ABSTRACT

   During the process of periodontitis-promoting oral squamous cell
   carcinoma (OSCC), the periodontitis microbiota can facilitate OSCC
   development by activating γδ T cells. Inhibiting γδ T cells through
   immunotherapy has been shown to significantly alleviate various types
   of cancer. However, the underlying mechanism by which inhibiting γδ T
   cells influenced cancer treatment has not been fully elucidated. In
   this study, a mouse model of OSCC with periodontitis was established,
   and γδ T cells were inhibited by antibodies. Gut samples from the mice
   were collected and analyzed by metabolomics, metagenomics, and 16S
   rRNA. Integrative analysis of the gut metabolome and microbiome
   revealed that targeting γδ T resulted in changes in the levels of
   metabolites associated with cancer in the gut. Although there was no
   difference in the α diversity of microbiota, β diversity was
   significantly different, with a more heterogeneous community structure
   in the mice receiving targeted γδ T immunotherapy. Statistical analysis
   of the gut microbiota at the species level revealed a significant
   enrichment of Lactobacillus murinus, which was significantly correlated
   with γδ T abundance. The functional analysis revealed that inhibiting
   γδ T could impact the functional gene. A comprehensive analysis
   revealed that L. murinus is especially associated with changes in
   adenine, which also had connection with the concentration of IL-17 and
   the abundance of γδ T.

IMPORTANCE

   This study revealed the effect of γδ T cells on gut microbial dysbiosis
   and identify potential links between gut microbiota and metabolism,
   providing new insights into the role of γδ T during the process of
   periodontitis-induced OSCC, and identifying relevant biomarkers for
   future research and clinical monitoring protocol development.

   KEYWORDS: γδ T, OSCC, metabolomics, metagenomics, microbiota

INTRODUCTION

   Oral squamous cell carcinoma (OSCC) is a common malignant tumor, with
   over 90% of cases occurring in squamous tissue of the head and neck. It
   is characterized by fast metastasis, high recurrence rates, and drug
   resistance, with over 350,000 cases and ~170,000 deaths from OSCC
   annually ([34]1, [35]2). The main risk factors for OSCC include
   smoking, heavy alcohol consumption, betel nut chewing, and the human
   papillomavirus (HPV). Additionally, differences in oral microbiota,
   dietary factors, immune status, environmental pollutants, occupational
   exposure, and genetic diseases are related to the development of OSCC
   ([36]3).

   Periodontitis is an oral inflammatory disease caused by bacterial
   plaque microorganisms. In addition to being an oral disease, chronic
   periodontitis has been shown to develop into a systemic disease
   ([37]4). Research has shown that there is a correlation between
   periodontitis and oral cancer ([38]5[39]–[40]7). Periodontitis is one
   of the risk factors for oral cancer, and patients with periodontitis
   have a significantly increased risk of developing OSCC ([41]8). Our
   previous research has also provided strong support for the promotion of
   OSCC development by periodontitis ([42]9).

   γδ T cells are a unique subset of lymphocytes that have been reported
   to have anti-tumor or pro-tumor functions in several cancer types and
   play an important role in immune responses ([43]10). As a key subset in
   oral barrier immune surveillance, γδ T cells can recognize various
   pathogen antigens ([44]11). IL-17^+ γδ T cells are the major source of
   interleukin-17 (IL-17), which has an immunosuppressive effect and can
   also directly promote cancer progression ([45]12). Our previous
   research has demonstrated that inhibiting γδT cells has a significant
   therapeutic effect on periodontitis-promoted OSCC, and γδT cells can be
   a key target for treating OSCC promoted by periodontitis ([46]9).
   Research has shown that gut microbiota can inhibit IL-17 production by
   cecal γδ T cells, and metabolites derived from these microbiota can
   reduce the production of IL-17 by gut γδ T cells ([47]13). The gut
   microbiota can shape overall immunity and affect health and disease
   status, including cancer, at the systemic level ([48]14). The sustained
   interaction between the gut microbiota and the mucosa regulates
   inflammation and mediates immune tolerance ([49]15). Changes in the gut
   microbiota may affect the stability of the oral microbiota composition
   through the gut-oral axis, and may also affect the occurrence of oral
   cancer through regulatory effects on the immune system ([50]16).

   The mechanism by which inhibiting γδ T cells promotes treatment of OSCC
   through periodontitis has not been fully elucidated. In this study, we
   collected and sequenced gut samples from OSCC mice treated with γδ T
   cell immunotherapy (Fig. S1), hoping to uncover the role of γδ T cells
   in the gut during the process of periodontitis-promoted OSCC and
   identify relevant biomarkers for future research and clinical
   monitoring protocol development.

MATERIALS AND METHODS

Collection and processing of oral saliva samples

   This experiment also included four healthy volunteers without
   periodontitis. All volunteers had no other maxillofacial or serious
   systemic infectious diseases, had not taken antibiotics, hormones, or
   antifungal drugs within 3 months, had no history of surgery,
   radiotherapy, chemotherapy, or pregnancy within the past 6 months. At 9
   a.m., all participants were asked to rinse their mouths after fasting
   for 1 hour, and ~2 mL of non-stimulated saliva was collected. After
   mixing the saliva samples, they were centrifuged at 8,500 g for 10
   minutes, and the lower precipitate was aliquoted and quickly frozen for
   storage. This study was approved and supervised by the Medical Ethics
   Committee of West China Hospital of Stomatology, Sichuan University
   (WCHSIRB-OT 2019-015), and all participants gave informed consent.

Experimental mouse model

   Six-week-old male BALB/c mice free of specific pathogens (SPF) were
   purchased from Chengdu Dossy Experimental Animals Co., Ltd. and were
   housed in the State Key Laboratory of Oral Diseases at Sichuan
   University. SPF mice were bred in an environment free of specific
   pathogens, without common pathogenic infections such as bacteria,
   viruses, and parasites. The use of SPF mice helps eliminate
   interference from external pathogens, ensuring more reliable
   experimental results. The mice were fed with sterile food and water
   throughout the experiment. All procedures in the animal experiment were
   performed in accordance with the regulations of the State Key
   Laboratory of Oral Diseases. This animal study was approved by the
   Ethics Committee of West China Hospital of Stomatology, Sichuan
   University (WCHSIRB-D-2019-015).

   The AOP-control group (establishment of periodontitis and submucosal
   injection of squamous cell carcinoma for 21 days, advanced OSCC with
   periodontitis, n = 6) and AOP-treated group (inhibition of γδ T cells
   by intraperitoneal injection of UC7-13D5 in the AOP-control group, n =
   5) mice were subjected to periodontitis model treatment: mice were
   given sterile water containing kanamycin (0.5 mg/mL) for three
   consecutive days, and then switched to sterile water without
   antibiotics after oral bacteria washing. A 5-0 sterile suture was tied
   around the second molar in the maxilla of the mouse. Microbiota
   inoculation was obtained from saliva of patients with periodontitis or
   healthy individuals. We collected 2 mL of saliva from each designated
   patient or healthy person. All saliva was mixed, aliquoted, and
   cryopreserved quickly. The frozen human saliva sample was centrifuged
   at 8,500 g for 15 minutes, and the resulting precipitate was thoroughly
   mixed with 3% carboxymethyl cellulose, ensuring that each mouse is
   inoculated with an equal volume of the mixture ([51]9). The mixture was
   repeatedly applied to the cheek and palatal surfaces of the mouse molar
   area. This was done for seven consecutive times, with 1-day intervals
   between each inoculation.

   The AOP-control and AOP-treated group mice were all subjected to OSCC
   model treatment: 5 × 10^6 SCC7 squamous cell carcinoma cells were
   resuspended in 50 µL DMEM medium and injected into the submucosa of the
   cheek of the mouse. After inoculation of cancer cells, the number of
   surviving mice was recorded every day. On the 21st day after cancer
   cell inoculation, the mice were euthanized, and tumor tissue was
   collected. The AOP-treated group mice were given intraperitoneal
   injection of TCR γδ monoclonal antibody (200 mg/mouse) every 2 days
   until sample collection.

Liquid chromatography-mass spectrometry metabolomics analysis

   Under sterile conditions, fecal samples were collected from each group
   of mice and stored at −80°C for testing. This test was completed by
   Guangdong Magigene Biotechnology Co., Ltd. ① After grinding and
   crushing 25 mg of the sample, 500 µL of extraction solution (40%
   methanol/40% acetonitrile/10% water) was added. ② The mixture was
   sonicated for 5 minutes and allowed to stand at −40°C for 1 hour. ③
   Under low-temperature conditions, the supernatant was centrifuged at
   12,000 g for 15 minutes and transferred to a small bottle for testing.
   ④ The target compounds were separated by chromatography using the
   Vanquish ultra-high-performance liquid chromatography system.
   ThermoFisher Q Exactive HF-X mass spectrometer was used for mass
   spectrometry data acquisition. ⑤ The original data were converted using
   ProteoWizard software and annotated with the mass spectrometry database
   after processing. ⑥ Differential metabolites were visualized in the
   form of volcano plots and radar charts. By analyzing the enriched
   pathways and topology of differential metabolites, the pathways with
   the highest correlation to metabolite differences were identified and
   displayed in a rectangular tree diagram. The above results were
   explained in terms of the AOP-treated group compared to the AOP-control
   group.

16S rRNA sequencing analysis

   ① Collected oral and fecal samples from each group of mice under
   sterile conditions and stored them at −80°C for later use. ② Extracted
   total DNA according to the instructions of the TIANamp Bacteria DNA
   kit, and measured the purity and concentration of DNA using a NanoDrop
   spectrophotometer. Stored the extracted DNA at −80°C for later use. ③
   Guangdong Magigene Biotechnology Co., Ltd. utilizes the PE250 strategy
   on the Illumina NovaSeq 6000 platform, employing primers 515F
   (GTGCCAGCMGCCGCGGTAA) and A806R (GGACTACVSGGGTATCTAAT) for
   high-throughput sequencing of the V4–V5 region of the bacterial 16S
   rRNA gene. ④ Sequence processing was performed after library
   construction. The sequencing results were annotated and clustered using
   the UPARSE algorithm ([52]17). ⑤ α-diversity analysis, β-diversity
   analysis, and differential analysis of some species composition were
   based on the OTU table.

Metagenomic sequencing analysis

   ① The sequencing was performed by Guangdong Magigene Biotechnology Co.,
   Ltd. Used the ALFA-SEQ DNA Library Prep Kit to generate sequencing
   libraries and added index codes. Evaluated the library quality using
   the Qubit 4.0 Fluorometer (Life Technologies, Grand Island, NY) and the
   QSEP400 High-Throughput Nucleic Acid Protein Analysis System (Houze
   Biology Technology Co., Houze Biology Technology, China). After quality
   control of the library, the library was sequenced on the Illumina
   Novaseq 6000 platform, producing paired-end reads with a length of 150
   base pairs. ② Species annotation was performed by comparing the
   sequencing data to the NCBI-NR database, and functional annotation was
   performed by comparing the sequencing data to the KEGG database.

Co-inertia analysis

   To help explain the relationship between the microbiome and
   metabolomics data sets, we integrated them using a multivariate method
   called co-inertia analysis (CIA) ([53]18). Principal component analysis
   (PCA) was applied to each individual data set as input to CIA. The
   sorting of the two data sets with the highest covariation was
   determined to identify shared biological trends between the two data
   sets. CIA was performed using the ade4 package in R (4.2.1).

Pearson correlation analysis

   In order to investigate whether there is a correlation between the
   microbiota in the gut and oral cavity, this study conducted correlation
   analysis using the major microbial genera in the gut, oral cavity,
   AOP-control, and AOP-treated groups. A P value of less than 0.05 was
   considered statistically significant, and Pearson correlation analysis
   was performed using the Hmisc package in R (version 4.2.1).

Statistical analysis

   The data are presented as mean ± SD of independent samples. Analysis of
   variance and Mann–Whitney U tests were used for analyzing parametric or
   non-parametric data, respectively. Student's t-test was used for
   comparing between two groups. P < 0.05 was considered statistically
   significant. The criteria for metabolomics data analysis used a cutoff
   of P < 0.05 for Student's t-test and a variable projection importance
   greater than one for the first principal component of the orthogonal
   projections to latent structures discriminant analysis (OPLS-DA) model.
   Correlation analysis was performed by calculating the Spearman
   correlation coefficient, and a heat map was drawn for variables with
   |r| > 0.8 and P < 0.05.

RESULTS

Inhibition of γδ T affects gut metabolism in periodontitis promoting OSCC

   In our prior research ([54]9), we observed that oral microbiota from
   periodontitis can facilitate OSCC development by activating γδ T. In
   the AOP-treated group, which consisted of OSCC mice treated with
   periodontitis and UC7-13D5 injection to inhibit γδ T, the tumor weight
   was significantly decreased in comparison to their respective controls
   without γδ T inhibition. Furthermore, the AOP-treated group exhibited a
   lower γδ T cell percentage in tumor tissues and lower IL-17 expression
   in serum in comparison to their respective controls. These OSCC mouse
   parameters are presented in Table S1.

   To further identify key biomarkers for γδ T-immunotherapy in
   periodontitis-associated OSCC, this study conducted non-targeted liquid
   chromatography-mass spectrometry metabolomics analysis on the feces of
   mice in the AOP-control and AOP-treated groups, which identified 838
   and 300 metabolites in positive and negative ion modes, respectively.
   After comparing the metabolic compositions of the AOP-treated and
   AOP-control groups, the differentially expressed metabolites were
   presented in a volcano plot ([55]Fig. 1A), revealing significant
   differences in 530 metabolites (positive ion mode) and 147 metabolites
   (negative ion mode). Both positive and negative ion modes were used in
   this experiment for metabolite analysis. A radar chart was employed to
   display a set of metabolites with the most significant changes in
   content ([56]Fig. 1B). Among them, the AOP-treated group exhibited a
   significant increase in the content of adenine, hydrocinnamic acid,
   5-(2-hydroxyethyl)−4-methylthiazole, thiamine, histamine, actinidine,
   deoxyadenosine, indole-3-carboxylicacid, indole-3-propionicacid,
   byssochlamicacid, 5-methylcytidine, and [4]-gingerdiol3,5-diacetate,
   when compared to the AOP-control group. Meanwhile, the content of
   beta-alanine, L-phenylalanine, cytosine, taurine, maleicacid, uridine,
   and (-)-epigallocatechin3-cinnamate decreased significantly. The fecal
   metabolites adenine ([57]19), hydrocinnamic acid ([58]20),
   indole-3-carboxylic acid ([59]21), deoxyadenosine ([60]22), thiamine
   ([61]23), and histamine ([62]24), which are enriched in the AOP-treated
   mice, have been shown to have inhibitory effects on cell proliferation.
   Additionally, 5-(2-hydroxyethyl)−4-methylthiazole ([63]25) and
   indole-3-propionic acid ([64]26) are associated with anti-inflammatory
   effects. Conversely, the observed reduction of uridine ([65]27, [66]28)
   and L-phenylalanine ([67]29) in the feces of AOP-treated mice is
   associated with an increased risk of cancer.

Fig 1.

   [68]Fig 1
   [69]Open in a new tab

   The effects of γδ T cell inhibition on the fecal metabolome of mice.
   (A) The volcano plot displays differential metabolites between the
   AOP-treated and AOP-control groups. The x-axis represents fold changes
   in each substance, and the y-axis represents the P value of the student
   t-test. The size of the scatter plot represents the VIP value of the
   OPLS-DA model. Red dots represent upregulated metabolites, and blue
   dots represent downregulated metabolites. (B) The radar plot shows
   trends in changes in the levels of differential metabolites. The ratios
   of the main differential metabolites were calculated, and a radar plot
   was made after logarithmic transformation with a base of 2. The purple
   shadow represents the AOP-control group, and the green shadow
   represents the AOP-treated group. (C) KEGG pathway enrichment analysis
   indicates 14 affected metabolic pathways. The size of the square
   represents the impact factor. The left panel represents the positive
   ion mode, and the right panel represents the negative ion mode. (D)
   Correlations between fecal metabolites, IL-17, γδ T cells, and tumor
   weight. The correlation coefficient (r value) is represented using a
   square color map, with red indicating positive correlation and blue
   indicating negative correlation. The darker the color, the higher the
   correlation. An "X" in a square indicates that the correlation analysis
   is not statistically significant.

   To gain a better understanding of the metabolic pathways involved in
   the promotion of OSCC through the oral microbiome in periodontitis by
   γδT cells, significant changes in metabolites were analyzed using KEGG
   pathway enrichment analysis ([70]Fig. 1C). The results revealed that
   the inhibition of γδT cells in mice with periodontitis-associated OSCC
   affected a total of 14 metabolic pathways, primarily involving taurine
   and hypotaurine metabolism, pyrimidine metabolism, primary bile acid
   biosynthesis, purine metabolism, phenylalanine, tyrosine, and
   tryptophan biosynthesis, beta-alanine metabolism, phenylalanine
   metabolism, and biotin metabolism, among others. Of these pathways,
   cell signaling and environmental stress response were identified as the
   most relevant biological processes.

   This experiment conducted correlation analysis between the identified
   differential metabolites, the expression of IL-17, the abundance of γδ
   T, and tumor weight. The results were visualized using a heatmap
   ([71]Fig. 1D). The metabolites adenine, hydrocinnamic acid,
   indole-3-carboxylic acid, deoxyadenosine, thiamine, histamine,
   5-(2-hydroxyethyl)−4-methylthiazole, and indole-3-propionic acid, which
   have been reported to have inhibitory effects on cell proliferation or
   inflammation, were found to be significantly and negatively correlated
   with the abundance of γδ T. Conversely, uridine and L-phenylalanine,
   which have been associated with a high risk of cancer, showed
   significant and positive correlation with the abundance of γδ T. After
   obtaining the matching information of differentially expressed
   metabolites in each group, we performed pathway search and regulatory
   interaction network analysis on the KEGG database of the corresponding
   species. The results of the regulatory analysis are presented as a
   network plot (Fig. S2).

   In the context of OSCC with periodontitis, targeting γδ T resulted in a
   substantial elevation in the levels of metabolites associated with
   anti-cancer effects and a concomitant decrease in those promoting
   cancer in the gut. This observation highlights the potential
   involvement of host metabolism in the immunotherapeutic efficacy of γδ
   T cell-targeting strategies and underscores the impact on multiple
   tumor-related metabolites. These findings further suggested that these
   metabolites might serve as crucial biological markers for the precise
   and effective immunotherapy of OSCC in the presence of periodontitis.

Targeting γδ T improves gut microbiota homeostasis in periodontitis promoting
OSCC

   In this study, fecal samples from both the AOP-control and AOP-treated
   groups of mice were subjected to 16S rRNA sequencing, resulting in a
   data set of 734,467 high-quality sequences and identifying 5,352 OTUs.
   Although analysis using the Chao1, Observed_richness, and Simpson
   indices revealed no significant differences in α-diversity of gut
   microbiota between the two groups ([72]Fig. 2A), the PCoA analysis
   showed that the targeted γδ T cell immunotherapy had a notable impact
   on the composition of gut microbiota, with significant differences in
   β-diversity of gut microbiota observed between the AOP-control and
   AOP-treated group mice ([73]Fig. 2B).

Fig 2.

   [74]Fig 2
   [75]Open in a new tab

   Analysis of diversity and abundance of gut microbiota in mice. (A)
   α-diversity of gut microbiota in each group of mice, as measured by
   Chao index, Observed richness, and Simpson index. (B) β-diversity of
   gut microbiota in each group of mice shown as a PCoA plot of UniFrac
   distances. (C) Abundance of major phylum, genus, and species of gut
   microbiota in mice. (D) Heatmap showing the relative abundance of 33
   major classified bacterial species within each group. (E) Analysis of
   inter-group differences in major gut microbiota at the species level.
   *P < 0.05.

   Furthermore, metagenomic sequencing was conducted to gain further
   insight into the gut microbiota of the two groups of mice, revealing
   that the dominant phyla were Bacteroidetes (35.4%), Firmicutes (27.2%),
   and Proteobacteria (5.2%). At the genus level, Bacteroides (12.3%),
   Prevotella (2.6%), Lactobacillus (2.5%), Alistipes (2.5%), and
   Clostridium (2.3%) were the most abundant genera. Among the species,
   Bacteroides acidifaciens (2.6%), bacterium J10(2018) (1.7%),
   Lachnospiraceae bacterium A4 (1.7%), Lactobacillus murinus (1.3%),
   Klebsiella pneumoniae (1.0%), Escherichia coli (0.9%), Muribaculum
   intestinale (0.86%), Firmicutes bacterium ASF500 (0.6%), and Prevotella
   sp. MGM1 (0.55%) were the most prevalent ([76]Fig. 2C).

   Based on the genus-level sequencing results of intestinal
   microorganisms, the experiment conducted a correlation analysis between
   all genus-level classifications and the IL-17 protein concentration, γδ
   T cell abundance, and tumor weight (Fig. S3). The analysis indicated a
   significant positive correlation between Bacteroides and the abundance
   of γδ T, while Lactobacillus was significantly negatively correlated
   with the abundance of γδ T. During the targeted inhibition of γδ T in
   the treatment of OSCC with periodontitis, the abundance of the
   beneficial intestinal bacteria ([77]30) Lactobacillus increased, which
   was significantly correlated with the IL-17 protein concentration and
   γδ T cell abundance.

   The study also presented a heatmap that displays the relative abundance
   of 33 major bacterial species identified in the AOP-control and
   AOP-treated group. The top three major bacterial species in the gut of
   mice in the AOP-control group were bacterium J10(2018), E. coli, and K.
   pneumoniae, while bacterium J10(2018), L. bacterium A4, and L. murinus
   were enriched in the gut of mice in the AOP-treated group ([78]Fig.
   2D). Statistical analysis of the gut microbiota at the species level
   revealed significant differences between the two groups, with the mice
   receiving targeted γδ T cell immunotherapy exhibiting a significant
   enrichment of L. murinus, Eubacterium plexicaudatum, and Acetatifactor
   muris in the gut, along with a significant decrease in K. pneumoniae,
   Bacteroides uniformis, and Bacteroides sp. NM69_E16B ([79]Fig. 2E).

   Overall, the results suggested that in the promotion of OSCC
   progression by periodontitis oral microbiota, targeting the inhibition
   of γδ T could simultaneously improve gut homeostasis and enrich
   probiotics.

The impact of inhibiting γδ T on microbiota functional genes in promoting
OSCC by periodontitis oral microbiota

   In the context of periodontitis oral microbiota promoting OSCC,
   inhibiting γδ T resulted in microbiota functional gene profiles that
   were almost identical between the two groups, with some differences
   still present, as shown by the Venn diagram ([80]Fig. 3A). To
   investigate the specific impact of inhibiting γδ T on functional genes,
   KEGG pathway enrichment analysis was performed, which indicated that
   the pathway distribution of the AOP-control and AOP-treated groups was
   similar, with metabolic pathways being the largest proportion at the L1
   level. Visualization of pathway abundance at the L2 level revealed that
   both groups had similar abundance levels, with amino acid metabolism,
   translation, and carbohydrate metabolism exhibiting higher abundance
   levels ([81]Fig. 3B).

Fig 3.

   [82]Fig 3
   [83]Open in a new tab

   Pathway enrichment analysis and abundance ratio. (A) The Venn diagram
   intuitively shows the number of common genes among groups. (B) The
   pathway distribution of AOP-control group and AOP-treated group at
   different classification levels. (C) The differences between groups in
   terms of pathways. The graph shows the LDA values of the different
   species, with colors representing the corresponding groups. The length
   of the bars in the graph represents the contribution of each species to
   the differences (LDA Score). LDA score is greater than 2. (D) Analysis
   of inter-group differences at the KO level. *P < 0.05.

   The analysis of the pathways that differed significantly between the
   AOP-control and AOP-treated groups were performed. The results showed
   that the AOP-treated group had significantly higher base excision
   repair, nucleotide excision repair, RNA polymerase, and Staphylococcus
   aureus infection pathways, while the AOP-control group had
   significantly higher other glycan degradation, lysosome,
   glycosaminoglycan degradation, pertussis, ascorbate and aldarate
   metabolism, glycosphingolipid biosynthesis-ganglio series, various
   types of N-glycan biosynthesis, and monobactam biosynthesis pathways
   ([84]Fig. 3C). Moreover, this study conducted further analysis of the
   pathways and key proteins (enzymes) that showed significant differences
   between the AOP-control and AOP-treated groups, revealing differences
   in the relative abundance of proteins [85]K01992, K07491 (a protein
   that plays an important role in genome evolution, adaptive evolution,
   and the immune system) ([86]31, [87]32), and K21573 between the two
   groups ([88]Fig. 3D).

   Additionally, this study investigated the correlation between these
   microbiota functional genes and the concentration of IL-17, the
   abundance of γδ T, and tumor weight. There were significant negative
   correlations between the abundance of γδ T and K07491 (Fig. S4).
   Moreover, K07491 and K07483 (a transposase that can cause changes in
   genome structure and gene regulation) ([89]33) were significantly
   negative correlated with the concentration of IL-17. Furthermore,
   significant positive correlations were observed between γδ T and the
   abundance of K21573 (involved in the degradation and utilization of
   starch) ([90]34, [91]35), K07165 (a type of receptor protein in
   bacteria that can sense signals in the environment), and K07485 (a type
   of transposase that can catalyze transposition reactions).

   The results indicated that the inhibition of γδ T cells could impact
   the functional gene profiles of oral microbiota associated with
   periodontitis. Furthermore, the observed differences in functional gene
   profiles and their relationship with the immune system highlight the
   potential importance of these factors as therapeutic targets for the
   prevention and treatment of OSCC.

Correlation between gut metabolites and microbiota in periodontitis oral
microbiota-promoted OSCC mice with γδ T suppression

   To investigate the potential correlation between the impact of γδ T
   suppression on alterations in microbiota and metabolites in the mouse
   intestine, this study performed bioinformatics analyses on the
   metabolomic and metagenomic data. PCA was conducted on both data sets,
   retaining two characteristic values, to visualize the correlation
   between the two. CIA was then carried out to explore the differences in
   metabolites and microbiota between the control and experimental groups.
   This analysis revealed a certain correlation between changes in
   intestinal metabolism and microbiota at the genus and species levels in
   both the AOP-control and the AOP-treated mice ([92]Fig. 4A).

Fig 4.

   [93]Fig 4
   [94]Open in a new tab

   Association between gut metabolites and microbes in mice. (A) The
   correlation between major microbiota at the genus (left) or species
   (right) level and major changed metabolites in the intestine is
   presented, where the starting points represent metagenomic features and
   the endpoints represent metabolomic features. The blue dots represent
   the AOP-control group, while the red dots represent the AOP-treated
   group. (B–C) The correlation between major microbiota at the genus (B)
   or species (C) level and major changed metabolites in the intestine is
   shown. The horizontal axis displays the major microbiota, while the
   vertical axis shows the different metabolites. The strength of the
   correlation is represented by the color of the circle, with red
   indicating a positive correlation and blue indicating a negative
   correlation. The darker the color, the stronger the correlation. An "X"
   inside the circle indicates that the correlation analysis is not
   statistically significant.

   The study further investigated the relationship between the main
   microbiota at the genus or species level and the major metabolites that
   underwent changes ([95]Fig. 4B and C). A comprehensive analysis
   revealed that L. murinus of the Lactobacillus genus, a type of
   intestinal microbiota, is associated with changes in multiple
   metabolites, as well as with the concentration of IL-17 protein and the
   abundance of γδ T. The key metabolite associated with L. murinus is
   adenine, which has been reported to have potential anticancer effects
   and is also correlated with the abundance of γδ T ([96]36).

   By conducting bioinformatics analysis, we found that there is a certain
   correlation between changes in intestinal metabolism and microbiota.
   Moreover, we identified L. murinus as the key microbiota associated
   with changes in multiple metabolites, the concentration of IL-17
   protein, and the abundance of γδ T. Adenine, a key metabolite
   associated with L. murinus, was also found to have potential as a
   therapeutic target for preventing and treating OSCC. These findings
   suggest that targeting L. murinus and adenine may offer promising
   approaches for the treatment of OSCC.

Investigating the impact of inhibiting γδ T on resistance genes, virulence
factors, and microbiota interaction

   In this study, we conducted a thorough investigation of the impact of
   inhibiting γδ T on resistance genes in periodontitis-associated OSCC.
   t-Tests were performed on the top four highest abundance resistance
   genes in the AOP-control and AOP-treated groups, and no significant
   difference in resistance genes was observed between the two groups
   ([97]Fig. 5A). However, further t-test analysis on virulence factors
   revealed a significant difference in AslA, an acidic lipopeptide found
   in some pathogens ([98]Fig. 5B). AslA can degrade important molecules
   of the host immune system, and help the pathogen evade immune
   surveillance. Moreover, AslA can also cleave important proteins on the
   surface of host cells, such as platelet-agglutinating factor,
   interfering with normal host cell function ([99]37, [100]38).

Fig 5.

   [101]Fig 5
   [102]Open in a new tab

   Differences in t-tests for mouse resistance genes and virulence
   factors, as well as at the microbial genus level. (A) t-Test of
   resistance genes between AOP-control group (blue) and AOP-treated group
   (yellow). (B) t-Test of independent bacterial factors. (C) The Venn
   diagram shows the number of major microbial genera at the level of the
   mouse gut and oral cavity. (D–F) Correlation analysis of microbial
   genera at the level of the gut (D) or oral cavity (E) or oral bacteria
   in the AOP-control group versus the AOP-treated group (F).

   In addition, we investigated the oral and gut microbiota and found
   significant differences at the genus level, with Bacteroides being the
   only overlapping genus ([103]Fig. 5C). We also examined the correlation
   at the genus level between the oral and gut microbiota. In the gut
   ([104]Fig. 5D), Bacteroides was positively correlated with
   Parabacteroides and negatively correlated with Clostridium. In the oral
   cavity ([105]Fig. 5E), the three pairs of Streptococcus and
   Corynebacterium, Staphylococcus and Corynebacterium, and
   Escherichia-Shigella and Enterococcus are positively correlated, while
   Porphyromonas and Citrobacter are negatively correlated. Furthermore,
   analysis of the correlation at the genus level of microbiota in the
   oral cavity of mice with periodontitis-associated OSCC ([106]Fig. 5F)
   showed that Bacteroides was significantly positively correlated with
   Enterococcus, Citrobacter, and Escherichia-Shigella, Enterococcus was
   positively correlated with Citrobacter, Escherichia-Shigella, and
   Bacteroides, and Streptococcus was positively correlated with
   Corynebacterium.

   Our study highlights the significance of inhibiting γδ T in reducing
   virulence factors in periodontitis-associated OSCC. Furthermore, our
   analysis revealed significant differences in the microbiota in the oral
   cavity and intestine. These findings provide valuable insights into the
   complex interactions among microbiota and virulence factors, and might
   contribute to the development of effective treatment strategies for
   OSCC.

DISCUSSION

   γδ T cells have strong plasticity and can polarize from one phenotype
   to another. It has been reported that γδ T cells can polarize into γδ
   T17 cells (producing only IL-17), γδ T1/17 cells (producing both
   IFN-gamma and IL-17), γδ T1 cells (producing both IFN-gamma and
   TNF-alpha), and γδ T2 cells (producing IL-4). γδ T cells and their
   secretion of IL-17 play an important role in cancer cell growth
   ([107]39), while γδ T cells can also weaken the anti-tumor ability of
   other immune cells ([108]40, [109]41). Due to the plasticity of γδ T
   cells, they can be regarded as “friend” or “foe” in different cancer
   contexts.

   In certain cancers, γδ T cells can shape an immune-suppressive tumor
   microenvironment through their own accumulation and function, or by
   enhancing the activity of other immune-suppressive cells, thereby
   promoting cancer progression. Our previous research has also
   demonstrated that γδ T cells are a key upstream node in the
   pro-carcinogenic effect of periodontal disease-associated oral
   microbiota.

   Previous studies have shown that there is a correlation between the gut
   microbiota and γδ T cells. The gut microbiota can inhibit IL-17
   production in cecal γδ T cells, and metabolites derived from these
   microbiota can reduce IL-17 production by gut γδ T cells ([110]13). In
   this study, there was a significant positive correlation between the
   abundance of γδ T cells and K07491, and a significant negative
   correlation with K21573. K07491 is the REP-associated tyrosine
   transposase (rayT), which belongs to the HUH superfamily of nucleases,
   whose members promote replication or mobilization of various mobile
   genetic elements (viruses, plasmids, and insertion sequences)
   ([111]42). In addition to the conserved residues involved in DNA
   cleavage catalysis, RAYT also carries characteristic structural motifs
   absent from typical IS200/IS605 transposases. In one-third of the
   cases, the DNA sequence flanking the rayT gene is arranged within
   bacterial interspersed mosaic elements (BIMEs) ([112]32), and recent
   studies have shown that DNA gyrase and DNA polymerase I can
   specifically recognize BIME DNA in vitro. These results suggest that
   BIMEs may play a role in the functional organization of bacterial
   nuclei ([113]43). K21573 is a TonB-dependent starch-binding outer
   membrane protein SusC, which is essential for bacterial growth on
   starch ([114]35). The process of nutrient acquisition by bacteria is
   executed by protein machines on the cell membrane. For many bacteria in
   the gut microbiota, this process is accomplished by a protein complex
   consisting of a substrate-binding protein (SusD) and a channel
   transporter protein (SusC) ([115]34). Therefore, we believe that
   targeting γδ T cells as a therapeutic approach can induce changes in
   the metabolic microenvironment within the intestines.

   It is worth noting that γδ T cells can respond to cytokine stimulation
   and change their function. Artificially altering the cytokine balance
   in the tumor immune microenvironment may be another new immunotherapy
   method targeting γδ T cells. Researchers advocate routine testing of
   IL-17 levels before starting γδ T cell adoptive immunotherapy to
   predict the immune status. In summary, whether using γδ T cell
   expansion or targeting γδ T cells, attention should be paid to their
   function and the regulation of cytokine balance in the tumor
   microenvironment.

   Researchers generally agree that there is a mutual interaction between
   the microbiome and γδ T cells, which can affect tissue homeostasis and
   disease development. The role of the microbiome in the development and
   function of γδ T cells has been a focus of research. However, the
   reverse regulation of the microbiome by γδ T cells has been
   underestimated by researchers. Although there are reports that lung γδ
   T cells in cancer patients are closely positively correlated with lung
   microbiome alpha diversity ([116]44), the impact of γδ T cells on the
   microbiome, especially in the oral microbiome, has not been fully
   determined. Studies have shown that ablating γδ T cells significantly
   alters the diversity of the oral microbiome, and similar microbiome
   changes have been reported in IL17R-deficient mice ([117]45),
   supporting the importance of IL-17 signaling in γδ T cell homeostasis.
   However, the bidirectional relationship between γδ T cells and the
   microbiome, especially the effect of γδ T cells on the microbiome, has
   not been fully determined. This study first analyzed the impact of
   ablating γδ T cells on gut ecology in promoting OSCC progression by the
   oral microbiome in periodontitis. The sequencing data of mouse feces
   supported that γδ T cells would reshape the host microbiome ecology in
   a reverse manner. After inhibiting γδ T cells, the content of
   anticancer metabolites in the intestines of tumor-bearing mice
   increased, the content of cancer-promoting metabolites decreased, and
   the probiotics were enriched in the intestines. Furthermore, it was
   found that L. murinus, adenine, and γδ T cell abundance were closely
   related.

   In summary, the influence of host microbiota should be considered in
   the process of targeting γδ T cell therapy for OSCC with concurrent
   periodontitis. First, the impact of environmental changes on the
   reshaping of γδ T cell function should be carefully considered,
   including the influence of oral microbiota structure on γδ T cell
   numbers and function, which in turn affects tumor development. In
   addition, the reverse effect of γδ T cells on the intestinal ecosystem
   is also worth exploring and utilizing.

ACKNOWLEDGMENTS