Age is a key factor influencing the composition of the oral microbiome, but its age-related dynamics remain unclear as most studies focus on specific age groups or disease-related changes. The objective of this study was to characterize age-related differences in the supragingival microbiome. Supragingival plaque samples were collected from 533 participants across four age groups including Child (3 ~ 5 year), Young adult (18 ~ 34 year), Mid-age (35 ~ 65 year) and Elder (over 65 year) groups. Microbial DNA was extracted and analyzed using 16S rRNA gene sequencing. Alpha and beta diversity were assessed. Taxonomic classification was performed using a Naïve Bayes classifier trained on the eHOMD database. Differential abundance analysis was conducted using LEfSe, and microbial network interactions were examined using SparCC. Alpha diversity differed among age groups, and beta diversity also showed significant differences among groups, except between the Mid-age and Elder groups. The relative abundance of Firmicutes and Proteobacteria was lower in the Mid-age and Elder groups, whereas Bacteroidetes and Fusobacteria were more abundant. Early colonizers such as Streptococcus, Veillonella, and Haemophilus were less abundant in these groups, while periodontopathogens including Porphyromonas, Fusobacterium, and Treponema were more abundant. Core microbiome analysis revealed Streptococcus dominance in the Child group, the presence of Rothia and Actinomyces in the Young adult group, and more pathogen-enriched microbiome in the Mid-age and Elder groups. Microbial network complexity also differed across age groups, with denser and more pathogen-centered networks observed in the older groups. Distinct age-related differences in the oral microbiome were observed in this cross-sectional study, with microbial diversity, taxonomic composition, and microbial interaction patterns. These findings suggest that understanding age-related microbial variation may be important for long-term oral health.

Background: The upper gastrointestinal (GI) tract is a complex environment characterized by sharp physicochemical gradients. While the oral microbiome is a major source of microbial seeding for downstream organs, it remains unclear how these communities correlate and diverge across different anatomical sites. This study provides a high-resolution re-analysis of a comprehensive multi-site dataset to delineate the microbial architecture and ecological signatures along the oral–upper GI axis. Method: Human oral, esophageal, gastric mucosal, and gastric juice microbiome sequencing data were retrieved from the publicly available National Center for Biotechnology Information (NCBI) BioProject PRJNA1049979 database. Using these publicly available 16S rRNA sequencing data, we performed an integrated ecological analysis. Microbial diversity, taxonomic composition, and niche-specific community structures were evaluated using Quantitative Insights Into Microbial Ecology 2 (QIIME2) and R-based tools, including linear discriminant analysis effect size (LEfSe) and phylogenetic mapping. Results: The esophageal microbiome showed significantly greater richness and evenness than the oral cavity and stomach. Beta diversity analysis demonstrated clear compositional separation between oral and downstream upper GI communities, whereas gastric samples, particularly gastric juice, showed greater heterogeneity. Although major phyla were shared across sites, their relative abundances differed markedly. Oral samples were enriched with periodontal-associated taxa, including Porphyromonas, Prevotella, Alloprevotella, and Fusobacterium. In contrast, gastric mucosal samples were enriched with Akkermansia muciniphila and Helicobacter pylori, whereas gastric juice was characterized by Sarcina ventriculi, Fusobacterium periodonticum, and Clostridium perfringens. These findings indicate both taxonomic continuity and pronounced site-specific ecological divergence along the oral–upper GI axis. Conclusion: The oral cavity, esophagus, stomach, and gastric juice share a common microbial framework but exhibit distinct community restructuring driven by local environmental selection. This study provides a detailed ecological view of the oral–upper GI microbiome and highlights the importance of site-specific microbial organization in upper GI health and disease.

BackgroundPeriodontitis is a chronic inflammatory disease driven by a dysbiotic subgingival microbiome. While 16S rRNA gene amplicon sequencing is widely used, whole-genome shotgun (WGS) metagenomics is increasingly applied for higher taxonomic and functional resolution.ObjectiveThe aim of this study was to directly compare 16S rRNA gene amplicon (V1-V2) sequencing and WGS metagenomic sequencing using matched subgingival plaque samples from patients with periodontitis.MethodsSubgingival plaque samples from 28 patients with periodontitis were analyzed using both 16S rRNA gene amplicon (V1-V2) sequencing and WGS metagenomics. Taxonomic composition, microbial diversity, differential abundance and functional analysis were compared across platforms.ResultsWGS generated markedly higher read counts than 16S rRNA gene amplicon but showed wide variability in non-human reads, whereas 16S rRNA gene amplicon yielded a consistent proportion of non-chimeric reads. High taxonomic overlap was observed at the phylum level but declined at higher taxonomic ranks. WGS preferentially detected taxa such as Actinomyces, Corynebacterium and Olsenella, while the 16S rRNA gene amplicon more frequently captured Saccharibacteria (TM7) and low-abundance taxa. Core genera, including Rothia, Neisseria and Cardiobacterium showed comparable abundance patterns across platforms. When patients were grouped depending on probing pocket depth (PPD), LEfSe analysis resulted in platform-specific enrichment patterns. Functional analyses revealed shared central pathways, such as pyruvate metabolism, while 16S-based PICRUSt2 emphasized reductive and degradative pathways and WGS-based HUMAnN highlighted oxidative and biosynthetic pathways. Notably, WGS-based functional profiles were strongly influenced by microbial read depth.ConclusionsThis comparative analysis demonstrates that 16S rRNA gene amplicon (V1-V2) sequencing and WGS both robustly capture core subgingival microbial signatures. While WGS provides higher species-level and functional resolution, the resolution was strongly constrained by microbial read depth in host-rich subgingival samples. These findings provide practical guidance for selecting appropriate sequencing strategies and optimizing sample preparation when designing WGS-based periodontal microbiome studies.

Background While the association between oral squamous cell carcinoma (OSCC) and microbial dysbiosis is well-established, high inter-study heterogeneity has historically hindered the identification of universal biomarkers.Objective To explore the profound influence of anatomical ecological niches on microbial profiles while identifying shared oncogenic signatures across diverse populations.Design An integrative meta-analysis of independent OSCC microbiome cohorts was conducted.Results Our results demonstrate that microbial community composition is predominantly driven by the sampling site rather than the disease state alone, with tissue-derived samples forming a distinct ecological cluster. Despite this site-specific dominance, a consistent core of anaerobic pathogens-including Fusobacterium, Capnocytophaga, and Treponema was significantly enriched in OSCC across multiple datasets. Network-based analysis identified Fusobacterium as top driver of microbial community restructuring. Diagnostically, saliva-based models achieved superior accuracy compared to tissue and mucosal models, suggesting that saliva serves as an integrative reservoir for oral pathological changes. Functional profiling further revealed a significant functional convergence toward pathways essential for rapid bacterial proliferation and environmental adaptation, such as ribosome biogenesis, DNA replication, and flagellar assembly.Conclusions These findings emphasize the necessity of standardizing sampling protocols and provide a robust scientific basis for non-invasive, saliva-based precision diagnostic strategies for OSCC.

This study aimed to compare oral microbiome profiles between obese and lean individuals without clinical periodontitis, and to assess changes in the oral microbiome of obese subjects following bariatric surgery. Individuals with a body mass index (BMI) > 30 were enrolled in the obese group, whereas those with a BMI < 23 served as controls. The obese surgery group, which consented to bariatric surgery, was followed up at 1, 3, and 6 months with clinical examinations. Oral examinations were conducted and periodontal disease was classified based on probing results. Saliva, buccal and subgingival microbiome samples were analyzed for community diversity, relative bacterial abundance, and differential abundance between control (n = 24) and obese group (n = 31). To evaluate effect size and statistical power, we used micropower, a simulation-based method for Permutational Multivariate Analysis of Variance-based β-diversity comparisons. The obese group exhibited distinct alpha diversity (buccal: Chao1 p = 0.0002, Shannon p = 0.0003, supragingival: Shannon p < 0.0001) compared with the control group. Bray-Curtis distance analysis indicated significant disparities in microbiome composition distribution in saliva (p = 0.003), buccal (p = 0.002), and subgingival plaque samples (p = 0.001). Although the obese and normal weight groups exhibited no significant periodontal differences, the obese group showed distinct species associated with periodontal disease, especially in subgingival plaque including Filifactor alocis, Peptostreptococcaceae spp., Prevotella spp., and Treponema maltophilum. Cluster analysis of the obese surgery group indicated the emergence of microbiomes associated with a healthy state that increased over time including Streptococcus salivarious and various Veillonella spp., whereas clusters containing periodontal pathogens including Porphyromonas spp., tended to diminish. The oral microbiome at 6 months post-bariatric surgery indicates a potential shift toward a healthy periodontal state, suggesting that weight loss interventions may positively impact oral microbial communities even in the absence of clinical periodontitis.

OBJECTIVE:Antiresorptives cases of adverse reactions to highly effective drugs used to treat skeletal-related events that occur during treatment of osteoporosis or malignancy have been reported in the jaw. To date, there is no clear pathophysiology for medication-related osteonecrosis of the jaw (MRONJ), but the role of inflammation or infection has been proposed as one of several possible pathogenesis theories. This study was designed to investigate the role of infection in the pathogenesis of MRONJ by analyzing the microbial communities observed in the bone samples from MRONJ patients. DESIGN:Oral microbial samples were collected from 39 MRONJ patients and 45 healthy patients and subjected to next generation sequencing analysis. Alpha diversity, beta diversity, linear discriminant analysis and effect size, and network analysis were applied to compare the microbiome features. RESULTS:The 10 predominant microbial strains observed in MRONJ patients were Streptococcus spp. S. constellatus, Tannerella forsythia, Parvimonas micra, Granulicatella adiacens, Bacteroidaceae G-1 bacterium HMT 272, Treponema socranskii, T. maltophilum, Pyramidobacter piscolens, and Fretibacterium fastidiosum. Network analysis revealed a significant correlation between T. forsythia and P. gingivalis. T. socranskii was closely related to T. maltophilum, Fretibacterium fastidiosum, and Peptostreptococcaceae nodatum. G. adiacens was closely related to Streptococcus parasanguinis, S. sanguinis, and Prevotella buccae. CONCLUSIONS:In conclusion, this study sheds light on the potential role of infection in the pathogenesis of MRONJ. A distinct microbial profile was identified in MRONJ patients, with notable predominance of S. constellatus, T. forsythia, P. micra and G. adiacens among others. Network analysis further revealed significant correlations between certain microbial strains, suggesting possible synergistic interactions in MRONJ pathogenesis. These findings contribute to a deeper understanding of the complex microbial dynamics underlying MRONJ and may inform future therapeutic strategies aimed at targeting specific bacterial species implicated in this condition.

Background/Objective: Oral lichen planus (OLP) is a chronic inflammatory disorder of the oral mucosa with unclear etiology. Increasing evidence implicates oral microbial dysbiosis in its pathogenesis, but little is known about supragingival plaque communities in relation to clinical subtypes. This cross-sectional case–control study aimed to characterize the supragingival plaque microbiota and microbial interaction networks in erosive OLP (E-OLP), non-erosive OLP (NE-OLP), and healthy controls (HCs), to elucidate microbial patterns associated with disease severity. Methods: Supragingival plaque samples were collected from 90 participants (30 per group) and analyzed using 16S rRNA gene sequencing. Alpha and beta diversity metrics, differential abundance, and co-occurrence network analyses were performed. Results: E-OLP exhibited pronounced dysbiosis, including the enrichment of pro-inflammatory taxa (e.g., Prevotella, Parvimonas) and depletion of health-associated commensals (e.g., Rothia, Capnocytophaga). Network analysis revealed the stepwise disintegration of microbial community structure from HC to NE-OLP to E-OLP, with reduced connectivity and increased dominance of pathogenic clusters in E-OLP. These microbial alterations aligned with clinical findings, as E-OLP patients showed significantly higher Reticulation/keratosis, Erythema, and Ulceration (REU) scores for erythema and ulceration compared to NE-OLP. Conclusions: Supragingival plaque dysbiosis and ecological disruption are strongly associated with OLP severity and subtype. This study highlights the utility of plaque-based microbial profiling in capturing lesion-proximal dysbiotic signals, which may complement mucosal and salivary analyses in future diagnostic frameworks. Multi-omics approaches incorporating fungal, viral, and metabolic profiling are warranted to fully elucidate host–microbe interactions in OLP.

ObjectiveNext generation sequencing is commonly used to characterize the microbiome structure. MiSeq is most commonly used to analyze the microbiome due to its relatively long read length. Illumina also introduced the 250 x 2 chip for NovaSeq. The purpose of this study was to compare the performance of MiSeq and NovaSeq in the context of oral microbiome study.MethodsTotal read count, read quality score, relative bacterial abundance, community diversity, and correlation between two platforms were analyzed. Phylogenetic trees were analyzed for Streptococcus and periodontopathogens.ResultsNovaSeq produced significantly more read counts and assigned more operational taxonomic units (OTUs) compared to MiSeq. Community diversity was similar between MiSeq and NovaSeq. NovaSeq were able to detect more unique OTUs compared to MiSeq. When phylogenetic trees were constructed for Streptococcus and periodontopathogens, both platforms detected OTUs for most of the clades.ConclusionTaken together, while both MiSeq and NovaSeq platforms effectively characterize the oral microbiome, NovaSeq outperformed MiSeq in terms of read counts and detection of unique OTUs, highlighting its potential as a valuable tool for large scale oral microbiome studies.

(1) Background: Peptides are appealing as pharmacological materials because they are easily produced, safe, and tolerable. Despite increasing gum-care awareness, periodontitis is still prevalent and is influenced by factors like high sugar consumption, smoking, and aging. Porphyromonas gingivalis is considered a major etiologic agent of periodontitis and activates the NLR family pyrin domain containing 3 (NLRP3) but is absent in melanoma 2 (AIM2) inflammasomes, resulting in pro-inflammatory cytokine release. (2) Methods: We examined the anti-inflammatory effects of 18 peptides derived from human stromal cell-derived factor-1 (SDF-1) on THP-1 macrophages. Inflammation was induced by P. gingivalis, and the anti-inflammatory effects were analyzed using molecular biological techniques. In a mouse periodontitis model, alveolar bone resorption was assessed using micro-CT. (3) Results: Of the 18 SDF-1-derived peptides, S10 notably reduced IL-1β and TNF-α secretion. S10 also diminished the P. gingivalis-induced expression of NLRP3, AIM2, ASC (apoptosis-associated speck-like protein), caspase-1, and IL-1β. Furthermore, S10 attenuated the enhanced TLR (toll-like receptor) signaling pathway and decreased the phosphorylation of nuclear factor-κB (NF-κB) and mitogen-activated protein kinases (MAPKs). In addition, S10 mitigated alveolar bone loss in our P. gingivalis-induced mouse model of periodontitis. (4) Conclusions: S10 suppressed TLR/NF-κB/NLRP3 inflammasome signaling and the AIM2 inflammasome in our P. gingivalis-induced murine periodontitis model, which suggests that it has potential use as a therapeutic treatment for periodontitis.

BackgroundThe study of the human microbiome is crucial for understanding disease mechanisms, identifying biomarkers, and guiding preventive measures. Advances in sequencing platforms, particularly 16S rRNA sequencing, have revolutionized microbiome research. Despite the benefits, large microbiome reference databases (DBs) pose challenges, including computational demands and potential inaccuracies. This study aimed to determine if full-length 16S rRNA sequencing data produced by PacBio could be used to optimize reference DBs and be applied to Illumina V3-V4 targeted sequencing data for microbial study.MethodsOral and gut microbiome data (PRJNA1049979) were retrieved from NCBI. DADA2 was applied to full-length 16S rRNA PacBio data to obtain amplicon sequencing variants (ASVs). The RDP reference DB was used to assign the ASVs, which were then used as a reference DB to train the classifier. QIIME2 was used for V3-V4 targeted Illumina data analysis. BLAST was used to analyze alignment statistics. Linear discriminant analysis Effect Size (LEfSe) was employed for discriminant analysis.ResultsASVs produced by PacBio showed coverage of the oral microbiome similar to the Human Oral Microbiome Database. A phylogenetic tree was trimmed at various thresholds to obtain an optimized reference DB. This established method was then applied to gut microbiome data, and the optimized gut microbiome reference DB provided improved taxa classification and biomarker discovery efficiency.ConclusionFull-length 16S rRNA sequencing data produced by PacBio can be used to construct a microbiome reference DB. Utilizing an optimized reference DB can increase the accuracy of microbiome classification and enhance biomarker discovery.

Background/purpose:Burning moouth syndrome (BMS) is a chronic pain condition similar to neuropathic pain. It is characterized by a persistent burning sensation in the oral cavity. Despite the lack of clarity regarding the etiology of BMS, recent studies have reported an association between the gut microbiome and neuropathic pain. However, few studies have investigated the association between the oral microbiome and orofacial pain, such as BMS. This study aimed to compare the oral microbial profiles of healthy controls (HC) and patients with BMS. Materials and methods:The BMS group was further divided into BMS_low and BMS_high groups according to pain intensity. A total of 60 patients with BMS (BMS_low, n = 16; BMS_high, n = 44) and 30 HC provided saliva samples, which were sequenced and analyzed for the V1-V2 region of the 16S rRNA gene. Results:The alpha diversity was similar among the three groups. However, a significant difference in the distribution of microbiome composition was observed between BMS_high and HC, as revealed by the Bray-Curtis distance analysis (P < 0.01). At the genus level, Prevotella and Alloprevotella were the most abundant genera in the BMS group. Compared to HC, BMS_high exhibited a relatively higher abundance of bacterial species. Some bacteria, including Prevotella spp., exhibit an increasing pattern with subjective pain intensity. Conclusion:These results suggest the potential involvement of oral microbiota in BMS pathogenesis. Additionally, variations in the microbiome may occur not only in the presence or absence of pain, but also with pain severity.

Recent studies have begun exploring the potential involvement of microbiota in the pathogenesis of oral lichen planus (OLP), yet comprehensive investigations remain limited. Hence, this study aimed to compare the microbial profiles in saliva samples obtained from patients with OLP against those from healthy controls (HC), along with a comparison between erosive (E) and non-erosive (NE) OLP patients. Saliva samples were collected from 60 OLP patients (E: n = 25, NE: n = 35) and 30 HC individuals. Analysis revealed no significant differences in alpha diversity, as assessed by the Chao1 and Shannon index, across the three groups. However, Bray–Curtis distance analysis indicated a significant disparity in microbiome composition distribution between HC and E-OLP, as well as HC and NE-OLP groups. The six most abundant phyla observed across the groups were Firmicutes, Bacteroidetes, Proteobacteria, Actinobacteria, Fusobacteria, and Saccharibacteria (TM7). Notably, OLP groups exhibited a higher prevalence of Bacteroidetes. Prevotella emerged as the predominant genus in the OLP groups, while Capnocytophaga showed a relatively higher prevalence in E-OLP compared to NE-OLP. This study’s findings indicate a notable difference in microbiota composition between HC and patients with OLP. Additionally, differences in the microbiome were identified between the E-OLP and NE-OLP groups. The increase in the proportion of certain bacterial species in the oral microbiome suggests that they may exacerbate the inflammatory response and act as antigens for OLP.

Porphyromonase gingivalis (P. gingivalis) is a type of bacteria that causes periodontitis, which is strongly correlated with systemic diseases such as diabetes. However, the effect of hyperglycemia on periodontitis are unclear. The present study examined the effects of high glucose levels on the response to P. gingivalis infection. The expression of P. gingivalis-induced interleukin-1β (IL-1β) and inflammasomes increased as the glucose concentration increased. High glucose conditions suppressed P. gingivalis–induced autophagy in human acute monocytic leukemia cell line (THP-1) macrophages. Zingerone increased autophagy and alleviated P. gingivalis-induced inflammatory response in THP-1 macrophages under high glucose conditions. In addition, P. gingivalis- induced inflammation in bone marrow-derived macrophages of diabetic mice was higher than in wild-type mice, but a zingerone treatment decreased the levels. Alveolar bone loss due to a P. gingivalis infection was significantly higher in diabetic mice than in wild-type mice. High-glucose conditions aggravated the inflammatory response to P. gingivalis infection by suppressing of autophagy, suggesting that autophagy induction could potentially to treat periodontitis in diabetes. Zingerone has potential use as a treatment for periodontal inflammation induced by P. gingivalis in diabetes patients.

EDITORIAL article Front. Cell. Infect. Microbiol., 13 February 2024Sec. Extra-intestinal Microbiome Volume 14 - 2024 | https://doi.org/10.3389/fcimb.2024.1378237

Recent advances in genomic technologies have enabled more in-depth study of the oral microbiome. In this study, we compared the amplicons generated by primers targeting different sites of the 16S rRNA gene found in the Human Oral Microbiome Database (HOMD). Six sets of primer targeting V1–V2, V1–V3, V3–V4, V4–V5, V5–V7 and V6–V8 regions of 16S rRNA were tested via in silico simulation. Primers targeting the V1–V2, V3–V4, and V4–V5 regions generated more than 90% of the original input sequences. Primers targeting the V1–V2 and V1–V3 regions exhibited a low number of mismatches and unclassified sequences at the taxonomic level, but there were notable discrepancies at the species level. Phylogenetic tree comparisons showed primers targeting the V1–V2 and V3–V4 regions showed performances similar to primers targeting the whole 16s RNA region in terms of separating total oral microbiomes and periodontopathogens. In an analysis of clinical oral samples, V1–V2 primers showed superior performance for identifying more taxa and had better resolution sensitivity for Streptococcus than V3–V4 primers. In conclusion, primers targeting the V1–V2 region of 16S rRNA showed the best performance for oral microbiome studies. In addition, the study demonstrates the need for careful PCR primer selections.

Healthy individuals often have different gut microbiota, and these differences can be influenced by their country of origin or their race. This study aimed to compare the gut microbiota compositions of healthy Germans and Koreans using 16S rRNA sequencing data extracted from public sources. Two cohorts, comprising 1592 samples (804 Germans and 788 Koreans), were analyzed for alpha and beta diversity, core microbiome, and abundances of specific taxa. The Prevotella enterotype was more prevalent in Koreans, and significant diversity differences were observed depending on cohorts and enterotypes. The core microbiomes across all enterotypes and cohorts included Bacteroides, Faecalibacterium, Parabacteroides, and Lachnospira. Several common core microbiomes were also found depending on enterotype. Koreans exhibited higher abundances of Faecalibacterium, Prevotella, and Bacteroides, while Germans had higher abundances of Blautia, Subdoligranulum, and Agathobacter. Distinctive microbiomes were identified by enterotype. The study enhances comprehension of gut microbiome variations linked to enterotype and geographical factors, and emphasizes the need for additional research to establish correlations between specific microbial properties and individual health status.

Background Periodontitis is initiated or accelerated by dysbiosis of oral microorganisms. When hypertension is accompanied in periodontitis patients, changes of oral microbiota occur. Since there are no reports on antihypertensives, we assessed their effect on the oral microbial profiles of patients with periodontitis. Methods This study involved 95 participants divided into two groups: those with periodontitis and hypertension (P_HT), and those with periodontitis and taking medications for hypertension (P_mHT). Plaque samples were collected from the buccal, supragingival, and subgingival sites of the oral cavities of these patients. DNA was extracted, and the V3-V4 region of the 16S ribosomal RNA was sequenced and analyzed. Results The P_HT and P_mHT groups were similar with respect to the alpha- and beta-diversity as well as the dominant phyla and genera, but differed in the relative abundance of bacterial species (85 species). In the P_mHT group, the relative abundance of major periodontal pathogens was greatly increased. In particular, Tannerella forsythia, Treponema denticola, and Fretibacterium fastidiosum increased nearly three times in the linear discriminant analysis score in the supragingival plaque. Also, there was an increase in the relative abundance of Prevotella spp., associated with periodontitis and nitrate reduction, which was also evident in the supragingival plaque. Conclusions These findings indicate that antihypertensives induce dysbiotic changes in the oral microbiota of patients with periodontitis, which are associated with increases in the relative abundance of periodontal pathogens. Therefore, more active periodontal treatment and supportive periodontal therapy are required in patients taking antihypertensives.

Intubated patients in intensive care units (ICUs) too frequently contract ventilator-associated pneumonia or Candida infections. Oropharyngeal microbes are believed to play an important etiologic role. This study was undertaken to determine whether next-generation sequencing (NGS) can be used to simultaneously analyze bacterial and fungal communities. Buccal samples were collected from intubated ICU patients. Primers targeting the V1-V2 region of bacterial 16S rRNA and the internal transcribed spacer 2 (ITS2) region of fungal 18S rRNA were used. V1-V2, ITS2, or mixed V1-V2/ITS2 primers were used to prepare an NGS library. Bacterial and fungal relative abundances were comparable for V1-V2, ITS2, or mixed V1-V2/ITS2 primers, respectively. A standard microbial community was used to adjust the relative abundances to theoretical abundance, and NGS and RT-PCR-adjusted relative abundances showed a high correlation. Using mixed V1-V2/ITS2 primers, bacterial and fungal abundances were simultaneously determined. The constructed microbiome network revealed novel interkingdom and intrakingdom interactions, and the simultaneous detection of bacterial and fungal communities using mixed V1-V2/ITS2 primers enabled analysis across two kingdoms. This study provides a novel approach to simultaneously determining bacterial and fungal communities using mixed V1-V2/ITS2 primers.