搜索结果：Microbial genomics

Sargassum seaweed is increasingly abundant in the Caribbean, creating ecological disruption but also providing biomass for agricultural inputs. This study compares the microbial diversity and safety of a Sargassum-based liquid biofertilizer (SBLB-INTEC) with those of a conventional product (LB-BANELINO) using 16S rRNA amplicon sequencing, rather than culture-dependent methods. Both formulations contained key nutrients (K, Ca, and Mg) and low levels of heavy metals. They harbored dense but relatively simple bacterial communities dominated by Firmicutes, particularly Bacilli, with Proteobacteria and other phyla at lower abundances. Staphylococcus (Staphylococcaceae) was highly abundant in both products, while SBLB-INTEC showed a somewhat more balanced community, including Delftia and other Comamonadaceae. Shannon diversity tended to be higher in SBLB-INTEC, but differences in alpha- and beta-diversity between formulations were not statistically significant. Because 16S data cannot distinguish viable from nonviable cells or resolve strain-level pathogenicity, these results do not prove the absence of pathogens; instead, they provide a genus-level baseline to guide targeted culture, qPCR, and functional assays. Overall, the combination of a favorable chemical profile and microbial groups commonly associated with nutrient cycling and plant-associated functions suggests that SBLB-INTEC could become a valuable component of integrated nutrient management in tropical agriculture, offering hope for a more sustainable future pending confirmatory plant-response and biosafety studies. We recommend integrating these microbial data into a national biofertilizer monitoring framework, combining metagenomic surveys with targeted qPCR and resistance gene screening.

Food is produced by a range of methods including extensive (organic and free range), intensive (conventional) and wild-caught production systems. Antimicrobial use varies between different food production systems, which may affect the microbial populations as well as the prevalence and diversity of antimicrobial resistance genes (ARGs) found on food at retail. In this study, shotgun metagenomics was used to investigate the microbial and ARG composition of 25 pork, 33 beef, 33 lamb, 60 chicken, 31 salmon and 41 leafy green samples collected in Norfolk, England, and labelled as extensive, wild caught or intensive. Food microbiomes consisted predominantly of spoilage-associated organisms including Pseudomonas, Lactococcus and Psychrobacter. Significant differences in bacterial diversity were found between intensive and extensive systems on chicken, and 22 differentially abundant genera were identified between production systems across beef, chicken and salmon. Genes conferring resistance to tetracyclines and beta-lactams comprised the majority of the food resistome across all commodities. Across most measures used to compare food resistomes between production methods, no significant differences were detected, except on chicken and salmon where differences in beta-diversity between production methods were detected, albeit with low effect sizes. Overall, these results suggest that differently produced foods, at least when tested at retail and in this region, may present a similar risk of antimicrobial resistance across the commodities investigated within this study. However, specific associations were identified with the microbial composition across chicken, beef and salmon, suggesting that production method may drive some variation in the microbial population structure on food products. Additional work at the farm or food processing levels is required to identify the drivers of these differences between production systems.

Environmental organic pollutants, identified as Polycyclic Aromatic Hydrocarbons (PAHs), are widespread and toxic. These hydrocarbons are commonly produced by industrial activities, burning fossil fuels, and crude oil discharges. Their high hydrophobicity, tendency to bioaccumulate, and mutagenic, carcinogenic, teratogenic, and genotoxic properties lead to significant environmental and human health risks. Additionally, their low bioavailability and chemical stability complicate PAHs remediation. In recent years, various methods have been explored to reduce their impact, including conventional physical and chemical treatments; however, these often face issues such as inadequate removal, high costs, lengthy processes, and environmental concerns. Bioremediation has emerged as a promising, environmentally friendly solution. This approach involves microorganisms such as bacteria, fungi, algae, and archaea utilizing specific enzymatic pathways-like dioxygenases, monooxygenases, peroxidases, and laccases-to transform PAHs into less toxic substances. Advances in genomics and metagenomics have identified key catabolic genes (e.g., nah, Phn, nid, pah) and regulatory mechanisms that enhance microbial resistance in PAH-contaminated environments. Since PAHs' low bioavailability and solubility often limit bioremediation alone, integrated strategies are gaining prominence. In-situ and ex-situ methods-including bioaugmentation, bio-stimulation, composting, and phytoremediation-boost microbial degradation of PAHs. Furthermore, advanced technologies such as multi-omics platforms, CRISPR-based genetic engineering, and artificial intelligence (AI) are transforming the field by enabling the development of targeted microbial strains, improving bioremediation efficiency, and creating predictive models. This review offers a recent, comprehensive outline by unifying PAHs toxicity, microbial degradation, traditional remediation, and advanced biotechnological tools into a single framework. A comprehensive and recent update of microbial and biotechnological approaches for sustainable PAHs bioremediation is offered by this review.

Persistent environmental pollutants require diverse microbial metabolic capabilities for effective degradation. While naturally occurring consortia or single strains often fall short in efficiency, synthetic microbial communities (SynComs) hold greater promise for enhanced degradation. To address this challenge, we developed GENIA (Genomically and Environmentally Networked Intelligent Assemblies), a genome-informed, machine learning-guided framework for the rational design of SynComs capable of degrading multiple pollutants. Using a microfluidic high-throughput cultivation platform, 2,155 bacterial strains were isolated from xenobiotic-enriched cotton detritusphere and screened for pollutant-specific growth. Whole-genome sequencing and functional annotation of 45 prioritized strains revealed metabolic traits associated with the degradation of lignin, atrazine, and PFAS. These genomic profiles were encoded into spline-based graph representations and integrated within the GENIA pipeline, which combines graph neural networks, pathway complementarity modeling, and functional redundancy minimization to predict optimal community assemblies. The resulting nine-member community, comprising Atlantibacter hermannii, Bacillus cabrialesii, Bacillus licheniformis, Bacillus pseudomycoides, Micrococcus luteus, Paenibacillus polymyxa, Pantoea dispersa, Pseudomonas fulva, and Pseudomonas pergaminensis, demonstrated broad catabolic capacity. Kinetic experiments in minimal medium showed simultaneous multipollutant degradation: lignin (91.6% by day 5), atrazine (91.4% by day 3), and PFOS (93.1% within 7 days), representing 2.2-fold improvement over best individual performers. Full-length 16S rRNA metabarcoding confirmed stable community composition with predicted hub strains expanding to 14-15.6% relative abundance. Soil microcosm validation demonstrated >70% degradation at 3 weeks. GENIA establishes a scalable framework that integrates systems genomics, phenotypic screening, and predictive modeling to engineer microbial consortia for complex environmental bioremediation.

Anthropogenic activity, driven by industrialization, agricultural practices, and waste disposal, has emerged as a predominant contributing factor to environmental pollution. These activities release substantial amounts of toxic pollutants into the environment, such as heavy metals, organic pollutants, microplastics, and nanomaterials, adversely affecting various ecosystems. These toxic substances can exert considerable stress on various microorganisms, including bacteria, fungi, and microalgae. The impact of anthropogenic pollutants on microorganisms is a nascent area of study, particularly as environmental stressors continue to increase in both quantity and complexity. This review aims to enhance our understanding of how microorganisms (bacteria, microalgae, and fungi) respond to the anthropogenic pollutants including heavy metals, organic pollutants such as polycyclic aromatic hydrocarbons (PAHs), nanomaterials and microplastics. It explores the toxic effects of these pollutants on diverse microbial species. Furthermore, the review covers studies that examine the molecular mechanisms underlying microbial resistance both through natural resistance processes and adaptive laboratory evolution or evolutionary engineering strategies. The review also highlights how omics technologies such as genomics, transcriptomics, proteomics and metabolomics reveal conserved and unique molecular mechanisms to gain insight into the pollutant-specific and organism-specific adaptation strategies. Nevertheless, limitations in community-level multi-omics studies, the relatively limited data on fungi, and the challenges associated with studying mixed cultures hinder a comprehensive understanding of microbial response and resistance mechanisms to anthropogenic pollutants. Addressing these gaps will be pivotal in leveraging the molecular mechanisms to guide the development of novel strategies to obtain pollutant-tolerant strains for bioremediation, bio-monitoring, and synthetic biology applications.

Competitive exclusion (CE) strategies represent a promising complementary approach to control foodborne pathogens in poultry production by modulating gut microbiota assembly. This field study evaluated the impact of a CE intervention applied at chick placement on cloacal microbiota temporal development, structure, and pathogen integration in commercial broiler chickens. Fifteen broiler farms with a history of Salmonella Infantis persistence were enrolled, including CE-treated farms and untreated controls. Cloacal samples were collected at 7, 18, 31, and 42 days of age and analyzed by 16S rRNA gene amplicon sequencing. Microbiota dynamics were evaluated through diversity metrics, differential abundance analysis, and microbial association network inference. CE treatment significantly altered microbiota composition and structure throughout the production cycle. Treated flocks showed a progressive increase in microbial richness and Shannon diversity from day 18 onward, while evenness remained largely unaffected. Beta-diversity analyses revealed persistent separation between treated and Un-Treated communities at all time points, indicating long-lasting treatment effects. Differential abundance analysis highlighted enrichment of beneficial genera, including Lactobacillus, Faecalicoccus, Roseburia, and Butyricimonas, in treated birds, whereas untreated flocks showed higher relative abundances of Campylobacter and other taxa associated with unstable community dynamics. Microbial network analysis revealed marked treatment-dependent differences in community organization. CE-treated networks exhibited higher modularity and edge density, suggesting increased structural complexity and potential resilience. Notably, Campylobacter showed strong early network integration and hub-like behaviour in untreated birds, while being completely disconnected in treated flocks at early life stages, indicating reduced ecological embedding. Overall, these findings demonstrate that competitive exclusion modulates broiler microbiota not only at the compositional level but also through restructuring microbial interaction networks. Early-life microbiota modulation appears to constrain pathogen ecological integration, providing a mechanistic framework for microbiota-driven control strategies under commercial poultry production conditions.

Mastitis is a major disease in dairy cattle, often caused by Staphylococcus aureus infection and increasingly complicated by antimicrobial resistance. As a result, bacterial strains able to effectively provide colonization resistance in the bovine mammary gland are being explored as potential probiotics to reduce reliance on antimicrobial treatments. Among these, some non-S. aureus staphylococci species have shown promise; however, their effects appear to be highly strain-dependent. In this study, a combination of machine learning approaches alongside standard genomic and phylogenetic analyses was used to compare and select putative probiotic Staphylococcus xylosus strains. The genomes of 82&#x2009;S. xylosus and 16 closely related Staphylococcus isolates from milk samples obtained from the Canadian Bovine Mastitis Research Network were assembled and annotated. In addition to identifying orthologous gene families and reconstructing phylogenetic relationships, each genome was screened for virulence factors, antimicrobial resistance (AMR) genes and bacteriocins. Random forest modelling and association rule learning were then applied to identify combinations of genes associated with isolates from milk samples collected from quarters exhibiting low inflammation, assessed using somatic cell counts (SCC). This approach identified 63 genes that frequently co-occurred in isolates from low SCC samples (low SCC <25,000&#x2009;cells&#x2009;ml-1) but were largely absent in those from high SCC samples (&#x2265;200,000&#x2009;cells&#x2009;ml-1). These gene sets were used as biomarkers in conjunction with phylogenetic and clustering analyses to guide the selection of a subset of S. xylosus isolates with potential probiotic properties.

Integrated chem-bio characterization of microbial strain libraries can streamline natural product discovery by prioritizing candidate producers. Here, we employ language- and transformer-based models to extract actionable insights from linked mass spectrometry (MS)-genome datasets. Our framework enables ranking of microbial producers to prioritise high-potential candidates for targeted validation. Across three representative case studies, this approach prioritized producers of diverse natural products with 75-100% precision. These findings demonstrate the transformative potential of AI-enabled chem-bio characterization to significantly accelerate natural product discovery and enable access to microbial chemical diversity beyond reference knowledge.

Marine sediments harbor diverse Vibrio populations that play critical roles in benthic microbial ecology; however, the genomic determinants underlying the dominance of sediment-associated Vibrio taxa remain insufficiently characterized at the genome level. In this study, culture-based enumeration revealed Vibrio diabolicus as the dominant Vibrio species in marine sediment samples, providing ecological rationale for genome-resolved investigation. Because sediment-associated vibrio species, often responds rapidly to environmental fluctuations and organic enrichment in coastal ecosystems, their distribution and genomic characteristics may also reflect changes in sediment microbial community structure and local environmental conditions. Four sediment-derived V. diabolicus isolates (Vdiab_L2, Vdiab_L3, Vdiab_VA, and Vdiab_B48) were subjected to whole-genome sequencing and comparative genomic analysis alongside closely related reference genomes. Average nucleotide identity (ANI) analyses confirmed species-level assignment, with all isolates exhibiting ANI values exceeding 95% relative to V. diabolicus references, while remaining clearly distinct from V. alginolyticus and V. parahaemolyticus references. Core-genome phylogenetic reconstruction resolved the sediment isolates into a coherent V. diabolicus lineage, consistent with ANI-based relationships and demonstrating strong concordance between whole-genome similarity metrics and evolutionary history inferred from conserved genes. Pangenome analysis revealed a relatively small, conserved core genome accompanied by a dominant accessory gene pool composed primarily of shell and cloud genes, indicative of an open pangenome structure. Accessory gene clustering and presence-absence profiling further highlighted strain-specific genomic heterogeneity within the species. Importantly, this study focuses specifically on comparative genomic structure rather than comparative functional genomics, aiming to establish a genome-level evolutionary framework for sediment-associated V. diabolicus populations. Together, these findings demonstrate that V. diabolicus combines ecological dominance in marine sediments with extensive genomic plasticity, a combination likely facilitating persistence and adaptation within heterogeneous benthic environments. This study provides a comprehensive comparative genomic baseline for understanding sediment-associated V. diabolicus populations and establishes a framework for future ecological and functional genomic investigations.

Urea is a major nitrogen form in natural and engineered ecosystems, yet the traits driving niche partitioning among nitrifiers during urea nitrification remain poorly understood. In this work, a stable urea nitrification microbial community was successfully established over prolonged cultivation characterized using 16S rRNA gene amplicon sequencing, qPCR and genome-resolved metagenomics coupled with comparative genomics. A clade A comammox Nitrospira closely related to Candidatus Nitrospira nitrosa became dominant (OTU330, 13.9%) and yielded the most abundant nitrifier metagenome-assembled genome (MAG). Genomes indicate comammox Nitrospira couples ATP-dependent urea ABC uptake to a streamlined urease-only module characterized by slow substrate turnover, whereas Nitrosomonas relies on passive urea channels and redundant urease/urea-amidolyase pathways, enabling rapid urea metabolism. These contrasting urea acquisition strategies suggest an affinity-capacity trade-off that underpins niche partitioning in urea-fed, oligotrophic nitrifying systems and provide targets for enhancing urea-based wastewater treatment processes.

Ecological guilds are groups of organisms that utilize the same class of resources and occupy similar niches, regardless of their taxonomic identities. Here we propose the Guild Model for Cystic Fibrosis Airway Microbial Ecology, which considers the ecological function and wider role of each microbe in the ecosystem. This model consists of four functional guilds: (i) "Brewers" metabolize host-derived substrates (e.g., mucins) and produce fermentation products; (ii) "Drunkards" exploit the metabolic niche built by Brewers, consuming fermentation products and secreting exopolysaccharides to build biofilms; (iii) "Putrifiers" produce toxic compounds causing inflammation and tissue necrosis; and (iv) "Nihilists" are specialist pathogens characterized by intracellular or lytic life cycles and cytotoxin production. By focusing on microbial function and the broader community context, this model offers a refined framework for interpreting cystic fibrosis airway ecology. Although developed for CF, the Guild Model is adaptable to other diseases influenced by microbial ecology.

Microbial competition for scarce resources shapes biodiversity patterns and ecosystem function across global biomes, yet quantifying this process from genomic data has remained elusive. Here, we introduce CaCo, a scalable metric that transforms metagenomic carbohydrate-active enzyme profiles into precise measures of niche overlap and competition potential (Resource Partitioning Score, RPS). Analyzing 14,691 high-quality metagenome-assembled genomes spanning Ocean, freshwater, soil, and human gut microbiomes, we reveal a striking macroecological pattern: Niche overlap increases from partitioned specialists in oligotrophic oceans to overlapping generalists in carbon-rich environments, including the human gut. This gradient aligns with classic niche theory, as phylogenetic signals indicate that closely related taxa may compete most intensely. Multitiered validation, spanning BIOLOG phenotypes, synthetic cocultures, and interaction gradients, confirms CaCo's predictive power and captures competitive exclusion. CaCo bridges genomic potential and ecological reality, providing niche-breadth metrics and enabling testable predictions of how resource availability shapes microbial competition and community structure.

Sphingobium yanoikuyae is a metabolically versatile, Gram-negative bacterium within the family Sphingobiaceae, recognized for its exceptional capacity to degrade a broad spectrum of xenobiotic compounds. Its ecological adaptability and enzymatic diversity enable the transformation of structurally complex pollutants into less toxic intermediates across diverse environmental settings. Although members of the genus Sphingobium have been widely studied, a focused and integrative synthesis specifically addressing the metabolic, genomic, and biotechnological attributes of S. yanoikuyae remains limited. This review presents a critical and up-to-date analysis of the taxonomy, genomic architecture, and xenobiotic degradation mechanisms of S. yanoikuyae, with particular emphasis on its functional role in microbial biotechnology. The organism exhibits broad substrate specificity toward hydrocarbons, pesticides, pharmaceuticals, and related compounds, mediated by coordinated enzymatic systems, including ring-hydroxylating dioxygenases, monooxygenases, and cytochrome P450-dependent pathways. Genomic analyses further reveal the presence of multiple catabolic operons, mobile genetic elements, and plasmid-associated gene clusters that collectively underpin its metabolic versatility, regulatory complexity, and adaptive potential. Beyond summarizing current knowledge, this review delineates key mechanistic features of degradation pathways, compares substrate-specific transformation efficiencies, and critically evaluates existing limitations, including incomplete pathway resolution, constraints in genetic manipulation, and variability in environmental performance. Furthermore, it highlights emerging opportunities in systems biology, enzyme engineering, and integrative omics approaches to enhance degradation efficiency and expand application scope. By consolidating current insights and identifying strategic research gaps, this work provides a coherent framework for advancing S. yanoikuyae as a robust platform for targeted applications in environmental remediation and sustainable biotechnology.

The widespread use of atrazine and nicosulfuron has been shown to adversely affect the growth of current crops, thereby diminishing productivity. Additionally, these compounds present a notable risk to subsequent crops, underscoring the importance of continued research and caution regarding their application. Microbial remediation technology has gained increasing attention for its effectiveness in mitigating herbicide contamination and residue. This approach offers key advantages, including operational simplicity and environmental sustainability. In the present study, a Priestia aryabhattai YB01 strain was isolated from cornfield soil and found to effectively degrade both atrazine and nicosulfuron. An artificial neural network and response surface methodology were used to optimize degradation conditions, achieving maximum degradation rates of 56.99% for atrazine and 44.51% for nicosulfuron. Whole-genome sequencing enabled the elucidation of the degradation mechanism. Molecular docking analyses revealed that functional proteins GE005880 and GE000952 had the highest binding affinities for atrazine and nicosulfuron, respectively, with binding energies of -6.85&#xa0;kcal/mol and&#xa0;-&#xa0;7.48&#xa0;kcal/mol. Molecular dynamics simulations confirmed that atrazine and nicosulfuron exhibit high mutual affinity, and their simultaneous presence may lead to an inhibitory interaction during co-degradation, with atrazine showing a stronger suppressive effect on nicosulfuron degradation.

Climate change poses significant threats to global agricultural productivity, necessitating innovative strategies to ensure food security and ecological sustainability. One promising avenue lies in the deliberate design and deployment of synthetic microbiomes and engineered rhizospheres to enhance plant resilience under environmental stress. This review places particular emphasis on multi-kingdom microbial interactions including bacteria, fungi, protists, and archaea and their potential for tailored, stress-specific applications within engineered rhizosphere systems. By integrating knowledge from microbial ecology, genomics, and systems biology, researchers have begun to unravel the complex interactions between plants and their associated microbial communities. Engineered microbial assemblies tailored to specific host plants and environmental conditions have shown potential in stabilizing crop performance during drought, salinity, and nutrient limitations. Moreover, the manipulation of root exudation patterns and soil physicochemical properties can be harnessed to recruit beneficial microbes and suppress harmful ones. The review also examines the role of synthetic biology tools, such as CRISPR-based genome editing and metabolic pathway engineering, in optimizing microbial traits for enhanced plant support. However, knowledge gaps remain in understanding multi-kingdom dynamics, optimizing SynComs for specific environmental contexts, and translating laboratory successes to reliable, field-scale applications. Additionally, advances in high-throughput screening, machine learning, and metagenomic profiling are accelerating the identification of key microbial taxa and functions relevant to plant health. Despite these promising developments, challenges remain in scaling these approaches for field applications and ensuring their ecological safety and consistency. This review explores the need for interdisciplinary efforts to translate laboratory insights into field-ready technologies, ultimately contributing to the development of climate-resilient and sustainable agricultural systems.

Mobile genetic elements (MGEs) play a central role in the acquisition and dissemination of antibiotic resistance genes (ARGs). This study analyzed the distribution of MGEs using the whole-genome sequences of 38 Acinetobacter isolates from patient, environmental, and pig waste samples. Pig waste isolates exhibited the highest mean number of plasmids, while prophages were more prevalent in environment-associated isolates. Interestingly, we observed a significant positive correlation between number of plasmids and number of defense systems. Co-localization of multiple ARGs within a single plasmid was observed, with up to 10 distinct ARGs observed within a single pdif module. Additionally, putative genomic resistance islands (GRIs) were identified in non-baumannii Acinetobacter species, representing the first documentation of GRIs outside the Acinetobacter calcoaceticus-baumannii (Acb) complex. This study provides new insights into the mechanisms of ARG dissemination in Acinetobacter, particularly the role of MGEs in facilitating hierarchical gene transfer processes.

Nasopharyngeal carcinoma (NPC) is strongly associated with Epstein-Barr virus (EBV) infection. The gut microbiome can influence outcomes of viral infections but the potential links among the gut microbiome, EBV infection and NPC remain unclear. To characterise gut microbiome alterations in EBV-associated NPC, evaluate microbiome-based diagnostic performance (alone and in combination with EBV markers), and explore associations between microbial features, EBV DNA burden, prognosis and the tumour microenvironment. We conducted a large-scale shotgun metagenomic study including 516 patients with EBV-associated NPC and 263 healthy controls. Microbiome dysbiosis, functional pathways and associations with plasma EBV DNA were assessed. Species-level markers were used to build a random forest classifier for NPC diagnosis, and performance was evaluated alone and in combination with EBV-specific markers. Survival analyses were performed to identify microbial features associated with NPC-related mortality and relationships with an immune-suppressive tumour microenvironment were explored. NPC was characterised by gut microbiome dysbiosis, including depletion of short-chain fatty acid-producing species and reduced butanoate metabolism, which were significantly associated with plasma EBV DNA. A random forest classifier based on species-level markers distinguished NPC from controls with an area under the curve (AUC) of 0.917; performance improved to an AUC of 0.984 when combined with EBV-specific markers. Specific microbial species were associated with NPC-related mortality and prognostic microbial features were linked to an immune-suppressive tumour microenvironment. EBV-associated NPC is associated with distinct gut microbiome and functional alterations that correlate with plasma EBV DNA. Microbial markers show strong diagnostic potential, particularly when integrated with EBV-specific markers, and prognostic microbial features may be linked to an immune-suppressive tumour microenvironment, supporting a potential role of the gut microbiome in NPC tumourigenesis.

The issues of how microorganisms survive very long periods of desiccation and how they react during both drying and rehydration phases have long been topics of interest in a range of relevant fields, including desert ecosystem microbiomics, food storage, ancient microbe studies, and even astrobiology. The recently published study by Carini et al., who used a combination of transcriptomics and metabolomics to investigate steady-state gene expression and cellular metabolite profiles at different states of bacterial cellular desiccation, during both drying and rewetting phases, adds some valuable insights into how members of bacterial communities can survive in the driest habitats on earth (P. Carini, A. Gomez-Buckley, C. R. Guerrero, M. R. Kridler, et al., mSystems 11:e00493-25, 2026, https://doi.org/10.1128/msystems.00493-25).

Insects such as black soldier fly larvae (Hermetia illucens, BSFL) are efficient bioconverters whose growth and physiological performance are strongly influenced by diet composition, gut microbiota, and the molecular regulation. This study investigated how a probiotic-based fermentation strategy modulates larval physiology, microbiome dynamics, and gene expression when BSFL are reared on fermented watermelon waste. Watermelon waste was fermented for 14 d using a consortium of Bacillus subtilis, Enterococcus faecalis, and Aspergillus oryzae, resulting in a nutritionally enhanced substrate. BSFL fed on fermented diet exhibited significantly increased growth performance, biomass yield, and nutritional content of the insect biomass. Metagenomic analysis revealed marked enrichment of gut microbes belonging to genera known to include beneficial and commensal species (Enterococcus, Vagococcus, Carnobacterium, Tetragenococcus, and Blautia) along with a reduction in genera containing species previously associated with opportunistic or pathogenic traits (Mycobacterium, Pseudomonas, Morganella, Pedobacter, and Serpula), indicating diet-induced modulation of host-microbe interactions. Transcriptomic profiling highlighted an upregulation of key genes involved in growth and development (CK1, HIB, and PDK1), protein and fat biosynthesis (DVL, GSK3, and Lpin), and immune defense (PGRP-SA, Spz, Toll, and Cactus). Functional enrichment analysis further confirmed their participation in critical signaling pathways, including Hedgehog, Wnt, mTOR, Toll and Imd, and MAPK. Overall, this study demonstrates that probiotic fermentation improves nutrient utilization, regulates host-microbe interactions, and activates molecular pathways associated with growth and immune resilience in BSFL, providing new insights into the physiological and molecular basis of dietary adaptation in insects.