Identification of hyperglycemia-associated microbiota alterations in saliva and gingival sulcus

Journal Pre-proof Identification of hyperglycemia-associated microbiota alterations in saliva and gingival sulcus Yu-Shan Wei, Ya-Chun Hsiao, Guan-Wei Su, Yi-Ru Chang, Hsiu-Pin Lin, Yi-Shiuan Wang, Yi-Ting Tsai, En-Chi Liao, Hsin-Yi Chen, Hsiu-Chuan Chou, Mei-Lan Ko, WenHung Kuo, Shing-Jyh Chang, Wen-Chi Cheng, Hong-Lin Chan PII:

S0003-9861(19)30991-9

DOI:

https://doi.org/10.1016/j.abb.2020.108278

Reference:

YABBI 108278

To appear in:

Archives of Biochemistry and Biophysics

Received Date: 4 November 2019 Revised Date:

27 December 2019

Accepted Date: 20 January 2020

Please cite this article as: Y.-S. Wei, Y.-C. Hsiao, G.-W. Su, Y.-R. Chang, H.-P. Lin, Y.-S. Wang, Y.-T. Tsai, E.-C. Liao, H.-Y. Chen, H.-C. Chou, M.-L. Ko, W.-H. Kuo, S.-J. Chang, W.-C. Cheng, H.-L. Chan, Identification of hyperglycemia-associated microbiota alterations in saliva and gingival sulcus, Archives of Biochemistry and Biophysics (2020), doi: https://doi.org/10.1016/j.abb.2020.108278. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Inc.

Author Contributions Statement Hong-Lin Chan and Wen-Chi Cheng conceived of the presented idea. Ya-Chun Hsiao, Guan-Wei Su and Yi-Ru Chang verified the analytical methods. Yu-Shan Wei wrote the main manuscript text and prepared all figures with support from Hsiu-Pin Lin and Yi-Shiuan Wang. Hsiu-Chuan Chou, Yi-Ting Tsai, En-Chi Liao, Hsin-Yi Chen, Mei-Lan Ko, Wen-Hung Kuo and Shing-Jyh Chang, helped supervise the project. All authors discussed the results and contributed to the final manuscript.

1

Identification of hyperglycemia-associated microbiota alterations in saliva and

2

gingival sulcus

3 4

Yu-Shan Wei1, Ya-Chun Hsiao2, Guan-Wei Su3, Yi-Ru Chang4, Hsiu-Pin Lin4,

5

Yi-Shiuan Wang1, Yi-Ting Tsai1, En-Chi Liao1, Hsin-Yi Chen1, Hsiu-Chuan Chou5,

6

Mei-Lan Ko5,6, Wen-Hung Kuo7, Shing-Jyh Chang8, Wen-Chi Cheng3,*, and

7

Hong-Lin Chan9,*

8 9

1

Institute of Bioinformatics and Structural Biology, National Tsing Hua University,

10

Hsinchu, Taiwan

11

2

Department of endocrinology and metabolism, Hsinchu Mackay Memorial Hospital

12

3

Dental department of Hsinchu Mackay memorial hospital

13

4

General Biologicals Corporation, Hsinchu, Taiwan

14

5

Department of Biomedical Engineering and Environmental Sciences, National Tsing

15

Hua University, Taiwan

16

6

17

Branch, Hsinchu 300, Taiwan.

18

7

Department of Surgery, National Taiwan University Hospital, Taipei, Taiwan

19

8

Department of Obstetrics and Gynecology, Hsinchu MacKay Memorial Hospital,

20

Hsinchu, Taiwan

21

9

22

Science, National Tsing Hua University, Hsinchu, Taiwan

Department of Ophthalmology, National Taiwan University Hospital Hsin-Chu

Institute of Bioinformatics and Structural Biology and Department of Medical

23 24 25 26 1

27

*Correspondence to:

28

Dr. Hong-Lin Chan, Institute of Bioinformatics and Structural Biology & Department

29

of Medical Science, National Tsing Hua University, No.101, Kuang-Fu Rd. Sec.2,

30

Hsinchu, 30013, Taiwan. Tel: 886-3-5742476; Fax: 886-3-5715934; E-mail:

31

[email protected]

32

Dr. Wen-Chi Cheng, General Biologicals Corporation, No. 6, Innovation First Road,

33

Hsinchu Science Park, Hsinchu 30076, Taiwan. Tel: (+886)-3-577-9221 ext. 270; Fax:

34

886-3-577-9227; E-mail: [email protected]

35 36

Keywords: MALDI-TOF mass spectrometry, Oral Microbiology, Hyperglycemia

37

2

38 39

Identification of hyperglycemia-associated microbiota alterations in saliva and gingival sulcus

40 41

Abstract

42

Oral microbes are a contributing factor to hyperglycemia by inducing an increase

43

in insulin resistance resulting in uncontrolled blood glucose levels. However, the

44

relationship between the distribution of oral flora and hyperglycemia is still

45

controversial. Combining the power of MALDI-Biotyper with anaerobic bacterial

46

culture, this study explores the correlation between anaerobic bacteria in the oral

47

cavity and blood glucose levels. The results demonstrated that altered blood glucose

48

levels contributed to a varied bacterial distribution in the oral cavity. Specifically,

49

Veillonella spp. and Prevotella spp. were identified in a higher proportion in people

50

with elevated blood glucose levels. Six bacterial species identified in this study

51

(Prevotella

52

Streptococcus mitis, Streptococcus salivarius, and Veillonella parvula) not only

53

demonstrated a positive association with higher blood glucose levels, but also likely

54

contribute to the development of the condition. The data demonstrated MALDI-TOF

55

MS to be a simpler, faster, and more economical clinical identification tool that

56

provides clarity and depth to the research on blood glucose and oral microbiota.

melaninogenica,

Campylobacter

57 3

rectus,

Streptococcus

gordonii,

58

Introduction

59

Oral microbes play an important role in the human health. The synergy between

60

oral microbes and the body helps in the defense against external stimuli. However, an

61

imbalance in the oral microbiome can lead to a number of oral diseases, such as dental

62

caries, periodontitis, periodontal disease, oral cancer [1], in addition to other systemic

63

diseases affecting the gastrointestinal, nervous, cardiovascular, endocrine, and

64

immune systems [2, 3]. Specifically, oral microbes have been reported as one of the

65

factors causing hyperglycemia. Studies indicate that oral microbes induce an increase

66

in insulin resistance leading to uncontrolled blood glucose levels [4, 5]. Two

67

indicators commonly used by diabetics to monitor glycemic status are fasting blood

68

glucose (GluAC) and glycated hemoglobin (HbA1c). The GluAC levels reflect

69

short-term glycemic changes whereas HbA1c signify long-term (2-3 months) blood

70

glucose levels [6, 7]. An increase in blood glucose results in a concomitant rise in the

71

glucose concentration in the gingival space [8, 9], possibly affecting the oral

72

microbiome distribution [10, 11]. Previous studies have demonstrated that, compared

73

to people with lower blood glucose levels, those with higher blood glucose levels

74

have

75

Capnocytophaga, Campylobacter, Eikenella, Fusobacterium, Gemella, Neisseria,

76

Parvimonas,

77

Terrahemophilus, Tannerella, TM7, Veillonella, and lower proportions of Eubacterium,

a

higher

proportion

of

Peptostreptococcus,

members

of

Leptotrichia,

4

Aggregatibacter,

Selenomonas,

Actinomyces,

Streptococcus,

78

Filifactor, Porphyromonas, Prevotella, Pseudomonas, Synergistetes, Tannerella and

79

Treponema [11-15]. At the species level, Eikenella corrodens, Fusobacterium

80

nucleatum, Prevotella intermedia, Streptococcus agalactiae, Treponema denticola,

81

Veillonella parvula, and V. dispar were identified more frequently in populations with

82

poor glycemic indices [10, 12-14]. However, some studies indicated that the

83

proportion of P. gingivalis and Tannerella forsythia is higher in subjects with healthy

84

blood glucose status compared to those with a poor one, in contrast to studies that

85

reported the opposite. Furthermore, other groups identified no correlation between the

86

distribution of P. gingivalis and T. denticola, and blood glucose levels [13, 15-20].

87

Therefore, the impact of blood glucose status on the oral bacterial flora is still

88

inconclusive [10, 11].

89

In the previous reports, identification of the microorganisms in the correlation

90

studies between oral microbiota and glycemic states was performed using classical

91

laboratory procedures, such as checkerboard DNA-DNA hybridization, polymerase

92

chain reaction, and 16S rDNA Sanger sequencing. In these studies, MALDI BioTyper

93

was frequently used to identify clinical pathogens, such as pathogens in the blood,

94

urine, intestinal tract, and oral cavity [21-26]. The advantage of MALDI TOF MS for

95

identifying microorganisms compared to conventional identification methods is that

96

the identification can be achieved without any prior knowledge of the taxonomic 5

97

affiliation [27-29], in addition to being more economical, rapid and accurate [25, 29,

98

30]. The MALDI BioTyper obtains the microbial identification information by

99

comparing the test sample spectra with the reference spectra in the database, using

100

scoring algorithms [21]. Several studies have demonstrated the efficacy of the

101

MALDI-Biotyper for accurate species-level identification at a rate greater than 70%

102

[31-33].

103

The conflicting reports on the relationship between blood glucose status and oral

104

microbiota warrant further research [10, 11]. In addition, the primary group of

105

pathogenic bacteria in the oral cavity were anaerobic [34]. Therefore, this study

106

combined the advantages of MALDI-Biotyper with anaerobic cultivation of bacteria

107

in order to explore the correlation between the anaerobes in the mouth and blood

108

glucose levels. By investigating the correlation between blood glucose monitoring

109

indicators and anaerobic bacteria, we can find anaerobic microbial distribution that

110

are affected by blood glucose and potentially cause blood glucose to be uncontrolled.

111 112 113 114 115 6

116 117 118 119

Materials and methods

120 121

Settings and Participants

122

The participants in the study were enrolled from a single institution: Mackay

123

Memorial Hospital in Hsinchu, Taiwan. The protocol approval (No: NCT03351530,

124

November 24, 2017) was obtained from Mackay Memorial Hospital Institutional

125

Review Board on February 10th, 2017, and informed written consent was obtained

126

from all participants. All methods were performed in accordance with the relevant

127

guidelines and regulations. General inclusion criteria were as follows: >30 years of

128

age, no use of systemic antibiotics or anti-inflammatory drugs in the 6 months prior to

129

enrollment, and no use of immunological agents in the previous 3 months, except

130

hypoglycemic agents for diabetes control. Oral inclusion criteria were average

131

probing depth (PD) < 6 mm and all subjects had at least 16 teeth. The exclusion

132

criteria included pregnancy, use of birth control pills, severe systemic diseases

133

(hepatitis, acquired immune deficiency syndrome), and a previous history of smoking

134

and/or betel nut use. 7

135 136

Sample collection

137

Saliva and gingival sulcus samples were collected from all participants. The

138

participants did not refrain from eating before collection, but rinse their mouth with

139

water for five seconds and discard were required before collection. The saliva used in

140

this study was unstimulated saliva and the cells were not removed from the entire

141

saliva[35]. For saliva collection, participants were asked to spit 3 ml saliva into a

142

15-mL sterilized tube. Gingival sulcus samples were collected with two sterilized

143

paper points each participant. Immediately after collection, the samples were

144

transferred to a CMPTM Anaerobic TranSwab container and stored at 4°C. After

145

removal from the CMPTM Anaerobic TranSwab, all samples were transferred into

146

Tryptic soy broth (TSB), and mixed with glycerol in a 1:1 ratio, and stored at -80°C

147

immediately. Ten-fold serial log dilutions of the sample were then prepared: 20× to

148

2,000,000× dilutions (beginning from 2 × 10-1 to 2 × 10−6) of the saliva samples, and

149

2× to 2,000× dilutions (beginning from 2 to 2 × 10−3) of the gingival sulcus samples.

150

The diluted samples (100 µL) were pour-plated onto pre-reduced Anaerobic Blood

151

Agar (TSB with 0.5% yeast extract, 0.05% cysteine HCl-H2O, 0.5 mg/ml hemin and

152

2 µg/ml vitamin K1) and Chocolate agar for anaerobic incubation (anaerobic gas

153

mixture, 80% N2, 10% CO2, 10% H2, 37 °C, 4 days) in a Whitley DG250

154

Workstation.

155 156

Microbial identification and quantification: Identification by MALDI-TOF MS

157

analysis

158

All bacterial colonies from both medium were transferred to a target polished 8

159

steel plate (MBT 384, Bruker Daltonics Inc.). The proteins from the colonies were

160

extracted with one microliter of 70% formic acid (Sigma). After air-drying, they were

161

overlaid with one microliter of a matrix solution (10 mg/ml solution of

162

α-cyano-4-hydroxycinnamic acid (HCCA), in a mixture of 50% acetonitrile, 47.5%

163

ultra-pure water, and 2.5% trifluoroacetic acid). After repeated the air-drying process,

164

the samples were analyzed on an Autoflex III MALDI-TOF mass spectrometer

165

(Bruker Daltonik GmbH, Leipzig, Germany). The range of spectra (between 2000 to

166

20,000 Da) was recorded at the maximal laser frequency. The raw spectra were

167

analyzed automatically using the MALDI BioTyper 3.1 software package (default

168

settings; Bruker Daltonik GmbH, Bremen, Germany, BioTyper® database renewed at

169

2018/5/15). Approximately 7311 main spectra profile (MSP) were detected. The log

170

scores obtained ranged from 0 to 3.00, which conformed to the criteria recommended

171

by the manufacturer. A score of ≥ 1.7 was considered as the ‘confidence identification’

172

cutoff, below which scores were considered unreliable for protein identification. Each

173

sample had greater than 100 colonies that could be reliably identified.

174 175

Data sources and measurement

176

Colonies that met the confidence identification scores were counted and

177

represented with the number of resulting colonies (colony-forming units, or CFU) as 9

178

means and standard deviation of the means (SD). Statistical significance was

179

determined using the Mann-Whitney Statistical test (P < 0.05) on the GraphPad Prism

180

7 Software. Shannon diversity index [36-38] was calculated as followed: Shannon -

181

Wiener Index (H'); H' = - ∑ (pi x ln (pi)); N = Number of all individuals in

182

genus/species; ni = Number of individuals in genus/species; pi = the proportion of

183

individuals in the genus/species (pi = ni / N). Pearson correlation was used to analyze

184

the association between all studied parameters using IBM SPSS Statistics. P values <

185

0.05 were considered statistically significant.

186 187

Results

188

Demographic and clinical data of participants

189

Of the 25 study participants, the GluAC levels of 11 and 14 participants was

190

<100 mg/dL (average 94.5. ±3.8 mg/dL) and >100 mg/dL (140.3±44.2 mg/dL),

191

respectively. The corresponding HbA1c levels were 5.5±0.3% and 6.6±1.0%,

192

respectively (P< 0.05, Mann-Whitney). The demographic characteristics between

193

these two groups, including age, waist circumference, and body mass index (BMI)

194

were similar (Table 1).

195

In order to study the differences in the anaerobic bacterial oral flora as a result of

196

changes in the blood glucose level, the bacteria cultured in the anaerobic environment 10

197

were identified by MALDI Biotyper. This study analyzed the anaerobic bacterial

198

distribution in saliva samples and gingival sulcus samples by MALDI BioTyper. A

199

total of 37 bacterial genera comprising of 87 unique species were identified in this

200

study.

201 202

Association of glycemic states with anaerobic bacterial genera identified by the

203

MALDI BioTyper

204

In order to investigate the relationship between anaerobic microbial diversity and

205

blood glucose status, the MALDI Biotyper was used to analyze the distribution of

206

anaerobic bacteria isolated from saliva or gingival sulcus samples. First, Shannon

207

diversity index was used to calculate the diversity at the genus level, but revealed no

208

statistical differences between the two populations (between 100 mg/dl), in both

209

saliva and gingival sulcus samples (Figure 1). Although there was no statistical

210

significance found in the results, the pattern still showed a positive tendency (Table 2

211

and Figure 2a,b, e,f).

212

The distribution of the bacterial genera in saliva and gingival sulcus fluid is

213

shown in Figure 3 and Figure 4, respectively. The abundance of each bacterial genera

214

is indicated by the area of the circles. The eleven columns on the left represent the

215

distribution of bacterial genera from participants with GluAC <100 mg/dL whereas 11

216

the rightmost column represents the distribution of the genera of patients with GluAC

217

>100 mg/dL. The main anaerobic genera in saliva and gingival sulcus fluids were

218

Streptococcus (relative abundance: 36.3–95.0% (saliva); 4.1–89.5% (gingival sulcus)),

219

Actinomyces (relative abundance: 0.4–34.8% (saliva); 0–54.9% (gingival sulcus)), and

220

Veillonella (relative abundance: 0.3–24.2% (saliva); 0-13.2% (gingival sulcus)).

221

The association between blood glucose status and oral anaerobic genera analyzed

222

in this study revealed no statistically significant correlation, as seen by the Shannon

223

diversity index (Figure 1, 2a,b, e,f and Table 2). Therefore, the correlation between

224

the distribution of the genera in the saliva and gingival sulcus fluid obtained by the

225

anaerobic culture method and the MALDI Biotyper technique was analyzed. The

226

Pearson correlation coefficient analysis of the two datasets identified five bacterial

227

genera associated with elevated GluAC levels: Campylobacter spp. (0.592, P=0.026*),

228

Porphyromonas spp. (0.992, P<0.001***), Prevotella spp. ( 0.661, P=0.014*), Rothia

229

spp. (0.938, P=0.018*), and Veillonella spp. (0.602, P=0.006**; Table 3). Among

230

them, Porphyromonas spp. also had a positive correlation with HbA1c levels (Pearson

231

correlation coefficient: 0.893, p=0.017*; Table 3). The five genera associated with

232

blood glucose status may represent a factor influencing the pathogenesis of poor

233

glycemic states.

234 12

235

Association of glycemic states with anaerobic bacterial species identified by MALDI

236

BioTyper

237

Comparison of the Shannon diversity indices in the species level samples of

238

saliva and gingival sulcus revealed a significant difference between the two

239

populations (P< 0.05, Mann-Whitney) (Figures 1). The Shannon diversity index for

240

the species level analysis data of saliva samples for participants with GluAC >100

241

mg/dL and <100 mg/dL was 1.81 ± 0.43 and 2.24 ± 0.21 (P = 0.0152*), respectively.

242

The Shannon diversity in species level in gingival sulcus samples was 1.54 ± 0.49

243

(>100 mg/dL GluAC group) and 1.97 ± 0.38 (<100 mg/dL GluAC group;P =

244

0.0038**). In addition, the Pearson correlation coefficient was used to investigate the

245

relationship between glycemic states and Shannon diversity index in species level.

246

The Shannon diversity index in species level of saliva positively correlated with

247

GluAC and HbA1c (correlation coefficient 0.427, p=0.033*; 0.556, p=0.004**,

248

respectively; Table 2 and Figure 2). The results suggest that the diversity of anaerobic

249

bacteria at the species level may be impacted by blood glucose levels and showed an

250

increase with increasing blood glucose concentrations.

251

The distribution of species in saliva and gingival sulcus were identified from the

252

anaerobic culture followed by MALDI Biotyper-based analysis. The correlation

253

between blood glucose status and the oral anaerobic microbial species was then 13

254

investigated using Pearson correlation coefficient. The distribution of the species of

255

saliva and gingival sulcus fluid is shown in Figure 5 and Figure 6, respectively. The

256

bacterial abundance of each species corresponds to the area of the circle. The eleven

257

columns on the left and the fourteen columns on the right indicate the distribution of

258

the species from participants with GluAC <100 mg/dL and >100 mg/dL, respectively.

259

The main anaerobic species identified in saliva were Streptococcus pneumoniae

260

(relative

261

Streptococcus parasanguinis (0–14.8%). The main anaerobic bacteria identified in the

262

gingival sulcus fluid were Streptococcus oralis (relative abundance: 0–64.5%),

263

Streptococcus pneumoniae (0–22.9%), and Veillonella parvula (0–14.1%; Figures 5

264

and 6).

abundance:

0–58.5%),

Streptococcus

salivarius

(0–52.4%),

and

265

At the species level identification, seven species were associated with GluAC:

266

Prevotella melaninogenica (Pearson correlation coefficient 0.914, P=0.03*),

267

Campylobacter

268

P=0.001**), Streptococcus gordonii (0.772, P=0.009**), Streptococcus mitis (0.693,

269

P=0.013*), Streptococcus salivarius (0.909, P=0.012*), and Veillonella parvula

270

(0.518, P=0.033*). Among them, three species were associated with HbA1c:

271

Prevotella melaninogenica (Pearson correlation coefficient: 0.959, p=0.001**),

272

Porphyromonas gingivalis (0.960, p=0.040*), and Streptococcus mitis (0.652,

rectus

(0.705,

P=0.03*),

14

Porphyromonas

gingivalis

(0.999,

273

p=0.022*; Table 4). The tendency of an increased abundance of glycemic

274

state-associated bacteria in oral samples might predispose the individual to a greater

275

risk of uncontrolled blood glucose.

276 277

Discussion

278

Previous studies have reported that when the blood glucose rises, the glucose

279

concentration in the gingival sulcus also increases affecting the microbial distribution

280

in the oral cavity [8-11]. However, conflicting studies identified that hyperglycemia

281

does not affect the microbial distribution in the oral cavity [39]. Thus, the influence of

282

blood glucose on the distribution of oral microbes is an open question and a subject of

283

active research [10, 11]. The current study attempted to answer whether blood glucose

284

levels affect the distribution of oral microbial flora using the MALDI-TOF MS to

285

identify anaerobic microorganisms from saliva and gingival sulcus samples. A total of

286

25 participants were enrolled in this study and 37 genera of bacteria including 87

287

strains were identified in the oral specimens. We also determined the total bacterial

288

counts, microbial diversity and Pearson correlation coefficient for the samples. Our

289

results demonstrated that in both saliva and gingival sulcus samples, the population

290

with a higher blood glucose concentration displayed greater anaerobic bacterial

291

species diversity compared to the population with lower blood glucose concentration. 15

292

In addition, the anaerobic bacterial diversity had a positive Pearson correlation

293

coefficient with blood glucose concentration regardless of the identification level or

294

sample type. In summary, the data demonstrated that the diversity of anaerobic

295

bacteria is positively correlated with blood glucose concentrations. Advancing

296

previous studies, which identified that hyperglycemics have a higher abundance of

297

anaerobic bacteria than normoglycemics [11], our results revealed that high blood

298

glucose also diversified the anaerobic microbial flora. One potential reason for the

299

increased diversity of the anaerobic flora may be due to the different

300

microenvironments caused by the differences in blood glucose, resulting in the

301

proliferation of microorganisms with different growth requirements in specific oral

302

niches. The microenvironment of hyperglycemia can attract microorganisms that have

303

a higher demand for glucose. After initial colonization by these microorganisms that

304

potentially generate conducive conditions, others that depend on the growth of the

305

primary

306

concentration-dependent alterations in the oral microbial composition [10, 11].

microorganisms

will

proliferate,

resulting

in

a

blood

glucose

307

In all, 37 unique genera were identified in this study, of which Pearson

308

correlation coefficient analysis identified a positive correlation with blood glucose

309

concentration for five genera: Campylobacter spp., Porphyromonas spp., Prevotella

310

spp., Rothia spp., and Veillonella spp. This is in agreement with previous studies that 16

311

identified Veillonella spp. at a higher proportion in the oral cavity of people with

312

hyperglycemia [10, 11, 13, 15]. Interestingly, Veillonella spp. was mostly reported to

313

be present in healthy periodontal states [13, 40][41]. In agreement with previous

314

reports, our data identified a positive correlation between both Veillonella parvula and

315

Prevotella spp., and elevated blood glucose levels [10, 13]. Members of both of the

316

genera Veillonella spp. and Prevotella spp. can utilize carbohydrates [10]. A potential

317

scenario based on our data is that when the glucose in the blood increases, the

318

environment changes to stimulate the growth of the genera that preferentially utilize

319

carbohydrates [9, 13]. This might be the reason for the selective increase in the

320

richness of Veillonella spp., Prevotella spp in the anaerobic oral microbiota.

321

Our MALDI-TOF MS analysis identified 87 unique anaerobic bacterial species,

322

of which seven were positively associated with varying blood glucose status, as

323

determined

324

Campylobacter

325

Streptococcus mitis, Streptococcus salivarius, and Veillonella parvula. Among them,

326

Porphyromonas gingivalis has been extensively studied. Although the results of our

327

study are consistent with previous results that found the population with higher blood

328

glucose had a higher trend of richness of P. gingivalis in the oral cavity [12, 40, 42],

329

other studies have reported conflicting data [11, 13-15, 17]. The reasons for the

by

Pearson rectus,

correlation

analysis:

Porphyromonas

17

Prevotella

gingivalis,

melaninogenica,

Streptococcus

gordonii,

330

differences in the distribution of strains are still unclear. Some reports indicate that the

331

distribution of microbial flora can be affected by differences in lifestyle and

332

geographical location [15, 43, 44].

333

In addition to differences in geography and living position, there are some

334

limitations that constrained our research. Due to the study exclusion criteria, we

335

enrolled relatively few participants. We excluded people who used systemic

336

antibiotics or anticancer drugs in the past six months, pregnant women, those

337

suffering from systemic diseases such as hepatitis or acquired immunodeficiency

338

syndrome, smokers, and betel nut users. Moreover, at least 16 teeth for a participant

339

were required for inclusion in this clinical trial. However, it’s difficult to meet this

340

requirement for participants with hyperglycemia. With a higher sample number, there

341

will probably be an increase in the credibility of these potential bacteria and obviate

342

the statistical limitations of small sample numbers. In spite of this limitation, the data

343

identified the anaerobic flora in saliva and gingival sulcus by MALDI-TOF MS.

344

Further, the data suggests that the distribution of the flora cultured in the anaerobic

345

environment varies with the blood glucose concentration, and this change may be due

346

to changes in the oral environment resulting in the proliferation of certain bacterial

347

groups [9, 13]. In addition to the limited number of samples, sample preservation and

348

culture techniques were also potential constraints. In this study, the anaerobic 18

349

sampling tube (CMPTM Anaerobic TranSwab container) was used to preserve the

350

samples immediately after sampling, stored at 4 °C, and processed in an anaerobic

351

console (Whitley DG250 Workstation) within 24 hours of collection. In spite of all

352

these precautions, it is still impossible to avoid the loss of some sensitive bacteria

353

post-sampling, after leaving the original oral environment. Furthermore, due to the

354

bacterial diversity in the samples, growth conditions for revival are also diverse. In

355

this study, only two media (anaerobic blood Agar and chocolate agar) were used.

356

Although 87 different species were identified, other media conditions need to be

357

considered in the future. Diverse culture conditions are added to increase the variety

358

of bacteria identified by the experiment. Another factor that can be varied is the

359

incubation time. Although some studies indicate that culture time does not affect the

360

identification rate [45, 46], time is still an important factor in the process of

361

cultivating microorganisms [47]. Finally, technical limitations undoubtedly play a role.

362

The use of MALDI-TOF MS to identify microorganisms relies on the number and

363

quality of reference spectra in the database. Therefore, identification of strains that do

364

not exist in the database is not possible. However, the MALDI-TOF bacterial database

365

is still expanding and many studies have used MALDI-TOF MS to identify

366

microorganisms [26, 47-49]. A recent report identifying the players in microbial

367

communities in peri-implantitis pockets also showed that MALDI-TOF MS is an asset 19

368

in studying disease-related oral microbiota [50]. Therefore, even in the current

369

scenario, MALDI-TOF MS is fast, sensitive, and more economical than the classical

370

laboratory procedures used to study the relationship between oral microbes and blood

371

glucose status [51-53]. However, the expansion of the MALDI-TOF database and the

372

establishment of the in-house MALDI-TOF MS libraries are still indispensable and

373

will help the development of microbe-related research, improve the accuracy of

374

microbial identification, and help identify complete microbiotas [47, 49, 50, 54, 55].

375

One of the complementary method to MALDI-TOF-based microbiota analysis, is

376

RNA sequencing analysis (RNA-Seq) which has become a standard strategy for

377

analyzing whole bacterial transcriptome and metatranscriptome information[56].

378

Besides, MS-based instruments, such as gas chromatography-mass spectrometry

379

(GCeMS), and liquid chromatography-mass spectrometry (LC-MS), might resolve the

380

whole metabolite patterns of the microbiome in the future.

381

In this study, MALDI-TOF MS for microbial identification was used to

382

investigate the distribution of oral flora under different blood glucose concentrations.

383

Veillonella spp. and Prevotella spp. were identified at a higher proportion in people

384

with higher blood glucose levels, corroborating several previous studies. The data also

385

showed differences in bacterial distribution due to differences in the physiological

386

environment and that Veillonella spp. and Prevotella spp. may play a certain role in 20

387

patients with higher blood glucose levels. In addition, other species identified in this

388

study that were positively associated with blood glucose, namely Prevotella

389

melaninogenica, Campylobacter rectus, Streptococcus gordonii, Streptococcus mitis,

390

Streptococcus salivarius, and Veillonella parvula, also have the potential to participate

391

in the pathogenesis, resulting in higher blood glucose levels. Our results also

392

demonstrate that with the expansion of the bacterial database, MALDI-TOF MS will

393

be a simpler, faster, and more economical clinical identification tool, providing clarity

394

and comprehensiveness to the research on blood glucose levels and oral microbial

395

flora.

396 397

Author Contributions Statement

398

Hong-Lin Chan and Wen-Chi Cheng conceived of the presented idea. Ya-Chun Hsiao,

399

Guan-Wei Su and Yi-Ru Chang verified the analytical methods. Yu-Shan Wei wrote

400

the main manuscript text and prepared all figures with support from Hsiu-Pin Lin and

401

Yi-Shiuan Wang. Hsiu-Chuan Chou, Yi-Ting Tsai, En-Chi Liao, Hsin-Yi Chen,

402

Mei-Lan Ko, Wen-Hung Kuo and Shing-Jyh Chang, helped supervise the project. All

403

authors discussed the results and contributed to the final manuscript.

404 405

Funding 21

406 407

This work was funded by R&D Piloting Cooperation Projects between Industries and Academia 104A19 and 105A24

408 409

Additional Information

410

All authors: none to declare.

411 412

Additional information

413

Competing Interests: The authors declare no competing interests.

414 415

22

416 417

Figure legends

418

Figure 1: Shannon Diversity Index at the genus and species level for the different

419

sites. (a) Samples from saliva. (b) Samples from gingival sulcus.

420 421

Figure 2: Correlation of glycemic states, fasting blood glucose (GluAC) and

422

glycated hemoglobin (HbA1c) with Shannon Diversity Index at the genus and

423

species level. Correlation of GluAC with Shannon Diversity Index at the genus level

424

of samples from (a) saliva (b) gingival sulcus, and at the species level of samples from

425

(c) saliva (d) gingival sulcus. Correlation of HbA1c with Shannon Diversity Index at

426

the genus of samples from (e) saliva (f) gingival sulcus, and at the species of samples

427

from (g) saliva (h) gingival sulcus.

428 429

Figure 3: The distribution of the genera identified by MALDI BioTyper of saliva

430

from participants. The y-axis shows the genera identified by MALDI BioTyper of

431

saliva. The relative abundance of each genus is indicated by the area of the circles.

432

Each of the 25 columns represents the profile of an individual participant in this study.

433 434

Figure 4: The distribution of the genera identified by MALDI BioTyper of 23

435

gingival sulcus from participants. The y-axis shows the genera identified by

436

MALDI BioTyper of gingiva. The relative abundance of each genus is indicated by

437

the area of the circle. Each of the 25 columns represents the profile of an individual

438

participant in this study.

439 440

Figure 5: The distribution of the species identified by MALDI BioTyper of saliva

441

from participants. The y-axis shows the species identified by MALDI BioTyper of

442

saliva. The relative abundance of each genus is indicated by the area of the circle.

443

Each of the 25 columns represents the profile of an individual participant in this study.

444 445

Figure 6: The distribution of the species identified by MALDI BioTyper of

446

gingival sulcus from participants. The y-axis shows the species identified by

447

MALDI BioTyper of gingival sulcus. The relative abundance of each genus is

448

indicated by the area of the circle. Each of the 25 columns represents the profile of an

449

individual participant in this study.

450 451 452 453 24

Table 1 Demographic and clinical data of participants Subjects

25

Glycemic states (mg/dL) Subjects

≧ 100

< 100

GLU-AC

P value

11

44%

14

56%

9

82%

8

57%

Females (N, %) Age (years)

46.3 ± 10.8

53.3 ± 12.8 0.1

Waistline (cm)

81.3 ± 8.7

90.7 ± 12.7 0.11

BMI

23.6 ± 3.5

26.1 ± 5.2

0.27

Glycemic states GLU-AC (mg/dL) HbA1c (%)

94.5 ± 3.8

140.3 ± 44.2 <0.0001****

5.5 ± 0.3

6.6 ± 1

0.0002***

Values were expressed as mean ± SD (standard deviation) or percentage BMI: body mass index;GLU-AC fasting blood glucose;HbA1c Hemoglobin A1c Statistical test performed with Mann-Whitney Statistical test P<0.05 454 Table 2 The Pearson Correlation of bacterial diversity with GluAC and HbA1c Analytic results

GluAC

HbA1c

Pearson correlation

P value

N

S Genus

0.169

0.419

25

S Species

0.427*

0.033

25

G Genus

0.381

0.060

25

G Species

0.378

0.062

25

S Genus

0.134

0.523

25

S Species

0.556**

0.004

25

G Genus

0.149

0.476

25

G Species

0.275

0.183

25

*Statistical test performed with Mann-Whitney Statistical test P<0.05 S: Saliva G: gingival sulcus 455 456 457 25

Table 3 The correlation of bacterial genus in gingiva sulcus with GluAC and HbA1c in the results of MALDI BioTyper Pearson correlation

P value

N

0.592*

0.026

14

Porphyromonas spp.

0.992**

<0.01

6

Prevotella spp.

0.661*

0.014

13

Rothia spp.

0.938*

0.018

5

Veillonella spp.

0.602**

0.006

19

Porphyromonas spp.

0.893*

0.017

6

Analytic results Campylobacter spp. GluAC

HbA1c

*Significant (P<0.05) 458 Table 4 The correlation of bacterial species in saliva and gingiva sulcus with GluAC and HbA1c Pearson P value correlation

Analytic results

GluAC

HbA1c

N

S species

Prevotella melaninogenica .914*

0.03

5

G species

Campylobacter rectus

0.034

9

G species

Porphyromonas gingivalis .999**

0.001

4

G species

Streptococcus gordonii

.772**

0.009

10

G species

Streptococcus mitis

.693*

0.013

12

G species

Streptococcus salivarius

.909*

0.012

6

G species

Veillonella parvula

.518*

0.033

17

S species

Prevotella melaninogenica .959**

0.01

5

G species

Porphyromonas gingivalis .960*

0.040

4

G species

Streptococcus mitis

0.022

12

.705*

.652*

*Statistical test performed with Mann-Whitney Statistical test P<0.05 S: Saliva G: gingival sulcus 459 460

References

461 462 463

1.

464

2.

Darveau, R.P., Periodontitis: a polymicrobial disruption of host homeostasis. Nat Rev Microbiol, 2010. 8(7): p. 481-90. Gao, L., et al., Oral microbiomes: more and more importance in oral cavity and whole body. 2018. 9(5): p. 488-500. 26

465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487

3.

He, J., et al., The oral microbiome diversity and its relation to human diseases.

4.

Folia Microbiol (Praha), 2015. 60(1): p. 69-80. Kocher, T., et al., Periodontal complications of hyperglycemia/diabetes mellitus: Epidemiologic complexity and clinical challenge. Periodontol 2000,

5.

6. 7.

8.

9. 10. 11. 12.

2018. 78(1): p. 59-97. Demmer, R.T., et al., The subgingival microbiome, systemic inflammation and insulin resistance: The Oral Infections, Glucose Intolerance and Insulin Resistance Study. J Clin Periodontol, 2017. 44(3): p. 255-265. 6. Glycemic Targets: Standards of Medical Care in Diabetes-2019. Diabetes Care, 2019. 42(Suppl 1): p. S61-s70. Lee, J.E., Alternative biomarkers for assessing glycemic control in diabetes: fructosamine, glycated albumin, and 1,5-anhydroglucitol. Ann Pediatr Endocrinol Metab, 2015. 20(2): p. 74-8. Chopra, P. and T.S. Kumar, Correlation of glucose level among venous, gingival and finger-prick blood samples in diabetic patients. J Indian Soc Periodontol, 2011. 15(3): p. 288-91. Ficara, A.J., et al., A comparison of the glucose and protein content of gingival fluid from diabetics and nondiabetics. J Periodontal Res, 1975. 10(3): p. 171-5. Longo, P.L., S. Dabdoub, and P. Kumar, Glycaemic status affects the subgingival microbiome of diabetic patients. J Clin Periodontol., 2018. Ganesan, S.M., et al., A tale of two risks: smoking, diabetes and the subgingival microbiome. ISME J, 2017. 11(9): p. 2075-2089. Aemaimanan, P., P. Amimanan, and S. Taweechaisupapong, Quantification of

488 489

key periodontal pathogens in insulin-dependent type 2 diabetic and non-diabetic patients with generalized chronic periodontitis. Anaerobe, 2013.

490 491

22: p. 64-8. Casarin, R.C., et al., Subgingival biodiversity in subjects with uncontrolled

492 493 494 495 496

13.

14.

type-2 diabetes and chronic periodontitis. J Periodontal Res, 2013. 48(1): p. 30-6. Sbordone, L., et al., Periodontal status and subgingival microbiota of insulin-dependent juvenile diabetics: a 3-year longitudinal study. J Periodontol, 1998. 69(2): p. 120-8.

497 498 499

15.

Zhou, M., et al., Investigation of the effect of type 2 diabetes mellitus on subgingival plaque microbiota by high-throughput 16S rDNA pyrosequencing. PLoS One, 2013. 8(4): p. e61516.

500 501 502

16.

Yuan, K., et al., Detection of putative periodontal pathogens in non-insulin-dependent diabetes mellitus and non-diabetes mellitus by polymerase chain reaction. J Periodontal Res, 2001. 36(1): p. 18-24. 27

503 504 505 506 507 508 509 510 511 512 513 514

17.

Campus, G., et al., Diabetes and periodontal disease: a case-control study. J

18.

Periodontol, 2005. 76(3): p. 418-25. Hintao, J., et al., The microbiological profiles of saliva, supragingival and subgingival plaque and dental caries in adults with and without type 2

19.

20.

diabetes mellitus. Oral Microbiol Immunol, 2007. 22(3): p. 175-81. Sardi, J.C., et al., Periodontal conditions and prevalence of putative periodontopathogens and Candida spp. in insulin-dependent type 2 diabetic and non-diabetic patients with chronic periodontitis--a pilot study. Arch Oral Biol, 2011. 56(10): p. 1098-105. Field, C.A., et al., Investigation and quantification of key periodontal pathogens in patients with type 2 diabetes. J Periodontal Res, 2012. 47(4): p. 470-8.

515 516 517

21.

Bhatti, M.M., et al., Rapid identification of positive blood cultures by matrix-assisted laser desorption ionization-time of flight mass spectrometry using prewarmed agar plates. J Clin Microbiol, 2014. 52(12): p. 4334-8.

518 519 520 521

22.

Haiko, J., et al., Identification of urinary tract pathogens after 3-hours urine culture by MALDI-TOF mass spectrometry. J Microbiol Methods, 2016. 129: p. 81-4. Turvey, M.E., et al., Identification of beer spoilage microorganisms using the

522 523 524 525 526 527

23.

24.

25.

MALDI Biotyper platform. Appl Microbiol Biotechnol, 2016. 100(6): p. 2761-73. Samb-Ba, B., et al., MALDI-TOF identification of the human Gut microbiome in people with and without diarrhea in Senegal. PLoS One, 2014. 9(5): p. e87419. Jang, K.S. and Y.H. Kim, Rapid and robust MALDI-TOF MS techniques for microbial identification: a brief overview of their diverse applications. J Microbiol, 2018. 56(4): p. 209-216.

528 529

Harder, S., et al., Analysis of the intraimplant microflora of two-piece dental implants. Clin Oral Investig, 2013. 17(4): p. 1135-42. Seng, P., et al., Ongoing revolution in bacteriology: routine identification of bacteria by matrix-assisted laser desorption ionization time-of-flight mass spectrometry. Clin Infect Dis., 2009(1537-6591 (Electronic)).

530 531 532 533 534

26.

535 536 537

28.

Patel, R., Matrix-assisted laser desorption ionization-time of flight mass spectrometry in clinical microbiology. Clin Infect Dis., 2013(1537-6591 (Electronic)).

538 539 540

29.

Cherkaoui, A., et al., Comparison of two matrix-assisted laser desorption ionization-time of flight mass spectrometry methods with conventional phenotypic identification for routine identification of bacteria to the species

27.

28

level. J Clin Microbiol, 2010. 48(4): p. 1169-75.

541 542 543 544 545 546 547 548 549 550 551 552 553 554 555

30. 31. 32.

33.

34. 35.

556 557 558 559

36.

560 561 562 563

38.

564 565

37.

39.

40.

Singhal, N., et al., MALDI-TOF mass spectrometry: an emerging technology for microbial identification and diagnosis. Front Microbiol, 2015. 6: p. 791. Decristophoris, P., et al., Identification of Staphylococcus intermedius Group by MALDI-TOF MS. Syst Appl Microbiol, 2011. 34(1): p. 45-51. Schulthess, B., et al., Use of the Bruker MALDI Biotyper for identification of molds in the clinical mycology laboratory. J Clin Microbiol, 2014. 52(8): p. 2797-803. Schulthess, B., et al., Evaluation of the Bruker MALDI Biotyper for identification of Gram-positive rods: development of a diagnostic algorithm for the clinical laboratory. J Clin Microbiol, 2014. 52(4): p. 1089-97. Tanner, A.C., Anaerobic culture to detect periodontal and caries pathogens. J Oral Biosci, 2015. 57(1): p. 18-26. Kaczor-Urbanowicz, K.E., et al., Salivary exRNA biomarkers to detect gingivitis and monitor disease regression. 2018. 45(7): p. 806-817. Shannon, C.E., A mathematical theory of communication. Bell system technical journal, 1948. 27(3): p. 379-423. Clarke, K.R., et al., Change in marine communities: an approach to statistical analysis and interpretation. 2014: Primer-E Ltd. Magurran, A.E., Ecological diversity and its measurement. 1988: Princeton university press. Taylor, J.J., P.M. Preshaw, and E. Lalla, A review of the evidence for pathogenic mechanisms that may link periodontitis and diabetes. J Clin Periodontol, 2013. 40 Suppl 14: p. S113-34. Makiura, N., et al., Relationship of Porphyromonas gingivalis with glycemic level in patients with type 2 diabetes following periodontal treatment. Oral Microbiol Immunol, 2008. 23(4): p. 348-51.

566 567

Paster, B.J., et al., Bacterial diversity in human subgingival plaque. J Bacteriol, 2001. 183(12): p. 3770-83. Tervonen, T., et al., Prevalence of periodontal pathogens with varying metabolic control of diabetes mellitus. J Clin Periodontol, 1994. 21(6): p. 375-9.

568 569 570 571 572

41.

573 574 575

43.

Haffajee, A.D., et al., Subgingival microbiota of chronic periodontitis subjects from different geographic locations. J Clin Periodontol, 2004. 31(11): p. 996-1002.

576 577 578

44.

Kim, T.S., et al., Differences in subgingival microflora of Korean and German periodontal patients. Arch Oral Biol, 2009. 54(3): p. 223-9. Valentine, N., et al., Effect of culture conditions on microorganism

42.

45.

29

579

identification

580 581 582

spectrometry. Appl Environ Microbiol, 2005. 71(1): p. 58-64. Carbonnelle, E., et al., Rapid identification of Staphylococci isolated in clinical microbiology laboratories by matrix-assisted laser desorption ionization-time

583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603

46.

47.

48.

49. 50.

51.

by

matrix-assisted

laser

desorption

ionization

mass

of flight mass spectrometry. J Clin Microbiol, 2007. 45(7): p. 2156-61. Stingu, C.S., et al., Rapid identification of oral Actinomyces species cultivated from subgingival biofilm by MALDI-TOF-MS. J Oral Microbiol, 2015. 7: p. 26110. Stingu, C.S., et al., Rapid identification of oral anaerobic bacteria cultivated from subgingival biofilm by MALDI-TOF-MS. Oral Microbiol Immunol, 2008. 23(5): p. 372-6. Veloo, A.C., G.W. Welling, and J.E. Degener, The identification of anaerobic bacteria using MALDI-TOF MS. Anaerobe, 2011. 17(4): p. 211-2. Yeh, H.C., et al., Identification of microbiota in peri-implantitis pockets by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. Sci Rep, 2019. 9(1): p. 774. Socransky, S.S., et al., Use of checkerboard DNA-DNA hybridization to study complex microbial ecosystems. Oral Microbiol Immunol, 2004. 19(6): p. 352-62.

53.

Shibli, J.A., et al., Composition of supra- and subgingival biofilm of subjects with healthy and diseased implants. Clin Oral Implants Res, 2008. 19(10): p. 975-82. Persson, G.R. and S. Renvert, Cluster of bacteria associated with

54.

peri-implantitis. Clin Implant Dent Relat Res, 2014. 16(6): p. 783-93. Veloo, A.C., et al., Comparison of two matrix-assisted laser desorption

52.

604 605

ionisation-time of flight mass spectrometry methods for the identification of clinically relevant anaerobic bacteria. Clin Microbiol Infect, 2011. 17(10): p.

606 607 608 609 610

1501-6. De Carolis, E., et al., Development and validation of an in-house database for matrix-assisted laser desorption ionization-time of flight mass spectrometry-based yeast identification using a fast protein extraction procedure. J Clin Microbiol, 2014. 52(5): p. 1453-8.

611 612 613

55.

56.

Kaczor-Urbanowicz, K.E., et al., Novel approaches for bioinformatic analysis of salivary RNA sequencing data for development. Bioinformatics, 2018. 34(1): p. 1-8.

614

30

a 3

Saliva *

2.5 2 1.5 1 0.5 0

Diversity of Diversity of Genus Species Glycemic states (mg/dL) <100 >100

Shannon's diversity index

Shannon's diversity index

b 3 2.5

Gingival sulcus **

2 1.5 1 0.5 0

Diversity of Diversity of Genus Species Glycemic states (mg/dL) <100 >100

Figure 1

Diversity of Species

Diversity of Genus

b

Saliva

a

f

d

e

h

c

g

GluAC

Diversity of Genus

HbA1c

Diversity of Species

Gingival sulcus

Figure 2

GluAC

Figure 3

GluAC

Figure 4

GluAC

Figure 5

10^2

GluAC

Figure 6