Growing use of sequencing technologies has accelerated investigation of the ocular surface microbiome, yet this environment is characterized by extremely low microbial biomass, complicating data interpretation. This review assesses current evidence linking microbial communities to ocular surface disease, discusses methodological and biological factors influencing interpretation of microbiome-disease associations, and proposes a framework in which microbial roles may be considered as drivers, modifiers, or markers. Studies across multiple ocular surface diseases report alterations in microbial composition, including reduced α-diversity and shifts in dominant taxa. Genera such as Staphylococcus , Corynebacterium , and Cutibacterium are frequently reported as resident members of the ocular surface microbiome, although their abundance varies across individuals and sampling sites. Across diseases, microbial patterns often overlap and remain inconsistent between studies. Emerging mechanistic evidence has identified specific microbial products, such as lipoteichoic acid, that promote ocular surface inflammation through defined signaling pathways, providing initial support for a potential driver or modifier role. In low-biomass environments such as the ocular surface, contamination, host DNA predominance, and methodological variability can strongly influence detected microbial signals. Interpretation of ocular surface microbiome data remains inherently challenging in this low-biomass context. However, the emergence of mechanistic studies suggests a transition from purely associative observations toward functional and translational investigation. Future studies should be designed to better define microbial roles by integrating standardized methodologies with multiomics approaches and detailed clinical phenotyping. Until such evidence emerges, microbiome research is best viewed as advancing biological insight rather than informing clinical decision-making.

Hypervariable region-specific detection of an avian gut pathobiont in multi-primer 16S rRNA metagenomics: the V9 region identifies Gallibacterium anatis undetected by conventional V3-V4 approaches.

The factors influencing the oral microbiome during childhood and adolescence remain under-explored at the population level. Furthermore, details on how the oral microbiome differs with age, varies between individuals of different ethnicities, or is associated with socioeconomic factors and diet in children and adolescents are almost entirely unknown. Saliva samples and detailed demographic, health, diet and socioeconomic data were collected from children and adolescents that attended the Ontario Science Centre (Toronto, Canada) and were enrolled in the Spit for Science cohort. We characterised the bacterial microbiota of 4812 samples using 16S rRNA gene sequencing to make this the largest population cohort of the paediatric oral microbiome to date. Exploration of limited participant genotyping information and more than 50 variables encompassing the demographics, health, diet, socioeconomic status and living environment of participants revealed that almost all of the investigated variables were associated with overall community structure and/or the abundance of specific bacterial genera. However, most of these associations were modest (R2 < 0.01) and the correlation between genetic relatedness and salivary bacteriome similarity was weak (R2 = -0.014), while the strongest determinants of oral bacteriome composition were shared family/household environment (R2 = 0.61 in the subset of participants from multi-child households), age (R2 = 0.014) and ethnicity (R2 = 0.01). We show that older children and adolescents have higher richness but lower evenness than younger children, suggesting that their oral bacteriome changes as they are exposed to more influences outside the home, and that the oral bacteriome is more consistent with more core taxa among children and adolescents than adults. We also find that diet variables related to the frequency of sugar consumption have the largest impact on the oral bacteriome of children and adolescents, and that microbial differences attributed to ethnicity and diet are likely intertwined. This study provides an atlas of the demographic, health and lifestyle factors that are associated with the salivary bacteriome of children and adolescents. These findings highlight the complex interplay between social, environmental, and biological factors in shaping the developing oral microbiome and underscore the importance of inclusive, demographically diverse cohorts in microbiome research. This presents a reference for the variables that are important to account for in paediatric oral microbiome studies. Video Abstract.

Gnotobiology, from the Greek gnotos (meaning 'known') and bios (meaning 'life'), is a research discipline that uses organisms with a defined microbiological status to study the interaction between hosts and microbes. This review traces six decades of Czech gnotobiology, beginning with the launch of a dedicated gnotobiology programme at Nový Hrádek in 1962 by Jaroslav Šterzl, whose visionary aims anticipated by decades the current recognition of the microbiota as a central determinant of immune and broader physiological function. The site - originally established in 1953 as the Biological Station - was thereby transformed into one of only four gnotobiological laboratories worldwide at that time and the first in Central and Eastern Europe. The facility pioneered the rearing of germ-free piglets, rats, rabbits, and mice, establishing the experimental foundation for the laboratory's work on immune ontogeny, mucosal immunity and tolerance, and microbiota-host interactions in immune development and regulation. This review discusses the key discoveries made using these models. Among them, work at the Institute of Microbiology (Prague and Nový Hrádek) demonstrated that germ-free animals have underdeveloped lymphoid tissue and impaired adaptive immunity. The review also describes the subsequent development of gnotobiotic models of human metabolic, immune-mediated, neoplastic, and neuropsychiatric diseases. The completion of the Human Genome Project in 2001 and the emergence of microbial metagenomics in the early 2000s sparked renewed interest in host-microbe interactions and led to a rediscovery of gnotobiotic approaches as essential tools for establishing causation in microbiome research. We examine how integrating these approaches with high-throughput sequencing, metabolomics, and other omics technologies has shifted the focus from cataloguing the microbiome to mechanistically dissecting host-microbe interactions. Finally, we outline future directions, including humanized gnotobiotic models, microbiota-based therapeutics, and the convergence of gnotobiology with personalized medicine and synthetic biology.

The human facial skin microbiome is a complex and dynamic ecosystem that plays a central role in maintaining skin health, immune regulation, and preventing dermatological skin conditions. Cutibacterium acnes (C. acnes) and Staphylococcus epidermidis (S. epidermidis) are the most prominent bacterial species, with shifts in their relative abundance correlating with skin site, age, skin site, and health status. Exploring the facial microbiome offers exciting opportunities, though it requires careful methodological consideration. Sampling techniques vary in invasiveness and depth, which can influence the accuracy and reproducibility of microbiome profiles. While traditional cultivation methods provide valuable insights, they often miss nonculturable microbes, limiting the view of microbial diversity. Molecular approaches such as amplicon sequencing and metagenomics enable a more comprehensive understanding of microbial communities, even though they currently cannot distinguish between viable and nonviable microbes. Addressing these challenges will help unlock the full potential of facial microbiome research. A balanced facial skin microbiome is associated with healthy skin, whereas a dysbiosis of C.acnes and S. epidermidis is commonly observed in acne-prone skin and more pronounced clinically manifest acne. A comprehensive understanding of the diversity and distribution of C. acnes phylotypes, as well as distinct lineages of S. epidermidis associated with skin disorders, is crucial for developing targeted, microbiome-based cosmetic and medical treatments. Emerging strategies aim to restore microbial balance by leveraging the skin's native microbiota, including probiotic approaches. These strategies represent a promising yet still emerging approach, as current clinical evidence remains limited and further well-controlled studies are required, although they may offer benefits by enhancing microbial diversity and supporting skin barrier function.

Identifying robust microbial biomarkers is crucial for disease diagnosis and prediction, elucidation of biological mechanisms, and development of targeted therapies. Machine learning-based approaches, particularly the random forest model, have been widely used for biomarker identification during sample stratification. However, those biomarkers often vary considerably for the same disease, limiting their practical applicability. A robust framework for reliable biomarker identification in microbiome research is needed. To address this gap, we proposed a prevalence-aware feature selection framework (ParSlet) that incorporates a universal scaling relationship between taxon prevalence and selection frequency. We first identified a universal exponential scaling law linking the probability of a taxon being consistently recognized as a biomarker versus its prevalence. Then, we integrated this scaling law with taxa prevalence into the biomarker identification using random forest. We systematically evaluated this approach in both simulated microbiome datasets and real-world microbiome datasets and compared it with existing methods, finding that our integrated approach generally improved feature stability and reproducibility of biomarker identification. In colorectal cancer (CRC) datasets, our method robustly identified well-established microbial biomarkers such as Ruminococcus, Clostridium_XVIII, and Faecalibacterium. Integrating a prevalence-based scaling adjustment into feature importance enhances the stability of microbiome biomarker identification. This approach holds promise for enabling more reliable disease diagnostics, uncovering generalizable microbial signatures across cohorts, and guiding the development of targeted microbiome-based interventions. ParSlet is available at https://github.com/KelabatOSU/Feature_selection. Supplementary data are available at Bioinformatics online.

Voices of microbiome researchers in an artificial intelligence era.

The chemistry of the cobalt corrinoids - Recent advances and emerging themes. Part 3. Cobalamins and health.

The bacterial 16S rRNA gene is widely used to characterize host-associated and environmental microbiomes, most commonly through sequencing short hypervariable regions. Recent improvements in PacBio sequencing chemistry and concatenation approaches can now enable high-throughput, full-length 16S rRNA gene sequencing with high accuracy and depth. However, errors introduced during library preparation remain a major limitation, particularly during PCR amplification of full-length amplicons, where error accumulation may be elevated due to longer sequence lengths. These challenges are amplified when samples vary widely in microbial biomass, making it difficult to select a single optimal number of PCR cycles. Here, we evaluated PCR cycle autonormalization for PacBio Kinnex full-length 16S rRNA gene sequencing across seven agriculturally relevant specimen types. We compared conventional fixed-cycle PCR protocols (20, 24, and 30 cycles) with an autonormalization approach in which individual reactions were terminated during exponential amplification based on real-time fluorescence thresholds. Under the workflow tested here, autonormalized libraries generally retained a high proportion of sequences following denoising and chimera removal, exhibited low residual error rates (<0.005%), and yielded relatively even read distributions across heterogeneous sample inputs. Overamplified reactions (30 cycles) showed elevated residual error rates and greater sequence loss, particularly in samples with higher microbial biodiversity, whereas low-cycle libraries produced more variable read output among specimens. Importantly, the PCR protocol had relatively minor effects on overall community composition compared with specimen type. These results support PCR cycle autonormalization as a useful workflow strategy for heterogeneous full-length 16S library preparation, while also highlighting the importance of library design, pooling strategy, and downstream processing in shaping technical outcomes.IMPORTANCEAmplicon-based sequencing of the 16S rRNA gene is a foundational tool in microbiome research, yet PCR amplification remains a major source of library-preparation error. This challenge is magnified for full-length 16S rRNA sequencing and for workflows that process specimen types with widely varying microbial biomass. Selecting a single PCR cycle number can underamplify low-biomass samples or overamplify high-titer samples, increasing artifacts and sequence loss during downstream processing. Here, we show that PCR cycle autonormalization can be integrated into a PacBio full-length 16S rRNA workflow and, under the conditions tested, provides low residual error rates and relatively even sample representation across heterogeneous inputs. Autonormalization also enables blind pooling of amplicons without post-PCR quantification or equimolar normalization, reducing hands-on time and sample loss. These benefits make cycle autonormalization particularly valuable for high-throughput and production-scale library preparation applications handling diverse specimen types.

Breast milk has long been recognized as the optimal source of nutrition for newborns, yet emerging research has revealed an additional dimension of its complexity: a diverse and dynamic microbial community. The breast milk microbiome constitutes one of the earliest and most consequential sources of microbial colonization in the infant gut, with direct relevance to neonatal immune development and long-term health. The core milk microbiome, dominated by Staphylococcus, Streptococcus, Lactobacillus, and Bifidobacterium, arrives principally via the entero-mammary pathway, maternal skin, and retrograde infant oral transfer, and is substantially modulated by maternal factors including diet, mode of delivery, antibiotic use, BMI, and lactation stage. Beyond live microorganisms, human milk oligosaccharides serve as critical prebiotics that selectively promote beneficial microbial taxa in the infant gut, while also exhibiting direct antimicrobial and immunomodulatory properties. Extracellular vesicles in breast milk carry bioactive payload, including proteins, microRNAs, and microbial components, that influence infant gut epithelial development and immune signaling, representing an underexplored frontier in milk biology. This review synthesizes current knowledge on the composition, origins, and functional significance of the breast milk microbiome. Based on a wide-ranging literature survey, the functional roles of the breast milk microbiome in colonization resistance, immune education, and metabolic programming in the developing infant are evaluated. Disruptions to this microbial community have been associated with conditions such as infant colic, atopic disease, and obesity. By mapping the available literature, we identify emerging research trends in the breast milk microbiome research and their translational implications for infant health optimization.

Microbial network inference is an essential approach for revealing complex interactions within microbial communities. However, the lack of experimentally validated gold standards presents a significant obstacle in evaluating the biological accuracy of inferred networks. This study delivers a comprehensive comparative assessment of six widely used microbial network inference algorithms on four diverse real-world microbiome datasets alongside computationally generated samples, including synthetic, noisy, and bootstrap-derived variants. Our evaluation framework extends beyond conventional synthetic benchmarking by emphasizing reproducibility-focused assessments grounded in biologically realistic perturbations. Our analysis reveals that bootstrap resampling and low-level noisy datasets (≤10% perturbation) effectively preserve the key statistical properties of real microbiome data, serving as reliable proxies for assessing algorithmic consistency. Conversely, synthetic datasets generated via the widely used SPIEC-EASI method exhibit substantial divergence from real data. Notably, several algorithms fail to distinguish between structured and random networks, highlighting a lack of structural sensitivity and the limitations of overreliance on synthetic benchmarks. This study provides critical insights for the microbiome research community, emphasizing the need for more reliable and broadly applicable approaches to network evaluation. We propose a benchmarking framework that prioritizes real-data-derived perturbations and mandates rigorous statistical validation of synthetic datasets. Our findings highlight the importance of robustness and reproducibility analyses as complementary evaluation criteria for microbial network inference methods when validated biological ground truth is unavailable.

A causal inference framework to bridge association and mechanism in the gut-brain axis.

Microbiome research is a dynamic, rapidly growing, and interdisciplinary field that generates valuable insights across the human health, agricultural, and environmental sectors. Despite this growth, gaps remain in educational content and professional development opportunities specifically tailored for microbiome science rather than traditional microbiology. The National Microbiome Data Collaborative (NMDC) has developed a Microbiome Science Certificate Program aimed at undergraduates but available to any learner or researcher interested in this field. The curriculum includes 12 modules to further technical knowledge, as well as practical and professional skills. The modules each include prepared slide decks, recorded lectures, resource documents, expert interviews, reading assignments, knowledge assessments, and an overall glossary. The modular content can be readily applied within the classroom as a stand-alone semester-long course or as supplementary to existing curricula. An asynchronous, online, certificate-granting implementation of the content is available through the American Society for Microbiology. We have outlined future laboratory, workforce development, and data science "mini-modules" that can be further developed with the help of educators. Improvements will be made to the program content based on feedback from learners and educators. This program aims to promote practical skills to empower the next generation of microbiome researchers.

Power and sample-size estimation in human microbiome research.

Macrophage plasticity as a therapeutic target in inflammatory bowel disease: Immunomodulatory and regenerative strategies.

Recent studies from Western countries suggest an association between cervicovaginal microbiome (CVM) and HPV persistence. This study aims to identify CVM diversity within HPV-negative/positive women and to establish an association between HPV infection and CVM in Indian women. This was a pilot, hospital-based, prospective study conducted at a tertiary care center in India over 6 months. Seventy-two participants underwent HPV genotyping, yielding 20 HPV-positive samples. Of these, 16 samples with good-quality DNA, along with 16 matched HPV-negative samples, were identified for CVM analysis. Sequencing of bacterial 16S ribosomal RNA region and bioinformatic analysis were performed in 32 samples. Baseline characteristics were similar for HPV-positive and HPV-negative women. Out of 16 HPV-positive women, 9 had HPV-16 genotype, one had HPV-16&18, and 6 had other genotypes. Seven patients had cervical cancer on histopathology, 7 had NILM, 1-ASCUS, and 1-ASC-H on cytology. HPV-negative women's CVM had a larger abundance of lactobacillus species than HPV-positive women (43.24% vs 2.44%). Whereas CVM among HPV-positive women was characterized by a greater abundance of anaerobes, such as Prevotella (12.46% vs. 4.59%), Atopobium (6.66% vs. 1.98%), Anaerococcus (5.27% vs. 0.57%), and Porphyromonas (2.98% vs. 0.67%), compared with HPV-negative women. The current study from the Sub-Himalayan region of India characterizes the diversity of CVM in HPV-positive and HPV-negative women. This makes a significant geographical contribution to vaginal microbiome research worldwide.

Undergraduate research experience (URE) positively contributes to student success, STEM identity, and STEM post-graduate careers. Despite these benefits, traditional UREs are limited, and many students encounter barriers to gaining access to these opportunities. Course-based Undergraduate Research Experiences (CUREs) increase access to UREs by incorporating authentic research projects into the course curriculum. Despite the recognized benefits of CUREs, few have been developed for introductory or upper-level microbiology courses. This scarcity largely reflects the substantial planning and financial investment required of faculty to create effective CUREs that are affordable, relevant, and align with course learning objectives. These combined demands present significant challenges to widespread adoption of CUREs in microbiology education. We considered these limitations when developing the "MicrobioME" CURE for implementation in pathogenic microbiology laboratory courses. Students participating in the MicrobioME CURE isolate and identify bacterial isolates from their own skin microbiome and investigate the impact of isolate-produced small molecules on Staphylococcus aureus biofilm production. We studied MicrobioME CURE implementation in two laboratory courses at the University of Kansas (KU) and West Virginia University (WVU) in cohorts differing in class size, research experience, meeting times, and faculty expertise in microbiome research, providing an opportunity to compare student outcomes in varied environments. Here, we report student outcomes of and provide tools for implementing the MicrobioME CURE in microbiology laboratory courses at other universities. Our results suggest that the MicrobioME CURE is scalable, affordable, and modular, making it an accessible and practical solution to increasing access to authentic research experience.

Earthworms are vital ecosystem engineers that play fundamental roles in soil formation, nutrient cycling, organic matter decomposition, and the maintenance of soil fertility. Their ecological importance has led to the widespread adoption of vermiculture and vermicomposting technologies for sustainable waste management and agricultural productivity. However, the health and performance of earthworm populations can be adversely affected by a variety of diseases, parasites, pests, and environmental stressors that remain poorly documented and often overlooked. This review provides a comprehensive synthesis of current knowledge on the major biotic factors influencing earthworm health, with particular emphasis on economically important vermiculture species such as Eisenia fetida and Eudrilus eugeniae. The review examines bacterial diseases, fungal infections, viral associations, protozoan and helminthic parasites, and invertebrate pests including mites and predatory flatworms. In addition, physiological disorders associated with environmental stress, particularly acidic bed syndrome, moisture imbalance, poor aeration, and substrate deterioration, are discussed. The occurrence, symptoms, transmission pathways, ecological consequences, and management strategies associated with these health challenges are critically evaluated. Recent advances in microbiome research, metagenomics, and molecular diagnostics are highlighted for their potential to improve understanding of host–microbe interactions and disease detection in earthworm populations. The review also identifies significant knowledge gaps, including limited experimental validation of reported pathogens, inadequate disease surveillance, and insufficient information on disease dynamics under tropical vermiculture conditions. Emphasis is placed on integrated disease management, biosecurity measures, environmental monitoring, and sustainable vermiculture practices for maintaining healthy earthworm populations. By consolidating fragmented information and identifying future research priorities, this review serves as a valuable resource for researchers, soil biologists, and vermiculture practitioners working toward improved earthworm health, enhanced vermicomposting efficiency, and sustainable soil ecosystem management.

Best practices framework for using 16S rRNA gene sequencing in poultry microbiota research.

Endometrial cancer (EC) is a common gynecologic malignancy globally, but the role of the intratumoral microbiome remains poorly defined. While microbial dysbiosis is increasingly linked to cancer, the composition, function, and clinical relevance of the microbiota in EC are underexplored. This study aims to systematically profile the microbiome in EC patients by integrating culturomics and 16S rDNA sequencing, and to evaluate the functional properties of key bacterial strains in relation to tumor progression. We collected cancerous tissues (CATs), non-cancerous adjacent tissues (NATs), and vaginal swabs from 32 EC patients. Culturomics was performed on 57 samples from 19 patients to isolate and identify bacteria, while 16S rDNA sequencing assessed microbial diversity and composition. Functional assays, including flow cytometry for invasion efficiency and ELISA, were used to evaluate pro-inflammatory capacity. Culturomics identified 79 bacterial species, with Staphylococcus, Streptococcus, and Cutibacterium emerging as core genera shared across the vagina, NATs, and CATs. Functional analysis revealed that strains such as Staphylococcus epidermidis and Streptococcus anginosus exhibited high invasion efficiency (up to 84.8%) and significantly upregulated pro-inflammatory responses. 16S rDNA sequencing showed that Lactobacillus dominated the vaginal microbiota, whereas endometrial tissues were enriched with opportunistic pathogens (e.g., Stenotrophomonas). Critically, specific genera were associated with aggressive clinical features: Slackia with poorly differentiated (G3) tumors and Porphyromonas with deep myometrial invasion (≥ 50%). This study uncovers a distinct, translocally disseminated core microbiota in EC, with key strains demonstrating invasive and pro-inflammatory capacities that may play a role in tumor progression. These findings provide a foundation for microbial biomarkers and targeted interventions, highlighting the transformative potential of microbiome research in gynecologic oncology.