rhizosphere microbiome critically influences plant growth and health, yet the genetic mechanisms underlying host regulation of microbiome composition remain unclear. Here, we analyzed whole-genome genotype and 16S rhizosphere microbiome data from 432 globally sourced eggplant accessions (Solanum melongena L.). Eggplant population structure corresponded to geographic origins, with rhizosphere microbiomes varying significantly among subpopulations. Host genetics explains 9%-39% of the variation in individual microbial taxa abundance, with core taxa more affected by host genetic variation. Microbial genome-wide association studies (mGWAS) identified 1235 significant genetic variants associated with 46 core microbial taxa, revealing key regulatory loci including chr10:7799021 near MYB113 (associated with Stenotrophomonas, P = 2.16 × 10-15) and chr10:19786889 near BLH9 (associated with Mycobacterium, P = 1.24 × 10-12), as well as a chromosome 5 locus with specific regulatory effects on Rhizobiales. These microbiome-associated genetic variants were enriched in secondary metabolic pathways, including anthocyanin biosynthesis, benzoxazinoid biosynthesis, and brassinosteroid biosynthesis, indicating that hosts regulate microbial communities through complex metabolic networks. Notably, genetic loci controlling microbial community structure underwent strong directional selection across eggplant subpopulations from different geographic origins, providing evidence for host-microbe coadaptive evolution. This study elucidates genetic regulatory patterns of eggplant rhizosphere microbiomes, enriching the theoretical framework of plant-microbe coevolution, with broad implications for microbiome-assisted crop improvement and sustainable agriculture.

Top-down trophic interactions are major drivers of microbiome dynamics, yet their outcomes are difficult to predict and their consequences for pathogen control remain unclear. We combine synthetic bacterial communities of varying complexity with field studies and microcosm assays to test whether microbivorous nematodes reorganize microbiomes to suppress soilborne disease. Field studies show stronger nematode-microbe associations around healthy plants, and microcosm assays confirm that nematode presence produces stable suppression, whereas microbe-only communities collapse under pathogen invasion. Nematode predation depletes non-preferred bacterial taxa and enriches metabolically versatile taxa within Proteobacteria, increasing community-level antagonistic potential and promoting complementary resource-use interactions linked to pathogen inhibition, yielding suppression beyond individual or pairwise effects. A minimal four-component feedback loop linking a nematode predator, plant pathogens, and two plant-associated bacteria with complementary functions accounts for the emergent outcome. Together, these results reveal an animal-mediated pathway of microbiome assembly that enhances resistance to pathogen invasion and provide a trophically informed framework for designing stable, disease-suppressive microbiomes in agriculture.

ABSTRACT Fungal pathogens threaten the health of humans, animals, and plants. ITS sequencing offers an effective approach for detecting fungal pathogens; however, a comprehensive pathogen database and associated tailored pipeline are still lacking. This study introduces the multiple fungal pathogen detection (MFPD) pipeline, which incorporates an accurate and high‐speed sequence alignment algorithm for broad‐habitat pathogen identification. The curated MFPD database includes 95 660 full‐length ITS sequences from 4924 reported fungal pathogen species. In silico experiments show that the full‐length ITS achieves the highest accuracy in pathogen detection (average 99.34%), outperforming both the ITS1 and ITS2 subregions. Benchmarking against existing tools, including FUNGuild, FungalTraits, and ISHAM‐ITS, shows that MFPD achieves the highest F1 scores in mock communities (0.89 for both plant and human–animal pathogens) and detects the broadest spectrum of pathogenic taxa in real samples. In addition to identifying causal pathogens, MFPD can also detect coinfecting pathogens in biological and environmental samples. Together, our work supports pathogen surveillance across diverse sectors, including clinical, agricultural, and livestock systems within a One Health framework.

Many soil protists are bacterivores, yet how protist predation reshapes bacterial metabolic interactions and functions remains poorly understood. Here, we combine global soil samples with microbial metabolic simulations, along with soil microcosm-pot validations, to investigate the influence of protists on bacterial metabolic interactions. Across 3,785 metabolic simulations spanning 757 soils, increased protists predicted higher bacterial metabolic interaction potential and cross-feeding but lower metabolic resource overlap and competition. These patterns were confirmed using an independent rhizosphere dataset and metagenomic analysis. Protist predation selected bacterial communities containing GC-rich genomes, acid-carbon-preferring taxa, and enhanced metabolite exchange. Additionally, exposing a synthetic community (SynCom) to protist predation elevated the expression of bacterial genes associated with plant growth-promoting functions. Consistently, microcosm- and pot-based experiments showed that protist addition increased bacterial cross-feeding over time and improved plant performance. Together, we establish a scalable framework to evaluate protist-driven bacterial cooperation and function to guide rational rhizosphere microbiome engineering.

Understanding how plant-associated microbiomes resist phytopathogen invasion remains a key challenge in natural ecosystems. Here we combined genome-scale metabolic models with synthetic community experiments, both in vitro and in planta, to unravel the mechanisms driving pathogen suppression. We developed curated genome-scale models for each strain, incorporating 48 common resource utilization profiles to fully capture their metabolic capacities. Trophic interactions inferred from models effectively predicted pathogen invasion outcomes across diverse microbial communities and nutrient environments. Importantly, considering both substrate and metabolite features provided a more holistic understanding of pathogen suppression. In particular, cross-feeding metabolites within the native community emerged as crucial yet often overlooked predictors of community resistance, disproportionally favouring native species over invaders. This study lays the foundation for designing disease-resistant microbiomes, with broad implications for mitigating pathogen exposure in diverse environments.

Plant growth-promoting rhizobacteria(PGPR)have been widely used for the promotion of plant performance.Predatory protists can influence the taxonomic and functional composition of rhizosphere bacteria.However,research on the impact of the interaction between protist and PGPR on plant performance remains at a very early stage.Here,we examined the impacts of individual inoculation of protist(Colpoda inflata,Dimastigella trypaniformis,or Vermamoeba vermiformis)or the PGPR strain Bacillus velezensis SQR9 as well as the co-inoculation of the protist C.inflata and B.velezensis SQR9 on the growth of tomato plants.We found that all individual protists and Bacillus could promote plant growth compared to the control with no microbe inoculation,with the co-inoculation of C.inflata and B.velezensis SQR9 achieving the greatest performance,including plant height,fresh weight,and dry weight.Different protists harbored distinct rhizosphere bacterial communities,with the co-inoculation of protist and Bacillus resulting in the lowest bacterial diversity and driving significant changes in community structure and composition,particularly by increasing the relative abundance of Proteobacteria.Random forest model highlighted Cellvibrio as the most important bacterial predictor of plant growth,which was enriched after protist inoculation,especially after the mixed inoculation of protist and Bacillus.We further found that bacterial functional genes of nitrogen metabolism were the key determinants of plant growth.These results indicate that the interaction between protists and Bacillus can support plant growth by reshaping rhizosphere bacterial community composition and function.Understanding the interaction mechanisms between protist and PGPR is crucial for their effective utilization in sustainable agriculture.

Long-read sequencing has transformed metagenomics and improved the quality of metagenome-assembled genomes (MAGs). However, current binning methods struggle with identifying unknown species and managing imbalanced species distributions. Here, we present LorBin, an unsupervised binner specially designed to reconstruct MAGs in natural microbiomes. LorBin deploys a two-stage multiscale adaptive DBSCAN and BIRCH clustering with evaluation decision models using single-copy genes to maximize MAG recovery. LorBin outperforms six competing binners in both simulated and real microbiomes, including oral, gut, and marine samples. LorBin generated 15-189% more high-quality MAGs with high serendipity and identified 2.4-17 times more novel taxa than state-of-the-art binning methods. Together, LorBin is a promising long-read metagenomic binner for accessing species-rich samples containing unknown taxa and is efficient at retrieving more complete genomes from imbalanced natural microbiomes.

Bacterial social interactions play crucial roles in various ecological, medical, and biotechnological contexts. However, predicting these interactions from genome sequences is notoriously difficult. Here, we developed bioinformatic tools to predict whether secreted iron-scavenging siderophores stimulate or inhibit the growth of community members. Siderophores are chemically diverse and can be stimulatory or inhibitory depending on whether bacteria have or lack corresponding uptake receptors. We focused on 1928 representative Pseudomonas genomes and developed an experimentally validated coevolution algorithm to match encoded siderophore synthetases to corresponding receptor groups. We derived community-level iron interaction networks to show that siderophore-mediated interactions differ across habitats and lifestyles. Specifically, dense networks of siderophore sharing and competition were observed among environmental and nonpathogenic species, while small, fragmented networks occurred among human-associated and pathogenic species. Together, our sequence-to-ecology approach empowers the analyses of social interactions among thousands of bacterial strains and offers opportunities for targeted intervention to microbial communities.

Streptomyces spp. are known for producing bioactive compounds that suppress phytopathogens. However, previous studies have largely focused on their direct interactions with pathogens and plants, often neglecting their interactions with the broader soil microbiome. In this study, we hypothesized that these interactions are critical for effective pathogen control. We investigated a diverse collection of Streptomyces strains to select those with strong protective capabilities against tomato wilt disease caused by Ralstonia solanacearum. Leveraging a synthetic community (SynCom) established in our lab, alongside multiple in planta and in vitro co-cultivation experiments, as well as transcriptomic and metabolomic analyses, we explored the synergistic inhibitory mechanisms underlying bacterial wilt resistance facilitated by both Streptomyces and the soil microbiome. Our findings indicate that direct antagonism by Streptomyces is not sufficient for their biocontrol efficacy. Instead, the efficacy was associated with shifts in the rhizosphere microbiome, particularly the promotion of two native keystone taxa, CSC98 (Stenotrophomonas maltophilia) and CSC13 (Paenibacillus cellulositrophicus). In vitro co-cultivation experiments revealed that CSC98 and CSC13 did not directly inhibit the pathogen. Instead, the metabolite of CSC13 significantly enhanced the inhibition efficiency of Streptomyces R02, a highly effective biocontrol strain in natural soil. Transcriptomic and metabolomic analyses revealed that CSC13’s metabolites induced the production of Erythromycin E in Streptomyces R02, a key compound that directly suppressed R. solanacearum, as demonstrated by our antagonism tests. Collectively, our study reveals how beneficial microbes engage with the native soil microbiome to combat pathogens, suggesting the potential of leveraging microbial interactions to enhance biocontrol efficiency. These findings highlight the significance of intricate microbial interactions within the microbiome in regulating plant diseases and provide a theoretical foundation for devising efficacious biocontrol strategies in sustainable agriculture.

Rare taxa are an important constituent of the microbiome and play a crucial role in maintaining biodiversity and ecosystem dynamics. However, little is known about rare taxa within the core microbiome (i.e., core rare taxa), nor do we understand the factors that drive their distribution and occupancy in ecosystems. In this opinion article, we define and explore the role of core rare taxa and the ecological and genetic drivers of their persistence. We also discuss 'innate' and 'adaptive' resilience in relation to core rare taxa and their drivers. Finally, we emphasize the need to develop appropriate metrics to quantify core rare taxa and their functions, as this can have significant implications for biodiversity conservation and microbiome engineering in the long run.

Abiotic and biotic soil properties are strong predictors of plant yield globally1-5, but they become unreliable over large areas when plant health is threatened by pathogen6-8. Here we present a novel approach to predict plant health based on spatiotemporal changes in soil chemical and biological properties. We first demonstrate that plant health and soil properties consistently respond to environmental change (organic fertilization) regardless of the soil type or geographical origin. Second, we experimentally show that trackable shifts in soil properties reliably explain soil suppressiveness to the Ralstonia solanacearum bacterial pathogen and that a scalable spatiotemporal model predicts plant health with 84% accuracy across multiple climatic zones and cropping systems. Our results suggest that this tight coupling between soil properties and plant health could facilitate the development of agricultural practices aimed at sustainably improving crop yields while safeguarding crop health. ### Competing Interest Statement The authors have declared no competing interest.

Prolonged antibiotic usage in livestock farming leads to the accumulation of antibiotic resistance genes in animal manure. Composting has been shown as an effective way of removing antibiotic resistance from manures, but the specific mechanisms remain unclear. This study used time-series sampling and metagenomics to analyse the resistome types and their bacterial hosts in chicken manures. Composting significantly altered the physicochemical properties and microbiome composition, reduced antibiotic resistance genes by 65.71 %, mobile genetic elements by 68.15 % and horizontal gene transfer frequency. Source tracking revealed that Firmicutes, Actinobacteria, and Proteobacteria are the major bacterial hosts involved in the resistome and gene transfer events. Composting reduces the resistome risk by targeting pathogens such as Staphylococcus aureus. . Structural equation modelling confirmed that composting reduces resistome risk by changing pH and pathogen abundance. This study demonstrates that composting is an effective strategy for mitigating resistome risk in chicken manure, thereby supporting the One Health initiative.

We summarize here the use of SynComs in improving various dimensions of soil health, including fertility, pollutant removal, soil-borne disease suppression, and soil resilience; as well as a set of useful guidelines to assess and understand the principles for designing SynComs to enhance soil health. Finally, we discuss the next stages of SynComs applications, including highly diverse and multikingdom SynComs targeting several functions simultaneously.

The soil-borne bacterial pathogen Ralstonia solanacearum causes significant losses in Solanaceae crop production worldwide, including tomato, potato, and eggplant. To efficiently prevent outbreaks, it is essential to understand the complex interactions between pathogens and the microbiome. One promising mechanism for enhancing microbiome functionality is siderophore-mediated competition, which is shaped by the low iron availability in the rhizosphere. This study explores the critical role of iron competition in determining microbiome functionality and its potential for designing high-performance microbiome engineering strategies. We investigated the impact of siderophore-mediated interactions on the efficacy of Pseudomonas spp. consortia in suppressing R. solanacearum , both in vitro and in vivo. Our findings show that siderophore production significantly enhances the inhibitory effects of Pseudomonas strains on pathogen growth, while other metabolites are less effective under iron-limited conditions. Moreover, siderophores play a crucial role in shaping interactions within the consortia, ultimately determining the level of protection against bacterial wilt disease. This study highlights the key role of siderophores in mediating consortium interactions and their impact on tomato health. Our results also emphasize the limited efficacy of other secondary metabolites in iron-limited environments, underscoring the importance of siderophore-mediated competition in maintaining tomato health and suppressing disease.