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PubMed2026

Ecological Filtering by Tuber Compartments Shapes Stable Core Microbiomes That Underpin Potato Plant Growth Across Environments.

Harnessing plant microbiomes for sustainable agriculture requires understanding not only whether they can boost crop performance, but also how ecological processes govern their assembly, stability, and functional contributions across environments. While we previously showed that seed tuber microbiomes can predict potato vigour using machine learning, it remained unclear how ecological processes shape tuber microbiome stability and functionality across host genotypes, tuber compartments, soil types, and years. Here, we analyzed the national-scale dataset of 240 field-collected potato seedlots, spanning six genotypes, two soil types, and two growing years, with a focus on the spatially distinct heel and eye compartments of the potato tuber. By profiling over 1200 bacterial and fungal communities and linking microbiome composition to plant performance, we show that plant genotype and tuber compartment are the strongest determinants of microbial diversity and composition. Compartment-specific enrichment of functional traits revealed spatial partitioning of microbial functions, with organic compound conversion and nitrogen cycling dominant in the heel, and energy metabolism enriched in the eye. Applying a macroecological abundance-occupancy framework, we identified a stable core microbiome of bacterial and fungal taxa that persisted across all environments and years. These core members were more strongly associated with plant growth-related traits than non-core taxa, and core taxa in different tuber compartments showed distinct correlations with taxa of potential pathogenic relevance. Together, our findings demonstrate that tuber compartments act as ecological filters that structure persistent, functionally specialised microbiomes linked to plant growth-related traits across environments. By providing an ecological and functional framework for compartment-resolved, stable core microbiomes, this study advances mechanistic understanding of plant-microbe interactions and identifies stable microbial partners as promising targets for improving potato resilience and productivity.

Journal Article↗

PubMed2025

Pseudoalteromonas is a novel symbiont of marine invertebrates that exhibits broad patterns of phylosymbiosis.

Despite growing insights into the composition of marine invertebrate microbiomes, our understanding of their ecological and evolutionary patterns remains poor, owing to limited sampling depth and low-resolution datasets. Previous studies have provided mixed results when evaluating patterns of phylosymbiosis between marine invertebrates and marine bacteria. Here, we investigated potential animal-microbe symbioses in Pseudoalteromonas, an overlooked bacterial genus consistently identified as a core microbiome taxon in diverse invertebrates. Using a pangenomic analysis of 236 free-living and invertebrate-associated bacterial strains (including two new nematode-associated isolates generated in this study), we confirm that Pseudoalteromonas is a novel symbiont with substantial evidence of phylosymbiosis across at least three marine invertebrate phyla (e.g., Nematoda, Mollusca, and Cnidaria). Patterns of symbiosis were consistent irrespective of geography (including in Antarctica), with FISH images from nematodes indicating that bacterial symbionts form biofilms in the mouth and esophagus. The evolutionary history of Pseudoalteromonas is marked by substantial host-switching and lifestyle transitions, and host-associated genomes suggest that these bacteria are facultative symbionts involved in nutritional mutualisms. In marine environments, we hypothesize that horizontally-acquired symbionts may have co-evolved with invertebrates, using host mucus as a physical niche and food source, while providing their animal hosts with Vitamin B, amino acids, and bioavailable carbon compounds in return.

Marine Invertebrates↗

PubMed2025

Native edaphoclimatic regions shape soil communities of crop wild progenitors.

Unveiling the soil biological communities ecologically associated with crop wild progenitors (CWPs) in their habitats of origin is essential for advancing productive and sustainable agriculture. A field survey was conducted to investigate the edaphoclimatic conditions and soil bacterial, fungal, protist, and invertebrate communities of 125 populations of direct progenitors of major crops for world agriculture. The wild populations clustered into four ecoregions shaped by two edaphoclimatic dimensions: one summarizing variations in soil sand contents and nutrients concentrations, and the other featuring changes in aridity, soil pH, and carbon storage potential. We identified a common soil core community across CWPs that varied significantly along deserts to tropical seasonal forests and savannas. The assembly of the soil core community was driven by varying environmental preferences amongst soil biodiversity kingdoms, reflecting potential shifts in their functional profiles. The tropical ecoregion exhibited higher proportion of acidophilic bacteria, fungal, and protist parasites, whilst desert ecosystems harboured greater abundances of saprophytic fungi and heterotrophic protists. Moreover, CWPs displayed unique microhabitats that incorporate variability into the soil community assembly. Our work reveals the biogeography of soil communities associated with CWPs, the first step towards the development of microbial rewilding initiatives.

centres of origin↗

PubMed2026

In silico analysis and comparison of the metabolic capabilities of different organisms by reducing metabolic complexity.

BACKGROUND: Understanding how metabolic capabilities diverge across microbial species is essential for deciphering community function, ecological interactions, and the design of synthetic microbiomes. Despite shared core pathways, microbial phenotypes can differ markedly due to evolutionary adaptations and metabolic specialization. Genome-scale metabolic models (GEMs) provide a systems-level framework to explore these differences; however, their complexity hinders direct comparison. RESULTS: We introduce NIS (Neidhardt-Ingraham-Schaechter), a computational workflow that integrates the redGEM, lumpGEM, and redGEMX algorithms to systematically reduce genome-scale models into biologically interpretable modules. This approach enables direct, quantitative comparison of fueling pathways, biomass biosynthetic routes, and environmental exchange processes while retaining essential metabolic information. We first demonstrate the utility of NIS by analyzing Escherichia coli and Saccharomyces cerevisiae, which revealed both conserved and divergent strategies in central metabolism, biosynthetic cost, and substrate utilization. We then applied NIS to the core honeybee gut microbiome, uncovering distinct metabolic traits, functional redundancy, and complementarity that help explain auxotrophy, cross-feeding interactions, and microbial coexistence. CONCLUSIONS: NIS provides an automated, scalable, and reproducible framework for dissecting microbial metabolic networks beyond gene content or taxonomy. By linking metabolism to ecological function, NIS offers new opportunities to interpret microbial community dynamics and to support the rational design of microbiomes in health, agriculture, and environmental applications. Video Abstract.

Metabolic Networks and Pathways↗

PubMed2024

Shotgun metagenomic analysis of saliva microbiome suggests Mogibacterium as a factor associated with chronic bacterial osteomyelitis.

Osteomyelitis of the jaw is a severe inflammatory disorder that affects bones, and it is categorized into two main types: chronic bacterial and nonbacterial osteomyelitis. Although previous studies have investigated the association between these diseases and the oral microbiome, the specific taxa associated with each disease remain unknown. In this study, we conducted shotgun metagenome sequencing (≥10 Gb from ≥66,395,670 reads per sample) of bulk DNA extracted from saliva obtained from patients with chronic bacterial osteomyelitis (N = 5) and chronic nonbacterial osteomyelitis (N = 10). We then compared the taxonomic composition of the metagenome in terms of both taxonomic and sequence abundances with that of healthy controls (N = 5). Taxonomic profiling revealed a statistically significant increase in both the taxonomic and sequence abundance of Mogibacterium in cases of chronic bacterial osteomyelitis; however, such enrichment was not observed in chronic nonbacterial osteomyelitis. We also compared a previously reported core saliva microbiome (59 genera) with our data and found that out of the 74 genera detected in this study, 47 (including Mogibacterium) were not included in the previous meta-analysis. Additionally, we analyzed a core-genome tree of Mogibacterium from chronic bacterial osteomyelitis and healthy control samples along with a reference complete genome and found that Mogibacterium from both groups was indistinguishable at the core-genome and pan-genome levels. Although limited by the small sample size, our study provides novel evidence of a significant increase in Mogibacterium abundance in the chronic bacterial osteomyelitis group. Moreover, our study presents a comparative analysis of the taxonomic and sequence abundances of all genera detected using deep salivary shotgun metagenome data. The distinct enrichment of Mogibacterium suggests its potential as a marker to distinguish between patients with chronic nonbacterial osteomyelitis and chronic bacterial osteomyelitis, particularly at the early stages when differences are unclear.

Humans↗

PubMed2026

Genomic and biosynthetic landscape of high-temperature Daqu microbiome.

As the core starter for Chinese Baijiu, high-temperature Daqu is produced through open solid-state fermentation with recurrent inoculation by mature Daqu, forming a rich yet largely untapped reservoir of genomes and bioactive compounds. This study constructs the High-temperature Daqu Fermentation Microbiome catalog using 463 metagenomes spanning the full fermentation cycle. The catalog comprises 4,264 metagenome-assembled genomes that are dereplicated into 252 representative genome-based species, 82 % of which are absent from current global food microbiome databases. It further contains 14.3 million non-redundant genes, of which 17.3 % are novel, and 17,031 biosynthetic gene clusters, of which 62.63 % are novel, thereby substantially expanding the known genomic and biosynthetic space of food microbiomes. Genome-resolved analyses revealed a U-shaped ecological trajectory, shifting from early Bacillus velezensis-enriched assemblages to transient dominance of lactic acid bacteria during peak thermogenesis, before returning in late fermentation to thermotolerant, spore-forming Bacillota and Actinomycetota. In parallel, biosynthetic potential was further organized into four recurrent, stage-enriched profiles, from RiPP-rich thermogenic states to mature-state assemblages enriched in PKS-, NRPS-, and terpene-related capacities, with Bacillus, Kroppenstedtia, and Saccharopolyspora constituting the principal biosynthetic reservoir. Together, this work uncovers a largely unexplored genomic and biosynthetic reservoir in high-temperature Daqu fermentation, providing a target resource for mining thermotolerant industrial enzymes, flavor-related genes, and bioactive metabolites with biotechnological potential.

Microbiota↗

PubMed2026

Challenges and future directions in AI-driven biomaterials for microbiome-associated oral infectious diseases: A systematic review.

Oral biofilm-induced antimicrobial resistance is the core pathogenic mechanism of microbiome-associated oral infectious diseases (dental caries, periodontitis, peri-implantitis, and endodontic infection). Traditional therapies and biomaterials are limited by poor biofilm penetration, drug resistance induction, single functionality, and inadequate adaptation to dynamic oral microenvironmental changes (e.g., pH fluctuations, salivary rinsing, masticatory stimulation). Artificial intelligence (AI) has transformed the field by integrating materials science, microbiology, and stomatology data. Via machine learning, deep learning, and multi-physics simulation, AI optimizes biomaterial physicochemical properties, decodes microenvironmental signals, constructs precise sensing-response loops, and supports the full chain of material design, performance prediction, and action simulation, advancing treatment from empirical intervention to precision regulation. This systematic review retrieved literature from PubMed, Embase, and Web of Science (January 2016-January 2026) using keywords across three dimensions: AI, biomaterials, and oral microbiome. Following inclusion/exclusion criteria, 99 articles were included. It elaborates on five core mechanisms of AI-driven oral biomaterials (precise oral microbiome analysis, targeted material design/optimization, performance prediction/simulation, targeted delivery/intervention, effect evaluation/dynamic regulation), analyzes their applications in microbiome-targeted biomaterial research and development (R&D) and clinical practice for the four major oral infectious diseases, addresses technical bottlenecks (insufficient targeting specificity and precision of biomaterials, poor stability and durability in complex oral microenvironments, inadequate biofilm disruption capacity, and clinical translation obstacles), and proposes future directions (multimodal design to enhance targeting specificity, structural and component optimization to improve stability/durability, development of multi-mechanism synergistic biofilm disruption strategies, strengthening translational research for clinical application, and deep integration of AI in the full chain of biomaterial R&D). This work provides comprehensive theoretical and practical support for the R&D, optimization, and clinical translation of AI-driven microbiome-targeted oral biomaterials.

Humans↗

PubMed2026

Functional capacities drive recruitment of bacteria into plant root microbiota.

Root-associated microbiomes are shaped by the plant, yet vary across environments and hosts, challenging prediction and engineering. Here, to uncover principles of bacterial selection at the root-soil interface, we applied a systems-level approach using reconstitution studies with communities of isolates from Arabidopsis, barley and Lotus grown in soil. Functional divergence among the microbiota of the host plants reflected distinct strategies: in Arabidopsis and barley, recruitment was primarily shaped by inoculum, while Lotus root environment favoured fewer, functionally diverse isolates, akin to a 'Swiss army knife' strategy. Despite taxonomic variability, root microbiomes encoded overlapping functions. Across major taxa, isolates with broad but distinct functional repertoires within their families were consistently more abundant. Using a genome-to-function framework that is function centric, taxonomically inclusive and host-context aware, we identified 266 functions enriched across all root microbiomes. This functional backbone emerged as a core signature of plant-associated bacteria, providing a solid foundation for microbiome engineering in agriculture.

Journal Article↗

PubMed2026

Recurrent and niche-specific functional bacteriome of maize hybrid revealed by integrated metabarcoding and culturomics.

The plant microbiome plays a pivotal role in plant survival in natural habitats by facilitating nutrient acquisition, stress adaptation, and disease suppression, while also offering opportunities to enhance crop productivity and climate resilience. However, the distribution of persistent and culturable bacteriome across maize-associated niches and their functional potential remain poorly resolved. This study integrated metagenomic next-generation sequencing (mNGS-based metabarcoding) and culturomics to characterise the maize-associated bacteriome of bulk soil, rhizoplane, phylloplane, and cob of the maize hybrid PHM-1 under contrasting cropping and tillage systems, and to identify recurrent and agriculturally promising bacteriome components. The bacteriome exhibited pronounced niche-specific structuring, whereas overall bacterial community composition did not differ significantly across cropping and tillage treatments (ANOSIM, R = 0.038, p = 0.306). Proteobacteria predominated in the culturable bacteriome (69-84%; mean, 76.2%) but accounted for only 1% of the total bacteriome, whereas Patescibacteria and Firmicutes were relatively enriched. Niche-specific dominance was evident, with Pantoea accounting for 40.79% of the total and 56.27% of the culturable phylloplane bacteriome under cereal monocropping, while Serratia represented 31.59% and 59.40% of the total and culturable cob bacteriomes, respectively. Across niches, mNGS captured substantially greater bacteriome diversity, particularly uncultured and unidentified taxa in soil-associated compartments, whereas culturomics recovered a narrower but functionally accessible fraction. Culturomics yielded 99 isolates representing 32 species across 12 genera, including six genera shared with the mNGS-derived recurrent bacteriome: Bacillus, Enterobacter, Pantoea, Pseudomonas, Serratia, and Stenotrophomonas. Functional screening identified strong biocontrol and plant-beneficial traits among core-associated isolates. Pseudomonas oryzihabitans ZM-DL-PA10 inhibited Rhizoctonia solani, Macrophomina phaseolina, and Bipolaris maydis by up to 40.6%, 43.9%, and 45.2%, respectively, through secreted and volatile metabolites; exhibited P, K, and Zn solubilisation; and produced IAA and siderophores. It also recorded the lowest B. maydis disease index (ADI) of 1.00. Pantoea ananatis ZM-BH-EA4 showed 52.4% and 68.5% inhibition of R. solani and B. maydis, respectively, through volatile metabolites. Collectively, the integration of mNGS and culturomics revealed a strongly compartmentalised maize bacteriome and identified recurrent, culturable, and functionally promising bacterial taxa, providing a targeted resource for microbiome-based crop protection and climate-resilient maize production.

Zea mays↗

PubMed2026

Adaptive Evolution Reveals Metabolic Plasticity and Functional Redundancy in an Anaerobic Microbiome under Extreme Ammonia Stress.

Ammonia toxicity represents a primary biochemical bottleneck governing microbial community structure and performance during the anaerobic digestion of the organic fraction of municipal solid waste. However, the mechanistic basis of microbial adaptation to chronic ammonia levels remains poorly characterized. In this study, a long-term sequential enrichment strategy under progressively increasing ammonia concentrations (350-1500 mgN L-1), integrated with genome-centric metagenomics and metatranscriptomics, was employed to resolve the response of an organic waste-degrading microbiome over a 240 day period. Increasing ammonia pressure induced a progressive decline in methanogenesis and accumulation of volatile fatty acids, particularly acetate. Despite these inhibitory pressures, methane production was only halved relative to the initial baseline reflecting a resilient methanogenic community. This stability was driven by a restructuring of the microbiome, where functional redundancy across divergent taxa preserved core metabolic functions. Key adaptive responses included the reconfiguration of carbon fixation pathways, specifically via a variant of the Wood-Ljungdahl pathway coupled with the glycine cleavage system acting as an alternative acetate oxidation route, as well as sustained osmoprotectant biosynthesis. Cellular homeostasis was preserved through H+ replenishment via multiple energy-converting complexes and K+ influx to maintain cation-proton balance. Collectively, these findings demonstrate that metabolic plasticity and the preservation of core metabolic functions are the primary determinants of ammonia resilience, sustaining methane production under inhibitory conditions.

Ammonia↗

PubMed2026

The antimicrobial gut resistome of the Wayampi reveals a shared background of antibiotic and metal resistance genes with industrialized populations, underscoring the "robust-yet-fragile" architecture of human gut microbiomes.

BACKGROUND: Metagenomics enables detailed profiling of genes encoding antimicrobial resistance. However, most studies focus exclusively on antibiotic resistance genes (ARGs), excluding those associated with non-antibiotic antimicrobials (metals, biocides), and often rely on methods with low-sensitivity and low-specificity. Furthermore, they rarely examine populations exposed to minimal anthropogenic pollution. We analyzed fecal resistomes of 95 Wayampi individuals, an Indigenous community in remote French Guiana, using a targeted metagenomic capture platform covering 8667 genes, including ARGs, metal resistance genes (MRGs) and biocide resistance genes (BRGs) (PMID: 29335005). Resistome profiles were compared with those of Europeans to assess population-level differences. RESULTS: ARG richness was similar between groups (259 in Wayampi vs. 264 in Europeans, 159 shared), but MRGs + BRGs gene richness was significantly higher in Wayampi (11,930 vs. 7419). Most genes appeared in a minority of individuals (mean 5% for ARGs, 2% for MRGs + BRGs), but several ARGs for tetracyclines [tet(32), tet(40), tet(O), tet(Q), tet(W), tet(X), tetAB(P)], aminoglycosides (ant6'-I, aph3-III), macrolides (ermB, ermF, mefA), and sulfonamides (sul2) were present in all individuals. Tetracycline resistance genes predominated overall, while beta-lactam resistance genes were more common in Wayampi, and genes conferring resistance to aminoglycosides, amphenicols, and folate inhibitors were more frequent in Europeans. Among MRGs, copper and arsenic resistance genes prevailed in both groups, followed by those for zinc, iron, cobalt, and nickel. Up to 76% of Wayampiis carried acquired MRGs for copper (pcoABCDRS and tcrB), silver (silACFPRS), arsenic (ars), and mercury (mer) detoxification. Shannon diversity indices were similar for ARGs, MRGs, and BRGs, but composition and evenness differed significantly. UMAP and ADONIS analyses distinguished cohorts based on ARG profiles (p < 0.001), but not on MRGs or BRGs. Correlation analysis revealed conserved gene-sharing networks and introgression of acquired ARGs and MRGs within both gut microbiomes. CONCLUSIONS: The diverse and balanced Wayampi resistome reflects a less perturbed microbiome compared to industrialized populations, and reveals a background of "core" and "shell" acquired ARGs and MRGs, consistent with the "robust-yet-fragile" architecture of scale-free networks. The patchy yet resilient gene distribution suggests varying levels of conserved gene sharing highways among populations, likely shaped by long-term microbial-human evolution, and supports a broader view on acquired antimicrobial resistance. Video Abstract.

Humans↗

PubMed2026

Beyond antibiotics: artificial intelligence-enabled anti-infective ecosystems for next-generation precision therapeutics against antimicrobial resistance.

The rapid global expansion of antimicrobial resistance (AMR) threatens to undermine decades of progress in infectious disease management and highlights the limitations of conventional antibiotic-centered therapeutic strategies. Although emerging technologies-including antimicrobial peptides, bacteriophage therapy, CRISPR-based antimicrobials, microbiome therapeutics, anti-virulence approaches, nanotechnology-enabled drug delivery, and artificial intelligence (AI)-have individually demonstrated considerable promise, they are predominantly being developed as independent interventions rather than as coordinated components of an integrated therapeutic strategy. This Perspective proposes the Intelligent Anti-Infective Ecosystem (IAIE) as a conceptual systems-level framework that computationally integrates multimodal diagnostics, pathogen genomics, microbiome profiling, AI-assisted decision support, programmable precision therapeutics, ecological monitoring, and longitudinal clinical feedback within a continuously learning dynamically optimized workflow. Unlike existing paradigms that primarily optimize individual technologies or therapeutic decisions, IAIE emphasizes closed-loop coordination among complementary antimicrobial approaches to support precision-guided infection management while preserving microbiome integrity and mitigating resistance selection pressure. We further outline the core components, operational principles, translational challenges, and technology readiness of the major therapeutic platforms that could contribute to such an ecosystem, while distinguishing clinically established interventions from emerging experimental strategies. Importantly, IAIE should be interpreted as a prospective conceptual architecture rather than an existing clinical platform. Its proposed clinical value remains to be established through sequential computational, preclinical, and prospective clinical investigations using standardized microbiological, ecological, and patient-centered outcome measures. By framing antimicrobial innovation within an responsive systems perspective, IAIE provides a roadmap for future multidisciplinary research aimed at integrating artificial intelligence and systems microbiology to enable sustainable management of antimicrobial resistance.

Humans↗

PubMed2026

Identifying fundamental gaps in functional metagenomics: a step towards unlocking microbiome research potential.

Incomplete functional annotation limits biological interpretation in microbiome studies and their translational potential. Poor annotation arises from multiple causes, with incomplete gene-protein-reaction mapping being one tractable yet under-examined contributor. We address this gap by developing a comprehensive hierarchical framework that systematically integrates gene families in UniRef, proteins in UniProt, and metabolic reactions in MetaCyc and BioCyc through UniProtKB accession, EC number, and Pfam-domain matching. Applied to a human gut metagenome dataset via HUMAnN3, our MetaCyc-based mapping recovers up to 2.3-fold more unique reaction identifiers than the default pipeline and increases reaction prevalence across samples from ≈32% to 52% core reactions, addressing the data sparsity that limits statistical and machine-learning applications in microbiome research. Biological plausibility for the tested functions was supported by positive and negative controls: gut-microbial hormone-metabolism reactions previously linked to this dataset were recovered, while vertebrate-specific hormone-metabolism reactions remained correctly undetected. These gains derive from systematic database integration alone, without predictive algorithms, indicating that a tractable, mapping-related component of functional dark matter and data sparsity in microbiome studies is directly addressable. Because Pfam- and BioCyc-derived mappings trade specificity for coverage, confidence in any individual reaction assignment depends on the supporting evidence tier and source database.

Humans↗

PubMed2026

Synthetic community derived from the root core microbes of a desert shrub Caragana korshinskii enhances wheat drought tolerance.

BACKGROUND: Drought, intensified by climate change, poses a mounting threat to global food security by severely constraining crop productivity. While microbial inoculants offer promise for drought tolerance, their poor adaptability remains insufficient for extremely water-deficient environments. Desert plants host unique drought-adapted microbiomes that remain largely unexplored for agricultural applications. RESULTS: Here, we investigated the microbial community of the desert shrub Caragana korshinskii and identified a core set of drought-responsive strains. A synthetic microbial community (SynCom) derived from these strains significantly improved wheat growth under drought stress. Metagenomic analyses revealed that microbial functions related to biofilm formation, quorum sensing, and carbon metabolism were enriched, with Pseudomonas identified as a key functional taxon. Guided by inter-strain interactions in biofilm assembly, we streamlined the consortium into a five-member synthetic community, where quorum-sensing signals promoted community-wide biofilm formation. Community biofilm production improved strain colonization and conferred greater drought tolerance compared to monocultures. In plants, mechanistic investigations indicated that the simplified SynCom inoculation universally upregulated MAPK and jasmonic acid signaling pathways. Furthermore, carbohydrate metabolic pathways such as starch and sucrose metabolism were specifically activated, suggesting a multi-level mechanism underlying SynCom-mediated drought tolerance. CONCLUSIONS: These findings demonstrate that SynCom constructed on the endophytic flora of desert plants can significantly enhance crop drought tolerance. Our work highlights the pivotal role of community biofilm synthesis in facilitating root colonization and activating a multidimensional drought tolerance network in plants. This study not only gives an ecological perspective on desert microbiome adaptations but also offers a strategic framework for developing effective microbial inoculants for arid-region agriculture. Video Abstract.

Caragana↗

PubMed2020

The wastewater microbiome: A novel insight for COVID-19 surveillance.

Wastewater-Based Epidemiology is a tool to face and mitigate COVID-19 outbreaks by evaluating conditions in a specific community. This study aimed to analyze the microbiome profiles using nanopore technology for full-length 16S rRNA sequencing in wastewater samples collected from a penitentiary (P), a residential care home (RCH), and a quarantine or health care facilities (HCF). During the study, the wastewater samples from the RCH and the P were negative for SARS-CoV-2 based on qPCRs, except during the fourth week when was detected. Unexpectedly, the wastewater microbiome from RCH and P prior to week four was correlated with the samples collected from the HCF, suggesting a core bacterial community is expelled from the digest tract of individuals infected with SARS-CoV-2. The microbiota of wastewater sample positives for SARS-CoV-2 was strongly associated with enteric bacteria previously reported in patients with risk factors for COVID-19. We provide novel evidence that the wastewater microbiome associated with gastrointestinal manifestations appears to precede the SARS-CoV-2 detection in sewage. This finding suggests that the wastewaters microbiome can be applied as an indicator of community-wide SARS-CoV-2 surveillance.

COVID-19↗

PubMed2025

Sex as a modifier of genetic risk for type 1 diabetes.

Sex differences influence the pathogenesis of type 1 diabetes (T1D), yet most genetic studies have treated sex as a control covariate rather than a dynamic effect modifier. Sex influences immune cell behaviour, including CD4+ and CD8+ T cell activation, regulatory T cell stability, B cell autoantibody production, dendritic cell priming and monocyte/macrophage inflammation. Underlying mechanisms include hormone-responsive enhancers, X-escape gene dosage and sex-biassed chromatin states, intersecting with T1D-associated variants to produce sex-specific immune phenotypes. These insights help explain regional variation in sex ratios of T1D incidence, such as male predominance in high-risk populations and female excess in low-risk populations. Biological sex shapes T1D risk across multiple layers, including polygenic load; environmental exposures such as vitamin D deficiency and enteroviral infection; and sex-specific hormonal, chromosomal and epigenetic influences. An integrative G × E × S (genetic × environmental × sex-specific) liability-threshold framework is thus supported. Clinical and translational implications include developing sex-specific polygenic risk scores, biomarker panels and interventional strategies targeting pathways such as hormone signalling, vitamin D metabolism and the microbiome. Future multi-omic, longitudinal studies are warranted to test genotype-sex interactions, integrate sex as a core effect modifier and enable precision prevention and treatment of T1D in both males and females.

Humans↗

PubMed2026

Host genetic regulation of xylem-resident Pseudomonas enhances cucumber growth.

BACKGROUND: Although endophytic microorganisms play a critical role in plant growth and stress resilience, the genetic basis underlying host selection of beneficial microbiota-particularly within the xylem-remains poorly understood. Cucumber (Cucumis sativus), as a crop model with a well-developed system for studying vascular biology, offers a valuable system to investigate the host genetic determinants of xylem microbiome assembly. RESULTS: By conducting population-level microbiome profiling across 109 cucumber accessions, we identified a conserved xylem microbiota dominated by Proteobacteria. Within this community, 20 core amplicon sequence variants (ASVs) were consistently present in xylem sap. Genome-wide association mapping identified a host genetic locus, CsXPR1, which encodes a tetratricopeptide repeat protein that regulates the abundance of the dominant xylem-colonized Pseudomonas ASV_4. Colonization patterns of ASV_4 varied across host genotypes and were correlated with CsXPR1 expression levels, suggesting a precision genetic regulation of bacterial entry into vascular tissues. Pseudomonas fulva strain 220, with 97% 16S rRNA gene identity with ASV_4, could colonize in cucumber xylem by inoculation of either roots or leaves. Genome analysis and plate assays revealed the biosynthesis of indole-3-acetic acid (IAA), solubilization of phosphate, and a range of plant beneficial traits in strain 220. Inoculation with strain 220 significantly enhanced growth in cucumber, but only in CsXPR1 haplotype that exhibited high gene expression and higher recruitment capacity of the strain. These benefits included notable increases in plant height (38%), stem diameter (36%), leaf area (61%), fresh and dry weight (51% and 85%, respectively), and a 4.57-fold increase in 4-methyleneglutamine content within the xylem sap. CONCLUSION: Our findings reveal a complete "gene-to-function" pathway where the host gene CsXPR1 mediates a genotype-dependent growth promotion. It achieves this by regulating the xylem colonization of a beneficial bacterium, Pseudomonas fulva, which in turn enhances plant growth by enriching the xylem sap with the key metabolite 4-methyleneglutamine. Video Abstract.

Cucumis sativus↗

PubMed2026

Characteristics and assembly mechanisms of tobacco-associated bacteria in typical tobacco-planting regions across China.

INTRODUCTION: Plant-associated microbiota critically modulates host growth and environmental adaptation, yet assembly mechanisms, niche differentiation, and ecological strategies of bacterial communities inhabiting tobacco microhabitats remain poorly elucidated across geographical gradients. METHODS: Here, we systematically characterized bacterial microbiome assembly across five tobacco-associated niches (bulk soil, rhizosphere soil, root, stem, and leaf) from seven typical tobacco-planting regions using 16S rRNA amplicon sequencing, genome annotation, and niche breadth analysis. The independent and interactive effects of geographical location and host compartment on community structure, and further compared genomic traits, functional profiles, and life-history strategies between specialist and generalist bacterial populations were quantified. RESULTS: The results revealed a deterministic soil-plant continuum stratification of bacterial communities and diversity, with progressively simplified communities and decreasing alpha diversity from bulk soil to above-ground tissues, accompanied by progressive dominance of Proteobacteria. Geographical factors predominantly structured soil microbial communities via divergent edaphic properties, while host filtering acted as a universal dominant driver shaping endophytic microbiome assembly. Niche differentiation analysis demonstrated that niche-specialized bacterial ASVs overwhelmingly dominated all microhabitats and geographical sites, whereas generalist taxa only constituted auxiliary populations. Although specialist and generalist microbes exhibited highly conserved core genomic architectures and overall functional repertoires, they displayed distinct niche-specific functional divergence in metabolic pathways, stress resistance, and secondary metabolism across host compartments. Life-history strategy analysis further revealed that Y-strategist represented the core adaptive bacterial population, especially enriched in above-ground tobacco tissues. DISCUSSION: Our study establishes a hierarchical dual-filtering assembly model for tobacco microbiota, clarifies the ecological differentiation and functional adaptation of specialist and generalist bacteria, and provides fundamental insights into the assembly rules and adaptive mechanisms of crop-associated microbiomes for future microbial resource utilization and agricultural microbiome regulation.

biogeography↗