ABSTRACT

   Periodontitis is closely related to renal health, but the specific
   influence of Porphyromonas gingivalis (P. gingivalis), a key pathogen
   in periodontitis, on the development of acute kidney injury (AKI) in
   mice has not been fully elucidated. In our study, AKI was induced in
   mice through ischemia-reperfusion injury while administering oral
   infection with P. gingivalis. Comprehensive analyses were conducted,
   including 16S rRNA sequencing, liquid chromatography-mass spectrometry
   (LC-MS) metabolomics, and transcriptome sequencing. In vitro, the
   identified metabolite was used to stimulate mouse neutrophils.
   Subsequently, these modified neutrophils were co-cultured with mouse
   renal tubular epithelial cells. The results showed that oral infection
   with P. gingivalis significantly exacerbated AKI in mice. 16S rRNA
   sequencing revealed notable shifts in gut microbiota composition. LC-MS
   metabolomics analysis identified significant metabolic alterations,
   particularly the upregulation of 3-indoleacrylic acid in the serum.
   Transcriptome sequencing showed an increased expression of neutrophilic
   granule protein (Ngp), which was closely associated with
   3-indoleacrylic acid, and the presence of Porphyromonas. Cellular
   experiments demonstrated that 3-indoleacrylic acid could activate
   neutrophils, leading to an elevation in NGP protein levels, a response
   that was associated with renal epithelial cell injury. Oral infection
   with P. gingivalis exacerbated AKI through the gut-kidney axis,
   involving gut microbiota dysbiosis, metabolic disturbances, and
   increased renal expression of Ngp.

IMPORTANCE

   This study provides novel insights into the relationship between
   periodontal health and renal function. Porphyromonas gingivalis oral
   infection disrupted the balance of gut microbiota and was an important
   modifier determining the severity of acute kidney injury. Under the
   “gut-kidney axis,” P. gingivalis might cause an increase in the level
   of the gut microbial metabolite 3-indoleacrylic acid, interfering with
   kidney immunity and disrupting the maintenance of kidney epithelium.

   KEYWORDS: acute kidney injury, Porphyromonas gingivalis, gut-kidney
   axis, gut microbiota, metabolomics

INTRODUCTION

   Acute kidney injury (AKI) is a clinical syndrome characterized by a
   rapid decline in renal function. AKI encompasses a variety of
   pathophysiological processes, including inflammation, oxidative stress,
   epigenetic changes, and other mechanisms ([34]1). Persistent renal
   dysfunction is associated with irreversible damage to renal cells and
   nephrons, potentially progressing to chronic kidney disease ([35]2). In
   recent years, significant progress has been made in understanding the
   critical role of crosstalk between the kidneys and distant organs in
   AKI ([36]3, [37]4).

   Epidemiological evidence indicates that patients with periodontitis
   exhibit significant renal function impairment compared to the general
   population ([38]5), underscoring the importance of considering
   periodontitis as closely related to renal health. Porphyromonas
   gingivalis (P. gingivalis) is a major pathogen in periodontitis, known
   to cause the destruction of periodontal supporting tissues ([39]6,
   [40]7). Our previous studies have also confirmed that oral infection
   with P. gingivalis exacerbates renal damage in AKI in mice ([41]8).
   However, the specific mechanisms by which orally derived P. gingivalis
   damages distant renal tissues remains unclear.

   The gut microbiota is a complex ecosystem within the human intestine
   that continuously communicates with key host organ systems to regulate
   health ([42]9). Recently, the “gut-kidney axis” theory has provided a
   new perspective on the bidirectional communication between the gut
   microbiota and kidney disease ([43]10). On the one hand, impaired renal
   function can lead to gut microbiota dysbiosis ([44]11, [45]12); on the
   other hand, alterations in the gut microbiota may damage the intestinal
   mucosal barrier, allowing harmful bacteria to enter the bloodstream,
   trigger inflammatory responses, and accelerate kidney damage ([46]13).
   Additionally, an increase in nephrotoxic metabolites produced by
   dysregulated gut microbiota within the “gut-kidney axis” is another
   mechanism that exacerbates kidney disease ([47]14). Oral infection with
   P. gingivalis has been shown to disrupt the composition and function of
   the gut microbiota ([48]15, [49]16), not only disturbing the intestinal
   environment but also subsequently leading to extensive extra-intestinal
   physiological reactions, such as liver disease and autoimmune disorders
   ([50]17). Nevertheless, there is a lack of compelling evidence to
   support the notion that oral infection with P. gingivalis exacerbates
   AKI by disrupting the gut microbiota.

   This study aims to investigate the impact of oral infection with P.
   gingivalis on the gut microbiota and metabolism in a mouse model of AKI
   using multi-omics approaches. We seek to elucidate the direct
   pathophysiological role of gut microbiota dysbiosis induced by P.
   gingivalis in AKI.

MATERIALS AND METHODS

Experimental mouse model

   Male C57BL/6J mice, aged 8 weeks, were randomly assigned to various
   experimental groups. The study protocols were approved by the
   Institutional Animal Care and Use Committee of Capital Medical
   University.

   To induce AKI, a dorsal skin incision was made to expose both kidneys,
   and the renal pedicles were clamped for 30 minutes. In the sham group,
   mice underwent anesthesia and muscle incision without clamping
   ([51]18). The surgical procedures were performed on the 55th day
   following the initial bacterial inoculation. The C57BL/6 strain
   exhibited a pronounced AKI phenotype following surgery, making it a
   highly suitable model for our study. Using this well-characterized
   strain ensures reliable results and facilitates comparisons with prior
   research in the field.

   The P. gingivalis W83 bacteria were centrifuged to form a pellet, which
   was then resuspended in sterile 3% (wt/vol) carboxymethylcellulose to
   achieve a concentration of 1 × 10^10 CFU/mL. A 100 µL aliquot of this
   suspension was orally administered to the mice every 2 days for a total
   of eight inoculations ([52]19, [53]20).

   Antibiotic treatment was administered by providing autoclaved drinking
   water containing 1 g/L ampicillin (Solarbio, Cat#A8180-1), 1 g/L
   metronidazole (MCE, Cat#HY-B0318), 1 g/L neomycin (MCE, Cat#HY-B0470),
   and 0.5 g/L vancomycin (MCE, Cat#HY-B0671) ([54]21). This treatment
   started 7 days before the AKI surgery and continued throughout the
   study.

   Samples were collected on the first day post-surgery. Under sterile
   conditions, fecal samples were obtained from the mice and stored at
   −80°C. The mice were weighed, and blood samples were collected. After
   euthanasia by CO[2] asphyxiation, the kidneys were harvested and
   weighed. Serum was obtained from whole blood samples for renal function
   testing. The experimental workflow is illustrated in Fig. S1.

HE staining and Masson’s trichrome staining

   Kidney and intestinal tissues were embedded in paraffin. HE staining
   was performed according to the kit’s instructions (Servicebio,
   Cat#G1076). Masson’s trichrome staining was conducted as per the
   manufacturer’s guidelines (Servicebio, Cat#G1006).

Immunofluorescence staining

   Samples were pretreated with trypsin and permeabilized using 0.5% PBST.
   They were then incubated with hydrogen peroxide solution and blocked
   with goat serum. Primary antibodies specific for TLR4 (Abcam,
   Cat#ab22048), Ki67 (Cell Signaling Technology, Cat#9129), claudin-5
   (Proteintech, Cat#29767-1-AP), NGP (ThermoFisher, Cat#orb420374), and
   caspase-3 (Proteintech, Cat#82202-1-PBS) were applied, followed by the
   appropriate secondary antibodies and DAPI.

16S rRNA sequencing analysis

   DNA was extracted from mouse fecal samples using the TIANamp Bacteria
   DNA kit (Tiangen, Cat#4992448). High-throughput sequencing of the V4–V5
   region of the bacterial 16S rRNA gene was performed by Guangdong
   Magigene Biotechnology Co., Ltd. using the Illumina NovaSeq 6000
   platform with primers 515F (GTGCCAGCMGCCGCGGTAA) and A806R
   (GGACTACVSGGGTATCTAAT). Sequence processing followed library
   construction, with annotation and clustering performed using the UPARSE
   algorithm. Analysis of α-diversity, β-diversity, and differential
   species composition was based on the operational taxonomic unit (OTU)
   table.

Liquid chromatography-mass spectrometry metabolomics analysis

   Serum samples were analyzed for untargeted metabolomics by Shanghai
   Personal Biotechnology Co., Ltd. The analysis was performed using a
   Vanquish UHPLC System (Thermo Fisher Scientific), and metabolites were
   detected with an Orbitrap Exploris 120 (Thermo Fisher Scientific) using
   ESI ionization. Data were processed with ProteoWizard software,
   annotated through a mass spectrometry database, and analyzed using R
   software.

Transcriptome sequencing

   Transcriptome sequencing was performed by Guangdong Magigene
   Biotechnology Co., Ltd. Total RNA was extracted from renal tissues, and
   quality control assays were conducted. Sequencing was carried out on
   the Illumina HiSeq 2500 platform using the PE150 strategy.

Correlation analysis

   To explore the potential correlations between microbiota, metabolites,
   and gene expression, Pearson correlation analysis was conducted.
   Statistical significance was determined with a P value threshold of
   less than 0.05. The analysis utilized R software.

In vitro experiment

   Neutrophils were isolated from mice. Bone marrow was flushed from
   femurs and tibias using RPMI 1640 with 10% fetal bovine serum (FBS) and
   filtered through a 70 µm nylon cell strainer. Neutrophils were isolated
   using the EasySep Mouse Neutrophil Enrichment Kit (STEMCELL
   Technologies, Cat#19762) following the manufacturer’s instructions.
   Neutrophils were cultured in RPMI 1640 with 10% FBS. For experiments, 2
   × 10^5 neutrophils per well were plated in 12-well plates and treated
   with 10 µM 3-indoleacrylic acid (MCE, Cat#HY-W015273) for 12 hours.

   Neutrophils were treated with culture medium containing siRNA at 100
   nmol/L; siRNA targeting neutrophilic granule protein (Ngp)
   (ThermoFisher, Cat#1320001) and negative control siRNA (ThermoFisher,
   Cat#12935200) were used. After 24 hours, the medium was replaced with
   normal medium, and cells were treated with 10 µM 3-indoleacrylic acid
   for another 12 hours.

   Mouse renal tubular epithelial cells (TCMK-1) were cultured in MEM
   medium with 10% FBS. TCMK-1 cells (2 × 10^5) were seeded into 12-well
   plates and co-cultured with pretreated neutrophils at a 1:1 ratio for
   48 hours, with neutrophils in inserts with 0.4-micron pores and TCMK-1
   cells in the lower chamber.

Western blot

   Proteins were separated on a Mini-PROTEAN TGX Gel (Bio-Rad,
   Cat#4561093) and transferred to a membrane using the Trans-Blot Turbo
   system (Bio-Rad, Cat#1704156). Membranes were blocked and incubated
   with primary antibodies against NGP (1:1,000, ThermoFisher,
   Cat#600-401-GW9), β-actin (1:1,000, Proteintech, Cat#66009-1-Ig),
   claudin-5 (1:1,000, Proteintech, Cat#29767-1-AP), and GAPDH (1:1,000,
   Proteintech, Cat#60004-1-Ig). After washing, membranes were incubated
   with HRP-conjugated secondary antibodies and detected using the
   ChemiDocTM MP Imaging System (Bio-Rad).

qRT-PCR analysis

   Total RNA was isolated using the RNeasy Kit (Qiagen, Cat#74104).
   Reverse transcription was performed with the Transcriptor First Strand
   cDNA Synthesis Kit (Roche, Cat#04379012001). The cDNA was analyzed
   using SsoAdvanced Universal SYBR Green Supermix (Bio-Rad,
   Cat#172-5270).

Cell counting kit-8 assay

   Cells were washed with PBS, and cell counting kit-8 (CCK-8) solution
   was added post-stimulation. Absorbance at 450 nm was measured with a
   spectrophotometer.

Statistical analysis

   Data are expressed as mean ± standard deviation. Statistical analyses
   were performed using the Student’s t-test for comparing two groups,
   one-way ANOVA for multiple group comparisons of parametric data, or the
   Wilcoxon matched-pairs signed-ranks test for non-parametric data. A
   P-value of less than 0.05 was considered to indicate statistical
   significance.

RESULTS

Oral infection with P. gingivalis exacerbated kidney and intestinal injury in
AKI

   Emerging evidence points to a crucial involvement of gut microbiota in
   the development of AKI ([55]22). In our experiments, mice in the AKI +
   P. gingivalis group displayed an increased kidney mass ratio (KW/BW)
   and significantly elevated levels of blood urea nitrogen, creatinine,
   and uric acid, as detailed in Table S1. HE and Masson’s trichrome
   staining of kidney tissues showed that oral infection with P.
   gingivalis aggravated AKI-associated renal damage ([56]Fig. 1A and B).
   When Abx treatment was administered to the AKI + P. gingivalis group,
   kidney function improved significantly (Table S1). This improvement was
   accompanied by reduced renal damage ([57]Fig. 1A and B), suggesting a
   pivotal role of microbiota in the worsening of AKI by P. gingivalis.

Fig 1.

   [58]Histological comparison of kidney and intestinal tissues in sham,
   AKI, AKI with P. gingivalis, and AKI with P. gingivalis plus
   antibiotics groups using HE, Masson, and DAPI staining. Quantification
   of TLR4 expression shows significant differences.
   [59]Open in a new tab

   Exacerbation of kidney and intestinal injury in AKI mice with P.
   gingivalis oral infection. (A) HE staining of renal tissue. (B)
   Masson’s trichrome staining of renal tissue. (C) HE staining of
   intestinal tissue. (D) Immunofluorescence staining for TLR4 in
   intestinal tissue and quantitative analysis. The results are expressed
   as the mean ± SD. *P < 0.05; **P < 0.01; ***P < 0.001; and ****P <
   0.0001 by ANOVA.

   Further histological examinations of intestinal tissues revealed
   notable epithelial damage and desquamation, along with lymphocytic
   infiltration, which were more pronounced in the AKI + P. gingivalis
   group ([60]Fig. 1C). Toll-like receptor 4 (TLR4), which recognizes
   pathogen-associated molecular patterns such as the lipopolysaccharide
   from P. gingivalis ([61]23), was significantly upregulated in the
   intestinal tissues of this group. Immunohistochemical staining
   confirmed this increased expression of TLR4. Notably, Abx treatment
   mitigated the intestinal damage and decreased TLR4 expression in these
   mice ([62]Fig. 1D).

Impact of oral infection with P. gingivalis on gut microbiota composition in
AKI mice

   To further investigate the effects of oral infection with P. gingivalis
   on gut microbiota composition in AKI, we conducted 16S rRNA sequencing
   analysis on fecal samples. This analysis revealed 578 identical
   operational taxonomic units shared among the four experimental groups,
   with each group also harboring unique OTUs ([63]Fig. 2A). Notably, the
   most prevalent OTU remained consistent between the Sham and AKI groups
   but differed in the AKI + P. gingivalis and Abx treatment groups
   ([64]Fig. 2B). The α-diversity analysis did not reveal any
   statistically significant differences among the groups ([65]Fig. 2C).
   While AKI alone (without P. gingivalis infection) did not significantly
   alter the β-diversity of the gut microbiota compared to the Sham group,
   oral infection with P. gingivalis markedly changed the β-diversity
   within the AKI mice, indicating significant deviations from both the
   AKI and Sham groups. The Abx treatment also modified the gut microbiota
   composition in the AKI + P. gingivalis group, although these changes
   still varied from the Sham baseline ([66]Fig. 2D).

Fig 2.

   [67]Microbial diversity analysis depicts Venn diagram, chord diagram,
   diversity metrics, taxonomic composition, PCoA, and heatmaps
   illustrating microbial shifts across sham, AKI, P. gingivalis, and
   antibiotic-treated groups.
   [68]Open in a new tab

   Oral infection with P. gingivalis altered the gut microbiota
   composition in AKI mice. (A) The number of shared and unique
   operational taxonomic units among the various groups. (B) The OTU
   composition of microbial communities across the groups. (C and D)
   Analysis of the diversity and structure of the gut microbiota within
   each group. The α-diversity was presented by Chao1 and Simpson indices.
   ns, not significant; *P < 0.05; **P < 0.01; ***P < 0.001; ****P <
   0.0001, determined using the one-way ANOVA test with Scheffé’s post hoc
   analysis. The β-diversity was presented as a principal coordinate
   analysis (PCoA) plot based on UniFrac distances. (E) The percentages of
   the major phyla, classes, and genera within the gut microbiota. (F) The
   relative abundance of 18 major genera across the groups. (G) The
   clustering analysis of the top 30 OTUs with the highest abundances.

   Further analysis of the gut microbiota composition at various taxonomic
   levels showed that Firmicutes was the most abundant phylum across all
   groups. At the class level, Bacilli dominated in all groups. However,
   noticeable differences emerged at the genus level: Atopostipes was most
   abundant in the Sham and AKI groups, whereas Sporosarcina was more
   prevalent in the AKI + P. gingivalis and Abx treatment groups ([69]Fig.
   2E). Examination of the top 18 most abundant genera revealed that the
   AKI + P. gingivalis group exhibited uniquely high abundances of
   Porphyromonas, Staphylococcus, and Lachnospiraceae. Post-Abx treatment,
   the abundance of these microorganisms notably decreased ([70]Fig. 2F;
   Fig. S2A). A heatmap further illustrated the clustering analysis of the
   top 30 OTUs by abundance ([71]Fig. 2G). Phylogenetic tree analysis
   underscored the similarities between the AKI and Sham groups, as well
   as between the AKI + P. gingivalis and Abx treatment groups (Fig. S2B).
   Additionally, KEGG pathway prediction based on the 16S rRNA sequencing
   data were conducted for each group, providing insights into potential
   metabolic functions (Fig. S2C).

   In summary, our analysis confirmed that oral infection with P.
   gingivalis significantly impacted the gut microbiota. The notable
   increase in microbes in the AKI + P. gingivalis group might play a
   critical role in exacerbating AKI.

P. gingivalis-induced metabolic alterations in AKI serum and the further
elevation of 3-indoleacrylic acid

   To further investigate how oral infection with P. gingivalis impacts
   gut microbiota and subsequently affects disease progression through
   metabolism, we conducted non-targeted metabolomics sequencing on serum
   samples from mice. The sequencing results revealed a substantial number
   of differential metabolites among the groups. To gain a clearer picture
   of the overall metabolite profile, we performed a principal component
   analysis on the metabolites. The findings indicated significant
   differences in the composition of serum metabolites among the Sham,
   AKI, and AKI + P. gingivalis groups in both the positive and negative
   ion modes (Fig. S3A). Under positive ion mode, there were 666 common
   differential metabolites shared among the Sham, AKI, and AKI + P.
   gingivalis groups, while under negative ion mode, this number rose to
   1,186 ([72]Fig. 3A). A meticulous examination of the differential
   metabolites revealed a balanced distribution of upregulated and
   downregulated metabolites in the AKI + P. gingivalis group compared to
   the AKI group. However, when compared to the Sham group, both the AKI +
   P. gingivalis and AKI groups exhibited a higher prevalence of
   upregulated metabolites ([73]Fig. 3B). Cluster analysis further
   distinguished the serum metabolite compositions of the AKI + P.
   gingivalis and AKI groups from that of the Sham group, indicating a
   significant divergence ([74]Fig. 3C).

Fig 3.

   [75]Venn diagrams, bar charts, heatmap, and volcano plots illustrating
   metabolomic differences across sham, AKI, and P. gingivalis-infected
   groups in both positive and negative ion modes. Key metabolites are
   highlighted.
   [76]Open in a new tab

   Impact of oral infection with P. gingivalis on metabolic alterations in
   the serum of AKI mice. (A) The Venn diagram illustrates the number of
   shared and unique metabolites among the various groups. The left panel
   represents positive ion modes, while the right panel depicts negative
   ion modes. (B) Bar charts summarize the differential metabolites
   between each pair of groups. The upper section displays positive ion
   modes, and the lower section displays negative ion modes. The X-axis
   represents the number of differential metabolites, while the Y-axis
   indicates the comparison groups. Red bars indicate upregulation, and
   blue bars indicate downregulation. (C) Overall metabolite clustering
   analysis and heatmap visualization. (D) The volcano plot displays the
   distribution and trends of differential metabolites in the serum.

   To pinpoint the metabolic distinctions between the AKI and Sham groups,
   a volcano plot analysis was employed. This analysis highlighted an
   upregulation of metabolites such as L-isoleucine, quin, 3-indoleacrylic
   acid, stearic acid, 4-hydroxyphenylpyruvic acid, and phenyl sulfate in
   AKI mice, in contrast to the downregulation of pyroglutamic acid,
   deoxycholic acid, mycophenolic acid, adenine, and sorbitol ([77]Fig.
   3D). A comparative analysis between the AKI + P. gingivalis and AKI
   groups unveiled several significant alterations, with metabolites like
   pyroglutamic acid, deoxycholic acid, chenodeoxycholic acid,
   mycophenolic acid, 3-indoleacrylic acid, and sorbitol showing an
   increase, and alpha-dimorphecolic acid, prostaglandin-c2, stearic acid,
   ursodeoxycholic acid, and urocanic acid showing a decrease in the AKI +
   P. gingivalis group ([78]Fig. 3D). Notably, the alterations in
   3-indoleacrylic acid were particularly pronounced, with a marked
   increase in AKI mice and an even more substantial rise post-P.
   gingivalis infection (Fig. S3B).

   A KEGG pathway enrichment analysis of the differential metabolites
   between the AKI + P. gingivalis and AKI groups identified enrichment in
   pathways, including oxidative phosphorylation, intestinal immune
   network for IgA production, and Th17 cell differentiation, underscoring
   the profound impact of P. gingivalis infection (Fig. S3C). Our study
   not only uncovered metabolic differences between AKI mice and normal
   mice but also demonstrated the profound impact of oral infection with
   P. gingivalis on the serum metabolites of AKI mice, notably leading to
   a significant upregulation of metabolites like 3-indoleacrylic acid.

Multi-omics analysis discovered that oral infection with P. gingivalis
induced gut-kidney axis dysregulation

   Oral infection with P. gingivalis disrupted intestinal homeostasis in
   AKI mice. An in-depth analysis revealed a significant association
   between the top 14 most abundant gut microbiota and renal function,
   including genera such as Porphyromonas, Dubosiella, Staphylococcus, and
   Lachnospiraceae ([79]Fig. 4A). We also noted key metabolites,
   especially 3-indoleacrylic acid, exhibiting the most potent correlation
   with renal function ([80]Fig. 4B). Further investigation indicated a
   significant positive correlation between the increase of
   3-indoleacrylic acid and the abundance of Porphyromonas in the
   comparison between the AKI and AKI + P. gingivalis groups ([81]Fig.
   4C).

Fig 4.

   [82]Heatmaps and network analysis depict associations between bacterial
   genera, metabolites, and genes across conditions. Positive and negative
   correlations are visualized, highlighting key interactions and
   significant changes in microbial profiles.
   [83]Open in a new tab

   Correlation analysis among gut microbiota, serum metabolites, and renal
   transcriptome in mice. (A) Heatmap representation of correlations
   between the most abundant gut microbiota and renal function parameters.
   (B) Heatmap visualization of the associations between major serum
   metabolites and renal function parameters. (C) Correlation analysis of
   major differential gut microbiota and top 20 differential serum
   metabolites between the AKI + P. gingivalis group and the AKI group.
   (D) Correlation analysis of major differential gut microbiota and top
   30 differential renal tissue genes between the AKI + P. gingivalis
   group and the AKI group. (A–D) Spearman’s correlation coefficients (r
   value) are depicted using a square color map. Asterisks indicate
   statistically significant correlation analysis results: *P < 0.05; **P
   < 0.01. (E) Correlation network diagram depicting the interactions
   among differential gut microbiota, differential serum metabolites, and
   differential renal tissue genes in the AKI + P. gingivalis group
   compared to the AKI group. Green dots represent metabolites, yellow
   dots represent gut microbiota, and gray dots represent genes.

   Our previous research has shown notable changes in the kidney
   transcriptome of AKI mice following oral infection with P. gingivalis.
   We further analyzed the top 30 genes exhibiting the most significant
   differences between the AKI and AKI + P. gingivalis groups, finding a
   close correlation between inflammatory factors and renal function
   indicators, such as neutrophilic granule protein (Ngp), Il6, Il1r2,
   Osm, and Cxcl2 (Fig. S4A), Moreover, the results of qPCR experiments
   verified those of the transcriptome sequencing (Fig. S4B). A
   comprehensive multi-omics analysis revealed that these inflammatory
   factors not only have a significant connection with changes in gut
   microbiota but also correlate with metabolic differences in the host’s
   blood ([84]Fig. 4D; Fig. S4C). This suggests that oral infection with
   P. gingivalis may affect host metabolites by altering the gut
   microbiome, subsequently regulating the transcription level of kidney
   cells in mice.

   It is worth noting that 3-indoleacrylic acid increased under AKI
   conditions and further increased when AKI was exacerbated by oral
   infection with P. gingivalis (Fig. S3B). Additionally, it showed a
   significant positive correlation with the abundance of Porphyromonas in
   the intestine ([85]Fig. 4C). Through KEGG pathway enrichment analysis,
   we found that 3-indoleacrylic acid was primarily associated with immune
   diseases and signaling molecule interactions (Fig. S4D). Incorporating
   16S rRNA, metabolomics, and transcriptomics into network analysis, we
   identified a close, positive correlation among 3-indoleacrylic acid,
   Porphyromonas, and Ngp ([86]Fig. 4E). In conclusion, these multi-omics
   analyses indicated that oral infection with P. gingivalis might
   exacerbate AKI via the gut-kidney axis, involving 3-indoleacrylic acid
   elevation and Ngp expression in the kidney.

3-Indoleacrylic acid mediated neutrophil-derived NGP production leading to
renal tubular epithelial cell injury

   We have identified a potential association between 3-indoleacrylic acid
   and the elevated expression of NGP in the kidney. In our in vivo
   experiments, high concentrations of 3-indoleacrylic acid were
   associated with a significant increase in NGP expression in the AKI +
   P. gingivalis group compared to the other groups, with NGP expression
   predominantly localized near renal epithelial cells (Fig. S5A).
   Moreover, the expression of the epithelial junction protein CLDN5 was
   significantly reduced in the AKI + P. gingivalis group (Fig. S5B).
   Additionally, caspase-3 expression was markedly elevated in the AKI +
   P. gingivalis group relative to the other groups (Fig. S5C). Notably,
   the increase in NGP expression was closely accompanied by enhanced
   caspase-3 expression in kidney tissue, suggesting a potential role for
   NGP in promoting apoptosis during AKI (Fig. S6).

   Further in vitro experiments indicated that the stimulation of
   3-indoleacrylic acid led to increased NGP protein levels in mouse
   neutrophils ([87]Fig. 5A and B). Concurrently, we also observed an
   elevation in the expression of inflammatory factors such as Il6, Osm,
   and Cyp21a1 ([88]Fig. 5C). It was noted that 3-indoleacrylic acid
   stimulation did not affect renal epithelial cell proliferation
   ([89]Fig. 5D). However, the 3-indoleacrylic acid-pretreated neutrophils
   resulted in a reduction in the expression of Ki67, a protein related to
   nuclear division and proliferation in epithelial cells ([90]Fig. 5E).
   Interestingly, when NGP expression in neutrophils was inhibited, the
   3-indoleacrylic acid-pretreated neutrophils no longer affected the
   expression of Ki67 and CLDN5 in epithelial cells ([91]Fig. 5E and F).
   These experimental results suggested that 3-indoleacrylic acid could
   stimulate neutrophils to express NGP, which in turn caused damage to
   renal epithelial cells.

Fig 5.

   [92]Effects of 3-indoleacrylic acid on NGP expression, mRNA levels, and
   cell proliferation. Western blot, qPCR, and immunofluorescence confirm
   upregulation of NGP and associated markers. CLDN5 expression and
   proliferation were analyzed.
   [93]Open in a new tab

   Promotion of neutrophil-NGP by 3-indoleacrylic acid leading to renal
   epithelial cell injury. (A and B) Western blot analysis of NGP protein
   expression in neutrophils stimulated with 3-indoleacrylic acid,
   followed by quantitative analysis. (C) qRT-PCR analysis of Ngp, Il6,
   Osm, and Cyp21a1 gene expression in neutrophils, normalized to Gapdh.
   (D) CCK-8 assay of renal epithelial cell viability after exposure to
   3-indoleacrylic acid. (E) Ki67 immunohistochemical staining to assess
   the proliferative capacity of renal epithelial cells. (F) Western blot
   analysis of junctional proteins in renal epithelial cells co-cultured
   with neutrophils stimulated by 3-indoleacrylic acid after NGP
   inhibition. Results are expressed as mean ± SD. Statistical
   significance was determined using Student’s t-test: *P < 0.05; **P <
   0.01; ***P < 0.001; and ****P < 0.0001.

DISCUSSION

   Periodontitis not only leads to the destruction and loss of periodontal
   support tissues but is also associated with various systemic diseases
   such as hypertension and diabetes. A substantial body of recent
   research has indicated a significant correlation between periodontitis
   and kidney disease, with the treatment of periodontitis improving
   kidney function ([94]24, [95]25). However, current studies have
   primarily focused on chronic kidney disease ([96]26), and the
   connection with AKI remains unclear. Our study discovered that oral
   infection with P. gingivalis, a key pathogen in periodontitis,
   exacerbated AKI damage and significantly disrupted the gut microbiota
   in AKI mice, leading to changes in the proportions of gut microbial
   communities, particularly an increase in Porphyromonas, Staphylococcus,
   and Lachnospiraceae. Additionally, there was a noticeable change in
   serum metabolites and an increase in inflammatory factors in kidney
   tissue.

   The microbiota and its metabolites play a crucial role in shaping the
   immune system beyond the gut ([97]27). In chronic kidney disease,
   dysbiosis of the gut microbiota and the accumulation of gut-derived
   uremic toxins can further exacerbate the condition ([98]28). In a mouse
   model of renal ischemia-reperfusion injury, the gut microbiota has been
   shown to cause AKI, and the depletion of the gut microbiota can prevent
   such injury ([99]21). Furthermore, the supplementation of specific
   short-chain fatty acids or the introduction of bacterial strains that
   produce these short-chain fatty acids can mitigate ischemic post-AKI in
   mice ([100]29, [101]30). This suggests that the impact of the
   microbiota on the severity of AKI may be related to changes in
   bacterial strains that produce immunomodulatory metabolites. However,
   the influence of periodontitis on the gut microbiota and its metabolism
   in AKI, particularly the role of P. gingivalis oral infection, is not
   well understood. Our study found that after P. gingivalis oral
   infection, the gut microbiota changed, leading to a significant
   alteration in the host’s metabolic products, especially a further
   increase in serum 3-indoleacrylic acid. This metabolite, a tryptophan
   derivative produced by gut bacteria, profoundly impacts host
   physiology, including maintaining the epithelial barrier and immune
   function. Studies have shown that certain anaerobic streptococci
   produce 3-indoleacrylic acid by breaking down tryptophan, which can
   regulate tumor ferroptosis and promote the development of colorectal
   cancer ([102]31). Our research also found that the increase in
   3-indoleacrylic acid caused by P. gingivalis oral infection stimulated
   the high expression of NGP in neutrophils, thereby affecting the
   integrity of renal tubular epithelium. The stimulation of
   3-indoleacrylic acid alone did not significantly change the activity of
   renal tubular epithelium, further confirming that the severity of AKI
   is influenced by changes in bacterial strains that produce
   immunomodulatory metabolites. In addition, we also measured the serum
   levels of indole-3-acetic acid in mice. Indole-3-acetic acid is a
   metabolite that is involved in a variety of cellular processes and
   disease states. The results revealed that its levels were notably
   decreased in the AKI group in contrast to the Sham group. However, P.
   gingivalis infection did not have a significant impact on its levels
   (Fig. S7).

   Neutrophils play an important role in kidney disease, including
   participating in inflammatory responses, promoting renal fibrosis, and
   affecting the repair of kidney damage ([103]32). Single-cell sequencing
   analysis has revealed that NGP is expressed during neutrophil
   differentiation as part of the secondary granule genes and is involved
   in the maturation and functional acquisition of these cells ([104]33).
   In addition, NGP plays an important role in inflammation and
   phagocytosis and is closely related to the host’s defense mechanisms
   ([105]34). Our transcriptome data analysis found that after P.
   gingivalis oral infection, the expression of NGP in kidney tissue
   increased and was related to the level of 3-indoleacrylic acid. Our
   cellular experiment results also suggested that 3-indoleacrylic acid
   affected the expression of NGP in neutrophils, and silencing NGP could
   inhibit the damaging effect of neutrophils on the renal tubular
   epithelium.

   Regarding the potential guiding significance and application prospects
   for future clinical treatment, our study offers several important
   implications. In the aspect of diagnosis, the identified changes in gut
   microbiota, serum metabolites, and kidney tissue gene expression could
   potentially serve as biomarkers. For example, the level of
   3-indoleacrylic acid in the serum might be used to assess the risk of
   AKI exacerbation related to P. gingivalis infection. In terms of
   treatment, intervening in the gut microbiota, either by using
   antibiotics as in our study or through other means like beneficial
   bacteria supplementation, could be a new strategy. Additionally,
   developing drugs that target the 3-indoleacrylic acid-mediated
   mechanism could provide new therapeutic options.

   The gut microbiota-metabolic network was closely related to the
   development of AKI. P. gingivalis oral infection disrupted the balance
   of gut microbiota and was an important modifier determining the
   severity of AKI. Under the “gut-kidney axis,” P. gingivalis might cause
   an increase in the level of the gut microbial metabolite
   3-indoleacrylic acid, interfering with kidney immunity and disrupting
   the maintenance of kidney epithelium. Therefore, intervening in the gut
   microbiota may provide a new treatment strategy for AKI patients with
   periodontitis ([106]Fig. 6).

Fig 6.

   [107]Illustration of pathway linking P. gingivalis infection to AKI via
   gut microbiota dysbiosis, increased 3-indoleacrylic acid production,
   neutrophil activation, and NGP induction in renal epithelial cells.
   Arrows depict the progression and interactions.
   [108]Open in a new tab

   Proposed mechanism diagram. P. gingivalis oral infection disrupted the
   balance of gut microbiota and was an important modifier determining the
   severity of AKI. P. gingivalis might cause an increase in the level of
   the gut microbial metabolite 3-indoleacrylic acid, interfering with
   kidney immunity and disrupting the maintenance of kidney epithelium.

Supplementary Material

   Reviewer comments
   [109]reviewer-comments.pdf^ (812.1KB, pdf)

ACKNOWLEDGMENTS