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 The PPT/PDF document "The Oral Microbiome" is the property of its rightful owner. Permission is granted to download and print the materials on this web site for personal, non-commercial use only, and to display it on your personal computer provided you do not modify the materials and that you retain all copyright notices contained in the materials. By downloading content from our website, you accept the terms of this agreement.
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
.
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