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. Author manuscript; available in PMC: 2020 Nov 1.
Published in final edited form as: Oral Dis. 2019 Oct 11;25(8):2020–2029. doi: 10.1111/odi.13200

Influence of T2DM and Pre-diabetes on Blood DCs Subsets and Function in Subjects with Periodontitis

Mariana de Sousa Rabelo 1,2, Ahmed El-Awady 2, Adriana Moura Foz 1, Giovane Hisse Gomes 1, Mythilpriya Rajendran 2, Mohamed M Meghil 2, Scott Lowry 2, Giuseppe Alexandre Romito 1, Christopher W Cutler 2, Cristiano Susin 3
PMCID: PMC6933074  NIHMSID: NIHMS1051264  PMID: 31541516

Abstract

Objective

To compare the myeloid and plasmacytoid DCs counts and maturation status among subjects with/without generalized periodontitis (GP) and T2DM.

Methods

The frequency and maturation status of myeloid and plasmacytoid blood DCs were analyzed by flow cytometry in four groups of 15 subjects: healthy controls, T2DM with generalized CP (T2DM+GP), pre-diabetes with GP (PD+GP), and normoglycemics with GP (NG+GP). RT-PCR was used to determine levels of Porphyromonas gingivalis in the oral biofilms and within panDCs. The role of exogenous glucose effects on differentiation and apoptosis of healthy human MoDCs was explored in vitro.

Results

Relative to controls and to NG+GP, T2DM+GP showed significantly lower CD1c+ and CD303+ DC counts, while CD141+ DCs were lower in T2DM+GP relative to controls. Blood DC maturation required for mobilization and immune responsiveness was not observed. A statistically significant trend was observed for P.gingivalis levels in the biofilms of groups as follows: controls<NG+GP<PD+GP<T2DM+GP. Moreover, significantly higher P.gingivalis levels were observed in blood DCs of NG+GP than controls, whereas no differences were observed between controls and PD+GP/T2DM+GP. In vitro differentiation of MoDCs was significantly decreased and apoptosis was increased by physiologically relevant glucose levels.

Conclusion

T2DM appears to inhibit important DC immune homeostatic functions, including expansion and bacterial scavenging, which might be mediated by hyperglycemia.

Keywords: Type 2 diabetes, pre-diabetes, porphyromonas gingivalis, blood dendritic cells, periodontitis

Introduction

Periodontal destruction is the result of a biofilm-induced inflammatory response, which is modulated by the interplay of the innate and adaptive immunity (Pihlstrom et al. 2005, Costalonga et al. 2014). In this context, dendritic cells (DCs) play a major role as sentinels of infection, which are scavenged in the periphery. This can stimulate DC maturation, as well as DC migration towards secondary lymphoid organs (Pickup et al. 2004, Musilli et al. 2011, El-Awady et al. 2015). Recent studies suggest that DC accumulation in the gingival tissues may be a harbinger of periodontal destruction in humans. These DCs form immune conjugates with T cells in periodontal lesions in situ (Jotwani et al. 2001, Jotwani et al. 2003, Jotwani et al. 2004). In humans, there are three generally recognized blood DCs subsets including CD123+ CD303+ plasmacytoid DCs, CD19− CD1c+ (BDCA-1), and CD141+ myeloid DCs (Ziegler-Heitbrock et al. 2010). A previous clinical study by our research group has shown enhanced responsiveness of the pool of myeloid DCs in peripheral blood of subjects with generalized periodontitis (GP) when compared to healthy controls (Carrion et al. 2012). These results combined with two follow-up studies (Miles et al. 2013a; Miles et al. 2013b) indicate that the DC responsiveness seems to result from the microbial carriage of periodontopathogens such as Porphyromonas gingivalis (P. gingivalis) by DCs.

Type 2 diabetes mellitus (T2DM) is a metabolic disease characterized by insulin resistance, which affects over 400 million individuals worldwide.13 Diabetics have been shown to have an altered immune and inflammatory response, including impaired adherence, chemotaxis, and phagocytosis of neutrophils, and upregulated function of monocytes and macrophages (Naguib et al. 2004). Few studies have examined the effect of T2DM on DCs, although there are reports of fewer circulating DCs in diabetics when compared to non-diabetics, which may help explain the compromised immune response associated with T2DM (Seifarth et al. 2008; Blank et al. 2010).

To date, no studies have investigated the effect of T2DM on the pool of peripheral DC subsets in subjects with GP. Our aim was to investigate the distribution of circulating DC subsets, and their maturation status in subjects with and without PD/T2DM and GP. The carriage rate of the keystone pathogen P.gingivalis within these blood DCs and the effect of elevated glucose levels on DC expansion were also investigated.

Methods

This study was conducted between January 2014 and May 2015 in full accordance with the Helsinki Declaration of 1975, as revised in 2013. The study protocol and the informed consent form were reviewed and approved by the School of Dentistry Ethics Committee, University of São Paulo, Brazil (658.998/CEP). Participants were recruited from the School of Dentistry, University of São Paulo. Each participant signed an informed consent form.

Study sample

Subjects seeking treatment at the University of São Paulo School of Dentistry were invited to participate. Subjects 35 to 65 years old who were periodontally healthy or had been diagnosed with GP were eligible to participate. GP was defined as periodontal probing depth ≥5 mm in ≥10 teeth, and bleeding on probing in ≥30% of sites. Periodontally healthy controls had periodontal probing depth ≤4mm and bleeding on probing in <30% sites. Exclusion criteria included: pregnancy, current smoking, body mass index > 40 Kg/m2 (, having received periodontal therapy and systemic antibiotics in the last 6 months, current use of NSAIDS or steroidal anti-inflammatory medications. Participants were categorized with regards to their HbA1C levels into (ADA 2016): T2DM (HbA1C ≥6.5), PD (HbA1C ≥5.7 and ≤6.4) and NG (HbA1C ≤5.6). All participants were under the supervision of an endocrinologist and received dietary intervention and/or oral hypoglycemic agents as needed. All subjects were authorized by their endocrinologist to participate in the study. Sixty subjects were distributed into four experimental groups as follows: healthy controls (n=15), NG+GP (n=15), PD+GP (n=15), and T2DM+GP (n=15).

Interview, periodontal assessment, and blood sampling

Detailed medical and dental histories were obtained using a structured questionnaire. A full-mouth, periodontal exam, involving probing at six sites per tooth, was carried out by two calibrated examiners (M.S.R. and A.M.F.). The following parameters were recorded: visible plaque index (Ainamo & Bay 1975), gingival bleeding index (Ainamo & Bay 1975), bleeding on probing, periodontal probing depth, and clinical attachment level. A manual probe was used to measure periodontal probing depth and clinical attachment level (UNC-15, Hu-Friedy®, Chicago, IL, USA). Reproducibility during the study was assessed in 10 participants, and the intraclass correlation coefficients ranged from 0.91 to 0.94 for periodontal probing depth and from 0.85 to 0.88 for clinical attachment level. The percent agreement within 1mm was >76% for periodontal probing depth and >81% for clinical attachment level. Blood peripheral samples were collected by a nurse into appropriate tubes. For serum, Red top serum tubes and for blood cell quantitation Green Sodium Heparin tubes (BD Vacutainer®; Plus Plastic Serum Tubes, BD, Franklin Lakes, NJ, USA).

Blood sample processing

HbA1c was assessed by high-performance liquid chromatography (DisSTST hemoglobin A1c Analyzer System, BioRad® Laboratories, Hercules, CA, USA) at the University of São Paulo Clinical Analysis Laboratory. Peripheral blood mononuclear cells (PBMCs) were isolated from 25ml of whole blood collected from subjects, into appropriate tubes using Ficoll-Paque Plus density gradient centrifugation (GE Healthcare, Kings Park, NY). PBMCs and all other clinical samples were transported frozen on dry ice by international courier to the Periodontal Molecular Immunology Laboratory at Augusta University. All samples were found to be frozen upon arrival and they were thawed for analysis. Total blood DCs were isolated from the PBMCs by negative selection using an automated immunomagnetic cell separation equipment (RoboSep™, StemCell Technologies, Vancouver, Canada), yielding pan-DCs.

Flow cytometer phenotypic analysis

Immunophenotyping was performed by flow cytometry using a MacsQuant Analyzer 10 (Miltenyi Biotec, Bergisch Gladbach, Germany) following sequential gating. Pan-DCs were stained to determine their phenotype and maturation status using specific monoclonal antibodies: CD1C.FITC, CD303.APC, CD141.VIOBLUE, CCR6/CD196.PE-Vio 770 (Miltenyi Biotec, Bergisch Gladbach, Germany), as well as CCR7/CD197 (eBioscience, Santa Clara, California.). Appropriate isotype controls were used to verify phenotypic determination and FcR blocker to prevent antibody binding to fragment crystallizable region-receptor (FcR) (Table 1). Marker expression was analyzed as the percentage of positive cells in the relevant population defined by forward scatter and side scatter characteristics (Figure 1).

Table 1.

Cell surface markers utilized for the flow-cytometry staining

Reagent Supplier Lot Isotype Clone
CD1c (BDCA-1) Miltenyl Biotec 5151125647 Mouse IgG2a AD5–8E7
CD141(BDCA-3) Miltenyl Biotec 5150413645 Mouse IgG1 AD5–14H12
CD303(BDCA-2) Miltenyl Biotec 5150302145 Mouse IgG1 AC144
CD196(CCR6) Miltenyl Biotec 5150624054 REA Control REA190
CD197(CCR7) eBioscience 471979 Rat IgG2a 3D12
FcR Blocker Miltenyl Biotec NA NA
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Figure 1. Gating strategy for the flow cytometry analysis.

Figure 1.

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(A) Forward and Side Scatter plot to determine the target population; (B) Cells gated for singlets (doublets discrimination); (C) Gate used for the exclusion of dead cells.

Subgingival biofilm sampling

For each subject, subgingival biofilm samples were collected from the four deepest sites (one site per quadrant) that did not exhibit suppuration. Before sampling, the teeth were isolated with cotton rolls and the supragingival plaque was removed. A mini-five Gracey curette (Hu-Friedy®, Chicago, IL, USA) was gently inserted into the pocket in the most apical portion and the subgingival biofilm was collected with a single stroke. The samples were placed in polypropylene tubes with 1.5 ml containing 100 μl buffer solution (10 mM Tris-HCl, 0.1 mM EDTA, pH 7.6) and stored at −80 °C then on dry ice, for transport until use.

Quantitative qrt-PCR

To detect P. gingivalis, total RNA was isolated from the subgingival biofilm and from the PanDCs with the RNeasy Micro Kit (QiAGEN, Hilden, Germany) according to the manufacturer’s instructions. RNA quantity and purity were tested using the Spectrophotometer (Nanodrop™ 1000 Thermofisher, Waltham, Massachusetts, USA) and only ratios of absorbance at 260 and 280 nm of 1.8–2.0, were included in the study. Estimated colony forming units (eCFU) were calculated using expression of 16s rRNA of P. gingivalis. Individual TaqMan® gene expression primer set (forward, reverse and probe) was designed to target mRNA 16s rRNA of P. gingivalis (GenBank: AB035455.1) based on this gene sequence using an online tool (Thermofisher, Waltham, Massachusetts, USA). The primer was verified for specificity and sensitivity by running qrt-PCR of RNA isolated from a standard laboratory strain of P.gingivalis 381 vs. other bacterial species grown in the laboratory, including Streptococcus gordonii and Fusobacterium nucleatum in serial dilutions. In addition, the ability of primer to amplify mRNA 16s rRNA of P.gingivalis within blood DCs was determined by spiking human MoDCs with defined serial dilution of MOIs (from 1–100) of P. gingivalis 381 in vitro (positive control), as well as uninfected MoDCs (negative control). P.gingivalis uptake was confirmed by fluorescent microscopy. A standard curve was generated for cycle threshold (Ct) versus known CFU values, and regression analyses were carried out for each serial dilution to estimate CFU based on 16s rRNA expression (S1 Figure). CFU was quantified based on the raw Ct values. The equation of calculating CFU for P. gingivalis was y = 6E+10e−0.867x (R² = 0.81). For MoDCs invasion model and for quantification of bacteria within human cells each sample eCFU was normalized to the total cell count.

One-step qrt-PCR was performed using Express qPCR SuperMix (Invitrogen, Cat. no. A10312) to detect the presence of P. gingivalis in the biofilm and in panDCs. For qrt-PCR reactions, 5µl of the RNA sample, 25µl PCR master mix (2x) and 2.5µl TaqMan® gene assays were used per reaction. All PCRs were performed in duplicates and were carried out on a real-time PCR, (StepOne® Applied Biosystems, Foster City, California, USA).

Monocyte-Derived DC (MoDC) generation and glucose treatment

An in vitro experiment was conducted in order to explore the effect of elevated glucose on DC differentiation from monocytes. Human monocytes were isolated from mononuclear fractions of peripheral human blood from healthy donors by negative selection (EasySep™ Human Monocyte Isolation Kit, STEMCELL Technologies Inc. Vancouver, Canada). Cells were seeded in the presence of GM-CSF (1000 unit/ml) and IL-4 (1000 unit/ml) (Gemini Bio Products, West Sacramento, CA, USA) at a concentration (3–4 × 105 cells/ml) in RPMI 1640 (HyClone Laboratories, Logan, Utah, USA) containing 10% heat inactivated FBS (Atlanta Biologicals, Flowery Branch, GA, USA) and antibiotic/antimycotic (HyClone, Logan, UT, USA) for 6 days. DC phenotype was confirmed by flow cytometry: CD1a+Cd14-HLADR+. Simultaneously, other groups of monocyte culture were exposed to glucose concentrations of 25mM and 35mM (Bernal-Lopez et al. 2013, Nahman et al. 1992, Piga et al. 2007).

For apoptosis analysis, Annexin-V Apoptosis Detection Kit FITC (eBioscience) was used. Briefly, cells were washed once in 1X phosphate buffered saline (PBS) and once in 1X Annexin-V binding buffer and then stained with FITC-Annexin-V at a 1:20 dilution and mixed gently. After incubation for 20 minutes in room temperature in the dark, cells were washed and resuspended in 200 uL of 1X Annexin-V binding buffer. Consequently, to distinguish apoptotic cells from dead/necrotic cells, 5 uL of Propidium Iodide staining solution were added to the samples and incubated for 5 minutes. Samples were then analyzed using a MacsQuant Analyzer 10 (Miltenyi Biotec, Bergisch Gladbach, Germany).

Sample size calculation

The sample size calculation was based on results obtained in a previous study comparing the blood DC number in periodontally healthy individuals and those with GP. A sample size of 15 participants per experimental group was calculated based on a reduction in the number of DCs from 2150 (SD: 1000) to 1300 (SD: 600) assuming an alpha of 5% and power of 80%.

Statistical analysis

Flow cytometry data and qrt-PCR data were analyzed using non-parametric statistical methods (Stata for Mac, 13.1, StataCorp, College Station, Tx, USA). Kruskall-Wallis tests were used for overall comparison among groups followed by the Dunn test for intergroup comparisons using a Bonferroni correction. Medians, 25% and 75% quartiles are reported. A nonparametric trend test, Jonckheere-Terpstra, was used to analyze the trend distribution of DC cells subtypes and P. gingivalis in biofilm and within DCs. The effect of age on the DC subsets was assessed using a quantile regression for the median. No major deviations from normality were observed for clinical data, and ANOVA and Bonferroni tests were used to compare among groups. The significance levels were set at 0.05.

Results

HbA1c levels

As per study design, subjects with PD and T2DM had a greater HbA1c levels than normoglycemic subjects. Subjects with PD and T2DM were significantly older than healthy controls and no other significant differences were observed among groups. No significant differences in the periodontal parameters were observed among the GP groups while the control group was periodontally healthy (Table 2).

Table 2.

Demographic characteristics and clinical periodontal parameters at the baseline, according to experimental groups.

N=60 Control NG+GP PD+GP T2DM+GP p-value*
Age (years) (mean±SD) 45.3±7.9A 50.4±8.1AB 54.1±7.9B 56.1±8.7B 0.006*
Gender (% female) 33.3 60 60 33.3 0.23
HbA1c (%) (mean±SD) 5.41±0.25 5.46±0.2 6.08±0.26 8.1±1.46 <0.001*
Body Mass Index (mean±SD) 26.3±2.4A 27.2±4.5A 28.5±6.9A 29.2±4.4A 0.386
Visible plaque index (% sites; mean±SD) 12,7±6,2 A 75,7±16,2B 73,7±8,1B 81,8±14,7B <0.01*
Gingival Bleeding Index (% sites; mean±SD) 4,1±5,0 A 75,6±17,5B 78,2±13,5B 82,9±17,2B <0.01*
Bleeding on Probing (% sites; mean±SD) 5,4±5,2 A 73,1±14,3B 73,6±10,3B 75,5±14,7B <0.01*
Periodontal Probing Depth (mm; mean±SD) 1,9±0,3 A 3,3±0.5B 3,4±0,7B 3,6±0,7B <0.01*
Clinical Attachment Level (mm; mean±SD) 1,3±0,5A 3,9±0.9B 4,2±1,3B 4,9±1,3B <0.01*
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SD: standard deviation; HbA1c, glycated hemoglobin; NG, normoglycemic; PD, pre-diabetes; T2DM, type 2 diabetes mellitus; GP, generalized periodontitis.

*

p-value for comparison between experimental groups (One-way ANOVA). Estimates followed by different letters denote statistically significant differences between groups (p<0.05 ANOVA with Bonferroni correction for multiple comparisons).

Circulating DC subsets

Representative flow cytometry plots of circulating DCs subsets are presented for each group in Figure 2. DCs were gated by forward and side scatter and subsequently identified by CD1C+, CD303+, CD141+, CD1C+CCR6+, and CD1C+CCR7+. Figure 3 and table S2 depicts the results of the flow cytometry analysis of circulating DCs subsets. A statistically significant trend across groups was observed for CD1c+ (p<0.001), CD303+ (p<0.001), and CD141+ (p<0.001) DCs; DC counts were as follows: T2DM+GP < PD+GP < NG+GP < controls. In the pairwise comparisons, a significant lower count of blood myeloid DCs (CD1c+) was observed among PD+GP and T2DM+GP subjects compared to healthy controls, while no differences were observed between healthy controls and NG+GP subjects (Fig. 3A). T2DM+GP subjects had significantly lower levels of the CD141+ subpopulation of myeloid DCs than all other groups (Fig. 3B). A significant decrease in blood plasmacytoid DCs (CD303+) was observed among PD+GP and T2DM+GP subjects compared to healthy controls (Fig. 3C). No significant trends or pairwise differences in immature (CD1c+CCR6+) and mature (CD1c+CCR7+) myeloid DC levels were observed among groups (Fig. 3D and 3E). A separate analysis showed that age was associated with CD1C+, CD303+, and CD141+; however, adjusting for age using regression analysis had no major impact on the results.

Figure 2. Representative scatter plots from flow cytometry analysis for each group.

Figure 2.

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NG, normoglycemic; PD, pre-diabetes; T2DM, type 2 diabetes mellitus; GP, generalized periodontitis.

Figure 3. Flow cytometry analysis of circulating DCs subsets.

Figure 3.

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(A) CD1C+; (B) CD141+; (C) CD303+; (D) CCR6+; and (E) CCR7+. NG, normoglycemic; PD, pre-diabetes; T2DM, type 2 diabetes mellitus; GP, generalized periodontitis. Estimates followed by different letters denote statistically significant differences between groups (Kruskall-Wallis followed by Dunn test with Bonferroni correction for multiple comparisons; p<0.05). Trend analysis was estimated using the Jonckheere-Terpstra nonparametric trend test.

P.gingivalis levels in oral biofilms and blood DCs

A significant trend was observed for increased P.gingivalis levels in the biofilm as follows: healthy controls < NG+GP < PD+GP < T2DM+GP (p<0.001). In the pairwise comparison, subjects with GP had significantly higher levels of P.gingivalis in the biofilm than healthy controls irrespective of the diabetes status. With regards to the estimated P.gingivalis counts within myeloid and plasmacytoid DCs, no significant trends were observed (p=0.826). However, in the pairwise comparison, NG+GP had significantly higher P.gingivalis levels within DCs than healthy controls, whereas no significant differences were observed between healthy controls and PD+GP and T2DM+GP, even when considering the proportion of P.gingivalis per cell (Fig. 4).

Figure 4. Porphyromonas gingivalis counts in the biofilm (A) and in circulating DCs (B).

Figure 4.

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NG, normoglycemic; PD, pre-diabetes; T2DM, type 2 diabetes mellitus; GP, generalized periodontitis; CFU, colony forming unit. Estimates followed by different letters denote statistically significant differences between groups (Kruskall-Wallis followed by Dunn test with Bonferroni correction for multiple comparisons; p<0.05). Trend analysis was estimated using the Jonckheere-Terpstra nonparametric trend test.

Influence of glucose on MoDC differentiation and apoptosis

Compared to the control group, the differentiation rate of MoDCs was significantly decreased in the presence of glucose treatments of 25mM and 35mM, respectively (p<0.05, Figure 5). A significant trend was observed for decreased differentiation as follows: control < 25mM < 35mM. Analysis of apoptosis in glucose treated MoDCs suggest that glucose was increasing apoptosis of MoDCs. Percentage of Annexin-V expression in MoDCs was significantly increased in cultures treated with glucose 25mM and 35mM, respectively, relative to control (p<0.05, Figure 6A and B). A significant trend was also observed for increased expression of Annexin-V as follows: control < 25mM < 35mM. No significant differences in Propidium Iodide expression were observed with or without glucose treatment (Figure 6A), suggesting that apoptosis rather than necrosis is the main mode of glucose-induced cell death, by which MoDCs differentiation rate is compromised.

Figure 5. The effect of glucose on the differentiation rate of DCs.

Figure 5.

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Estimates followed by different letters denote statistically significant differences between groups (Kruskall-Wallis followed by Dunn test with Bonferroni correction for multiple comparisons; p<0.05). Trend analysis was estimated using the Jonckheere-Terpstra nonparametric trend test.

Figure 6. Annexin-V and PI expression in response to glucose treatment.

Figure 6.

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A) Flow cytometry scattergrams of annexin-V and PI expression in MoDCs in response to glucose treatment. B) Representation of Annexin-V percentage. Estimates followed by different letters denote statistically significant differences between groups (Kruskall-Wallis followed by Dunn test with Bonferroni correction for multiple comparisons; p<0.05). Trend analysis was estimated using the Jonckheere-Terpstra nonparametric trend test.

Discussion

The aim of the study was to compare the frequency of blood DC subtypes in subjects diagnosed with GP, according to their glycemic levels. Hyperglycemic subjects had significantly reduced myeloid and plasmacytoid DCs when compared to normoglycemic subjects, which supports the contention that glycemic state may affect immune responsiveness. Our in vitro experiments suggest that elevated glucose may hinder DC differentiation via apoptosis rather than necrosis, which may partially explain the present findings. Regarding the scavenging function of these DCs, hyperglycemic subjects with GP appeared to have reduced P.gingivalis carriage rate when compared to normoglycemic subjects with GP. To the best of our knowledge, this is the first study to investigate the DC subset distribution and microbial carriage state in subjects with GP and T2DM.

With regards to hyperglycemia, Seifarth et al. (2008) and Blank et al. (2010) also observed a reduction in circulating DCs in T2DM compared to healthy controls. Corrales et al., (2007) reported quantitative and functional abnormalities, such as decreased ability to secrete proinflammatory cytokines, in circulating monocytes and DCs of diabetic subjects. In contrast, Musilli et al. (2011) reported a significantly higher counts of circulating myeloid DCs in comparison to healthy controls. Our previous clinical study (Carrion et al. 2012) of myeloid DCs in periodontitis patients, also showed a positive correlation between the presence of P.gingivalis in the biofilm and within blood myeloid DCs after scaling and root planning (SRP), suggesting oral biofilm as the source of bacteremia, which were captured by blood DCs (Miles et al. 2014); P.gingivalis invades DCs via its minor Mfa1 fimbrial adhesin (Zeituani et al. 2009, Zeituani et al. 2010) and survives by evading autophagy (El-Awady et al. 2015, Tyagi et al. 2017). The resultant DCs are immature and immunosuppressive for CD4+ and CD8+ T cells via a- indoleamine 2,3- dioxygenase-dependent mechanism (Tyagi et al. 2017). Intracellular persistence of P. gingivalis in DCs was inhibited by blocking DC-SIGN, the surface receptor for Mfa-1, or by stimulating autophagy in myeloid DCs (Zeituani et al. 2009, El-Awady et al. 2015). The present study confirms that the blood DCs in GP patients, regardless of glycemic status, are immature, as evidenced by lack of CCR7 upregulation.

We observed a significant trend of higher P.gingivalis counts in the oral biofilm as the hyperglycemic levels increase; however, we did not observe a concomitant increase in P.gingivalis counts within DCs, suggesting that hyperglycemia may influence the scavenging function of DCs (Savina & Amigorena 2007). Our results suggest that hyperglycemia may alter the responsiveness of monocytes, DC progenitors, and their microbial capture or carriage capabilities, since the proportion of P.gingivalis in relation to the total number of DCs is reduced in these groups.

Current evidence supports the contention that persistent hyperglycemia leads to an exaggerated immune-inflammatory (local) response to the periodontal pathogens, resulting in severe periodontal tissue destruction (Preshaw et al. 2012). The interaction between the advanced glycation end products (AGE) and its receptors (RAGE) induces an oxidative stress that may induce a chronic monocytic upregulation and subsequent secretion of proinflammatory cytokines (Salvi et al. 1997, Graves et al. 2006). It is well established that AGE-RAGE interactions have a negative impact on cellular function of several inflammatory cells including impaired adherence, chemotaxis, and phagocytosis.14 The current study provides another aspect of how the effect of hyperglycemia could affect local immune sentinels (DCs) and systemic dissemination of the microbes they carry.

The true impact of hyperglycemia on DC cellular functions has not been fully explored. A recent in vitro study by Gilardini Montani et al. (2016) showed that high glucose-containing culture medium and hyperglycemic sera impaired monocyte differentiation into DCs. Mechanistic assays showed that DCs cultured in high glucose had reduced expression of activation and maturation markers upon exposure to lipopolysaccharide, indicating a possible dysfunction in the DC maturation in response to the classical activating stimuli represented by pathogen associated molecular patterns (PAMPs) or damage associated molecular patterns (DAMPs). Also, the presence of high glucose or hyperglycemic sera produced a high amount of reactive oxygen species (ROS) and displayed a hyper-activation of Wnt/β-catenin and p38 MAPK pathways causing impairment on DC differentiation/maturation. These results were in accordance with our in vitro data that showed a significantly decreased differentiation rate of MoDCs in response to glucose treatments of 25mM and 35mM. Moreover, glucose induced apoptosis in differentiating MoDCs. Percentage of Annexin-V expression in MoDCs was significantly increased by 2.8- and 3-fold in cultures treated with glucose 25mM and 35mM, respectively, relative to untreated groups.

A limitation of the present study relates to the use of DC cells from healthy donors for the in vitro experiment which evaluated the effect of glucose on MoDC differentiation and apoptosis. The amount of peripheral blood collected was not sufficient for the quantification of DC subsets, the study’s primary objective, and the glucose experiment since dendritic cells comprise between 0.1 and 1% of the total white blood cells; thus, blood samples from donors who were not diabetic were used. The experiment indicated that glucose may impede DC differentiation via apoptosis, which provides a possible mechanism that should be explored in future studies. It is important to acknowledge that other mechanisms may also explain the present results.

In conclusion, our findings suggest that T2DM may negatively affect the pool of myeloid and plasmacytoid peripheral DCs and its content of bacteremic keystone pathogen P.gingivalis. Further functional and clinical investigations are needed to explore the mechanisms behind the compromised DC response in hyperglycemic subjects and its relationship with periodontitis.

Supplementary Material

Supp TableS2. S2 Table. Medians values (75th/25th percentiles) of circulating DCs subsets.

NG, normoglycemic; PD, pre-diabetes; T2DM, type 2 diabetes mellitus; GP, generalized periodontitis. Capital letter represent between groups comparisons (Kruskall-Wallis Test followed by Dunn and Bonferroni).

Supp figS1

S1 Fig. P. gingivalis standard curve.

Acknowledgements

We would like to thank Dr. Marcia Pinto Alves Mayer and Dr. Priscila Larcher Longo for their collaboration during the preparation of the samples in the University of Sao Paulo, Brazil.

Funding information: U.S. Public Health Service Grants from National Institutes of Health/NIDCR R01 DE014328 and R21 DE020916 to C.W.C and São Paulo Research Foundation, FAPESP Grant 2011/06982–4 to G.A.R supported this work.

Footnotes

Conflicts of interest: none to declare.

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Associated Data

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

Supplementary Materials

Supp TableS2. S2 Table. Medians values (75th/25th percentiles) of circulating DCs subsets.

NG, normoglycemic; PD, pre-diabetes; T2DM, type 2 diabetes mellitus; GP, generalized periodontitis. Capital letter represent between groups comparisons (Kruskall-Wallis Test followed by Dunn and Bonferroni).

NIHMS1051264-supplement-Supp_TableS2.doc (35KB, doc)
Supp figS1

S1 Fig. P. gingivalis standard curve.

NIHMS1051264-supplement-Supp_figS1.tif (157.5KB, tif)

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