Microbiome Research Research Articles

Reproducing plant microbiome research reveals site and time as key drivers of apple tree phyllosphere bacterial communities

Reproducing plant microbiome research reveals site and time as key drivers of apple tree phyllosphere bacterial communities

Oncoviruses in the Oral Cavity: Recent Advances in Understanding Viral Infections and Tumorigenesis

Oncoviruses, such as Epstein–Barr virus (EBV), human papillomavirus (HPV), and Kaposi sarcoma-associated herpesvirus (KSHV), have been widely discussed for their oncogenic risk. Initially, the oral cavity was disregarded. In recent years, orientation has shifted to the importance of the oral cavity and cancer-related issues via Handbook 19 titled “Oral Cancer Prevention” by the International Agency for Research on Cancer, the WHO Global Oral Health Status Report 2022, and multiple other actions focused on reducing the oversight of this neglected area. Oncoviruses play a significant role in oral cavity malignancies by establishing persistent infections, evading host immune responses, and inducing cellular transformation through the disruption of normal regulatory pathways. Molecular biology and microbiome research have advanced our understanding of the complex interplay between oncoviruses and oral microbiota, demonstrating how coinfections and dysbiosis can enhance viral oncogenic potential. These findings improve the understanding of virus-induced oral cancers and support the development of novel diagnostic and therapeutic strategies. This narrative review focuses on the relationship between oncoviruses and the oral cavity by focusing on how a specific virus triggers tumorigenesis for each of the described viruses and how it affects oral cavity cancer development. Finally, we describe recent advances and future perspectives including vaccines and/or treatment.

Large-scale classification of metagenomic samples: a comparative analysis of classical machine learning techniques vs a novel brain-inspired hyperdimensional computing approach.

The growing complexity and dimensionality of biological data require more efficient and scalable machine learning approaches. HDC offers a novel alternative to conventional methods, showing resilience to high-dimensionality while maintaining competitive accuracy. This study demonstrates the effectiveness of HDC in classifying metagenomic samples based on their microbial composition. Our results suggest that HDC not only matches, but sometimes exceeds the performance of well-established methods. We make this approach accessible to the broader bioinformatics community with an open-source tool fully integrated into the Galaxy platform, facilitating its adoption and reproducibility, with the aim of integrating HDC into mainstream biological data analysis pipelines, especially for complex, high-dimensional tasks in microbiome research.

Skin microbiome-biophysical association: a first integrative approach to classifying Korean skin types and aging groups

IntroductionThe field of human microbiome research is rapidly expanding beyond the gut and into the facial skin care industry. However, there is still no established criterion to define the objective relationship between the microbiome and clinical trials for developing personalized skin solutions that consider individual diversity.ObjectivesIn this study, we conducted an integrated analysis of skin measurements, clinical Baumann skin type indicator (BSTI) surveys, and the skin microbiome of 950 Korean subjects to examine the ideal skin microbiome-biophysical associations.MethodsBy utilizing four skin biophysical parameters, we identified four distinct Korean Skin Cutotypes (KSCs) and categorized the subjects into three aging groups: the Young (under 34 years old), the Aging I group (35-50), and the Old group (over 51). To unravel the intricate connection between the skin’s microbiome and KSC types, we conducted DivCom clustering analysis.ResultsThis endeavor successfully classified 726 out of 740 female skin microbiomes into three subclusters: DC1-sub1, DC1-sub2, and DC2 with 15 core genera. To further amplify our findings, we harnessed the potent capabilities of the CatBoost boosting algorithm and achieved a reliable framework for predicting skin types based on microbial composition with an impressive average accuracy of 0.96 AUC value. Our study conclusively demonstrated that these 15 core genera could serve as objective indicators, differentiating the microbial composition among the aging groups.ConclusionIn conclusion, this study sheds light on the complex relationship between the skin microbiome and biophysical properties, and the findings provide a promising approach to advance the field of skincare, cosmetics, and broader microbial research.

Gut microbiota profiles of peninsular Malaysian populations are associated with urbanization and lifestyle

There is increasing evidence of distinct gut microbiome compositions between populations living industrialized and non-industrialized lifestyles worldwide. However, whether populations of Malaysia exhibit variations in their microbiome, and to what extent host lifestyle correlates with these variations, remains unclear. Malaysia’s extensive geographical and sociocultural diversity provides a unique opportunity to explore how lifestyle and environmental exposures are associated with the human gut microbiome. Here, we characterized the gut microbiome of three populations in peninsular Malaysia, each representing different lifestyle contexts, and identified host factors associated with microbiome variation. Our findings suggest that lifestyle-related factors are strongly associated with differences in microbial community composition across populations. In particular, urban and rural individuals harbor gut microbiota with distinct community structures. We further identified specific taxa as potential microbial signatures of host lifestyle, with the genera Prevotella and Cryptobacteroides enriched in rural populations, while Phocaeicola, Vescimonas, and Megasphaera were more prevalent among urban individuals. In addition to lifestyle, demographic factors such as age, sex, and BMI were also associated with variation in the gut microbiome. This study highlights the influence of urbanization, lifestyle, and diet on the gut microbiome landscape of Malaysian populations and underscores the importance of considering sociocultural context in future microbiome research.

Advancing metagenomic classification with NABAS+: a novel alignment-based approach.

Microbiome research has expanded rapidly in the last decade due to advances in sequencing technology, resulting in larger and more complex data. This has also led to the development of a plethora of metagenomic classifiers applying different algorithmic principles to classify microorganisms. However, accurate metagenomic classification remains challenging due to false positives and the need for dataset-specific tuning, limiting the comparability of distinct studies and clinical use. In this study, we demonstrate the discrepancy between current, commonly used classifiers and propose a novel classifier, NABAS+ (Novel Alignment-based Biome Analyzing Software+). NABAS+ uses BWA (Burrows-Wheeler aligner) alignment with strict RefSeq curation to ensure one reliable genome per species and filters for genomes with only high-quality reads for precise species-level identification from Illumina shotgun data. The performance of our algorithm and three commonly used classifiers was evaluated on in silico datasets modelling human gastrooral communities, as well as on deeply sequenced microbial community standards. Additionally, we illustrated the usefulness of NABAS+in detecting pathogens in real-world clinical data. Our results show that NABAS+, due to its extensive alignment process, is superior in accuracy and sensitivity compared to leading microbiome classifiers, particularly in reducing false positives in deep-sequenced microbial samples, making it suitable for clinical diagnosis.

Integrative systems biology approaches for analyzing microbiome dysbiosis and species interactions.

Microbiomes are crucial for human health and well-being, with microbial dysbiosis being linked to various complex diseases. Therefore, understanding the structural and functional changes in the microbiome, along with the underlying mechanisms in disease conditions, is essential. In this review, we outline the structure and function of different human microbiomes and examine how changes in their composition may contribute to diseases. We highlight critical information associated with microbial dysbiosis and explore various therapeutic strategies for restoring a healthy microbiome, including microbiota transplantation, phage therapy, probiotics, prebiotics, dietary interventions, and drug-based approaches. Further, to better understand microbiome dysbiosis, we discuss multi-omics approaches including metagenomics, metatranscriptomics, metaproteomics, and meta-metabolomics, alongside computational modeling approaches such as ecological and metabolic network analysis. We outline key challenges associated with multi-omics techniques and emphasize the importance of integrative systems biology approaches that combine multi-omics data with computational modeling. These approaches are crucial for effectively analyzing microbiome data, providing deeper insights into species interactions and microbiome dynamics. Finally, we offer insights into future research directions in the field of microbiome research. This review makes a unique contribution to microbiome research by presenting a holistic framework that integrates multi-omics data with multi-scale modeling to elucidate microbial interactions, microbiome dysbiosis, and their modulation in disease-associated contexts.

Advances in Microbiome Research: Implications for Infectious Disease Management and Treatment.

The human microbiome plays a pivotal role in health and disease, with microbial imbalances (dysbiosis) increasingly linked to heightened susceptibility to infections and exacerbated disease severity. This review explores how the microbiome confers protection through mechanisms, such as colonization resistance, immune modulation, and antimicrobial metabolite production, while also examining its potential as a predictive tool for infection risk and outcomes, as exemplified in COVID-19. This article synthesizes current literature on microbiome dynamics, leveraging advances in high-throughput sequencing, bioinformatics, and machine learning to analyze microbial profiles and identify biomarkers. It evaluates microbiome-based therapeutic strategies, including probiotics, prebiotics, and engineered microbes, and assesses challenges in translating these approaches into clinical practice. Microbiome profiles demonstrate prognostic value in predicting infection risk and severity, supported by enhanced analytical tools that enable precise biomarker discovery for diagnostics and personalized medicine. Therapeutic interventions show promise in restoring microbial balance and combating infections, though clinical adoption is hindered by variability, regulatory hurdles, and the need for standardized methodologies. Integrating microbiome insights into clinical practice requires rigorous clinical trials, standardized protocols, and resolution of ethical and regulatory challenges. Future research should focus on elucidating microbiome-host-pathogen interactions and developing targeted interventions, and advanced computational models are critical to unlocking the full potential of microbiome-based diagnostics and therapeutics for infectious disease management.

An early-life perspective is needed to explain the impact of gut microbiota on wild vertebrate phenotypes.

Vertebrates house dense and diverse communities of microorganisms in their gastrointestinal tracts. These communities shape host physiological and ecological phenotypes in diverse ways, with implications for animal fitness in nature. Exposure to microbes during the earliest stages of life is particularly important because, during critical developmental windows, the microbiome is exceptionally plastic and interactions with microbes can have long-lasting physiological impacts on the host. Despite our understanding that early-life microbial interactions are important to host function broadly, the majority of research in this area has been performed in human or model organisms that are not representative of animals in the wild. Specifically, most gut microbiome studies in wildlife are cross-sectional and compare microbial communities across life stages using different individuals, as opposed to tracking the microbial communities and phenotypes of the same individuals from early to later life. This knowledge gap may hinder wildlife microbiome research, as the current model lacks an early-life perspective that can contextualize host phenotypic and fitness differences observed between animals at later life stages. Further, considering early-life microbial dynamics may offer insights to applied research, such as determining the optimal age to manipulate microbiomes for desired conservation outcomes. In this Commentary, we consider current understanding of the importance of early-life host-microbe interactions to vertebrate physiology across the lifespan, discuss why this perspective is necessary in wildlife studies, and provide practical recommendations for experimental designs that can address these questions, including field and laboratory approaches.

Year-long, multiple-timepoint field studies show the importance of spatiotemporal dynamics and microbial functions in agricultural soil microbiomes.

Despite the recognition of the complexity of soil ecosystem dynamics, most soil microbiome studies sample one field, take one sample per field, or use limited samples throughout the year. This limits our understanding of the spatiotemporal role of the soil microbiome in relation to management practices. To address these limitations, we conducted a year-long investigation of the soil microbiome in two agricultural fields, sampling multiple plots at different soil depths every 5 weeks. We examined spatial and temporal variabilities in response to the application of organic amendments (one-time biochar and annual compost application) on bacterial and fungal communities, studying both the microbial composition (metabarcoding, phospholipid fatty acids [PLFA], hot-water extractable-carbon) and activity (metatranscriptomics). Indicated by metabarcoding and PLFA, fungal communities were less affected over time per field, whereas bacteria exhibited more pronounced temporal trends. In contrast, fungi displayed clear spatial effects, while bacterial spatial differences within the field were predominantly observed in the deeper soil layer. Effects on functional roles and metabolic processes of the active microbial community were mainly related to temporal trends, especially in the topsoil. Organic amendments did not affect the microbial activity and affected fewer than 2% of the bacterial and fungal amplicon sequence variants over time. This study reveals the predominance of spatiotemporal dynamics over management practices in shaping soil microbial communities within agricultural fields, emphasizing the importance of field-specific factors, sampling depth, and community type. This ushers in the need for a well-considered experimental design and sampling strategy that accounts for spatiotemporal trends.IMPORTANCEThis study addresses a critical gap in soil microbiome research by investigating spatiotemporal effects on soil bacterial and fungal composition and activity in relation to field management practices. Moving beyond single-field and limited sampling approaches, this research conducted monthly sampling events on two fields at various depths. By combining metabarcoding, phospholipid fatty acid analysis, and metatranscriptomics, the study examined bacterial and fungal community composition, biomass, and functionality. Key findings reveal distinct responses of bacterial and fungal communities to spatiotemporal variability and management practices. Functional categories were predominantly driven by temporal trends rather than compost amendments. Temporal changes were more pronounced in the topsoil. These insights into the complex interactions between soil microbial communities, management practices, and spatiotemporal dynamics contribute significantly to soil microbiome research and sampling strategies.

International Symposium on Ruminant Physiology: Stochastic and deterministic factors that shape the rumen microbiome.

International Symposium on Ruminant Physiology: Stochastic and deterministic factors that shape the rumen microbiome.

Identifying Optimal Machine Learning Approaches for Microbiome-Metabolomics Integration with Stable Feature Selection.

Microbiome research has been limited by methodological inconsistencies. Taxonomy-based profiling presents challenges such as data sparsity, variable taxonomic resolution, and the reliance on DNA-based profiling, which provides limited functional insight. Multi-omics integration has emerged as a promising approach to link microbiome composition with function. However, the lack of standardized methodologies and inconsistencies in machine learning strategies has hindered reproducibility. Additionally, while machine learning can be used to identify key microbial and metabolic features, the stability of feature selection across models and data types remains underexplored, despite its importance for downstream experimental validation and biomarker discovery. Here, we systematically compare Elastic Net, Random Forest, and XGBoost across five multi-omics integration strategies: Concatenation, Averaged Stacking, Weighted Non-negative Least Squares (NNLS), Lasso Stacking, and Partial Least Squares (PLS), as well as individual 'omics models. We evaluate performance across 588 binary and 735 continuous models using microbiome-derived metabolomics and taxonomic data. Additionally, we assess the impact of feature reduction on model performance and feature selection stability. Among the approaches tested, Random Forest combined with NNLS yielded the highest overall performance across diverse datasets. Tree-based methods also demonstrated consistent feature selection across data types and dimensionalities. These results demonstrate how integration strategies, algorithm selection, data dimensionality, and response type impact both predictive performance and the stability of selected features in multi-omics microbiome modeling.

Metabolic dysfunction-associated steatotic liver disease (MASLD): emerging insights into gut microbiota interactions and therapeutic perspectives

As the most prevalent hepatic disorder worldwide, metabolic dysfunction-associated steatotic liver disease (MASLD) afflicts over one-third of the global population, representing a significant public health challenge. The multifactorial pathogenesis of this condition is principally rooted in metabolic dysregulation. It is notable that emerging evidence highlights a critical role for gut microbiota (GM) in disease initiation and progression. This comprehensive review elaborates some representative GM species that influence hepatic lipid metabolism and elucidates the mechanisms through which GM dysbiosis exacerbates MASLD pathogenesis. Importantly, the positive or negative effects of intestinal bacterial communities on MASLD are largely dependent on their special metabolites, such as short chain fatty acids, ethanol, and trimethylamine N-oxide. Current therapeutic strategies targeting GM modulation, including prebiotics, probiotics, fecal microbiota transplantation, specific medicines, and bacteriphages, demonstrate promising efficacy that partially restores microbial equilibrium and mitigates hepatic steatosis. Although limitations still persist in achieving sustained clinical remission, the expanding frontier of microbiome research continues to refine our understanding of host-microbiota crosstalk in MASLD. Future investigations integrating multiple approaches and longitudinal clinical data hold potential to unravel complex microbial networks, paving the way for innovative therapeutic breakthroughs in metabolic liver disease management.

Analysis of Microbiome for AP and CRC Discrimination

Microbiome data analysis is essential for understanding the role of microbial communities in human health. However, limited data availability often hinders research progress, and synthetic data generation could offer a promising solution to this problem. This study aims to explore the use of machine learning (ML) to enrich an unbalanced dataset consisting of microbial operational taxonomic unit (OTU) counts of 148 samples, belonging to 61 patients. In detail, 34 samples are from 16 adenomatous polyps (AP) patients, while 114 samples are from 46 colorectal cancer (CRC) patients. Synthesis of AP and CRC samples was conducted using the Synthetic Data Vault Python library, employing a Gaussian Copula synthesiser. Subsequently, the synthesised data quality was evaluated using a logistic regression model in parallel with an optimised support vector machine algorithm (polynomial kernel). The data quality is considered good when neither of the two algorithms can discriminate between real and synthetic data, showing low accuracy, F1 score, and precision values. Furthermore, additional statistical tests were employed to confirm the similarity between real and synthetic data. After data validation, layer-wise relevance propagation (LRP) was performed on a deep learning classifier to extract important OTU features from the generated dataset, to discriminate between CRC patients and those affected by AP. Exploiting the acquired features, which correspond to unique bacterial taxa, ML classifiers were trained and tested to estimate the validity of such microorganisms in recognising AP and CRC samples. The simplified version of the original OTU table opens up opportunities for further investigations, especially in the realm of extensive data synthesis. This involves a deeper exploration and augmentation of the condensed data to uncover new insights and patterns that might not be readily apparent in the original, more complex form. Digging deeper into the simplified data may help us better grasp the biological or ecological processes reflected in the OTU data. Transitioning from this exploration, the synergy of ML and synthetic data enrichment holds promise for advancing microbiome research. This approach enhances classification accuracy and reveals hidden microbial markers that could prove valuable in clinical practice as a diagnostic and prognostic tool.

Functional Foods in Modern Nutrition Science: Mechanisms, Evidence, and Public Health Implications.

In recent years, functional foods have garnered increasing scientific and public health interest due to their potential to confer physiological benefits beyond basic nutritional value. International bodies such as EFSA, FDA, and WHO define functional foods as those containing bioactive components that may contribute to the prevention and management of chronic non-communicable diseases, including cardiovascular disease, type 2 diabetes, and certain cancers. The evolving paradigm of "food as medicine" reflects a broader shift in nutritional science towards proactive, health-oriented dietary strategies. This article provides a comprehensive, interdisciplinary overview of functional foods by examining their biological mechanisms, clinical evidence, public health significance, regulatory frameworks, and future prospects-particularly in the context of advances in personalized nutrition and nutrigenomics. A thorough literature review was conducted, drawing from recent peer-reviewed studies and guidelines from key health authorities. The review highlights the roles of specific compounds such as probiotics and prebiotics in modulating the gut microbiome, flavonoids and polyphenols in anti-inflammatory processes, omega-3 fatty acids in cardiometabolic regulation, and vitamins and minerals in supporting immune function. While an expanding body of clinical trials and meta-analyses supports the health benefits of these compounds-including reductions in LDL cholesterol, improved insulin sensitivity, and mitigation of oxidative stress-the integration of functional foods into everyday diets remains challenging. Socioeconomic disparities and limited health literacy often impede their accessibility and widespread adoption in public health practice. Functional foods represent a promising component of prevention-focused modern healthcare. To maximize their impact, a coordinated, evidence-based approach is essential, involving collaboration among healthcare professionals, nutrition scientists, policymakers, and the food industry. Looking forward, innovations in artificial intelligence, microbiome research, and genomic technologies may unlock novel opportunities for the targeted and effective application of functional foods in population health.

Innovative Interventions: Postbiotics and Psychobiotics in Neurodegenerative Disease Treatment.

Neurodegenerative disorders, including Huntington's disease, Amyotrophic lateral sclerosis, Alzheimer's disease, and Parkinson's disease, create more challenges as the population gets older and there are no curative therapies available. Recent advances in gut microbiome research have spotlighted postbiotics and psychobiotics as innovative therapeutic strategies targeting the gut-brain axis to alleviate neurodegenerative symptoms and slow disease progression. Postbiotics, which are metabolites and cellular components released by probiotic bacteria, and psychobiotics, a class of probiotics with potential mental health benefits, offer novel approaches to neuroprotection. This chapter examines the ways in which postbiotics and psychobiotics modulate inflammation, oxidative stress, neurotrophic factors, and gut barrier integrity to provide neuroprotective effects. We review scientific research that highlights the efficacy of specific microbial strains and their metabolites in enhancing cognitive function and reducing neurodegeneration. In addition, we explore the consequences of diet and specific nutrition on strengthening the therapeutic results of these medications. The purpose of this chapter is to provide a detailed analysis of the existing data supporting the use of postbiotics and psychobiotics in both the prevention and management of neurological diseases. By integrating perspectives from microbiology, neurology, and clinical nutrition, we highlight the potential of these interventions to enhance patient outcomes and quality of life. In addition, we discuss the translational limitations and future research approaches required to successfully transition these microbiome-based treatments from the laboratory to clinical practice, emphasizing the importance of a holistic and personalized approach in combating neurodegenerative diseases.

Significant associations between high-risk sexual behaviors and enterotypes of gut microbiome in HIV-negative men who have sex with men.

Our study's discovery that gut microbiome enterotypes are significantly associated with anal sex roles in HIV-negative MSM opens a new frontier in understanding the complex interplay between microbiology and sexual health. This finding underscores the urgency of delving into the mechanistic connections between the gut microbiome, sexual behaviors, and HIV infection. By identifying modifiable factors influencing gut microbiome composition, we have paved the way for developing personalized preventive strategies that could disrupt the transmission dynamics of HIV within this high-risk population. This research contributes to the fundamental understanding of the gut microbiome's role in the sexual health of MSM, making it a pivotal advancement in the fields of gut microbiome research and sexual health.

Urbanization and the Gut Microbiome: Insights from the Anna's Hummingbird (Calypte anna) in California.

Complex environmental characteristics shape the gut microbiome of wildlife with critical implications for host health, pathogen resistance, digestion, and overall fitness. While foundational avian microbiome research has focused mainly on domestic birds and poultry due to their proximity to humans and the associated zoonotic risks from imbalanced microbiomes, studying the gut microbiome of wild birds is also essential. Wild birds fill important roles in their habitats-serving as critical pollinators and bioindicators of ecosystem health. Despite this, their gut microbiomes remain less explored, particularly concerning how urbanization affects microbiome composition and diversity, which may provide insight into the health of birds in urban habitats. This study investigates the gut microbiome of the Anna's Hummingbird (Calypte anna), a species that thrives in urban and rural environments across the West Coast. We amplified the V1-V3 region of bacterial 16S rRNA extracted from fecal samples and used the QIIME2 platform to characterize bacterial communities. By analyzing bacterial communities from birds sampled across a range of anthropogenically altered locations, we explore correlations between microbiome composition and habitat characteristics (artificial light at night, human density, air pollution, and percent imperviousness). We show that air quality is an important driver of microbial diversity and offer insights into how the heterogeneity of urban landscapes impacts the microbiome composition of wild birds.

Towards Effective Helicobacter pylori Eradication: Emerging Therapies in the Wake of Antibiotic Resistance.

Helicobacter pylori (H. pylori) infection is a leading cause of gastritis, peptic ulcers, and gastric cancer, affecting more than half of the global population. Its persistence in the acidic gastric environment and its ability to evade host immunity present major treatment challenges. Although antibiotics remain the standard therapy, rising antimicrobial resistance has reduced treatment efficacy, prompting the search for alternative and adjunct approaches. Emerging therapies include probiotics, antimicrobial peptides (AMPs), and plant-derived compounds, which target H. pylori through membrane disruption, immunomodulation, or direct antimicrobial activity. Novel drug delivery systems and microbiota-sparing interventions are also being investigated. Additionally, vaccine development offers a promising strategy for long-term protection, though challenges related to antigenic variability and host-specific responses remain. Despite these advances, treatment variability and the limited clinical validation of alternatives hinder progress. A multifaceted approach integrating microbiome research, host-pathogen interactions, and new therapeutic agents is essential for future success.

Advancing periodontitis microbiome research: integrating design, analysis, and technology

Periodontitis, a chronic inflammatory disease affecting 20%-50% of adults worldwide, is driven by polymicrobial synergy and dysbiosis. Despite numerous studies on the oral microbiota in periodontitis, significant heterogeneity exists between findings, posing challenges for treatment strategies. To understand the sources of this variability and establish standardized protocols, we reviewed the literature to identify potential factors contributing to these discrepancies. We found most studies focus on microbial communities in periodontal pockets, with fewer investigating microbial composition within gingival tissue. Research indicates that bacterial communities in gingival tissue exist as biofilms, potentially serving as reservoirs for persistent infection. Therefore, further exploration of the microbiome within periodontal tissues is needed, which may offer new insights for treatment strategies. Metatranscriptomics provides valuable insights into gene expression patterns of the oral microbiota, enabling the exploration of microbial activity at a functional level. Previous studies revealed that most upregulated virulence factors in periodontitis originate from species not traditionally considered major periodontal pathogens. However, current studies have not fully identified or revealed the functional changes in key symbiotic microbes in periodontitis. We reviewed the analytical paradigms of metatranscriptomics and found that current analysis is largely limited to assessing functional changes in known periodontal pathogens, highlighting the need for a functional-driven approach. Beyond the limitations of current analytical paradigms, the metatranscriptomics also has inherent constraints. We suggested integrating emerging high-throughput microbial sequencing technologies with functional-driven analytical strategies to provide a more comprehensive and higher-resolution insight for microbiome reconstruction in periodontitis.