Porphyromonas gingivalis sialidase reshapes gut microbiota to affect serum metabolism and splenic immunity: insights from a murine study

Qibing Wu; Hongxia Li; Yixi Niu; Hanmo Li; Junchao Liu; Yaping Pan; Chen Li; Shuwei Zhang

doi:10.1080/20002297.2026.2710456

. 2026 Jul 30;18(1):2710456. doi: 10.1080/20002297.2026.2710456

Porphyromonas gingivalis sialidase reshapes gut microbiota to affect serum metabolism and splenic immunity: insights from a murine study

Qibing Wu ^a,^b, Hongxia Li ^a,^b, Yixi Niu ^a,^b, Hanmo Li ^a, Junchao Liu ^a, Yaping Pan ^a, Chen Li ^a,^b,^*, Shuwei Zhang ^a,^b,^*

PMCID: PMC13425540 PMID: 42540270

Abstract

Background

Porphyromonas gingivalis (P. gingivalis), a major periodontal pathogen can alter the gut microbial composition, serum metabolites and systemic immune status in mice. However, the role of its sialidase in modulating these parameters remains unexplored.

Objective

This study aims to investigate the effects of P. gingivalis sialidase on gut microbiota, serum metabolites and their correlations with systemic immune responses.

Design

C57BL/6 mice were orally inoculated with P. gingivalis W83, its sialidase-deficient ΔPG0352 mutant or PBS twice weekly following antibiotic pretreatment. After 40 days, spleen samples were collected for histological examination and cytokine analysis. Faecal samples were collected for 16S rRNA sequencing, and the serum samples were analysed by untargeted metabolomics.

Results

P. gingivalis W83 group exhibited severe splenic inflammation and higher inflammatory cytokine levels than the other two groups. 16S rRNA gene analysis identified 19 differential bacterial genera (including Staphylococcus and Prevotellaceae_UCG-001) between the P. gingivalis W83 and ∆PG0352 groups. Metabolomics detected 12 key differential metabolites mainly involved in energy metabolism and amino acid biosynthesis pathways. Correlation analysis revealed phosphatidylcholine as a central metabolite, positively correlated with Staphylococcus, Prevotellaceae_UCG-001 and IL-1β, confirming its hub role linking metabolic shifts to immune activation.

Conclusions

P. gingivalis sialidase exacerbated systemic inflammation by reshaping gut microbiota, driving phosphatidylcholine-centred metabolic reprogramming and amplifying splenic immunity.

Introduction

Periodontitis is a chronic inflammatory disease initiated by dental plaque, characterized by the progressive destruction of periodontal supporting tissues [1]. It imposes substantial economic and public health burdens, as it not only impairs oral health but is also associated with an elevated risk of various systemic diseases, including Alzheimer's disease, hypertension and diabetes mellitus [2,3]. The pathways by which periodontitis affects systemic diseases include systemic inflammation, immune responses and oral bacteria [4]. The oral–gut axis has been proposed as a potential mechanistic link underlying periodontitis-associated systemic diseases, and has garnered considerable attention [5,6]. Oral microorganisms can transfer to the digestive tract, where their biofilm-forming capability serves as a critical determinant of their survival in gastric acid and subsequent functional activity in the gut [7,8]. Accumulating evidence indicates that oral administration of periodontal pathogens can induce multiple pathological changes, including gut microbiota dysbiosis, increased intestinal permeability, intestinal inflammation and insulin resistance [8–12]. In mice, oral gavage with Porphyromonas gingivalis (P. gingivalis) modulates the gut microbiota, and this dysbiosis consequently drives perturbations in serum metabolite profiles and affects metabolic pathways linked to tryptophan and choline metabolism [5,13]. These pathological alterations can thereby affect systemic health by modulating immune responses [14]. The crosstalk between gut microbiota and the host immunity is mediated by pathogen-associated molecular patterns (PAMPs) and bacterial metabolites, which bind to their cognate receptors and thereby trigger a cascade of immune responses [15].

P. gingivalis, a major periodontal pathogen, harbours a diverse and complex pathogenic repertoire. Its secreted lipopolysaccharide (LPS) can inhibit the expression of host chemokines, thereby evading the host immune defences. Additionally, its toxic virulence factors such as gingipains can directly induce tissue degradation and elicit excessive inflammatory responses [16]. Sialidase, encoded by the gene PG0352, has also been identified as a critical virulence factor of P. gingivalis [17]. This glycoside hydrolase exerts its function by cleaving terminal sialic acid residues from sialylated glycoconjugates [18], and affects surface polysaccharide biosynthesis and/or assembly, biofilm formation and serum resistance [19]. Our previous work constructed a sialidase gene-deficient mutant (∆PG0352) of P. gingivalis strain W83, and found that sialidase deficiency did not affect the planktonic growth of P. gingivalis, while the pathogenicity of ∆PG0352 was lower than that of P. gingivalis W83, indicating that sialidase is essential of P. gingivalis to cause systemic infection in mice [17,19]. Also, we previously established a rat periodontitis model via ligature and P. gingivalis inoculation using wild-type P. gingivalis W83, ∆PG0352 and its complemented strain com∆PG0352. The ligature control showed mild alveolar bone resorption, while all three bacterial groups presented severe bone loss. Our results showed that no significant phenotypic differences were observed between the P. gingivalis W83 and com∆PG0352 groups, and ∆PG0352 induced stronger M1 polarization than P. gingivalis W83 [20]. Despite the reduced virulence of ΔPG0352, this mutant can successfully survive in periodontal lesions and trigger periodontitis and inflammatory responses. Nevertheless, the precise functional role of sialidase derived from P. gingivalis in mediating gut microbiota dysbiosis, as well as its downstream regulatory effects on systemic immune responses, remains largely undefined. Therefore, the present study aimed to investigate the effects of P. gingivalis sialidase on gut microbial composition and serum metabolite profiles in murine models, and to further decipher the correlative relationships between these microbial and metabolic alterations and systemic immune activation. Collectively, these results are expected to provide novel insights into the role of P. gingivalis sialidase in regulating host immune function via the oral–gut axis mediating this periodontitis-systemic connection.

Materials and methods

Cultivation of P. gingivalis

This study utilized the wild-type P. gingivalis W83 strain and its isogenic sialidase-deficient mutant ΔPG0352, whose construction has been described in our prior work [21]. P. gingivalis W83 and ΔPG0352 strains were cultured anaerobically on TSB solid agar plates containing 5% sterile defibrinated sheep blood, 5 μg/mL chlorhaematin, 1 μg/mL vitamin K and 5 μg/mL erythromycin (ΔPG0352 only). After 5–7 days, the bacteria were inoculated into TSB liquid medium for 16–18 h before centrifugation (3,500 × g, 5 min). Then, the bacteria were resuspended in PBS to 1 × 10⁹ colony forming units/mL (optical density at 600 nm).

Animal experiments

All experimental procedures were approved by and conducted in compliance with the institutional animal ethics guidelines of the Animal Care and Use Committee (ACUC) of China Medical University (Approval No. K2023016). Male C57BL/6 mice (6-week-old, weighing 18–20 g) were purchased from Beijing HFK Bio-Technology Co., Ltd. (Beijing, China).The mice were housed in a specific pathogen-free (SPF) facility under controlled conditions (24 °C, 50% humidity, 12 h light/dark cycle) and acclimatized for 1 week with ad libitum access to standard chow and sterile water. To ensure consistent gut microbial baseline in all experimental groups before administration of P. gingivalis, all mice were provided with drinking water supplemented with azithromycin (Sigma-Aldrich) at a concentration of 10 mg/500 mL for 5 consecutive days, followed by a 7-day antibiotic-free period [22]. Subsequently, the mice were randomized assigned into three groups: the P. gingivalis W83 group (n = 6), the ΔPG0352 group (n = 6) and the control group (n = 6). Mice were orally gavaged with 1 × 10⁸ CFUs of P. gingivalis W83 or sialidase-deficient ΔPG0352 mutant in 100 μL sterile PBS, while the control group received an equal volume of sterile PBS only. All mice were treated twice weekly for five consecutive weeks until euthanasia. Sample size was determined via the resource equation approach for exploratory animal research [23]. Briefly, this method controls the error degrees of freedom (DF) of one-way ANOVA within 10–20 to ensure adequate statistical power. For our one-way intergroup comparison design, the theoretical range of animals per group was calculated using the formulas: Minimum n = 10/k + 1 and Maximum n = 20/k + 1 (k = number of experimental groups). Therefore, we enrolled 6 mice per group, which complied with the calculated sample size range. Extra animals were prepared to offset potential loss during modelling and sample processing. The number of valid mice used for each analysis was recorded separately.

Tissue collection

Body weight was measured at the end of the experimental period. Blood samples were collected from the medial canthal vein and subjected to centrifugation (4 °C, 1,300 × g, 10 min), and serum supernatant was cryopreserved at −80 °C until subsequent analysis. Spleens were aseptically excised, weighed and randomly divided into two portions: one portion was fixed in 4% paraformaldehyde for histology, and the other was snap-frozen at −80 °C for subsequent cytokine analysis. The spleen index (mg/g) was calculated as the ratio of spleen weight (mg) to individual mouse body weight (g). Faecal samples (50 mg per mice) were aseptically collected into sterile tubes, immediately frozen at −80 °C for subsequent 16S rRNA sequencing and microbial community analysis (n = 6 for each group).

HE staining analysis

Spleen tissues were collected, dehydrated, cleared and embedded in paraffin, then cut into 4 μm-thick sections. The sections were subjected to routine haematoxylin and eosin (H&E) staining to observe inflammatory cell infiltration. Histopathological images were captured using an Aperio VERSA digital pathology scanner (Leica Biosystems), and further analysed via ImageScope software (Leica Biosystems).

Detection of IL-1β and IL-6 cytokines in tissue homogenates

The spleen tissues were aseptically rinsed with ice-cold PBS and homogenized thoroughly. The homogenates were centrifuged, and the harvested supernatants were aliquoted into sterile tubes and preserved at −80 °C for subsequent assays. The levels of IL-1β and IL-6 were measured using commercial ELISA kits (Cloud-Clone Crop., Wuhan) according to the manufacturer's instructions. All reagents and frozen samples were equilibrated to room temperature before experiments. All samples were assayed in triplicate (n = 3 for each group), and the cytokine concentrations were calculated based on the corresponding standard curves.

Faecal microbiome sequencing analysis

Total microbial genomic DNA was extracted from faecal specimens using the FastPure Stool DNA Isolation Kit (Shanghai Meiji Yuhua Biomedical Technology Co., Ltd) (n = 6 for each group). DNA quality was evaluated via agarose gel electrophoresis and quantified with a NanoDrop® ND-2000 spectrophotometer (Thermo Scientific Inc., USA). The V3–V4 hypervariable regions of bacterial 16S rRNA genes were amplified via PCR using the primer pairs 338F (5′-ACTCCTACGGGAGGCAGCAG-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′) on a T100 Thermal Cycler (BIO-RAD, USA) [24]. Purification of PCR products was performed using AMPure PB beads (Pacific Biosciences, Menlo Park, CA, USA) and DNA concentration was measured with a Quantus™ Fluorometer (Promega, Wisconsin, USA). Subsequently, a DNA library was constructed using the TruSeqTM DNA Sample Prep Kit (Illumina, USA), and subsequently subjected to paired-end sequencing on the Illumina PE300/PE250 platform (Illumina, San Diego, USA). Following barcode identification and length filtering, reads with lengths outside the range of 1,000–1,800 bp were removed. High-quality sequences were clustered into operational taxonomic units (OTUs) at a 97% threshold using UPARSE v7.1 [25]. To eliminate bias caused by uneven sequencing depth, all samples were normalized to the minimum sequencing depth for downstream analyses.

Untargeted metabolomic profiling of serum specimens

Serum metabolites were extracted and subjected to metabolic profiling via ultra-high-performance liquid chromatography coupled with high-resolution mass spectrometry (UHPLC-HRMS) on a Thermo Q Exactive HF-X platform (n = 6 for each group). Chromatographic separation was performed on an ACQUITY HSS T3 analytical column (100 × 2.1 mm, 1.8 μm) with a gradient elution program at a flow rate of 0.40 mL/min and a column temperature of 40 °C. The mobile phases comprised 0.1% formic acid in water-acetonitrile (95:5, v/v; mobile phase A) and acetonitrile-isopropanol (47.5:47.5, v/v; mobile phase B). Mass spectral data were acquired in both positive and negative electrospray ionization (ESI) modes with the optimized instrumental parameters as follows: sheath/auxiliary gas (50/13 psi), capillary/auxiliary heater temperatures (325 °C/425 °C) and ion spray voltage (±3,500 V). Full-scan mass spectra were collected over a mass-to-charge ratio(m/z) range of 70–1,050 at a resolving power of 60,000, followed by data-dependent MS/MS scans (7,500 resolution, 20–60 eV collision energy). To guarantee analytical reproducibility and data quality, pooled quality control (QC) samples, prepared by mixing equal volumes of all individual serum aliquots, were injected interspersed throughout the entire analytical sequence at 5-sample intervals. All metabolite identifications were performed following the consensus criteria proposed by the Metabolomics Standards Initiative (MSI). All annotated metabolites were assigned to confident MSI Levels 1–2. The MS/MS spectral match score (Fragmentation Score, 0–100), mass error (ppm), matched database and MSI confidence level for all annotated metabolites are provided in Supplementary Table S1.

Data analysis

For intestinal microbiota analysis, Mothur software (version 1.30.1) was employed to generate the rarefaction curves and calculate alpha diversity indices, including but not limited to the Chao and Shannon indices [26]. The Kruskal–Wallis H test was utilized to evaluate statistically significant assess differences in the Shannon and Chao indices among multiple groups. Principal coordinate analysis (PCoA) based on Bray–Curtis dissimilarity was performed using the Vegan R package (version 2.4.3) to determine the similarity of microbial community compositions among different samples. Bacterial taxa with significantly differentially abundant bacterial taxa (from phylum to genus level) between groups were identified via Linear Discriminant Analysis Effect Size (LEfSe), with a significance threshold of linear discriminant analysis (LDA) score > 2 and p < 0.05 [27]. To evaluate the impact of clinical variables on microbial community composition, distance-based redundancy analysis (db-RDA) was conducted using the Vegan package (v2.4.3). Furthermore, key clinical parameters identified from db-RDA were interrogated for their correlations with alpha-diversity indices using linear regression models.

Raw UHPLC-MS data were processed with Progenesis QI software (Waters, Milford, USA) for feature detection, retention time alignment and intensity normalization. Subsequently, metabolite annotation was performed by searching three databases, including the Human Metabolome Database (HMDB http://www.hmdb.ca/), METLIN (https://metlin.scripps.edu/) and the self-compiled Majorbio Database. The processed data matrix was analysed on the Majorbio Cloud Platform. Preprocessing steps involved retaining metabolic features detected in ≥80% of samples within each group, where missing values were imputed with the minimum observed intensity. Sum normalization was applied to mitigate technical variability, followed by exclusion of features with QC-derived relative standard deviation (RSD) > 30%. Log10 transformation was performed to generate the final datasets for downstream analyses.

Multivariate statistical modellings, including partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares-discriminant analysis (OPLS-DA), were performed using the ropls R package (v1.6.2). Significantly differential metabolites were screened based on dual criteria: variable importance in projection (VIP) > 1 derived from OPLS-DA and p < 0.05 (Student's t-test). Pathway enrichment analysis mapped the differential metabolites onto KEGG pathways, with functional relevance evaluated through hypergeometric testing using the Python packages ‘scipy.stats’ (https://docs.scipy.org/doc/scipy/).

The experimental data were statistically analysed using SPSS version 26.0. Specifically, quantitative data that conformed to a normal distribution were presented as mean ± standard deviation (SD), while those that did not conform to a normal distribution were presented as median (interquartile range, IQR). Intergroup differences were assessed via one-way analysis of variance (ANOVA), Kruskal–Wallis H test, or Wilcoxon rank-sum test, with statistical significance set at p < 0.05.

Results

Immune and inflammatory alterations in the spleen

To investigate the impact of P. gingivalis sialidase activity on host immune responses, a C57BL/6 mouse model was employed. Following 40 days of bacterial colonization, spleen samples were harvested for cytokine analysis and histological assessment, serum was subjected to untargeted metabolomic profiling and faecal samples underwent 16S rRNA sequencing for intestinal microbiota analysis (Figure 1A). Throughout the experimental period, mice in the PBS, P. gingivalis W83 and ∆PG0352 groups exhibited no significant differences in body weight (Figure 1B). However, significant differences were observed in the spleen index. Specifically, the spleen index was markedly increased in the P. gingivalis W83 group compared with the PBS control group. While the ΔPG0352 group also exhibited an upward trend in the spleen index relative to the PBS group, such elevation was substantially milder than that in the wild-type W83 group. These findings suggest that sialidase activity can facilitate immune activation (Figure 1C). Haematoxylin-eosin (HE) staining based histological analysis of the spleen tissues revealed evident histological alterations among the three experimental groups (Figure 1D). In the PBS control group, the spleen presented intact tissue structure, with clearly demarcated white pulp and red pulp. In comparison, mice splenic tissues from the P. gingivalis W83 group exhibited severe white pulp hyperplasia, characterized by enlarged germinal centres and extensive lymphocyte infiltration, which reflected intense systemic immune activation. The ∆PG0352 group showed moderate white pulp expansion and smaller germinal centres than the wild-type P. gingivalis W83 group, yet these pathological changes were still more obvious than those observed in the PBS group.

A six panel figure shows experimental design, body weight, spleen index, cytokine levels, and spleen histology. Six-panel figure: experimental design, body weight, spleen index, cytokine levels, and spleen histology. Panel a: 41-day animal experimental design including azithromycin, PBS, P gingivalis W83, or delta PG0352 gavage. Panel b: Weekly body weight (g) over 8 weeks. PBS and delta PG0352 groups increased from ~22g to ~28g. W83 group showed slightly lower but similar increases. Panel c: Spleen index (mg/10g). PBS: ~20. W83: ~40 (p<0.05 vs PBS). delta PG0352: ~35 (p<0.05 vs PBS). Panel e: IL-1 beta (pg/mL). PBS: ~40. W83: ~100 (p<0.05 vs PBS). delta PG0352: ~60 (p<0.05 vs PBS). Panel f: IL-6 (pg/mL). PBS: ~100. W83: ~700 (p<0.01 vs PBS). delta PG0352: ~200 (p<0.05 vs PBS). Panel d: H&E spleen staining. PBS: normal architecture. P gingivalis W83: prominent white pulp hyperplasia, enlarged germinal centers, dense lymphocytic infiltration. delta PG0352: moderate white pulp expansion, less severe than W83. Scale bars: 200 micrometers (upper), 100 micrometers (lower). — Impact of *P. gingivalis* sialidase on splenic immune response. (A) Schematic illustration of the animal experimental design. (B) Weekly changes in body weight (G) of mice in each group during the experimental period. (C) Spleen index (mg/10 g) of mice in each group, expressed as mean ± SD (n = 6); *p < 0.05, analysed by one-way ANOVA. (D and E): Concentrations of IL-1β and IL-6 in spleen tissues of mice from each group, presented as mean ± SD (n = 6); *p < 0.05, **p < 0.01, analysed by one-way ANOVA. (F) H&E staining of spleen tissues. The PBS group showed normal splenic architecture, while the *P. gingivalis* W83 group exhibited prominent white pulp hyperplasia, accompanied by enlarged germinal centres and dense lymphocytic infiltration. The ΔPG0352 group displayed moderate white pulp expansion, which was less severe than that observed in the *P. gingivalis* W83 group. Scale bars: 200 μm (upper panels), 100 μm (lower panels).

Cytokine analysis further confirmed that the expression levels of IL-1β and IL-6 were significantly upregulated in the P. gingivalis W83 group versus the PBS and ΔPG0352 groups (p < 0.05). Although the ΔPG0352 group exhibited lower inflammatory cytokine levels than the P. gingivalis W83 group, these levels remained markedly higher than those in the PBS group (Figure 1E and F). Collectively, these results indicate P. gingivalis triggers splenic immune and inflammatory responses, and such pathological alterations are strongly dependent on bacterial sialidase activity.

Gut microbiome alterations induced by P. gingivalis sialidase

Considering the pivotal involvement of oral–gut axis in the pathogenesis of multiple metabolic diseases, we conducted 16S rRNA gene sequencing on faecal specimens collected from mice in the three experimental groups. Both rarefaction curves and Shannon index curves verified that the sequencing depth and sample coverage were adequate for downstream bioinformatic analyses (Supplement Figure1A and B). Consistently, no obvious differences in the alpha-diversity indices including Shannon, Chao and Sobs were detected among the three groups (p > 0.05, Supplement Figure 1C–E). Principal coordinate analysis (PCoA) at the OTU levels exhibited distinct segregation of gut microbial community structures across the groups. Specifically, the microbial composition of the P. gingivalis W83 group was clearly separated from those of the PBS control and ΔPG0352 groups (p < 0.05, Figure 2A). Taxonomic composition analysis further confirmed marked alterations in the relative abundance of gut microbiota at both phylum and genus levels (Figure 2B and C). At the phylum level, Bacteroidota, Firmicutes and Proteobacteria dominated the intestinal microbial community in all groups. Notably, the abundance of Proteobacteria was significantly increased in both the W83 and ΔPG0352 groups compared with the PBS group. Circos plot analysis intuitively illustrated the intricate interaction relationships among predominant bacterial taxa, and further demonstrated that intestinal microbial interaction patterns were profoundly remodelled following colonization with P. gingivalis W83, or its ∆PG0352 mutant (Figure 2D). At the genus level, the relative abundances of Staphylococcus and Prevotellaceae_UCG-001 were remarkably increased in the P. gingivalis W83 group, whereas the ΔPG0352 group harboured a microbial community composition that was more similar to that of the PBS group (Figure 2E and F, p < 0.05).

A six-panel figure shows gut microbiota analysis. Panel a is PCoA, panels b and c are bar graphs, panel d is a Circos plot. The six-panel figure shows gut microbiota analysis. Panel a is a principal coordinates analysis scatter plot with PC1 on the horizontal axis from minus 0.4 to 0.4 and PC2 on the vertical axis from minus 0.6 to 0.4. Three clusters of points are visible, with W83 points clustered on the left, PBS points clustered in the middle, and delta PG0352 points clustered on the right. Panel b is a stacked bar graph showing community abundance at the phylum level for PBS, W83, and delta PG0352 groups. Bacteroidota and Firmicute are the most abundant phyla across all groups. Panel c is a stacked bar graph showing community abundance at the genus level for the three groups. Norank f Muribaculaceae and Lactobacillus are prominent. Panel d is a Circos plot visualizing associations between samples and bacterial species at the genus level. It shows connections between the three groups and various bacterial genera. Panel e is a box plot showing the proportion of Staphylococcus . The W83 group shows a significantly higher proportion than PBS and delta PG0352, with p less than 0.001 for both comparisons. Panel f is a box plot showing the proportion of Prevotellaceae UCG-001 . The W83 group shows a significantly higher proportion than PBS, with p less than 0.01. — Analysis of gut microbiota in three groups of mice. (A) Principal coordinates analysis (PCoA) of the gut microbiota at the OTU level. Red, blue and green dots represented the PBS, *P. gingivalis* W83 and ΔPG0352 groups, respectively. The closer the dots between two groups, the more similar their microbiota compositions. (B) Bar chart showing the composition of gut microbiota at the phylum level among the three groups. (C) Bar chart showing the composition of gut microbiota at the genus level among the three groups. (D) Circos plot visualizing the associations between samples and bacterial species at the genus level, illustrating the corresponding relationship between gut microbiota and individual samples. (E and F) Relative proportion of *Staphylococcus and Prevotellaceae_UCG-001* in each group, analysed using the Kruskal–Wallis H test. *p < 0.05, **p < 0.01, ***p < 0.001.

To further characterize intergroup microbial compositional differences, Venn diagram analysis identified 451 OTUs shared across the three groups, whereas the P. gingivalis W83 and ΔPG0352 groups harboured 75 and 115 unique OTUs, respectively (Figure 3A). Circos diagram showing genus-level microbiota-sample associations (Figure 3B). Linear discriminant analysis (LDA) further pinpointed the key bacterial taxa exhibiting significant intergroup variations (Figure 3C). In the P. gingivalis W83 group, the genera Staphylococcus and Prevotellaceae_UCG-001 were markedly enriched. In contrast, the ΔPG0352 group was enriched in beneficial taxa including Lactobacillus and Muribaculaceae. By comparison, the PBS group displayed specific enrichment in Dubosiella, a phenotypic feature that underscored its relatively balanced intestinal microbiome composition.

A threepanel figure shows a Venn diagram and a Circos plot of microbial diversity, plus a bar graph of LDA scores. The three panel figure illustrates microbial diversity and taxonomic differences. Panel A is a Venn diagram with three overlapping circles: PBS (80 unique items), W83 (75 unique items), and delta PG0352 (115 unique items). Overlaps are: PBS and W83 (493 items), W83 and delta PG0352 (510items), PBS and delta PG0352 (517 items). The central overlap of all three is 451 items. Panel B is a Circos plot showing connections between PBS, W83, delta PG0352, and microbial taxa like Lactobacillus, Dubosiella, and Escherichia-Shigella. The outer ring shows group sections with 0 to 90 percent tick marks. The inner ring displays connections with varying widths indicating abundance. Panel B is a Circos diagram showing genus-level microbiota-sample associations. Colored arcs demonstrate bidirectional connections: arcs radiate from microbial genera to samples, and each sample links to its affiliated genera via colored arcs. Panel C is a horizontal bar graph, LEfSe Bar, with LDA Score logarithm 10 (0 to 5.5) on the horizontal axis and microbial taxa on the vertical axis. Bars are grouped by PBS, W83, and delta PG0352. — Microbial diversity and taxonomic differences among the three groups. (A) Venn diagram analysis of OTUs in the gut microbiota of the three groups of mice, illustrating the overlap and uniqueness of OTUs among groups.(B) Circos plot illustrating the association between samples and taxa at the genus level. The colored arcs visualize bidirectional relationships between microbial genera and samples: arcs extending from each genus radiate to corresponding samples, and arcs originating from individual samples connect to their associated microbial genera. (C) LDA histogram depicting statistically significant differences in microbial taxa among the three groups, only taxa with an LDA score greater than 3.5 were included in the visualization.

Serum metabolic alterations in response to P. gingivalis W83 and ∆PG0352 colonization

Gut microbiota dysregulation modulates systemic circulatory metabolism via the biosynthesis of microbial metabolites and direct crosstalk with host metabolic signalling pathways [28]. To elucidate the systemic metabolic impacts of P. gingivalis sialidase activity on host physiology, untargeted serum metabolomic profiling was performed in this study. As shown in Figure 4A, the serum metabolic profiles of the three groups exhibited a distinct segregation. Specifically, samples from the W83 and PBS groups exhibited tight intragroup clustering with favourable sample consistency, whereas the ΔPG0352 group displayed a more dispersed clustering pattern with partial overlaps with both the PBS and P. gingivalis W83 groups. These results indicated that the serum metabolic signature of the ∆PG0352 group possessed both convergent and divergent characteristics relative to the PBS and P. gingivalis W83 groups. Volcano plot-based differential metabolite analysis quantified metabolic alterations across groups. Compared with the PBS group, the P. gingivalis W83 group harboured 133 significantly upregulated and 146 significantly downregulated metabolites (Figure 4B). Additionally, a total of 159 metabolites were markedly upregulated in the P. gingivalis W83 group relative to the ΔPG0352 group (Figure 4C). In contrast, the ∆PG0352 group exhibited only minor metabolic variations compared with the PBS groups (Figure 4D). These findings demonstrate that colonization with the sialidase-deficient P. gingivlias mutant triggers a much weaker perturbation of host systemic metabolism than wild-type P. gingivalis infection.

A four panel figure shows a PLS-DA plot and three volcano plots comparing metabolic differences between groups. The four panel figure shows a PLS-DA plot and three volcano plots. Panel a, a PLS-DA plot, displays Component 1 on the horizontal axis from minus 140 to 80 and Component 2 on the vertical axis from minus 25 to 25. Three groups are shown: PBS as circles, W83 as diamonds, and delta PG0352 as triangles. The PBS group clusters on the right, W83 on the lower right, and delta PG0352 on the left. Panels b, c, and d are volcano plots. Each plot has Log2FC on the horizontal axis and minus Log10 pvalue on the vertical axis, ranging from 0 to 4. Panel b, W83 versus PBS, shows a distribution of points with many upregulated points on the right and some downregulated points on the left. Panel c, W83 versus delta PG0352, shows a similar distribution with upregulated points on the right and downregulated points on the left. Panel d, delta PG0352 versus PBS, shows upregulated points on the right and a greater number of downregulated points on the left. Each volcano plot includes a legend for significant upregulation, no significant difference, and downregulation, along with a VIP value legend. — Results of serum metabolics analysis and intergroup difference comparisons. (a) PLS-DA score plot showing pairwise separation among the three groups (PBS, *P. gingivalis* W83, and ΔPG0352). (b–d) Volcano plots of pairwise differential metabolites between each pair of the three groups (PBS, *P. gingivalis* W83 and ΔPG0352). Red dots represent significantly upregulated metabolites, blue dots represent significantly downregulated metabolites and gray dots represent metabolites with no significant differences. The size of the dots reflects the VIP value.

Metabolic pathway enrichment analysis was performed via topology-based approaches using the KEGG database (Figure 5A). The top five perturbed metabolic pathways induced by P. gingivalis colonization and its sialidase activity were involved in nucleotide metabolism, alanine-aspartate-glutamate metabolism, purine metabolism, tyrosine metabolism and lysine degradation-changes indicative of profound perturbations in host energy and amino acid metabolic homeostasis. KEGG pathway classification analysis identified several significantly enriched pathways, with glycerophospholipid metabolism showing a significant enrichment (Figure 5B). Furthermore, heatmap coupled with VIP scoring revealed pronounced metabolic discrepancies between the ∆PG0352 and P. gingivalis W83 groups (Figure 5C). Of the 12 key differential metabolites identified, phosphatidylcholine (PC(20:5(6E, 8Z, 11Z, 14Z, 17Z)-OH(5)/22:2(13Z, 16Z))) (VIP = 2.8919) exhibited the highest contribution to intergroup metabolic differences, followed by phosphatidylethanolamine (PE(P-18:0/18:1(12Z)-2OH(9,10))) (VIP = 2.8876) (Supplementary Table S2). Moreover, ROC curve analysis confirmed that all 12 key metabolites had an area under the curve (AUC) value greater than 0.8, indicative of superior discriminatory ability (Figure 5D and E). This robust discriminatory capability highlighted the critical biological role and substantial contribution of these metabolites in distinguishing the ∆PG0352 group from the P. gingivalis W83 group. The AUC values of all differential metabolites between P. gingivalis W83 and ∆PG0352 are provided in Supplementary Table S3.

A five panel figure shows KEGG topology analysis, KEGG enrichment analysis, a heatmap, and two ROC analyses. The five panel figure shows KEGG topology analysis, KEGG enrichment analysis, a heatmap, and two ROC analyses. Panel a, a bubble plot, shows KEGG topology analysis. The horizontal axis is Impact Value from 0 to 0.24. The vertical axis is negative logarithm of P value from 0 to 4.5. Bubbles are distributed across the plot, with larger bubbles indicating higher impact and darker bubbles indicating lower P value. Panel b, a horizontal bar graph, shows KEGG enrichment analysis. The horizontal axis is Enrichment ratio from 0 to 0.4. Bars extend to various enrichment ratios, with some marked with asterisks for significance. Panel c, a heatmap, illustrates expression profiles of differential metabolites and their VIP scores. The heatmap displays a grid of metabolite expression, with a scale from negative 3 to 3. To the right, a vertical bar graph shows VIP scores for each metabolite, ranging from 1.5 to 3.5. Several bars are marked with asterisks for significance. Panel d, a line graph, shows ROC analysis of key differential metabolites. The horizontal axis is 1 minus Specificity from 0 to 1. The vertical axis is Sensitivity from 0 to 1. Multiple lines represent different metabolites, generally rising from the lower left towards the upper left. Panel e, a line graph, also shows ROC analysis of key differential metabolites, with axes identical to panel d. — Differential metabolite KEGG enrichment analysis and ROC analysis. (A) KEGG topology analysis of serum differential metabolites between the *P. gingivalis* W83 and ΔPG0352 groups. Each bubble represents a KEGG metabolic pathway, with the bubble size indicating the impact value and color representing the p-value. (B) KEGG enrichment analysis of the identified differential metabolites, displaying the enrichment ratio of various metabolic pathways. (C) Heatmap illustrating the expression profiles of differential metabolites and their corresponding VIP scores. (D and E) Receiver operating characteristic (ROC) curve analysis of key differential metabolites, illustrating their diagnostic potential for distinguishing between the *P. gingivalis* W83 and ΔPG0352 groups.

Comprehensive correlation analysis of gut microbiota species, serum metabolites and splenic immune traits

To further explore the systemic regulatory effects of P. gingivalis sialidase activity on the crosstalk among gut microbiota, serum metabolites and immunity responses, a comprehensive correlation analysis was performed in this study. The heatmap depicted in Figure 6A illustrated the correlative relationships between 12 pivotal serum metabolites, 19 differentially abundant gut microbial taxa and two key immune indicators (IL-1β and IL-6). The results showed that phosphatidylcholine, a core differential metabolite, was strongly and positively correlated with multiple gut microbial taxa, including Staphylococcus and Prevotellaceae_UCG-001, as well as the pro-inflammatory marker IL-1β. In contrast, Pelargonidin 3-sophoroside exhibited significant negative correlations with Staphylococcus and splenic immune factor IL-1β and IL-6, indicating the distinct roles of these molecules in regulating intestinal microbial composition and inflammatory responses.

A two-panel figure shows a heatmap of correlations and a distance-based redundancy analysis of gut microbiota composition. The two-panel figure shows a heatmap and a distance-based redundancy analysis. Panel a presents a heatmap displaying correlations. The vertical axis lists 12 serum metabolites and the horizontal axis lists 19 gut microbiota species and 3 splenic immune indicators. The heatmap shows a range from minus 1 to 1, with darker regions indicating negative correlations and brighter regions indicating positive correlations. Many cells show asterisks indicating statistical significance. Panel b displays a distance-based redundancy analysis on genus level. The horizontal axis is CAP1 32.82 percentage and the vertical axis is CAP2 14.88 percentage. Data points are clustered into three groups: PBS, delta PG0352, and W83. Arrows indicate associations between gut microbiota composition and mouse body weight, spleen index, immune factors, and key serum metabolites. The arrows for N prime Formylkynurenine, IL1 beta, and IL6 point towards the upper right. The arrows for body weight, spleen index, and PE P 18 0 over 18 1 12Z minus 2OH 9 10 point towards the lower left. The arrows for Inosine 2 prime phosphate, N Acetyl 1 aspartylglutamic acid, Cytosine, L Methionine S oxide, 4 Allylpyrocatechol sulfate, Medicagenic acid 3 O b D glucosyl 1 to 2 b D glucoside 28 O b D xylosyl 1 to 4 a L rhamnosyl 1 to 2 a L arabinosyl ester, and 17 Dimethylaminogeldanamycin point towards the lower right. — Comprehensive correlation analysis of gut microbiota, serum metabolites and splenic immune indicators. (A) Heatmap showing the correlations among 12 differential serum metabolites, 19 differential gut microbiota species and splenic immune indicators (spleen index, IL-1β and IL-6) in *P. gingivalis* W83 and ΔPG0352 groups. (B) Distance-based redundancy analysis (db-RDA) of gut microbiota composition at the genus level across the three groups, illustrating the associations between gut microbiota composition and mouse body weight, spleen index, immune factors and key serum metabolites.

Distance-based redundancy analysis (db-RDA) at the genus level provided further insights into the interactive relationships among gut microbial composition, serum metabolites and immune parameters (Figure 6B). The gut microbiota of the P. gingivalis W83 group was predominantly clustered in the third and fourth quadrants, while the ΔPG0352 group displayed a broader microbial distribution across the second, third and fourth quadrants. Among all identified metabolites, phosphatidylcholine displayed the longest loading vector, indicating its dominant contribution to the variation in gut microbial distribution. Additionally, the acute angles between the vector of phosphatidylcholine and the vectors of IL-1β and IL-6 confirmed positive correlations between this metabolite and the two inflammatory immune markers.

Discussion

Accumulating evidence has demonstrated that patients with periodontitis may swallow up to 10¹² free bacteria daily [29]. Upon entering the gastrointestinal tract, these bacteria can elicit alterations in the host immune response, which serves as a key mechanism underlying the systemic effects of periodontitis [30,31]. P. gingivalis, a key pathogen implicated in periodontal disease, is well recognized for its capacity to trigger local tissue destruction and systemic immune dysregulation [32,33]. In addition to driving periodontal inflammation, P. gingivalis is closely linked to the development of multiple systemic disorders, such as cardiovascular disease, diabetes and neurodegenerative diseases. Such associations are largely explained by its capability to alter host immune responses and facilitate chronic inflammatory progression [34–36]. Among its diverse virulence factors, sialidase plays a critical role in enhancing bacterial survival, promoting virulence and facilitating biofilm formation [17,37]. P. gingivalis lacks intrinsic sialic acid synthetic machinery and must harvest free sialic acids via extracellular breakdown of host glycans. Classified as essential glycosyl hydrolases, sialidases catalyse the hydrolysis of terminal sialic acid linkages on glycoconjugates [38]. The removal of surface sialic acids distorts host-pathogen molecular recognition, allowing bacteria to disguise themselves as host-derived components, evade immune clearance and successfully invade host cells [20,39]. These camouflaging features may aid the ectopic intestinal colonization of P. gingivalis along the oral–gut axis. Sialidase modulates host immunity via diverse mechanisms, leading to profound dysregulation in both innate and adaptive immunity. Specifically, sialidase facilitates immune evasion by suppressing M1 macrophage polarization, impairing antigen presentation and phagocytosis in infected macrophages [20]. It also perturbs the cytokine homeostasis by decreasing IL-12 secretion in macrophages, while concurrently reducing the production of IL-1β and TNF-α production in both epithelial cells and macrophages [19,21,40]. Such immune dysregulation sustains a chronic inflammatory milieu typical of periodontitis. Targeting sialidase therefore holds a promising therapeutic potential. Consistent with this notion, our prior study revealed that DANA, a sialidase inhibitor, disrupts biofilm formation, suppresses the expression of virulence factors and mitigates periodontal inflammation and subsequent tissue damage [41].

The oral gavage model of P. gingivalis is widely adopted in periodontal research. It can induce chronic alveolar bone resorption and inflammatory responses that closely recapitulate the pathological features of human periodontal disease [42,43]. In the present study, mice were orally gavaged with wild-type P. gingivalis W83 and the sialidase-deficient mutant ΔPG0352, with a focus on the role of sialidase in regulating gut microbiota composition and host immune responses. Our results showed that the spleen indices were markedly increased in both the W83 and ΔPG0352 groups relative to the PBS group, suggesting obvious splenomegaly. However, the levels of immune factors, including IL-1β and IL-6, in the splenic tissue homogenates from the ΔPG0352 group were significantly decreased compared to those in the P. gingivalis W83 group. Integrated analyses of gut microbiota and serum metabolites identified 451 differentially abundant bacterial genera and 171 differential metabolites between mice challenged with P. gingivalis W83 and ΔPG0352 mutant. Subsequent correlation analysis confirmed that 3 key bacterial genera and 12 metabolites were tightly associated with strain-specific variations.

Our analyses revealed reduced abundance of Staphylococcus and Paraprevotella in mice challenged with the sialidase-deficient mutant ΔPG0352, when compared to the wild-type P. gingivalis W83 group. Staphylococcus aureus, the primary pathogenic species within the genus Staphylococcus, releases enterotoxins that elicit systemic toxic responses [44]. Similarly, Paraprevotella is categorized as a genus of pathogenic bacteria and is correlated with carcinogenesis, with documented roles in exacerbating intestinal mucosal deterioration in ulcerative colitis [45]. The decreased abundance of these two genera in the sialidase-deficient strain group suggests that sialidase deficiency in P. gingivalis restricts the colonization of potential pathogens. Sialidase derived from P. gingivalis mediates the acquisition of environmental sialic acid, which serves as a key nutrient source for the growth of P. gingivalis and its symbiotic community [46]. Collectively, these results confirm that P. gingivalis sialidase alters gut microbiota structure. It drives the expansion of opportunistic pathogens and limits the abundance of probiotics such as Lactobacillus, which may lead to adverse outcomes for host systemic health.

Accumulating evidence has confirmed the regulatory role of gut microbiota in serum metabolism [47]. Herein, we explored the differences in serum metabolites driven by gut microbiota dysbiosis following exposure to two distinct P. gingivalis strains. In our mouse model, P. gingivalis and its sialidase mainly disrupted nucleotide metabolism, alanine-aspartate-glutamate metabolism, purine metabolism, tyrosine metabolism and lysine degradation, with glycerophospholipid metabolism as the most significantly enriched pathway. These pathway alterations are highly consistent with metabolic characteristics reported in recent clinical research on periodontitis patients. Accumulating clinical untargeted metabolomic data from saliva and gingival crevicular fluids have also identified disturbances in amino acid metabolism, purine and nucleotide metabolism, as well as abnormal glycerophospholipid metabolism in periodontitis patients [48,49]. Our results indicated that gut microbial distribution was predominantly associated with the differential metabolite phosphatidylcholine, PC(20:5(6E, 8Z, 11Z, 14Z, 17Z)-OH(5)/22:2(13Z, 16Z)). This oxidized phosphatidylcholine participates in lipid degradation and membrane remodelling [50], and its elevated abundance is associated with increased risk of adverse cardiovascular events [51]. Another differential metabolite, PE (P-18:0/18:1(12Z)-2OH(9,10)), is an oxidized phosphatidylethanolamine. Its abnormal accumulation induces mitochondrial dysfunction, a key driver of multiple disorders [52]. As major glycerophospholipids, phosphatidylcholine and phosphatidylethanolamine account for approximately 50% of cellular membrane phospholipids. These lipids are essential for protein recognition, transmembrane signalling and intracellular immune regulation [53,54]. Previous work has shown that low-fat pectin supplementation increases intestinal Lactobacilli species and rescues gut microbiota diversity [55]. This intervention normalizes serum profiles of phosphatidylcholine and phosphatidylethanolamine, improves glycerophospholipid metabolism and protects mice against dextran sulfate sodium (DSS)-induced colitis [55]. Consistently, elevated Lactobacillus abundance can alleviate glycerophospholipid metabolic disturbances, restore serum levels of phosphatidylcholine and other glycerophospholipids and modulate host immune response [56].

In comparison with the P. gingivalis W83 group, mice receiving the ΔPG0352 strain showed a notable decline in serum phosphatidylcholine and a rise in phosphatidylethanolamine. Our findings imply that P. gingivalis sialidase mediates changes in serum glycerophospholipids in a gut microbiota-dependent manner. The molecular mechanisms behind this crosstalk still need to be fully explored. The overlapping metabolic signatures between our animal model and human clinical samples suggest that the above metabolic pathways are conserved during P. gingivalis infection along the oral–gut axis, and further support the role of sialidase in mediating periodontitis-associated metabolic disorders. Overall, P. gingivalis sialidase may facilitate the progression of systemic diseases by altering gut microbial structure and perturbing host serum metabolic homeostasis.

This study has several limitations that should be acknowledged. Firstly, though male mice were widely used in experimental model of periodontitis, the lack of female mice is an inevitable limitation, since sex differences including oestrous cycle and sex hormone fluctuations strongly affect periodontal inflammation progression. Future studies incorporating both and female mice are required to validate and extend our findings. Secondly, based on our previous study showing no significant phenotypic differences were observed between the P. gingivalis W83 and com∆PG0352 groups [20], our present study just utilized the wild-type P. gingivalis W83 strain and the sialidase-deficient ΔPG0352 mutant strain, without the inclusion of genetic complementation experiments. Future studies incorporating complementation assays will help fully validate the causal relationship between sialidase expression and the observed gut microbial, metabolic and systemic phenotypic alterations, thereby further consolidating the current findings. Although we conducted correlation heatmap and db-RDA to link gut microbiota and serum metabolites, the absence of comprehensive multi-omics integrated interpretation impairs the overall biological coherence of our results. Thirdly, this study focused on only two cytokines (IL-1β and IL-6) and a limited set of metabolites (e.g. phosphatidylcholine and phosphatidylethanolamine), which may have overlooked other key molecules or signalling pathways critical to systemic immunity and metabolism. Future research should expand the scope to include additional pro-inflammatory and anti-inflammatory factors, such as TNF-α, IFN-γ, IL-10 and TGF-β, to gain a more comprehensive understanding of the underlying regulatory networks. Additionally, since this study adopted an intragastric administration model focusing on the role of sialidase encoded by PG0352 in regulating gut microbiota composition and host immune responses, metabolomic and bacterial profiles of oral lesions were not analysed. Further studies combining local oral tissue detection are warranted. Nonetheless, our findings indicated that P. gingivalis sialidase may modulate systemic immunity by regulating the gut microbiota and altering serum metabolite profiles, thereby providing a novel therapeutic target for the clinical intervention of periodontal disease and its associated systemic comorbidities.

Conclusions

In conclusion, this study demonstrates that P. gingivalis triggers systemic immune dysregulation via remodelling gut microbial composition and disrupting host metabolic networks. Phosphatidylcholine is identified as a pivotal metabolite bridging gut microbial dysbiosis to splenic inflammation. Notably, ablation of sialidase in the ΔPG0352 mutant largely abolishes these adverse systemic effects, verifying the essential role of P. gingivalis sialidase in mediating oral–gut–immune crosstalk. These findings deepen our mechanistic understanding of oral–gut axis communication and highlight P. gingivalis sialidase as a viable therapeutic target for alleviating periodontitis-associated systemic comorbidities. Furthermore, this work provides novel insights into the complex microbiome-metabolite-immune regulatory network that drives inflammatory disorders.

Supplementary Material

Supplementary material

Supplementary Information

ZJOM_A_2710456_SM4058.docx^{(16.5KB, docx)}

Supplementary Table 2.docx

ZJOM_A_2710456_SM9316.docx^{(12.9KB, docx)}

Supplementary material

Supplementary Table 1.xlsx

ZJOM_A_2710456_SM9315.xlsx^{(117.1KB, xlsx)}

Supplementary material

SFigure 1.tif

ZJOM_A_2710456_SM9313.tif^{(1.5MB, tif)}

Supplementary material

Supplementary Table 3.xlsx

ZJOM_A_2710456_SM9314.xlsx^{(17.6KB, xlsx)}

Acknowledgements

All authors read and approved the final manuscript.

Funding Statement

This study was supported by the National Natural Science Foundation of China (No. 82401128) and Natural Science Foundation of Liaoning Provincial (2024-MS-034).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

All raw data will be made available upon request.

Ethical approval

This study was approved by the Institutional Animal Care and Use Committee of China Medical University.

Supplementary material

Supplemental data for this article can be accessed at https://doi.org/10.1080/20002297.2026.2710456.

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[56]. Zhang Q, Zhong D, Sun R, et al. Prevention of loperamide induced constipation in mice by KGM and the mechanisms of different gastrointestinal tract microbiota regulation. Carbohydr Polym. 2021;256:117418. doi: 10.1016/j.carbpol.2020.117418 [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary material

Supplementary Information

ZJOM_A_2710456_SM4058.docx^{(16.5KB, docx)}

Supplementary Table 2.docx

ZJOM_A_2710456_SM9316.docx^{(12.9KB, docx)}

Supplementary material

Supplementary Table 1.xlsx

ZJOM_A_2710456_SM9315.xlsx^{(117.1KB, xlsx)}

Supplementary material

SFigure 1.tif

ZJOM_A_2710456_SM9313.tif^{(1.5MB, tif)}

Supplementary material

Supplementary Table 3.xlsx

ZJOM_A_2710456_SM9314.xlsx^{(17.6KB, xlsx)}

Data Availability Statement

All raw data will be made available upon request.

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PERMALINK

Porphyromonas gingivalis sialidase reshapes gut microbiota to affect serum metabolism and splenic immunity: insights from a murine study

Qibing Wu

Hongxia Li

Yixi Niu

Hanmo Li

Junchao Liu

Yaping Pan

Chen Li

Shuwei Zhang

Roles

Abstract

Background

Objective

Design

Results

Conclusions

Introduction

Materials and methods

Cultivation of P. gingivalis

Animal experiments

Tissue collection

HE staining analysis

Detection of IL-1β and IL-6 cytokines in tissue homogenates

Faecal microbiome sequencing analysis

Untargeted metabolomic profiling of serum specimens

Data analysis

Results

Immune and inflammatory alterations in the spleen

Figure 1.

Gut microbiome alterations induced by P. gingivalis sialidase

Figure 2.

Figure 3.

Serum metabolic alterations in response to P. gingivalis W83 and ∆PG0352 colonization

Figure 4.

Figure 5.

Comprehensive correlation analysis of gut microbiota species, serum metabolites and splenic immune traits

Figure 6.

Discussion

Conclusions

Supplementary Material

Acknowledgements

Funding Statement

Disclosure statement

Data availability statement

Ethical approval

Supplementary material

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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