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The Oral Microbiome
The Oral Microbiome

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and Salivary Biomarkers in Health and Disease Farah Ibrahim AlMarzooq MBChB MMedSc PhD Sharjah Institute for Medical Research University of Sharjah Sharjah UAE amp ID: 541241 Download Presentation

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Slide1

The Oral Microbiome

and

Salivary Biomarkers in Health and Disease

Farah Ibrahim Al-Marzooq MBChB , MMedSc , PhDSharjah Institute for Medical Research, University of Sharjah, Sharjah, UAE & Natheer Al-Rawi College of Dental Medicine, University of Sharjah, Sharjah, UAESlide2

IntroductionSlide3

Introduction There is a relation between oral and systemic diseases, but the question remains whether

the oral diseases are the cause or the consequence of pathological process in other body sites?Slide4

Obesity : a body mass index (BMI) >30.0 kg/m2

It is a major public health problem today. Obesity

is a risk factor for several chronic diseases, most notably hypertension, dyslipidemia, coronary heart disease & diabetes. Diabetes is a chronic disease that occurs either when the pancreas does not produce enough insulin (Type 1 diabetes) or when the body cannot effectively use the insulin it produces (Type 2 diabetes)

(WHO, 2016)International Diabetes Federation statistics , 2015Introduction By 20401/10 adults (642 million) will have diabetes

In

2015

1/11

adults

(415 million)

have diabetes Slide5

Human saliva is an ultra-filtrate from plasma; thus, it can be used as an alternative to serum for the detection of diagnostic biomarkers (Jinhua

et al., 2012)

BiomarkersIntroduction

A biomarker, or biological marker, generally refers to a measurable indicator of physiologic health, a pathogenic process, or a pharmacologic response

to a therapeutic intervention.

Whether produced by healthy individuals or by individuals affected by a particular systemic disease, these molecules

can be used to monitor health status, disease onset, treatment response and outcome

.Slide6

Resist-in: (

resist insulin) an

adipo-cytokine, produced by adipocytes and macrophages.It was originally proposed as the link between obesity and insulin resistance in mice.Insulin resistance is a fundamental aspect of the

etiology of type 2 diabetes. Previous studies reported a positive correlation between serum and salivary resistin, which were both correlated to BMI in type 2 diabetes patients (Jinhua et al., 2012)Data suggests that resistin could be one of the molecular links connecting obesity, diabetes and periodontitis, and may serve as a marker that links periodontal disease with other systemic diseases (Archana et al., 2014).

Introduction Slide7

Introduction

After smoking,

obesity is the second strongest risk factor for inflammatory periodontal tissue destruction

(Jagannathachary et al., 2010) The relationship between diabetes and periodontal disease :Diabetes is a risk factor for severe periodontal diseasesPeriodontitis is a risk factor for worsening blood glucose control in patients with diabetes and may also increase the risk of diabetic complicationsSlide8

Gingivitis

is associated with an increased number of gram-negative genera, such as:

Fusobacterium spp.

Periodontitis is associated with an increased number of anaerobic species called “Red complex bacteria” : Pophyromonas gingivalisTannerella forsythiaTreponema denticola

Introduction

Stages of

periodontal infections

(

Scannapieco

, 2013)

Periodontal infectionsSlide9

The oral microbiome plays a relevant role in human health and it is a key element in a variety of oral and systemic diseases.

Traditional culture techniques

used to identify

oral bacteria have significant shortcomings :Difficulty in culturing: some oral bacteria need strict anaerobic conditions & some need special media as they are very fastidious in nature.Non-cultivability of many oral species. Low sensitivity: bacteria in limited amount can not be detected Only viable bacteria can grow on culture; therefore, strict sampling and transport conditions are essential Time-consuming and expensive (Topcuoglu & Kulekci , 2015).The use of molecular techniques facilitates the characterization of both cultivable and non-cultivable members of the oral microbiome. This leads to the identification of many oral bacterial species which have never been cultivated and identified.

≥ 1,000

phylotypes

could potentially colonize the oral cavity (

Scannapieco

, 2013)

Introduction Slide10

Objectives We aim to compare the

bacterial community and the level of

selected biomarkers in the saliva of adults in health and disease conditions. Obese (diabetics and non-diabetics) were compared with non-obese adults

:Resistin (as a biomarker of insulin resistance)Bacteria associated with dental infections Slide11

MethodologySlide12

A cross sectional study (December 2015 - April 2016)

Methodology

*CDC (2016)Slide13

Supernatant

Pellet

ELISA

DNA extractionMethodology Slide14

Absorbance values (at wavelength of 450 nm) were measured using Micro Plate Reader (Hospitex

Diagnostics, Italy)A Standard Curve was generated using the standard concentrations (0.25 -16 ng/mL) on the x-axis and the corresponding mean 450 nm absorbance on the y-axis.

Methodology

Determination of salivary resistin concentration by sandwich ELISA Quantitative measurement of Resistin

concentrations in saliva samplesSlide15

Real-time PCR

Real-time PCR was carried out with Rotor-Gene® Q PCR thermocycler (

Qiagen, Germany).Amplification was performed using master mix containing Eva Green as a florescent dye

The fold-difference (N) in the number of the target organism-specific gene copies relative to the number of 16S rRNA gene copies was determined as follows: N = 2 - ∆ Ct ∆ Ct = Ct target – Ct Universal primers 16S rRNA (Kabeerdoss, et al. 2013).Oral Infection

Bacterial species

(target gene)

Reference

Periodontitis

Porphyromonas

gingivalis

(16S

rRNA

)

Kuboniwa

et al

., 2004

Treponema

denticola

(16S

rRNA

)

Yoshida

et al.,

2004

Tannerella

forsythia

(16S

rRNA

)

Shelburne

et al

., 2000

Actinobacillus

actinomycetemcomitans

(

iktA

)

Yoshida

et al

., 2003

Gingivitis

Fusobacterium spp.

(16S

rRNA

)

Suzuki

et al.,

2004

Dental caries

Bifidobacterium

(16S

rRNA

)

Matsuki

et al.,

2002

Universal primers

(16S

rRNA

)

Nadkarni

et al., 2002

Methodology

Slide16

Results &

Discussion Slide17

Results & Discussion 90 adult patients (ages: 40-60 years) were recruited in this study

Parameters

Obese

Control (non-obese)DiabeticsNon-DiabeticsParticipants (M/F)30 (15/15)

30 (15/15)

30 (15/15)

Age (years)

51.7

± 5.3

46.8

± 5.5

47.1

± 5.1

Body mass index (kg/m2)

*

34.2

± 4.2

34.96

± 4.6

27.3

± 2.5

Weight (Kg)

*

93.6

± 12.6

98.3

± 13.1

77.2

± 11.91

Height (cm)

166.5

± 10.6

167.9

± 8.8

167.99

± 10.6

Glucose (mg/

dL

)

*

200.9

± 80.98

105.6

± 24.3

97

± 19.8

Resistin

(ng/mL)

*

14.3

± 3.6

14.1

± 4.3

10.76

± 5.9

*

Significant difference (

p

< 0.05) between obese and non-obese groups

Except for

blood glucose

level, the difference between Diabetic and Non-Diabetic obese patients was not significant for all the tested variables Slide18

The Standard curve used for the calculation of

Resistin

concentrations.Samples Standards

Salivary resistin was significantly higher in the obese patients (diabetics and non-diabetics) compared to the non-obese control.

Results & Discussion

Salivary

resistin

can be used as a biomarker for

Obesity:

Obesity

is

a

hyper-inflammatory state

that predispose to 

insulin resistance

(

Genco

et al

., 2005)

It has recently been found that

resistin

participates in the

inflammatory response

levels of

resistin

are increased in various chronic inflammatory conditions such as rheumatoid arthritis, chronic kidney diseases, atherosclerosis, coronary heart diseases, and

periodontitis.

Significant correlation between

salivary

resistin

levels

and both

BMI

&

weight

(P values: 0.001 & 0.012, respectively)

There was no correlation between

salivary

resistin

levels

and

blood glucose

(P values: 0.286)

Slide19

Universal primer PCR

Fusobacterium spp.

(16S rRNA) ∆ Ct = Ct target – Ct Universal primers 16S rRNA N = 2 - ∆ Ct

Bifidobacterium

(16S

rRNA

)

Porphyromonas

gingivalis

(16S

rRNA

)

Treponema

denticola

(16S

rRNA

)

Tannerella

forsythia

(16S

rRNA

)

Actinobacillus

actinomycetemcomitans

(

iktA

)

Detection of bacteria involved in

Oral Infections

Results & Discussion Slide20

P value

N

range (mean ± SD) for each groupBacterial species

Obese Non-Diabetics (n=26)Obese Diabetics (n=26)Non-Obese (n=26)

0.003

*

60 × 10

-5

– 80.7× 10

-2

12 × 10

-5

- 100.7 × 10

-2

3 × 10

-5

– 6.9 × 10

-2

Fusobacterium spp.

(

10.1

±

20.5

× 10

-2

)

(

11.5

±

22.5

× 10

-2

)

(

1.1

±

1.6

× 10

-2

)

 

Total:

78

 

26

26

26

+

ve

cases per group

< 0.001

*

0.06 × 10

-5

– 46 × 10

-2

1.87 × 10

-5

– 13.49 × 10

-2

0.45 × 10

-5

– 0.7 × 10

-2

P.

gingivalis

(5.2 ± 11.6 × 10

-2

)

(1.6 ± 3. 4 × 10

-2

)(0.08 ± 0.16 × 10-2) Total: 76 

24

26

26

+

ve

cases per group

< 0.001

*

17.1 × 10

-5

– 14.46 × 10

-2

3.7

× 10

-5

2.3

× 10

-2

0.5 × 10

-5

– 0.14 × 10

-2

T. forsythia

(

1.9

±

3.2

× 10

-2

)

(0.73 ± 0.74 × 10-2)(0.04 ± 0.04 × 10-2)  Total: 76 262624+ve cases per group

N= Fold-difference in the number of the target organism-specific gene copies relative to the number of 16S rRNA gene copies* Significant difference (p < 0.05)

Results & Discussion

Quantification of Oral bacteriaSlide21

P value

N

range (mean ± SD) for each group

Bacterial speciesObese Non-Diabetics (n=26)Obese Diabetics (n=26)Non-Obese (n=26)

0.068

0.2 × 10

-5

-

8.2

× 10

-3

0.1 × 10

-5

- 4.6 × 10

-3

0.2 × 10

-5

– 0.34 × 10

-3

T.

denticola

(

58.9

±

168.2

× 10

-5

)

(

37.99

±

92.8

× 10

-5

)

(

8.9

±

9.9

× 10

-5

)

 

Total:

69

24

25

20

+

ve

cases per group

0.097

6.8 × 10

-4

– 24.5 × 10

-3

0.2 × 10

-4

– 4.9 × 10

-3

0.3 × 10

-4

– 2.1 × 10

-4

A.

actinomycetemcomitans

(5.7 ± 8.9 × 10

-4

)

(0.9 ± 1.7 × 10

-4

)(0.5 ± 0.8 × 10-4) Total: 24 

9

8

7

+

ve

cases per group

0.172

0.94 × 10

-5

– 11.8 × 10

-3

1.37 × 10

-5

- 25 × 10

-3

0.05 × 10

-5

– 2.3 × 10

-3

Bifidobacteria

(

0.9

±

2.8

× 10

-3

)

(

1.4

±

5.1

× 10

-3)(0.29 ± 0.54 × 10-3) Total: 64 172423+ve cases per groupN= Fold-difference in the number of the target organism-specific gene copies relative to the number of 16S rRNA gene copies

Results & Discussion

Quantification of Oral bacteriaSlide22

Total number of bacteria per group

Results & Discussion

No of cases per group

(number of bacteria)PeriodontalConditionObese Non-Diabetics(n=26)Obese Diabetics

(n=26)

Non-Obese

(n=26)

22

(

3-6)

25

(

4-6)

20

(

3-6)

Healthy (n=67)

4

(

4-6)

1

(6)

6

(

3-6)

Non-healthy (n=11)

3-6

4-6

3-6

Total number of bacteria

There was no significant difference between obese and non-obese groups (Diabetics and non-

Diabetics)

with respect to the

total

number of bacteria

and

periodontal health condition

(P values: 0.185 and 0.164 respectively) Slide23

Pathogenesis of periodontal disease

Polymicrobial

synergy + dysbiosisVirulence factors in dysbiotic bacteriaHost immune response dysregulation :Subversion by the microbial community

Host immuno-regulatory defects(Hajishengallis, 2015)Bacteria in the saliva:Colonization : Saliva can serve as a reservoir for bacterial colonization Clinical infection : Detection of certain bacterial species in saliva can reflect their presence in dental plaque and periodontal pockets. Slide24

No correlation was found between the levels of salivary

resistin

and: The total number of oral bacteria. quantity of different oral bacteria. Both salivary resistin

& certain oral bacterial species [Fusobacterium (associated with gingivitis), P. gingivalis and T. forsythia (associated with periodontitis)] were detected in significantly higher quantities in the obese patients (diabetics and non-diabetics) compared to the non-obese controlPrevious research showed incremental elevation of resistin with periodontal disease activity and a reduced level of resistin, after periodontal therapy (Archana et al, 2014).In animal studies, it was found that the expression of resistin can be upregulated by microbial antigens such as lipopolysaccharide, a component of the cell wall of Gram-negative bacteria that has been demonstrated to induce inflammatory reactions (Lu et al,

2002).

The connection is likely made through

inflammation

, initiated and propagated by the actions of

oral biofilms

, which exacerbates chronic systemic inflammation in obese individuals.

Increased systemic inflammation

has been linked to

insulin resistance

and the development of

diabetes

, as well as its complications (

Scannapieco

, 2013)

Results & Discussion Slide25

Mechanisms linking periodontitis to systemic inflammation and diseases

(

Hajishengallis, 2015)Multidirectional associationProinflammatory cytokines :a multidirectional link between periodontitis,

obesity & other chronic diseases(Jagannathachary et al., 2010) Slide26

Conclusion Slide27

This study highlighted the importance of saliva as a non-invasive sample for the detection of biomarkers

and microbes

associated with oral and systemic diseases. Conclusion

Detection of biomarkers Saliva collection is a non-invasive It may represent an alternative for patients in whom blood drawing is difficult (e.g. children and older patients) Fast screening of large population it can be performed by individuals with modest training, including patients themselves. This may pave the way for the development of salivary screening tests that can be done by patients at homeSaliva as a diagnostic fluidSlide28

Conclusion

Salivary diagnosis

is poised to revolutionize the delivery of health and dental care by providing

chairside, non-invasive diagnosis and health monitoring, which can contribute to the development of personalized medicine and personalized dental medicine.

Detection of

microbes

Using saliva as a PCR template reflects the overall condition of the oral cavity

Relative quantification

is better than

absolute quantification

, which requires very precise sample collection (Yoshida

et al

., 2002).

Quantitative real-time PCR of oral biofilms :

Diagnosis :

establishing the

etiology

of oral infectious diseases, especially

unculturable

oral bacteria

Prognosis:

monitoring the effect of therapy and evaluating treatment.

Saliva as a diagnostic fluidSlide29

Recommendations

Periodontal diseases are silent and chronic in nature; thus, many patients do not realize they have them.

Meanwhile, physicians may not know that the patient has a condition that affects sugar control and makes diabetes management more difficult

The relationship between oral and general health will challenge dentists & physicians to work together in managing patients with periodontal diseases and systemic diseases like diabetes.Patients with diabetes should consult a dentist for periodontal screening Patients with periodontal disease should be screened for diabetes if signs or symptoms are present.

Dentists should explain to the obese individuals about the possible oral complications of obesity, and should follow up their oral condition in order to diminish morbidity associated with obesity.Slide30

Archana D., Rahul K., Nagappa

G., D. Gopalakrishnan

, and Paulami B. Resistin: A Potential Biomarker for Periodontitis Influenced Diabetes Mellitus and Diabetes Induced Periodontitis. Disease Markers. 2014; vol. 2014, Article ID 930206Matsuki TK, Watanabe J, Fujimoto Y, Miyamoto T, Takada K, Matsumoto K, et al. Development of 16S rRNA

-gene-targeted group-specific primers for the detection and identification of predominant bacteria in human feces. Appl Environ Microbiol . 2002; 68: 5445-51.Nadkarni MA, Martin FE, Jacques NA, Hunter N. Determination of bacterial load by real-time PCR using a broad-range (universal) probe and primers set. Microbiology. 2002; 148:257-266.Shelburne CE, Prabhu A, Gleason RM, Mullally BH, Coulter WA. Quantitation of Bacteroides forsythus in subgingival plaque comparison of immunoassay and quantitative polymerase chain reaction. J Microbiol Methods . 2000; 39:97-107.Suzuki N, Yoshida A, Saito T, Kawada M, Nakano Y. Quantitative microbiological study of subgingival plaque by real-time PCR shows correlation between levels of Tannerella

forsythensis

and

Fusobacterium

spp. J

Clin

Microbiol

.

2004; 42:2255-2257.

Yoshida A, Suzuki N, Nakano Y, Oho T, Kawada M, Koga T. Development of a 5′fluorogenic nuclease-based real-time PCR assay for quantitative detection of

Actinobacillus

actinomycetemcomitans

and

Porphyromonas

gingivalis

. J

Clin

Microbiol

.

2003; 41:863-866.

Yoshida A, Kawada M, Suzuki N, Nakano Y, Oho T, Saito T, Yamashita Y.

TaqMan

real-time polymerase chain reaction assay for the correlation of

Treponema

denticola

numbers with the severity of periodontal disease.

Oral

Microbiol

Immunol

.

2004; 19:196-200.

References Slide31

References

Hajishengallis G. Periodontitis: from microbial immune subversion to systemic inflammation. Nature reviews Immunology.

2015;15(1):30-44. Jinhua Y., Hongfei G., Jing Y., Lu X., and Ming L. Measurement of Salivary Resistin Level in Patients with Type 2 Diabetes. International Journal of Endocrinology. 2012; vol. 2012, Article ID 359724Jagannathachary

S, Kamaraj D. Obesity and periodontal disease. Journal of Indian Society of Periodontology. 2010;14(2):96-100. Genco RJ, Grossi SG, Ho A, Nishimura F, Murayama Y. A proposed model linking inflammation to obesity, diabetes, and periodontal infections. J Periodontol. 2005;76:2075–84Scannapieco FA. The Oral Microbiome: Its Role in Health and in Oral and Systemic Infections. Clinical Microbiology Newsletter. 2013; 35(20): 163–169Suzuki N, Yoneda M, Hirofuji T. Mixed Red-Complex Bacterial Infection in Periodontitis. International Journal of Dentistry. 2013; 2013: 587279. Topcuoglu N & Kulekci G. 16S

rRNA

based microarray analysis of ten periodontal bacteria in patients with different forms of periodontitis.

Anaerobe

. 2015; 35(Pt A):35-40.

Kuboniwa

M, Amano A, Kimura KR,

Sekine

S, Kato S, Yamamoto Y,

Okahashi

N, Iida T,

Shizukuishi

S. Quantitative detection of periodontal pathogens using real-time polymerase chain reaction with

TaqMan

probes.

Oral

Microbiol

Immunol

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2004; 19:168-176.

Lu SC, Shieh WY, Chen CY, Hsu SC, Chen HL. Lipopolysaccharide increases

resistin

gene expression in vivo and in vitro.

FEBS Lett.

2002; 530 (1–3): 158–62Slide32

Acknowledgment University of Sharjah for supporting this project

Members of the Wound Healing & Oral Diagnosis Research Group

The Director and researchers in the Sharjah Institute for Medical Research, University of Sharjah, UAEThe students (College of Dental Medicine, University of Sharjah, UAE) who collected the samples :Walaa MaherYasmina Waleed

Yassmina YasserFarah GhanimAhmed AlghafriAhmed SheriffFeras MohammedSlide33

Thanks for

your kind attention