Transcript n=26 - Molecular Biology Conferences

Farah Ibrahim Al-Marzooq
MBChB , MMedSc , PhD
Sharjah Institute for Medical Research, University of Sharjah, Sharjah, UAE
&
Natheer Al-Rawi
College of Dental Medicine, University of Sharjah, Sharjah, UAE
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?
Introduction

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 , 2015
In 2015
1/11 adults (415 million) have diabetes
By 2040
1/10 adults (642 million) will have diabetes
Introduction
Biomarkers

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.

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)
Introduction

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

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 diseases

Periodontitis is a risk factor for worsening blood
glucose control in patients with diabetes and may also
increase the risk of diabetic complications
Introduction
Periodontal infections

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 gingivalis
Tannerella forsythia
Treponema denticola
Stages of periodontal infections (Scannapieco , 2013)
Introduction


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)
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
Methodology

A cross sectional study (December 2015 - April 2016)
Study population
Patients attending to the University of Sharjah Dental Hospital were
invited to participate in the study
Consent form
Medical history & random blood glucose measurement
Anthropometric measures: (weight & height)
BMI calculation : weight (kg) divided by squared height (m2)
BMI ≥ 30 : obese , BMI < 30 : non-obese *
*CDC (2016)
Methodology
Obese
Diabetics
(n=30) •
Obese
nondiabetics
(n=30) •
nonObese
(n=30) •
Oral examination and Saliva Sample Collection
Centrifugation
Supernatant
ELISA
Pellet
DNA
extraction
Methodology
Determination of salivary resistin concentration by sandwich ELISA

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

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
Oral Infection
Bacterial species (target gene)
Reference
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
Periodontitis

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).
Results & Discussion

90 adult patients (ages: 40-60 years) were recruited in this study
Obese
Diabetics
Non-Diabetics
Control
(non-obese)
Participants (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
34.2 ± 4.2
34.96 ± 4.6
27.3 ± 2.5
93.6 ± 12.6
98.3 ± 13.1
77.2 ± 11.91
166.5 ± 10.6
167.9 ± 8.8
167.99 ± 10.6
Parameters
Body mass index (kg/m2)
Weight (Kg)
*
Height (cm)
*
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
Results & Discussion

Salivary resistin was significantly higher in the obese patients (diabetics and nondiabetics) compared to the non-obese control.
 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)
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.
The Standard curve used for the
calculation of Resistin concentrations.
Samples
Standards
Results & Discussion


Porphyromonas
gingivalis
(16S rRNA)
Detection of bacteria involved in
Oral Infections
Tannerella forsythia
(16S rRNA)


Fusobacterium spp.
(16S rRNA)

Treponema denticola
(16S rRNA)

Actinobacillus
actinomycetemcomitans
(iktA)

Bifidobacterium
(16S rRNA)
Universal primer PCR
∆ Ct = Ct target – Ct Universal primers
16S rRNA
N = 2 - ∆ Ct
Results & Discussion
Quantification of Oral bacteria
Bacterial species
Non-Obese
(n=26)
N range
(mean ± SD) for each group
Obese Diabetics
Obese Non-Diabetics
(n=26)
(n=26)
Fusobacterium
spp.
3 × 10-5 – 6.9 × 10-2
12 × 10-5 - 100.7 × 10-2
60 × 10-5 – 80.7× 10-2
(1.1 ± 1.6 × 10-2)
(11.5 ± 22.5 × 10-2)
(10.1 ± 20.5 × 10-2)
+ve cases per group
26
26
26
0.45 × 10-5 – 0.7 × 10-2
1.87 × 10-5 – 13.49 × 10-2
0.06 × 10-5 – 46 × 10-2
P. gingivalis
0.003 *
Total: 78
< 0.001*
(0.08 ± 0.16 × 10-2)
(1.6 ± 3. 4 × 10-2)
(5.2 ± 11.6 × 10-2)
26
26
24
0.5 × 10-5 – 0.14 × 10-2
3.7 × 10-5 – 2.3 × 10-2
17.1 × 10-5 – 14.46 × 10-2
+ve cases per group
T. forsythia
P value
Total: 76
< 0.001*
(0.04 ± 0.04 × 10-2)
(0.73 ± 0.74 × 10-2)
(1.9 ± 3.2 × 10-2)
24
26
26
+ve cases per group
Total: 76
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 bacteria
Bacterial species
Non-Obese
(n=26)
0.2 × 10-5 – 0.34 × 10-3
T. denticola
+ve cases per group
A.
actinomycetemcomitans
+ve cases per group
Bifidobacteria
+ve cases per group
N range
(mean ± SD) for each group
Obese Diabetics
Obese Non-Diabetics
(n=26)
(n=26)
0.1 × 10-5 - 4.6 × 10-3
P value
0.2 × 10-5 - 8.2 × 10-3
0.068
(8.9 ± 9.9 × 10-5)
(37.99 ± 92.8 × 10-5)
(58.9 ± 168.2 × 10-5)
20
25
24
0.3 × 10-4– 2.1 × 10-4
0.2 × 10-4– 4.9 × 10-3
6.8 × 10-4 – 24.5 × 10-3
Total: 69
0.097
(0.5 ± 0.8 × 10-4)
(0.9 ± 1.7 × 10-4)
(5.7 ± 8.9 × 10-4)
7
8
9
0.05 × 10-5 – 2.3 × 10-3
1.37 × 10-5 - 25 × 10-3
0.94 × 10-5 – 11.8 × 10-3
Total: 24
0.172
(0.29 ± 0.54 × 10-3)
(1.4 ± 5.1 × 10-3)
(0.9 ± 2.8 × 10-3)
23
24
17
Total: 64
N= Fold-difference in the number of the target organism-specific gene copies relative to the number of 16S rRNA gene copies
Results & Discussion

Total number of bacteria per group
Periodontal
Condition
Healthy (n=67)
Non-Obese
(n=26)
20
(3-6)
No of cases per group
(number of bacteria)
Obese Diabetics Obese Non-Diabetics
(n=26)
(n=26)
25
22
(4-6)
(3-6)
Non-healthy (n=11)
6
(3-6)
1
(6)
4
(4-6)
Total number of bacteria
3-6
4-6
3-6
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)
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.
Pathogenesis of periodontal disease
• Polymicrobial synergy + dysbiosis
• Virulence factors in dysbiotic bacteria
• Host immune response dysregulation :
 Subversion by the microbial
community
 Host immuno-regulatory defects
(Hajishengallis, 2015)
Results & Discussion

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 nondiabetics) compared to the non-obese control
Previous 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)
Multidirectional
association
Proinflammatory
cytokines :
a multidirectional
link between
periodontitis,
obesity
&
other chronic
diseases
(Jagannathachary et al.,
2010)
Mechanisms linking periodontitis to systemic inflammation and diseases
(Hajishengallis, 2015)
Conclusion
Saliva as a diagnostic fluid

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.

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 home
Conclusion
Saliva as a diagnostic fluid


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.
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.
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.
References







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 930206
Matsuki 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: 544551.
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









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 359724
Jagannathachary 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;76:2075–84
Scannapieco FA. The Oral Microbiome: Its Role in Health and in Oral and Systemic
Infections. Clinical Microbiology Newsletter. 2013; 35(20): 163–169
Suzuki 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): 158–62
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, UAE

The students (College of Dental Medicine, University of
Sharjah, UAE) who collected the samples :

Walaa Maher

Yasmina Waleed

Yassmina Yasser

Farah Ghanim

Ahmed Alghafri

Ahmed Sheriff

Feras Mohammed