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Terpenoids represent the largest and most structurally diverse class of secondary metabolites, with extensive applications in the pharmaceutical, nutraceutical, cosmetic, agricultural, fragrance, and biofuel industries. The growing demand for these compounds has resulted in extensive exploitation of plant-derived terpenoids, raising concerns regarding resource availability and sustainability. Consequently, microbial production has emerged as a promising alternative because of the high genetic tractability, rapid growth, and ease of metabolic engineering offered by microbial hosts. Various metabolic engineering strategies, including heterologous gene insertion, targeted gene deletion, and redirection of carbon flux from primary metabolism toward terpenoid biosynthesis, have been employed to enhance terpenoid production. The selection of an appropriate microbial host is a critical determinant of production efficiency, as it influences metabolite yield, cultivation feasibility, genetic manipulability, scalability, environmental sustainability, and economic viability. Genetically engineered microorganisms have therefore become well-established platforms for the production of diverse classes of terpenoids. Although substantial progress has been made in reconstructing and expressing terpenoid biosynthetic pathways in microbial hosts, further strain optimization requires systematic integration of computational approaches. In this context, artificial intelligence (AI) and machine learning (ML) have emerged as powerful tools for metabolic engineering by enabling pathway prediction, metabolic flux optimization, enzyme engineering, and identification of bottlenecks throughout terpenoid biosynthesis. Coupled with advances in genomics, systems biology, and synthetic biology, these technologies are accelerating the development of robust microbial cell factories for the sustainable, large-scale production of terpenoids through industrial bioprocesses.
The advent of microbial single-cell sequencing technology provides unprecedented resolution to study microbial ecosystems, revealing cellular heterogeneity, interactions, and genetic evolution of microorganisms. This review systematically compiles current microbial single-cell sequencing technologies, providing a detailed synthesis of their underlying technical principles, illuminating the strengths and limitations of various approaches. In-depth summaries of the technical challenges they encounter in different microbial domains and their practical applications are provided. Finally, we summarized the emerging field of microbial spatial omics, with a particular focus on advanced imaging techniques utilizing sequencing and fluorescence in situ hybridization.
Integrating cover crops (CCs) with nitrogen (N) management can enhance soil health, yet their combined effects on soil microbiomes and multifunctionality remain insufficiently characterized in short-term, multi-location corn systems in Mississippi. We evaluated how CC identity (single species and mixtures) and reduced N fertilization shape soil microbial communities and soil multifunctionality across a three-year field experiment (2021-2025) conducted at two locations in Mississippi, USA (Starkville and Newton). Treatments included six CCs with a no-cover control under two N rates (0 and 112 kg N ha-1). At corn V4 growth stage, soil samples (0-15 cm) were analyzed for physicochemical and biological indicators (pH, total C and N, POXC, glomalin, and enzyme activities), bacterial and fungal communities (16S rRNA V4 and ITS2 amplicon sequencing), and N-cycling functional genes (amoA and nifH via qPCR). Across years and locations, CCs, especially ryegrass, consistently increased soil biological indicators and elevated a composite multifunctionality index, while N fertilization reduced soil pH and exerted comparatively smaller effects on multifunctionality. Microbial community structure was primarily driven by interannual variation and location factors, with N fertilization consistently increasing bacterial α-diversity, and CC effects were most evident in specific years and were strongest under ryegrass. Network analyses indicated higher bacterial network complexity under 0 N, whereas fungal networks showed greater connectivity under N addition. Bacterial diversity and key bacterial taxa were positively associated with multifunctionality, and structural equation modeling indicated that microbial diversity and N-cycling functional groups mediate management effects on multifunctionality. Overall, ryegrass enhanced microbial-linked ecosystem services under reduced N inputs, though these shifts did not translate into consistent corn yield gains.
Mitochondrial genomes are among the most powerful sources for resolving evolutionary relationships in eukaryotes; however, their application in fungal phylogenetics remains underutilized. In this study, we employed comparative genomics and phylogenomics to evaluate the utility of the mitogenome within the genus Amanita, specifically at the species complex level. Our results demonstrate that mitochondrial coding regions provide robust species-level resolution, while structural features - including intron dynamics, synteny breaks and ORFs - reveal evolutionary trajectories often obscured by traditional nuclear markers. We identified highly variable gene synteny patterns and significant fluctuations in intron and ORF content, particularly within the cob and cox1 loci. Notably, these mitogenomic features varied considerably even among closely related species within the same section. Our findings underscore the dual utility of the mitochondrial genome for both interspecific inference and the identification of new cryptic lineages within established species complexes. Furthermore, we report a significant mitonuclear discordance, suggesting complex evolutionary histories such as ancestral hybridization or incomplete lineage sorting. These results highlight the necessity of integrating mitogenomics into fungal systematics to improve our understanding of species boundaries and evolutionary dynamics.
The human gut microbiota is a highly complex ecological system closely linked to host health, yet the functional mechanisms underlying its dynamic behavior remain poorly understood. Accurate modeling of microbial community dynamics is essential for elucidating these mechanisms. However, most existing approaches rely on densely sampled time-series data and often lack biological interpretability. To address these challenges, we propose gNODE, a framework that integrates the generalized Lotka-Volterra (gLV) model with neural ordinary differential equations (NeuralODEs) to jointly predict microbial community dynamics, infer species interactions, and quantify the functional contributions of key taxa. By embedding ecological equations into a neural architecture, gNODE incorporates biological constraints directly into its model structure, enabling biologically meaningful parameter estimation and accurate inference even under sparse temporal sampling. Through simulations and real datasets, gNODE demonstrates superior performance in parameter estimation, trajectory prediction, and perturbation response modeling compared with existing methods. In a Clostridioides difficile infection dataset, gNODE accurately captured post-infection community trajectories and identified key inhibitory taxa, highlighting its potential to discover microbes that suppress pathogens. In a probiotic cocktail colonization dataset, gNODE identified diet-specific keystone species, underscoring its utility for assessing perturbation responses and guiding the design of probiotic consortia. gNODE provides a robust and interpretable framework for modeling complex microbial community dynamics, offering new mechanistic and functional insights into the ecological processes that shape host-associated microbiomes.
The soil matrix is a heterogeneous mixture composed of aggregates-three-dimensional complexes composed of organic materials and mineral particles. Soil aggregates vary considerably in physical and chemical properties by size, making them unique habitats for distinct microbial communities and metabolic pathways. Yet, this microscale spatial variability is often overlooked in studies that use homogenized soil cores. We investigated the microbial taxonomy, functional gene composition, and metabolic products observed in four aggregate size fractions ranging from 8 mm to free particles (below 53 μm) collected from agricultural soils under two different management practices. The functional gene composition differed significantly among aggregate sizes, with higher abundances of genes for the degradation of plant-derived compounds in the macroaggregates and for biomass recycling in the two smallest size fractions. These differences were corroborated by significant differences in the composition of the metabolome but not in specific enzyme activities. Both taxonomic profiling and reconstruction of genomes from metagenomes revealed a higher abundance of ammonia-oxidizing archaea in the macroaggregates in comparison to other aggregate sizes, and analysis of their genomes revealed complementary metabolisms potentially enabling them to colonize different niches within the same habitat. Together, our results show that soil microbial communities and their functions are shaped by the size of soil aggregates, likely driven by differences in resource availability between macro- and microaggregates.
Dulse (Palmaria palmata) is a macroalgal feed ingredient rich in polysaccharides and bioactive compounds that offers a sustainable strategy to enhance animal health and productivity through modulation of gut microbiota. However, the impact of dulse supplementation on the taxonomic composition and genetic repertoire of the broiler chicken caecal microbiota remains poorly characterised. We applied long-read shotgun metagenomic sequencing on 18 caecal samples collected from 27-day-old male Ross 308 broilers following a 7-day feeding trial with three dietary treatments - a reference diet, a soyabean meal-supplemented diet, and a diet supplemented with 30% dulse - to investigate the effects of dulse inclusion on microbial community composition, genetic diversity, and antimicrobial resistance (AMR) and virulence determinants. Across all dietary treatments, the Clostridia class predominated (71%), whereas primary fermenters (L. phocaeense), lactic acid bacteria (L. salivarius), and hydrogenotrophic cross-feeders (B. hydrogenotrophica) were enriched in the reference diet, dulse-supplemented and soyabean meal-supplemented groups, respectively (KW p < 0.05), contributing to potential improvements in caecal function, immune resilience, and nutrient utilisation while reducing pathogen load. The overall resistome profiles were comparable across dietary treatments and were dominated by genes conferring resistance to tetracyclines, lincosamides, and aminoglycosides. In contrast, the virulome displayed diet-associated shifts: Enterobacteriaceae were enriched in the dulse and reference diets relative to the soyabean meal diet, with an expanded functional repertoire of virulence-associated genes, particularly those involved in adhesion, iron acquisition, and secretion systems. Multidrug resistance genes, virulence determinants, and Col/IncF-type plasmid replicons were associated with E. coli reads, highlighting its potential resistance and virulence arsenal within the caecal microbiota. Our findings suggest that the benefits of dulse extend beyond its nutritional value, residing in its ability to foster ecosystem resilience; by promoting a diverse, niche-stabilised microbiota, dulse minimises the risk of opportunistic pathogen proliferation, supporting its use as a sustainable, functional feed ingredient.
Methanotrophs are key microbial regulators of soil methane (CH4) sinks, but the global impact of their functional gene abundance on CH4 oxidation remains unquantified. This gap limits the integration of key functional genes abundance parameters (e.g., pmoA) into soil CH4 sink model. We integrated meta-analysis, machine learning, and process-based modeling to assess the relationship between pmoA gene abundance and soil CH4 uptake. Our developed Functional Gene Abundance-Based Methanotrophy Model (FGA-MeMo) incorporates pmoA as a proxy for CH4 oxidation capacity, significantly improving model simulations. FGA-MeMo estimates global upland soil CH4 uptake at 45.74 ± 0.26 Tg year-1, which is 56%-58% higher than MeMo model. Under SSP5-8.5 scenario, this increases to 64.68 ± 0.35 Tg year-1 by 2100, with mid- and high-latitude regions showing enhanced CH4 oxidation due to greater pmoA abundance. These findings highlight the importance of integrating microbial functional genes into Earth system models for improved CH4 cycle predictions.
Astroviruses are becoming a growing concern in public and veterinary health. In humans, astrovirus infections can cause severe diarrhea and may lead to neuropathological encephalitis, whereas in wildlife, these enteropathogenic viral infections often lack overt symptoms and thus remain unnoticed. Yet their close interaction with the host's gastrointestinal microbiome might drive cascading effects with disadvantages for host health. Bats harbor many zoonotic viruses without showing signs of disease, and many species move freely along the gradient from pristine to agricultural landscapes. To better understand the impact of astrovirus (AstV) infection under a One Health framework, we investigated the gut microbiome of naturally AstV-infected Seba's short-tailed bats (Carollia perspicillata, n = 234) inhabiting old-growth lowland forests or forest fragments embedded in an agricultural matrix in Panama. AstV prevalence was higher in forest fragments. We observed that AstV infection is associated with a shift in microbial beta but not alpha diversity, which points towards the replacement of common gut microbial taxa when infected. Indeed, potentially beneficial bacteria, such as Lactococcus, decreased in abundance, whereas potentially pathogenic bacteria from the Helicobacter genus increased in AstV-positive bats. Two Helicobacter haplotypes closely related to avian Helicobacter species were identified. We conclude that even though the impact of infection on the microbiome was not amplified in forest fragments, the higher infection likelihood in landscapes altered by humans implies more frequent or prolonged health repercussions for bats.
Microorganisms dominate life in the hadal zone, yet extreme sampling difficulty and low biomass have precluded characterization of their in situ activities. Here, we analyze microbiome samples collected from hadal seawaters via in situ filtration during 12 human-occupied vehicle dives. DNA-protein co-extraction and metagenome-guided metaproteomic analysis identify 135,073 non-redundant active proteins, with over 95% being hadal-specific. Metaproteomic quantification distinguishes highly active and less active taxa that differ in biogeographic origins and genomic traits. Hadal microorganisms operate a metabolic regime fundamentally distinct from the upper ocean, preferentially utilizing refractory organic matter (aromatics, halogenated compounds, and D-amino acids) and expanded electron acceptors (thiosulfate and heavy metals), collectively shaping hadal element cycling. Active viruses extend beyond "Piggyback-the-Winner" dynamics, enhancing host adaptation through auxiliary metabolic genes. These findings provide proteome-level evidence of hadal microbial activities and reveal biogeochemical cycling distinct from that of the upper ocean, highlighting the underappreciated significance of hadal microbiomes within global ocean ecosystems.
Black pepper (Piper nigrum Linn.), one of the world's most economically important spice crops, is increasingly challenged by climate-related stresses, emerging pests and diseases, and declining soil health, all of which threaten its productivity and sustainability. While previous reviews have predominantly focused on black pepper genomic resources, breeding strategies, and disease management, the integration of multi-omics technologies, microbiome science, and artificial intelligence (AI) to enhance its stress resilience has received comparatively limited attention. This review synthesizes recent advances in the molecular mechanisms underlying black pepper responses to biotic and abiotic stresses, with emphasis on omics approaches (such as genomics and transcriptomics), as well as the roles of beneficial microbial communities in enhancing stress tolerance, nutrient acquisition, and disease suppression. We further discuss emerging microbiome-assisted strategies, including the development of beneficial microbial consortia and targeted manipulation of microbial functions, for enhancing black pepper resilience under changing environmental conditions. In addition, we explore how AI-driven analytical approaches can integrate complex multi-omics and microbiome datasets to unravel the complex molecular networks governing black pepper-microbe interactions under stress conditions and accelerate precision breeding. By integrating genomics, microbial ecology, and AI, this review presents a systems-level framework for understanding and improving stress resilience in black pepper. This interdisciplinary perspective highlights new opportunities to accelerate the development of climate-resilient cultivars and advance sustainable black pepper production.
Barley (Hordeum vulgare L.) provides a suitable model for studying domestication-driven plant-microbiome interactions. Although wild, landrace, and modern genotypes host distinct rhizosphere communities, the extent to which roots and microbes reciprocally influence each other remains unclear. Here, we applied an integrated multi-omics approach combining long-read metagenomics, root transcriptomics, and plant genomics to understand genotype-specific host-microbiome coordination. Oxford Nanopore whole metagenome sequencing (WMS) revealed genotype-associated shifts in rhizosphere communities across seasons. Functional profiling showed a conserved metabolic backbone including amino acid metabolism, energy production, and secondary metabolite biosynthesis, alongside genotype-dependent variation in carbohydrate metabolism and transport-associated pathways. Genome-resolved analysis through metagenome-assembled genomes (MAGs) further detailed the taxonomic and functional architecture of key rhizosphere lineages. Root transcriptome profiling identified extensive differential expression associated with microbial perception, signaling, defense, and metabolic processes. Integration of host and microbiome data revealed coordinated molecular patterns, indicating that barley genotypes are associated with distinct microbial assemblages and corresponding transcriptional responses. These findings indicate that domestication has shaped coordinated associations between barley genotypes and their rhizosphere microbiomes, reflected in both microbial community composition and host transcriptional regulation. This work provides new insights into the evolutionary tuning of plant-microbiome relationships and highlights opportunities for microbiome-informed strategies in barley improvement.
The genus Bacillus, particularly endophytic species, has been widely studied as a source of plant growth-promoting bacteria in agricultural systems. These microorganisms contribute to plant performance through nutrient acquisition, phytohormone production, pathogen suppression, microbiome modulation, and enhanced tolerance to biotic and abiotic stresses. However, their ecological roles, functional plasticity, and genomic diversity remain poorly integrated into conceptual frameworks that extend beyond crop-based applications. Functional plasticity is reflected in their ability to colonize diverse plant hosts and tissues and to promote similar plant responses through distinct molecular mechanisms. Likewise, genomic diversity is evidenced by variation in accessory genomes, biosynthetic gene clusters, and regulatory networks that shape ecological functions and metabolite production. This review examines endophytic Bacillus as a model for understanding how metabolically versatile and genomically plastic bacteria establish functional, but context-dependent, associations with plants. Drawing on evidence from functional genomics, pangenomics, metabolomics, and microbial ecology, we discuss mechanisms associated with plant growth promotion and emphasize their dependence on host identity, environmental conditions, and microbial interactions. We address functional convergence arising from distinct genetic and metabolic routes, the contribution of accessory genomes and regulatory variation, and the ecological consequences of microbial inoculation in resident plant-associated microbiomes. We also highlight the limitations of in vitro screening approaches and the need for experimental validation across multiple biological scales to establish robust genotype-phenotype relationships. Finally, we extend the discussion beyond agricultural systems to consider the use of endophytic Bacillus in wild plant systems and ecological restoration, emphasizing the importance of evaluating both functional outcomes and ecological impacts.
Bovine-associated Klebsiella pneumoniae is an important bacterial species linking animal health, microbial ecology, and One Health-oriented antimicrobial resistance research. In this study, we performed a global genomic analysis of 1291 publicly available bovine-associated K. pneumoniae genomes collected from 18 countries between 2005 and 2024 using data retrieved from NCBI. MLST, core-genome phylogenetic analysis, pangenome analysis, CARD, VFDB, and PlasmidFinder were used to characterize sequence types, genomic diversity, antimicrobial resistance-associated genes, virulence-associated genes, and plasmid replicons. A total of 256 sequence types were identified, among which ST107 was the most common. Core-genome phylogenetic analysis revealed multiple genomic lineages, while pangenome analysis identified 46,325 gene clusters, including 1967 core genes and 40,595 cloud genes, indicating an open pangenome structure and substantial accessory gene diversity. Virulence-associated genes were unevenly distributed, with yagZ/ecpA being the most frequently detected determinant. In total, 138 antimicrobial resistance-associated genes or potential resistance determinants were detected across 16 antimicrobial categories, including clinically important β-lactamase- and carbapenemase-associated genes. IncF-family plasmid replicons, particularly IncFIB(K)_1_Kpn3, were frequently detected, suggesting widespread plasmid replicon-associated genomic backgrounds; however, physical co-localization between resistance genes and specific plasmid backbones could not be confirmed. Overall, this study reveals the genetic diversity, resistance-associated gene reservoir potential, heterogeneity of virulence-associated genes, and plasmid replicon backgrounds of bovine-associated K. pneumoniae. Importantly, the genome-predicted AMR potential identified in this study should not be interpreted as confirmed phenotypic resistance without further experimental validation. These findings provide genomic insights for risk surveillance, candidate control-target screening, and microbiota-oriented intervention research.
Seagrass restoration practices are evolving to leverage microbiome applications, similar to agricultural systems that have demonstrated how targeted microbial communities enhance crop resilience in challenging environments. While adult seagrass microbiome research has expanded significantly, research on the seed microbiome remains critically understudied. This gap is important given that seeds represent a large portion of restoration efforts. Advancing seed microbiome research requires standardized experimental systems for controlled plant-microbe interaction studies, which are currently lacking in seagrass research. Here, we tested fabricated ecosystem devices (EcoFAB 2.0) as a standardized system for growing Zostera marina (eelgrass) seedlings, enabling a controlled study of aquatic plant-microbe interactions. Using these chambers, we addressed three key questions: (i) can we reliably grow eelgrass in a controlled laboratory setting, (ii) can we manipulate eelgrass microbiota assembly and its long-term trajectory, and (iii) can we detect shifts in the microbiota during plant development (host filtering)? Host morphology measurements and 16S rRNA gene amplicon sequencing were used to track microbiota assembly across three early developmental stages of the host. Because plants were grown in a sterile environment, surface sterilization of seeds (bleach and ethanol) removed epiphytes without disturbing the shared endophytic community, yet microbiota composition remained divergent at Stage 6 (143 differentially abundant ASVs), indicating that seed coat epiphytes make a lasting and distinct contribution to assembly trajectory. We also identified 26 stage-specific indicator ASVs across eelgrass development, suggesting stage-specific microbial associations during seedling establishment. This work demonstrates the potential for targeted manipulation of the microbiome in seagrass for restoration efforts.IMPORTANCEUsing the Fabricated Ecosystem 2.0 (EcoFAB 2.0), we were able to successfully control the microbial environment of eelgrass, Zostera marina, resulting in the reduction of epiphytes and maintaining low microbial diversity across plants without compromising the morphology and growth of seedlings. Our findings advance the marine plant model system, Z. marina, by identifying taxonomic indicators across life stages. This work lays the foundation for a targeted understanding and application of microbiomes for seagrass restoration, bridging the critical knowledge gap between agricultural seed microbiome success and marine restoration applications.
Metabolic syndrome (MetS) is a multifaceted disorder influenced by genetic and environmental factors. MetS is associated with obesity, dyslipidemia, hypertension, and hyperglycemia. Recently, attention has turned to gut microbiota, a diverse microbial community in the gastrointestinal tract implicated in metabolic diseases, including MetS. Berry cactus (Myrtillocactus geometrizans) contains polyphenols, pectins, sterols, and betalains with reported hypoglycemic, hypolipemic, anti-inflammatory, and antiproliferative properties. This study evaluated the impact of berry cactus juice concentrate (BJC) on metabolic markers, gut microbiota composition and predicted microbial functions in high-fat diet-induced MetS rat model. Metabolic markers related to MetS and comprehensive analyses of microbial 16 S rRNA gene were obtained after 140 days of treatment. HFD feeding induced a MetS-like phenotype in rats; however, BJC supplementation did not significantly reverse the main metabolic alterations under the experimental conditions evaluated. Notably, BJC treatment was associated with changes in gut microbiota composition, including alterations in dominant phyla and relevant genera such as Parabacteroides. Predicted functional analyses suggested associations with pathways involved in fatty acid metabolism and promotion of the availability of berry cactus bioactive molecules. In addition, the presence of betalains and flavonoid derivatives was identified in BJC, which may contribute to its microbiota-modulating effects. These findings suggest that BJC may act primarily as a dietary modulator of microbiota in the context of HFD-induced metabolic disturbances. Although further studies are needed to determine whether these microbial changes translate into significant metabolic benefits.
Bacteriocins are ribosomally synthesized antimicrobial peptides that contribute to microbial competition, niche establishment, and community structure. The 2020 taxonomic reorganization of the former broad Lactobacillus genus provides a useful framework for reinterpreting bacteriocin diversity in lineage-specific ecological contexts. This review focuses on bacteriocins produced by species now assigned to Limosilactobacillus and Ligilactobacillus, two host- and food-associated genera that include several reported bacteriocin producers. These genera were selected because they contain bacteriocins with diverse structural features, including cyclic peptides, class IIa and IIb peptides, class IId peptides, defensin-like peptides, and larger proteinaceous bacteriocins, and because many producer strains originate from competitive ecological niches such as the gastrointestinal tract, oral cavity, vagina, milk, poultry, livestock, and fermented foods. We synthesize evidence on bacteriocin biosynthetic gene clusters, molecular diversity, antimicrobial mechanisms, ecological functions, physicochemical stability, and translational potential. This review also distinguishes bacteriocin-specific evidence from effects attributable to bacteriocin-producing strains, particularly for immunomodulation, co-aggregation, pathogen exclusion, and microbiota modulation. Finally, we address how comparative genomics, structured genome mining and artificial intelligence-aided prediction can speed bacteriocin discovery, emphasizing the necessity of experimental validation, standardized activity assays, safety evaluation and scalable production. This study gives a systematic framework to understand the bacteriocins of Limosilactobacillus and Ligilactobacillus in the post-Lactobacillus era by integrating taxonomy, ecology, mechanism and translational evidence.
Epidemic preparedness depends on tracking microbial evolution that drives shifts in ecological behaviors such as disease emergence. However, the genetic constraints mediating microbial emergence for generalist and specialist behaviors remain poorly described. Here, we addressed this question by combining comparative and functional genomics with phylogeny-based evolutionary analyses of the cereal pathogen Xanthomonas translucens. We show that a generalist X. translucens subgroup arose from a specialist ancestor, and the loss of a single effector gene, xopAL1, contributed to the generalist host expansion by promoting host jump from barley to wheat. Deleting barley-specialist X. translucens xopAL1 recapitulated the host jump to wheat and demonstrates risk across each globally distributed genetic lineage. However, this niche expansion via XopAL1 loss incurs a significant fitness cost to colonize barley. Moreover, the specialist lineage gained an additional effector gene, xopAJ, which enhanced virulence on barley while restricting oat infection, thereby reinforcing niche specialization. We further conducted transcriptomic analysis of wheat and determined that XopAL1 triggers a defense response that involves the reduction of photosynthetic processes. Our work provides an experimentally validated evolutionary framework to understand mechanisms of intergenera host jump. Overall, we demonstrate that single events of gene loss and gain shape ecological behaviors by creating a dynamic trade-off between niche breadth and specialization.
Previous research suggests that early-life stress (ELS) increases the risk of mental health disorders later in life. It is hypothesised that ELS disrupts the developing gut microbiome, which in turn may alter neuroendocrine and immune system development, thereby increasing disease susceptibility. However, the specific microbial taxa and pathways mediating these effects remain poorly characterised. Here, we used rat models to investigate whether ELS leads to long-term alterations in the gut microbiome. Microbial composition was assessed using 16S rRNA Nanopore sequencing of DNA extracted from faecal pellets of adolescent male and female rats exposed to: (i) early postnatal dexamethasone (DEXA; a synthetic glucocorticoid) or saline control, (ii) prenatal stress (PRS) and controls, or (iii) postnatal stress (POS) and controls. Microbiome structure was evaluated using richness, evenness, dominance and diversity indices. We show that ELS induces model-specific and sex-dependent changes in gut microbiome composition, primarily at the level of overall community structure rather than individual taxa. DEXA exposure produced the most consistent compositional signature, particularly in males, whereas PRS showed minimal detectable effects and POS exhibited a more heterogeneous response characterised by increased dispersion and limited taxonomic shifts. More broadly, these findings demonstrate that integrating beta-diversity analyses with machine learning approaches can identify reproducible microbiome patterns associated with ELS, even in the absence of large taxonomic changes. Applying similar frameworks in larger and longitudinal cohorts will be important to determine how these subtle microbial signatures contribute to long-term physiological outcomes.
Zoonotic spillover, the transmission of pathogens between animals and humans, is increasingly recognized as a major driver of emerging infectious diseases. However, most spillover research continues to approach emergence as a pathogen-specific or event-based phenomenon, limiting the ability to generalize findings across biological systems or to anticipate emergence before outbreaks occur. In this review, we build upon the existing eco-evolutionary framework to develop a pathogen-agnostic conceptual and operational framework that positions spillover as an emergent outcome of interacting ecological and evolutionary processes operating across hosts, environments, and time. The framework is organized around four interacting system-level pillars: pathogen evolvability, host network architecture, environmental mediation of persistence and exposure, and selective filtering and amplification under ecological and anthropogenic change. We propose that spillover risk emerges nonlinearly from the convergence of these interacting processes rather than from any single factor acting independently. Importantly, we move beyond conceptual synthesis by outlining measurable indicators and empirically testable approaches derived from genomics, ecology, environmental surveillance, and network analysis to operationalize the framework across diverse pathogen systems. Additionally, we discuss comparative and longitudinal study designs to evaluate spillover dynamics among viruses, bacteria, parasites, and other microbial threats. By reframing spillover as a dynamic eco-evolutionary system rather than an isolated transmission event, this review provides a comparative and operational foundation for studying zoonotic emergence and for informing surveillance, early warning, and intervention strategies amid accelerating environmental changes.