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PubMed2026

A Risk Score for Polycystic Ovary Syndrome Based on Meta-Analysis and Machine Learning of Gut Microbiota Signatures.

Polycystic Ovary Syndrome (PCOS) is a prevalent endocrine and metabolic disorder among reproductive-age women, in which emerging evidence suggests a substantial role played by the gut microbiota. To comprehensively evaluate gut microbiota alterations in PCOS and identify microbial biomarkers through integrated analysis, a systematic search of PubMed, Web of Science, and Embase was conducted for studies employing 16S rRNA gene sequencing of fecal samples from PCOS cohorts. Ten eligible PCOS cohorts, comprising 858 individuals, were included in the study, from which a risk score was derived using a 20-gene gut microbial signature associated with PCOS. Meta-analysis at the genus level identified that Subdoligranulum, NK4A214_group, and Collinsella significantly decreased, and Bacteroides increased in PCOS across multiple cohorts. Machine learning analysis identified a 20-genus microbial signature using the least absolute shrinkage and selection operator (LASSO) method, which was used to construct a risk score with an AUC of 0.835 in diagnosis prediction. Network analysis further identified Negativibacillus and Lachnospiraceae_UCG_010 as potential driver microbes in PCOS. The analysis in this study highlights key alterations in the gut microbiota across PCOS cohorts. The identified gut microbial signature and derived LASSO-based risk model offer novel insights and a potential tool for PCOS diagnosis.

Polycystic Ovary Syndrome↗

PubMed2026

Meta-CD: a metagenomic sequencing coverage and depth calculator for target species.

Metagenomic Coverage and Depth Calculator (Meta-CD) is a convenient, biologist-friendly tool for determining coverage and depth to enhance taxonomic detection, functional profiling, and metagenome-assembled genome (MAG) recovery in metagenomics. It supports experimental design and post-sequencing analysis, modeling how genome size, relative abundance, sequencing depth, and DNA quantity influence detection of target species.

metagenomics↗

PubMed2026

Coral color morphs exhibit distinct microbial and proteomic profiles linked to stress and immune mechanisms in a changing ocean.

BACKGROUND: Coral phenotypic plasticity facilitates acclimation and adaptation to environmental variability. Coral species often display a variety of color morphs, yet key biological and ecological implications of such phenotypic variation remain underexplored. Here, we present the first proteomic and untargeted lipidomic and metabolomic survey to explore the biological characteristics and potential ecological significance of different color morphs (pink and brown) of healthy Pocillopora verrucosa sampled along a latitudinal gradient. RESULTS: Our multi-omic approach elucidated distinct mechanisms associated with these dominant color morphs. We discovered bacterial indicators specific to each morph: putative pathogens such as Salmonella, Escherichia-Shigella, and carotenoid-producing Gemmatimonas were notably associated with the pink morph, whereas the brown morph was associated with potentially beneficial bacteria, such as Lysobacter, Acinetobacter, and Endozoicomonas. Despite these microbiome differences, the lipidome and metabolome of P. verrucosa were surprisingly homogeneous across colors and locations, suggesting similar metabolic performances during summer conditions. Key polar and apolar lipid classes, such as fatty acids, glycerophosphocholines, and retinoids, were prevalent. Notably, our proteomic analysis revealed morph-specific expressions, with pink morphs exhibiting enhanced levels of GFP-like proteins, Ankyrin, and the enzyme pullulanase, suggesting novel putative protective roles. In contrast, the brown morphs showed a higher abundance of heat shock proteins, indicating putative differential stress response capabilities. CONCLUSION: This comprehensive study provides the first proteomic survey of P. verrucosa and identifies key physiological pathways and trade-offs linked to color morphs, which can further contribute to enhancing our understanding of coral resilience in the face of climate change. SIGNIFICANCE STATEMENT: Understanding the phenotypic plasticity of corals is crucial for uncovering mechanisms of resilience in warming oceans, yet the biological significance of coral color morphs still needs to be explored. Using an innovative multi-omic approach (proteomics, lipidomics, and metabolomics), we provide the first comprehensive analysis of differences between pink and brown morphs of Pocillopora verrucosa. Our data reveal key taxa, potentially pathogenic or beneficial, associated with each morph, and suggest different strategies for each color morph to cope with heat stress, either expressing proteins involved in UV protection and heterotrophic activity or enhanced levels of heat stress resilience and DNA repair. These findings offer insights into the phenotypic plasticity of coral color morphs and their differential responses to climate change. Video Abstract.

Anthozoa↗

PubMed2026

From bacterial to microbiome-derived vesicles: genome-informed identity, source qualification, and translational quality for skin-directed cosmetics.

Bacterial extracellular vesicles (BEVs) are increasingly proposed as materials for skin-directed cosmetics, yet rapid adoption of "exosome" terminology has outpaced clarity on their origin, composition, and manufacturing quality. This review argues that the value of BEVs depends on scientific discipline rather than marketing appeal, and that they are promising because they are biologically potent, not because they are intrinsically benign. We retain BEVs as the scientific umbrella term for vesicles released by bacteria and we propose the term microbiome-derived vesicles (MDVs) as the consumer-facing designation for qualified commensal BEVs - a surface term that avoids the difficulty of "bacterial" while its definition preserves bacterial provenance. We develop a three-axis framework for BEV identity that integrates compositional analysis, producer-strain genomics, and functional or safety profiling, and we position whole-genome sequencing (WGS) as decisive for source qualification and mechanistic interpretation but insufficient to prove the efficacy of a purified preparation. Building on this framework, we summarize isolation, purification, and analytical characterization requirements; interpret current skin-efficacy evidence in light of its methodological limits; and discuss formulation, cosmetic application, regulatory positioning, and manufacturable quality. We conclude that transparent bacterial provenance, reproducible preparation, and evidence proportionate to the claims made are prerequisites for evaluating commensal BEVs, described for skin applications as MDVs, as a scientifically defined cosmetic platform.

Bacterial extracellular vesicles↗

PubMed2026

The COVID-19 pandemic influenced the temporal dynamics of antimicrobial resistance markers and bacterial community across urban wastewater treatment plants.

Urban wastewater systems represent important interfaces between human activity and the environmental occurrence of antimicrobial resistance (AMR) markers. We assessed the temporal dynamics of intI1, ermB, and the 16 S rRNA gene by quantitative PCR across three wastewater systems (EPC, CJC, and JW) in Fortaleza, Brazil, from November 2021 to November 2023. Bacterial communities were additionally characterized by 16 S rRNA gene metabarcoding in 18 samples collected in December 2021 and January 2022. A synchronized decline in 16 S rRNA gene and intI1 concentrations beginning in late 2022 was observed across all three wastewater systems, suggesting a shift toward lower microbial abundance. The ermB gene showed higher and more variable concentrations during part of the pandemic period, followed by convergence toward lower levels; however, the absence of antimicrobial-consumption data precluded attribution of this pattern to changes in macrolide selective pressure. Normalized antimicrobial resistance marker abundances were comparatively stable at EPC and JW but more variable at CJC. EPC exhibited the highest ASV richness, whereas CJC and JW showed greater diversity according to Shannon and inverse Simpson indices. Beta-diversity analyses identified wastewater system as the principal factor associated with bacterial community structure, while the effect of sampling period was smaller and metric-dependent. Neither ermB nor intI1 was individually associated with community composition, although intI1 showed a limited effect after adjustment for wastewater system in one model. Physicochemical parameters were not significantly associated with normalized marker abundances in the exploratory paired analysis. Arcobacter, Acinetobacter, and other potentially relevant genera were detected, but no direct associations between these taxa and the monitored AMR markers could be established. These findings highlight the value of integrating longitudinal qPCR, microbiome profiling, and environmental characterization to improve the interpretation of targeted AMR markers in One Health wastewater surveillance.

Wastewater↗

PubMed2026

The mechanism by which long-term exposure to TDCIPP promotes cognitive impairment in 3 ×Tg-AD mice: Insights from multi-omics studies.

Tri(1,3-dichloro-2-propyl) phosphate (TDCIPP) is a commonly used organophosphate ester that has the potential to adversely affect human health. Although previous studies have closely associated TDCIPP with cognitive impairment, the underlying mechanisms remain unclear. To elucidate the neurotoxic effects of TDCIPP and its mechanistic contribution to cognitive impairment in 3 ×Tg-AD mice, a multi-omics approach incorporating proteomics, untargeted metabolomics, and 16S ribosomal RNA (rRNA) gene sequencing was employed to evaluate the impact of TDCIPP exposure on neurobehavioral function. TDCIPP exposure promoted cognitive impairment in 3 ×Tg-AD mice. Proteomic analyses revealed that this promotion is associated with disturbances in the hippocampal mitochondrial autophagy pathway. Furthermore, TDCIPP may interfere with the PINK1/Parkin-mediated mitophagy pathway at the functional level, without altering PINK1 protein abundance. Untargeted metabolomic analysis of urine samples demonstrated that TDCIPP exposure altered the metabolic profile of 3 ×Tg-AD mice, with 58 metabolites upregulated and 11 downregulated. Additionally, 16S rRNA sequencing revealed substantial modifications in gut microbiome composition following exposure to TDCIPP. Notably, significant correlations were identified between the perturbed bacterial genera and the differential metabolites. In conclusion, exposure to TDCIPP promotes cognitive impairment in 3 ×Tg-AD mice, which is associated with the interference with the PINK1/Parkin-mediated mitophagy pathway, as well as alterations in the urinary metabolome and gut microbiota. These findings suggest the potential to mitigate such cognitive impairment by targeting the microbiota-gut-brain axis.

Animals↗

PubMed2026

ZILA-SRM: a probabilistic framework with zero-inflated latent models for robust strain reconstruction from metagenomes.

UNLABELLED: Resolving bacterial strain diversity from shotgun metagenomic data is fundamental to understanding intra-host evolution, transmission dynamics, and phenotypic heterogeneity. However, current probabilistic approaches face a severe "identifiability limit" when disentangling highly similar genomes. Under high-noise conditions, sequencing errors, coverage overdispersion, and collinearity confound standard expectation-maximization algorithms, resulting in overfitting and spurious "ghost" strains. Here, we introduce zero-inflated latent allocation for strain reconstruction from metagenomes with adaptive sparsity regularization (ZILA-SRM) to overcome this barrier through three innovations. First, we integrate a zero-inflated Poisson mixture model to decouple "structural zeros" (true strain absence) from "sampling zeros" (stochastic dropout), addressing overdispersion in standard Poisson-based tools. Second, we impose a convex adaptive sparsity regularization penalty that leverages biological sparsity priors to shrink noise artifacts dynamically. Third, we implement a graph-theoretic refinement step using maximal clique enumeration to resolve haplotype collinearity. Benchmarking against StrainFinder and MixtureS on 702 synthetic data sets shows that ZILA-SRM achieves a 20% improvement in precision in high-complexity scenarios while maintaining over 80% recall for minor variants at 0.5% abundance. Re-analysis of deep-sequencing data from 195 Mycobacterium tuberculosis clinical samples reveals cryptic low-abundance drug-resistant variants in 12% of patients, including a minor clone carrying the rpoB S450L mutation. Furthermore, application to skin microbiome data sets further reveals a strong negative correlation between dominant Staphylococcus aureus and Staphylococcus epidermidis strains, providing genomic evidence for competitive exclusion. These findings establish ZILA-SRM as a robust tool for resolving strain-level diversity in complex metagenomes. IMPORTANCE: Understanding microbial communities at the strain level is critical because closely related strains can differ dramatically in traits such as drug resistance, virulence, and ecological interactions. However, resolving individual strains from metagenomic sequencing data remains difficult, especially when strains are highly similar or present at low abundance. As a result, biologically meaningful diversity is often obscured or misinterpreted as noise. In this study, we introduce a new framework that improves the reliability of strain reconstruction from complex metagenomic data. By reducing false-positive strain detection while preserving sensitivity to rare variants, our approach enables more accurate characterization of microbial populations. This improved resolution reveals previously hidden subpopulations in clinical and microbiome datasets, providing clearer insights into microbial evolution, competition, and the emergence of clinically relevant traits such as antibiotic resistance.

Metagenomics↗

PubMed2026

Causal Relationships Between Oral Microbiota and Inflammatory Skin Diseases.

INTRODUCTION AND AIMS: The oral microbiome has been increasingly linked to systemic inflammation and immune dysregulation, but whether specific oral bacteria causally contribute to inflammatory skin diseases remains unclear due to confounding and reverse causation. This study aimed to assess the causal effects of 43 oral microbiota taxa on the risk of five inflammatory skin diseases using a Mendelian randomization (MR) approach. METHODS: We performed a two-sample MR analysis using genetic instruments for oral microbiota derived from publicly available genome-wide association studies and outcome data from the FinnGen consortium. Causal effects of oral taxa on systemic lupus erythematosus, vitiligo, pemphigus, localized scleroderma, and dermatitis herpetiformis were estimated. The inverse-variance weighted method served as the primary analysis, complemented by sensitivity analyses to evaluate horizontal pleiotropy, heterogeneity, and reverse causality. RESULTS: MR analyses identified several putative causal associations between oral microbiota and inflammatory skin diseases. Genus Granulicatella and an unknown Streptococcus species (ASV0009) showed causal effects on systemic lupus erythematosus. Family Lachnospiraceae_[XIV] and an unknown Rothia species (ASV0016) were associated with vitiligo. Five oral microbiota taxa demonstrated causal associations with pemphigus. Actinomyces species micronuciformis was linked to localized scleroderma. Order Fusobacteriales and an unknown Neisseria species (ASV0004) were associated with dermatitis herpetiformis. No significant heterogeneity or horizontal pleiotropy was detected in sensitivity analyses. CONCLUSION: This MR study provides genetic evidence supporting a causal role of specific oral bacteria in the development of several inflammatory skin diseases, highlighting the oral microbiome as a potential contributor to cutaneous autoimmunity and inflammation. CLINICAL RELEVANCE: Our findings highlight the putative role of the oral microbiome as a plausible candidate for mechanistic and clinical investigations into the prevention or adjunctive management of selected inflammatory skin diseases. However, oral hygiene improvement, targeted antimicrobials, and other microbiota-directed interventions were not directly tested in this MR study and remain hypothetical strategies requiring validation in experimental and clinical studies.

Humans↗

PubMed2026

Multi-omics revealed the effects of rumen to blood path on early lactation performance in transition dairy cows.

BACKGROUND: The transition period is vitally important to the life cycle of dairy cows. However, the function of the microbiota during both pre- and post-partum and their relationship with ruminal, plasma, and milk metabolites still require systematic investigation. To address this, the 7 highest- and 7 lowest-performing animals among a cohort of 100 dairy cows were selected based on their postpartum energy-corrected milk yield. Rumen fluid and plasma samples were collected during both pre- and post-partum periods, whereas milk samples were obtained postpartum. Shotgun metagenomics of rumen contents in addition to metabolomics of rumen, plasma, and milk samples were performed to evaluate the associations between ruminal microbes and early lactation performance in transition dairy cows. RESULTS: Compared with prepartum cows, postpartum high-yield cows had greater concentrations of ruminal volatile fatty acids and plasma total bile acid. Moreover, plasma urea nitrogen and most amino acids, peptides, and their derivatives in plasma and milk were increased in postpartum high-yield cows, relative to postpartum low-yield cows. Metagenomic analysis revealed that the relative abundances of several species within the Prevotella, Succinimonas, Succinatimonas, and Methanosphaera increased, while other bacteria belong to Alistipes and Bacteroides, and archaeal Methanobrevibacter species decreased in postpartum cows, particularly in postpartum high-yield cows. Co-occurrence network and correlation analysis suggested that Prevotella and Succinatimonas were negatively correlated to Alistipes, Bacteroides, and Methanobrevibacter, potentially contributing to the nutritionally efficient phenotype of postpartum high-yield cows. A metabolic pathway analysis of our metagenomic data revealed that postpartum high-yield cows possessed more microbial genes involved in starch utilization and amino acid synthesis, while a wide range of microbial genes involved in cellulose utilization, acetogenesis, and amino acid degradation were found in prepartum cows with low-yield in postpartum. A structural equation model analysis showed that the increased relative abundances of Prevotella tf.2-5 and Succinatimonas CAG_777 were related to greater concentrations of plasma chenodeoxycholic acid glycine conjugate, milk 5-Methoxytryptophan, and energy-corrected milk yield. Finally, pan-genomic analysis confirmed that Alistipes, Bacteroides, and Methanobrevibacter possess genetic conservation of both hydrogenases and dehydrogenases, which may contribute to energy loss in the rumen via hydrogen dissipation. CONCLUSION: In summary, our findings provide a fundamental understanding of how microbiome-dependent mechanisms contribute to early lactation performance in dairy cows during the transition period. The increased abundance of Prevotella, Succinimonas, and Succinatimonas in postpartum cows suggest that they are important microbes during the transition period and may help in coping with metabolic challenges, while improving nutrient utilization efficiency during this period. Our study underscores the importance of the ruminal microbiome during the transition period and highlights the need for rumen-based nutritional intervention strategies to improve production efficiency in ruminants. Video Abstract.

Animals↗

PubMed2026

Microbiome features associated with persistent intestinal carriages of Escherichia coli ST131 in a Southeast Asian cohort study.

Escherichia coli sequence-type 131 (ST131) is the dominant global extraintestinal pathogen capable of asymptomatic intestinal carriage and sustained household transmission, challenging infection control. Despite its clinical significance, the ecological determinants of gut persistence remain poorly understood. We performed shotgun metagenomics on fecal samples to investigate gut microbiome features associated with ST131-positive samples, distinct host carrier statuses (persistent, intermittent and non-carriers) and household risks in a study of a Southeast Asian cohort. Here, we show that ST131 carriage was associated with compositional shifts without reducing species alpha-diversity. Regression analyses identified depletion of commensal taxa and the 1,5-anhydrofructose degradation pathway in ST131-positive samples. Persistent carriers exhibited highly perturbed microbiome enriched with pathobionts, aerobactin- and lipopolysaccharide (LPS)-biosynthesis pathways. Comparing household risk groups to control, revealed that biotin biosynthesis and 1,5-anhydrofructose degradation may influence ST131 co-colonization through both direct and indirect mechanisms. Machine learning analyses identified metabolic pathways as stronger discriminators of persistent carriage than taxonomic features. Genomic-resolved analysis of clinical ST131 isolates revealed conserved genes for iron-acquisition, LPS and antibiotic resistance determinants. Overall, while commensals and metabolism may influence initial ST131 colonization, persistent carriage is associated with specific microbial and metabolic adaptations, providing potential targets to limit intestinal ST131 persistence.

Humans↗

PubMed2024

Assessment of antibiotic resistance genes in soils polluted by chemical and technogenic ways with poly-aromatic hydrocarbons and heavy metals.

Anthropogenic activities are leaving lots of chemical footprints on the soil. It alters the physiochemical characteristics of the soil thereby modifying the natural soil microbiome. The prevalence of antimicrobial-resistance microbes in polluted soil has gained attention due to its obvious public health risks. This study focused on assessing the prevalence and distribution of antibiotic-resistance genes in polluted soil ecosystems impacted by industrial enterprises in southern Russia. Metagenomic analysis was conducted on soil samples collected from polluted sites using various approaches, and the prevalence of antibiotic-resistance genes was investigated. The results revealed that efflux-encoding pump sequences were the most widely represented group of genes, while genes whose products replaced antibiotic targets were less represented. The level of soil contamination increased, and there was an increase in the total number of antibiotic-resistance genes in proteobacteria, but a decrease in actinobacteria. The study proposed an optimal mechanism for processing metagenomic data in polluted soil ecosystems, which involves mapping raw reads by the KMA method, followed by a detailed study of specific genes. The study's conclusions provide valuable insights into the prevalence and distribution of antibiotic-resistance genes in polluted soils and have been illustrated in heat maps.

Soil Pollutants↗

PubMed2025

Large Quantities of Bacterial DNA and Protein in Common Dietary Protein Source Used in Microbiome Studies.

Diet has been shown to greatly impact the intestinal microbiota. To understand the role of individual dietary components, defined diets with purified components are frequently used in diet-microbiota studies. Defined diets frequently use purified casein as the protein source. Previous work indicated that casein contains microbial DNA potentially impacting results of microbiome studies. Other diet-based microbially derived molecules that may impact microbiome measurements, such as proteins detected by metaproteomics, have not been determined for casein. Additionally, other protein sources used in microbiome studies have not been characterized for their microbial content. We used metagenomics and metaproteomics to identify and quantify microbial DNA and protein in a casein-based defined diet to better understand potential impacts on metagenomic and metaproteomic microbiome studies. We further tested six additional defined diets with purified protein sources with an integrated metagenomic-metaproteomic approach and found that contaminating microbial protein is unique to casein within the tested set as microbial protein was not identified in diets with other protein sources. We also illustrate the contribution of diet-derived microbial protein in diet-microbiota studies by metaproteomic analysis of stool samples from germ-free mice (GF) and mice with a conventional microbiota (CV) following consumption of diets with casein and non-casein protein. This study highlights a potentially confounding factor in diet-microbiota studies that must be considered through evaluation of the diet itself within a given study.

Animals↗

PubMed2025

Unveiling novel antimicrobial peptides from the ruminant gastrointestinal microbiomes: A deep learning-driven approach yields an anti-MRSA candidate.

INTRODUCTION: Antimicrobial peptides (AMPs) present a promising avenue to combat the growing threat of antibiotic resistance. The ruminant gastrointestinal microbiome serves as a unique ecosystem that offers untapped potential for AMP discovery. OBJECTIVES: The aims of this study are to develop an effective methodology for the identification of novel AMPs from ruminant gastrointestinal microbiomes, followed by evaluating their antimicrobial efficacy and elucidating the mechanisms underlying their activity. METHODS: We developed a deep learning-based model to identify AMP candidates from a dataset comprising 120 metagenomes and 10,373 metagenome-assembled genomes derived from the ruminant gastrointestinal tract. Both in vivo and in vitro experiments were performed to examine and validate the antimicrobial activities of the AMP candidates that were selected through bioinformatic analysis and subsequently synthesized chemically. Additionally, molecular dynamics simulations were conducted to explore the action mechanism of the most potent AMP candidate. RESULTS: The deep learning model identified 27,192 potential secretory AMP candidates. Following bioinformatic analysis, 39 candidates were synthesized and tested. Remarkably, all synthesized peptides demonstrated antimicrobial activity against Staphylococcus aureus, with 79.5% showing effectiveness against multiple pathogens. Notably, Peptide 4, which exhibited the highest antimicrobial activity against methicillin-resistant Staphylococcus aureus (MRSA), confirmed this effect in a mouse model with wound infection, exhibiting a low propensity for resistance development and minimal cytotoxicity and hemolysis towards mammalian cells. Molecular dynamics simulations provided insights into the mechanism of Peptide 4, primarily its ability to disrupt bacterial cell membranes, leading to cell death. CONCLUSION: This study highlights the power of combining deep learning with microbiome research to uncover novel therapeutic candidates, paving the way for the development of next-generation antimicrobials like Peptide 4 to combat the growing threat of MRSA would infections. It also underscores the value of utilizing ruminant microbial resources.

Animals↗

PubMed2025

Effects of Different Feeding Patterns on the Gut Virome of 6-Month-Old Infants.

The gut microbiome is essential for infant health, and in recent years, the impact of enteroviruses on infant health and disease has received increasing attention. The transmission of breast milk phages to the infant gastrointestinal tract contributes to the shaping of the infant gut virome, while breastfeeding regulates the colonization of the infant gut virome. In this study, we collected fecal samples from healthy infants and analyzed the distribution characteristics of infant viral communities by viral metagenomic analysis, and analyzed the differences in infant viral communities under different feeding practices. Our results indicate that the infant intestinal virome consists of phages and eukaryotic viruses. Caudovirales and Microviridae dominated the phage composition, and except for Siphoviridae, which was more predominant in the intestines of formula-fed infants, there were no significant differences in the overall abundance of other Caudovirales and Microviridae in the intestines of infants with different feeding patterns. Breastfeeding can lead to a higher diversity of infant gut viruses through vertical transmission, and a highly diverse gut virome helps maintain the maturation of the gut microbiome. This study informs the shaping of gut virome in healthy infants by breastfeeding and contributes to further research on infant gut virome characteristics and formation processes.

Humans↗

PubMed2026

META-DIFF: a k-mer-based pipeline that detects differentially abundant sequences in metagenomics whole genome sequencing.

Traditional case-control metagenomic studies are constrained by their dependence on taxonomic and functional databases. Because annotation occurs before differential analysis, they are limited to known elements and keep function and taxonomy separate. Although binning strategies have emerged to reconstruct genomes and mitigate this issue, they still require an assembly step, preventing the use of all available sequencing data. Here, we introduce META-DIFF, a pipeline based on differentially abundant k-mers independently of any prior annotation. From those k-mers, it reconstructs longer sequences and provides biological context, as well as the best set of unitigs to discriminate between conditions. Across both taxonomy-centric and functionally-centric benchmarks, it showed robust performance and displayed great reproducibility. It also behaved more conservatively than did other univariate methodologies, i.e. it maintained a high precision at the expense of recall, particularly in conditions of low fold-change and limited sequencing depth. The efficacy of META-DIFF was further validated through its application to a real-world colorectal cancer dataset, which produced both confirmatory and novel results compared with those of previous publications. The pipeline is able to exploit all reads and identify differentially abundant elements, including unknown DNA, prior to annotation. With the guidelines provided, META-DIFF provides users with great exploratory power to unravel microbiome changes.

Metagenomics↗

PubMed2026

Association between the gut microbiome and plasma metabolites linked to vocalization-based temperament in Merino sheep.

BACKGROUND: Temperament, as a determinant of behavioural and emotional responses, has a substantial adaptive value in different environments. This study aims to investigate the association between the gut microbiota and temperament plasticity, and clarify the potential metabolic mechanism that underpins that association by running a multi-omics study in sheep. METHODS: The TrackSheep research cohort was generated using 200 healthy juvenile Merino ewes, and the rumen microbiota, plasma metabolome, and temperament phenotype was measured. RESULTS: Rumen metagenomic analysis identified 25 microbial species and 16 MetaCyc pathways that explained 37.5% and 11.1%, respectively, of the variation in temperament as estimated using the vocal reactivity to stress. Among these, the γ-aminobutyric acid (GABA) shunt and allantoin degradation pathways showed the strongest associations with vocal behaviour. Multi-omic integration linked these microbial pathways to plasma metabolites that are involved in neurotransmission, antioxidant defense, and energy metabolism, including acetyl-L-carnitine (ALCAR) and urocortisone, which partially mediated the effects of microbial pathways on vocalisations. Notably, functional genomic and mediation analyses indicated that the abundance of Cryptobacteroides sp902761655 was associated with the activity of GABA shunt pathway, where GABA co-occurred with succinate production, in turn correlating with reduced inhibitory effects of ALCAR on stress-susceptible temperament. Although plasma metabolite shifts observed immediately after behavioural tests reflected stress exposure, their associations with rumen microbiota highlight microbiome-metabolite interplay that could underly behavioural variation. CONCLUSIONS: Our study provides the first large-scale multi-omics evidence linking the rumen microbiome to a dimension of emotional reactivity in livestock, while underscoring the need for longitudinal and experimental validation to establish causal mechanisms. Video Abstract.

Animals↗

PubMed2026

Metabolome-driven rhizosphere microbiome assembly determining the health of medicinal herb (Angelica sinensis) against root rot.

BACKGROUND: The rhizosphere-associated microbiota plays a crucial role in plant responses to disease stress. Plant secondary metabolites are recognized as crucial mediators in the assembly of rhizosphere microbial communities, particularly by enhancing the colonization of beneficial microorganisms. Despite this recognized importance, a deeper understanding of how such metabolome-driven microbiome assembly specifically determines plant resistance against soil-borne diseases is still lacking. RESULTS: Here, we focused on the widely planted medicinal plant Angelica sinensis and demonstrated that root rot-diseased rhizosphere soils (DRS) exhibited a higher relative abundance of Fusarium and a lower relative abundance of Streptomyces compared to healthy rhizosphere soils (HRS). Shotgun metagenomic sequencing revealed that metabolism-associated genes, particularly those related to steroid degradation, are significantly enriched in HRS samples. Subsequent genome and functional gene analysis of Streptomyces revealed that the steroid degradation-related genes are associated with rhizosphere colonization in hosts. Rhizosphere Streptomyces S15 directly antagonized Fusarium and enhanced the root resistance of A. sinensis. Comparative metabolomics showed that A. sinensis plants from HRS secreted more lipid and lipid-like molecules than those from DRS, especially sterol lipids and long-chain fatty acids, which promoted the growth of Streptomyces S15 isolates. Transcriptome analysis validated that the lipid hormones are essential for sporulation, biofilm formation, and streptomycin biosynthesis of S15 strain. Finally, exogenous application of synbiotics (lipid prebiotics and S15) to A. sinensis resulted in the enrichment of S15-homologous Streptomyces amplicon sequence variant (ASV), further establishing beneficial bacterial communities in Fusarium-stressed rhizospheres. CONCLUSIONS: Our study proposes that A. sinensis recruits steroid-metabolizing Streptomyces species by exuding key lipid compounds (i.e., methyl jasmonate and brassinolide) to combat Fusarium root rot. This study provides novel insights into using functional synbiotics as a promising strategy for manipulating plant-microbiome interactions to promote sustainable agriculture. Video Abstract.

Rhizosphere↗

PubMed2026

Enrichment of Lysobacter in a long-term organically managed agricultural field with low soilborne disease incidence.

Disease-suppressive soils, in which soilborne pathogens are naturally suppressed, offer a promising model for sustainable crop protection, particularly in organic farming systems where chemical disease control options are limited. Although disease suppression in these soils is considered to rely on biological control, the underlying mechanisms remain poorly understood. In this study, we investigated soil from a long-term organically managed field in Shiga Prefecture, Japan, where soilborne disease incidence has remained consistently low, to identify bacterial community features potentially associated with this field. The 16S rRNA gene amplicon sequencing indicated that this soil harbored a bacterial community distinct from those of nearby agricultural soils. Following the application of organic compounds, the genus Lysobacter, a taxon with known antagonistic activity against plant pathogens, was markedly enriched in response to proteinaceous organic inputs. This enrichment was consistent across sampling times and specific to certain proteinaceous organic inputs, whereas minimal effects were observed on chitin, N-acetyl-d-glucosamine, or cysteine. Broader soil surveys indicated that Lysobacter enrichment was not strictly associated with whether soils had been managed under organic or conventional farming practices. Stepwise multiple regression analysis identified 10 co-occurring bacterial genera that were strongly associated with Lysobacter abundance. These findings highlight condition-dependent Lysobacter enrichment as a characteristic microbial response to proteinaceous organic amendments in this low-disease-incidence field and provide microbial insights that may inform microbiome-based strategies for sustainable soil management.

Lysobacter↗