Frontiers in microbiomes最新文献

The human gut microbiome represents a complex and dynamic therapeutic target whose effective interrogation requires system-level analytical approaches beyond single-omics or reductive methods. This mini-review synthesizes recent advances in computational modeling and multi-omics integration relevant to the development of predictive, patient-tailored microbiome therapies. We critically assess the analytical strengths and limitations of genome-scale metabolic models (GEMs); generalized Lotka-Volterra and ODE-based community models; agent-based simulations; and statistical machine-learning frameworks and examine how their integration with metagenomics, metatranscriptomics, metaproteomics, and metabolomics can help bridge microbial functional potential with clinically relevant phenotypes. Representative applications-including MintTea for disease module identification, gNOMO2 for integrative microbiome profiling, and AGORA-based community metabolic modeling-illustrate the translational scope of these frameworks across inflammatory, metabolic, and infectious disease contexts. Hybrid ML-GEM frameworks have not yet been directly applied to FMT outcome prediction; however, the mechanistic principles underlying both approaches - metabolic compatibility modeling and data-driven responder stratification - suggest a compelling direction for future investigation, contingent on prospective validation in adequately powered and independent clinical cohorts. Persistent methodological challenges-such as data heterogeneity, batch effects across sequencing platforms, incomplete multi-omics coverage, and limited interpretability of complex machine-learning models-are being actively addressed through standardized preprocessing pipelines, explainable Artificial intelligence (AI) strategies, and federated analytics. While federated approaches enable privacy-preserving, multi-institutional model training, they introduce additional constraints related to non-identically distributed data, communication overhead, and uneven computational capacity. Overall, the convergence of mechanistic modeling, data-driven learning, and distributed analytical infrastructures may assist in advancing microbiome research from a largely correlational perspective toward mechanistic and ultimately prescriptive frameworks for precision microbiome medicine.

Introduction: Investigations into the location and load of protists in the environment arounddairies are scarce but are essential to maintaining the health of livestock.Moreover, the design of dairy barns has fluctuated over the decades to maximizecattle health and milk production without regard to influences on environmentalmicrobiomes. Beyond cost, the major emphasis of barn design is the managementof appropriate temperature and comfort for cattle. However, there havebeen no corresponding investigations into whether these design changes affect protist communities within barns.

Methods: In this study, community shotgun metagenomic analysis was used to define the spatial composition and relative abundance of protist communities from 118 samples of manure, lagoons, troughs, and house and stable flies at a commercial dairy implementing two free-stall management systems: flow-through and cross-vent. Sequence reads were mapped to the CosmosID database. Viability was not assessed; therefore, results reflect DNA detection only not viability or disease occurrence.

Results: The protist composition differed significantly between dairy components. Ecological findings showed that troughs and lagoons harbored high protist diversity, including the possible pathogen Neobalantidium coli and potential carriers Paramecium biaurelia and Acanthamoeba. Manure had the lowest protist diversity. Stable flies carried more protist taxa than house flies. Both fly species uniquely carried the non-pathogenic alveolate parasite Hammondia hammondi. The water mold plant pathogen Pseudoperonospora cubensis was identified in all sample types. Of the total relative abundance of protists, 2.10% were amoebas, 7.63% alveolate parasites, 62.71% water molds, 23.31% ciliates, 1.74% foraminifera, and 2.50% diatoms.

Discussion: These results describe preliminary spatial overlaps and possible avenues of dissemination, providing a basis for assessing appropriate management systems and identifying protist reservoir sites within dairy operations.

Introduction: Neurodegenerative disorders such as Alzheimer's disease (AD) and Parkinson's disease (PD) have been increasingly linked to alterations of the gut microbiota, although reported microbial signatures remain heterogeneous and often lack taxonomic resolution.

Methods: In the present study, we applied full-length 16S rRNA gene sequencing to characterize gut microbiota composition in 152 individuals, including patients with AD (n = 37), PD (n = 65), and age-matched healthy controls (n = 50), using a unified bioinformatic and statistical framework with adjustment for relevant demographic covariates.

Results: Alzheimer's disease was associated with a modest but significant reduction in microbial richness and Shannon diversity compared with controls, whereas no alpha diversity differences were observed in PD. Beta diversity analyses revealed significant compositional differences across diagnostic groups, driven primarily by PD and modulated by sex but not age. Species-level differential abundance analysis identified a PD-associated microbial signature characterized by reduced abundances of short-chain fatty acid-producing bacteria, including Faecalibacterium prausnitzii, Agathobacter rectalis, Roseburia intestinalis, and Faecalicatena fissicatena, together with increased abundance of Ruminococcus sp. JE7A12. In contrast, AD exhibited minimal species-level changes, with only Bacteroidales bacterium CF showing reduced abundance compared with controls.

Discussion: Overall, these findings indicate that Parkinson's disease is characterized by a targeted disruption of beneficial butyrate-producing bacteria, whereas Alzheimer's disease exhibits subtler and less consistent microbiome alterations. Our results underscore the importance of species-level resolution for identifying disease-associated microbial signatures.

Millions of microorganisms-including bacteria, viruses, fungi, archaea, and protists-reside on and within the human body, collectively forming the human microbiota. This complex and dynamic community plays a crucial role in modulating physiological processes, particularly the development and regulation of the immune system. Modern behaviors such as frequent washing, excessive hygiene, and widespread use of antimicrobial agents can disrupt the natural composition and functional balance of the microbiota, leading to altered immune responses and increased susceptibility to disease. In this review, we focus primarily on the bacterial component of the human microbiome. While we acknowledge the importance of viruses, fungi, archaea, and protists, these components are beyond the scope of the current review. We highlight recent advances in bacterial microbiome research that are reshaping our understanding of host-microbe interactions, immune modulation, and the health consequences of microbiota dysbiosis.

The microbiome is highly important to the physiologies of all multicellular organisms, particularly metazoans. However, the microbiomes of many wild animals remain understudied and poorly understood. Peromyscus mice are commonly used as models of adaptation, mental health, and human disease in biomedical research, and are also common in the environment across North America, frequently coming into close contact with humans. Additionally, Peromyscus sonoriensis are implicated as the primary reservoir for Sin Nombre hantavirus, a rare but severe disease of high morbidity and mortality in humans. Here we characterize the fecal microbiomes of 311 Peromyscus sonoriensis, the western deer mouse, collected from across New Mexico, USA, which will further their usefulness as models of disease and behavior as well as increase our understanding of their ecology. The animals used in this study are geographically diverse, collected from multiple ecoregions, and encompass mice of all ages and sexes. We sequenced the entire 16S rRNA gene in a PCR independent approach and characterized the microbiomes with Shannon entropy, Faith phylodiversity, and weighted UNIFRAC. We found that these mice have diverse microbiomes, with individuals varying in the presence and proportions of various identified bacteria. We analyzed the total population of mice according to age, sex, and trapping location, and found that trapping location was the only condition to significantly impact the microbiome. When the mice were subdivided by the location of collection, there were mild effects of age and sex. When comparing mice from archival museum storage, storage of samples in 95% ethanol resulted in significant alterations to the microbiome when compared to cryopreservation. Differential bacterial family presence was determined using ANCOMBC at the 0.05 significance threshold, and there were many differentially abundant families across all groups of mice. This data set can now be used as a reference for further research into the microbiomes of related Peromyscus species, enhance the use of P. sonoriensis as model laboratory animals, and as a source of novel research questions regarding the physiology of these rodents.

Microorganisms are now widely acknowledged as essential contributors to the health and resilience of coastal environments. Yet, mangrove ecosystems, despite offering numerous ecological and economic services, remain relatively overlooked in microbial research. In this study, we examined the bacteriome of the rhizosphere and the phyllosphere of two mangrove tree species: Avicennia germinans and Rhizophora mangle. Both species were sampled along the banks of the Sinnamary estuary in French Guiana. Our results revealed notable differences in microbiome composition between the two organs and between the two tree species. On average, only 0.3% of ASVs were shared between the leaves and roots and 2.2% between A. germinans and R. mangle. The taxonomic differences were characterized mainly by the significant presence of Rhodothermia and Bacteroidia in the leaves and Cyanobacteria and Planctomycetia in the roots. Furthermore, our results showed that the root microbiome of both species was only weakly influenced by the surrounding water and sediment, with an average of less than 0.7% of ASVs shared. Finally, our study indicates a strong specificity in the bacterial communities of both the phyllosphere and rhizosphere and also raises questions regarding the near absence of Gammaproteobacteria in both the leaves and roots, which remain to be elucidated.

The human gut microbiota is critical for regulating host metabolism, immune responses, epithelial integrity, and systemic homeostasis, and disturbance has been linked to metabolic, inflammatory, and immune-mediated illnesses. Despite significant advances in microbiome research, the interpretation of gut microbiota-disease relationships is still limited by an overreliance on taxonomic profiling and observational study designs, which frequently overlook functional, strain-level, and mechanistic aspects of host-microbiota interactions. Growing research suggests that microbial functional capacity, metabolic activity, and ecological features such as resilience and functional redundancy are better markers of gut health than compositional measurements alone. Nonetheless, significant inter-individual variability, methodological heterogeneity, and dependence on fecal-based analysis continue to limit reproducibility and causal inference across studies. This review integrates current evidence on gut microbiota composition, functional features, and important influencing variables, while emphasizing mechanistic linkages between microbial dysbiosis and major human illnesses, filling significant conceptual gaps in modern microbiome research.

Introduction: MicroRNAs (miRNAs) are key post-transcriptional regulators that also take part in immune responses and recovery processes following exercise. While both gut microbiota composition and peripheral blood mononuclear cell (PBMC)-derived miRNAs are known to be influenced by endurance training, potential correlations between these two systems in athletes remain largely unexplored.

Objective: This study aimed to investigate correlations between gut microbiota composition and PBMC miRNA expression following a maximal exercise stress test in endurance athletes.

Methods: Fifty-eight participants (22 runners, 18 cyclists, and 18 controls) underwent maximal exercise testing, with blood samples collected pre- and post-maximal exercise stress test for small RNA sequencing of PBMCs. Baseline fecal samples were analyzed via 16S rRNA gene sequencing to characterize gut microbiota. Expression data of PBMC miRNAs and microbial taxonomic profiles were integrated to assess potential correlations.

Results: Thirteen significant correlations (|r| = 0.41-0.51, p < 0.005) were identified between gut bacterial taxa known to produce short-chain fatty acids (SCFAs)-including Veillonella, Blautia, Coprococcus, Butyrivibrio, Propionibacterium, and Parabacteroides-and the expression of PBMC miRNAs following a maximal exercise test. The significantly expressed PBMC microRNAs included hsa-miR-545-3p, hsa-miR-126-3p, hsa-miR-1292-3p, hsa-miR-6805-5p, hsa-miR-3668, hsa-miR-196b-5p, hsa-miR-602, hsa-miR-324-5p, and hsa-miR-365a-3p, some of which are known to modulate inflammatory pathways and immune cell signaling.

Conclusion: This is the first study demonstrating an association between resting gut microbiota composition and PBMC miRNA expression following maximal exercise stress test in endurance athletes. These findings raise the possibility of a complex association between gut microbial composition and PBMC miRNA expression in response to exercise. While causality cannot be inferred, the observed correlations suggest a candidate microbiota-miRNA that warrants further investigation in the context of exercise-induced immune regulation and recovery in athletes.

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder marked by difficulties in communication, social interaction, and restricted, repetitive behaviors. The gut microbiota has emerged as a key factor in the gut-brain axis relevant to ASD. We conducted a cross-sectional study comparing the gut bacterial composition of children with ASD (n=29) and their neurotypical siblings (NT, n=29). To minimize environmental and lifestyle confounders, all pairs were 4 to 10 years old and cohabiting in the same household in Uruguay. We used full-length 16S rRNA gene (V1-V9) sequencing with the latest R10.4.1 Oxford Nanopore Technologies chemistry, enabling high-resolution microbial characterization. While overall β-diversity did not differ significantly between the ASD and NT groups, we identified specific taxonomic shifts. The ASD group was enriched in taxa like Sellimonas, while the NT group showed enrichment of genera like Faecalibacterium and Coprococcus. Furthermore, we found GI symptoms to be significantly more prevalent in the ASD group and some bacterial genera associated with GI symptomatology. In addition, we explored the association of pharmacological treatments. Antipsychotic use was associated with reduced Akkermansia abundance, whereas melatonin and methylphenidate use were associated with the enrichment of Negativibacillus. This study provides novel insights into the gut microbiome of Uruguayan children with ASD, delineating the influence of GI symptoms and pharmacological load on microbial diversity and composition.