University of Florida Thesis Or Dissertation Formatting

THE DEVELOPMENT OF AN EXTRACELLULAR VESICLE ISOLATION PROTOCOL FOR GINGIVAL CREVICULAR FLUID

ESTELA L. TRUZMAN

A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE

UNIVERSITY OF FLORIDA

2019

To my family and my husband for their infinite support, inspiration and love

ACKNOWLEDGMENTS

I would like to thank my mentors Dr. Shannon Holliday and Dr. Wellington Rody for their guidance and support throughout this process. I would also like to thank the faculty, staff and residents at the University of Florida’s Department of Orthodontics for their unwavering support throughout these past 3 years. Last, but not least, I would like to thank my wonderful parents and husband for always encouraging me to pursue my dreams.

TABLE OF CONTENTS

page

ACKNOWLEDGMENTS ...... 4

LIST OF TABLES ...... 7

LIST OF FIGURES ...... 8

LIST OF ABBREVIATIONS ...... 10

ABSTRACT ...... 11

CHAPTER

1 INTRODUCTION ...... 13

Background ...... 13 Biomarkers in Orthodontics ...... 16 Osteoclastogenesis Related Factors (RANKL/OPG) ...... 16 Biomarkers of Inflammation ...... 17 Extracellular Matrix Degradation Factors (MMPs) ...... 17 Dentin Breakdown Products ...... 18 Extracellular Vesicles (EVs) ...... 20 Biomarkers in Oral Fluids ...... 26 Saliva...... 26 Gingival Crevicular Fluid (GCF) ...... 27 GCF Collection ...... 27 Gingival Washing Method...... 28 Capillary Tubing or Micropipettes ...... 28 Absorbent Filter Paper Strips ...... 29 Durapore Filter Membranes ...... 30 Specific Objectives or Hypotheses ...... 31 Objective ...... 31 Hypotheses ...... 31

2 MATERIALS AND METHODS ...... 33

Participants, Eligibility Criteria ...... 33 Study Design ...... 33 Sample Collection ...... 33 PerioPaper Strip ...... 34 Durapore Filter Membrane ...... 34 Microcapillary Tube ...... 34 Control Samples Collection ...... 35 Sample Preparation ...... 35 EV Isolation with Nanoparticle Tracking Analysis (NTA) ...... 35

Statistical Analysis ...... 36

3 RESULTS ...... 41

4 DISCUSSION ...... 51

Main Findings ...... 51 Limitations ...... 55

5 CONCLUSIONS ...... 57

LIST OF REFERENCES ...... 58

BIOGRAPHICAL SKETCH ...... 63

LIST OF TABLES

Table page

2-1 Outline of Inclusion and Exclusion Criteria ...... 37

3-1 Raw Data with Average Concentration Obtained from the NanoSight Software (version 2.3, NanoSight) ...... 44

3-2 Volume of GCF Collected with PerioPaper and Average Concentration of Nanoparticles ...... 45

3-3 Average Nanoparticle Concentration of Control Samples ...... 45

3-4 Comparison of Raw Data to Control Values ...... 45

3-5 Adjusted Data- Average Control Values Subtracted from the Raw Data ...... 46

3-6 Concentration Difference between the three Collection Methods ...... 46

3-7 Correlation between the adjusted values of the three methods ...... 46

LIST OF FIGURES

Figure page

1-1 Size distribution and concentration of nanoparticles recovered from control and GCF samples. Analysis performed on the NanoSight instrument available at UF-ICBR ...... 32

2-1 PerioPaper, 2018. Courtesy of Dr. Estela Truzman. PerioPaper collection method on tooth #8 ...... 38

2-2 Periotron 8000®, 2018. Courtesy of Dr. Estela Truzman GCF volume determination with the Periotron 8000® (Oraflow, Plainview, NY, USA) ...... 38

2-3 Durapore, 2018. Courtesy of Dr. Estela Truzman Volume of GCF collected with PerioPaper and concentration of nanoparticles ...... 38

2-4 Microcapillary tube, 2018. Courtesy of Dr. Estela Truzman Microcapillary tube collection method on tooth #8 ...... 39

2-5 Leuer port, 2018. Courtesy of Dr. Estela Truzman Installation of the leuer port on the NanoSight NS300 ...... 39

2-6 Leuer Port Connection, 2018. Courtesy of Dr. Estela Truzman Connection of the leuer port into the top-plate of the NanoSight NS300...... 40

2-7 Top-plate, 2018. Courtesy of Dr. Estela Truzman 1 ml disposable syringe connected to a luer port on the top-plate of the NanoSight NS300...... 40

3-1 Bar graph with raw data obtained from the NanoSight software. The average concentration of each method including the Standard deviation are shown ...... 47

3-2 Graph from the NanoSight showing the average nanoparticle concentration of a PerioPaper sample ...... 47

3-3 Graph from the NanoSight showing the average nanoparticle concentration of a Durapore membrane sample ...... 48

3-4 Graph from the NanoSight showing the average nanoparticle concentration of a micropipette ...... 48

3-5 Bar graph with adjusted data. The average control values were subtracted from the raw data for each sample. This number was multiplied by 15.7, which was the dilution media ...... 49

3-6 PerioPaper vs. Durapore Spearman correlation coefficient ...... 49

3-7 PerioPaper vs. micropipette Spearman correlation coefficient ...... 50

3-8 Durapore vs. micropipette Spearman correlation coefficient ...... 50

LIST OF ABBREVIATIONS

CBCT Cone Beam Computed Tomography

CT Scan Computerized Tomography Scan

DMP-1 Dentine Matrix Protein-1

DPP Dentine Phosphoproteins

DSP Dentine Sialoprotein

EVs Extracellular Vesicles

FACS Fluorescence Activated Cell Sorting

GCF Gingival Crevicular Fluid

IL-1 훽 Interleukin-1훽

IL-6 Interleukin-6 miRNA microRNA

MMP Matrix Metalloproteinase

NTA Nanoparticle Tracking Analysis

PBS Phosphate Buffered Saline

TRAP Tartrate-resistant acid phosphatase

OPG Osteoprotegerin

PDL Periodontal Ligament

PTH Parathyroid Hormone

RANKL Receptor Activator of Nuclear Factor Kappa B Ligand

RANK Receptor Activator of Nuclear Factor Kappa B

TNFα Tumor Necrosis Factor Alpha

TIMPs Tissue inhibitors of metalloproteinases

Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science

THE DEVELOPMENT OF AN EXTRACELLULAR VESICLE ISOLATION PROTOCOL FOR GINGIVAL CREVICULAR FLUID

Estela L. Truzman

May 2019

Chair: Lexie Shannon Holliday Major: Dental Sciences – Orthodontics

In recent years, health care has focused on developing strategies to diagnose, monitor progression, and predict treatment outcomes of conditions and diseases using non-invasive methods. Extracellular vesicles have been extensively studied in recent years due to their diagnostic potential as well as their possible use in therapeutics. One of the most widely used fluids for biomarker discovery of oral diseases is gingival crevicular fluid (GCF). The objective of this study was to identify the GCF collection method best suited for EV recovery. This was a cross-sectional study comparing three

GCF collection methods: PerioPaper, Durapore filter membrane, and micropipettes.

GCF was collected from the buccal side of the upper central incisors of 19 adult volunteers. Sampling was carried out in one single visit. Control samples for each method were prepared and compared with the GCF samples. The size and concentration of nanoparticles in each sample were evaluated using Nanoparticle

Tracking Analysis (NTA). The results show that most of the nanoparticles were in the size range of 30-400 nm, consistent with EV size range. No significant correlation was detected between the GCF volume collected with PerioPaper and the average

nanoparticle concentration. Significant differences between the GCF samples and the control samples were detected by all three methods. The highest number of nanoparticles in the size range of EVs were recovered using the Durapore membrane strips, followed closely by the PerioPaper strips. Statistical analysis indicates that

PerioPaper and micropipette differ (borderline significance), and that Durapore and micropipette differ significantly. However, PerioPaper and Durapore do not differ significantly. This study concluded that there was a significant benefit in the use of

PerioPaper and Durapore membranes compared to the use of Microcapillary tubes.

CHAPTER 1 INTRODUCTION

Background

In recent years, health care has focused on developing strategies to diagnose, monitor progression, and predict treatment outcomes of conditions and diseases using non-invasive methods. With significant advancements in molecular technologies, the field of proteomics has developed at a fast rate to fulfill those needs. This field focuses on the study of proteins in the large scale. One of the main areas of application of clinical proteomics has become protein biomarker discovery.1

A biomarker or biologic marker is any substance, structure or process objectively measured in an organism that can act as an indicator of health or disease.2 Biomarkers have assumed an important role in health sciences by providing the tools to make earlier diagnosis of diseases and develop targeted treatment strategies.3 The examination of biologic fluids has been used to develop a patient-specific biomarker panel for diagnosis of oral and systemic diseases.3 The most widely used fluids for biomarker discovery of oral diseases are gingival crevicular fluid (GCF) and saliva.4

Saliva is currently being used as a source for novel biomarkers for diagnosis of a broad range of diseases including autoimmune disorders, cardiovascular disease and infectious diseases.2 Studies have shown that predisposition to certain diseases as well as their progression have been correlated with changes in the composition of whole saliva.5 On the other hand, GCF has a greater potential of containing biomarkers able to identify local processes.6

Biologic markers have several diagnostic and prognostic applications in orthodontics. The protein content in GCF can be analyzed to predict slower or faster

rates of tooth movement, as well as to diagnose potential complications of orthodontic treatment including periodontal disease and root resorption.7 Recently, research has focused on analyzing GCF to discover the presence and expression of proteins associated with orthodontic tooth movement. This has demonstrated to be of diagnostic value to monitor orthodontic treatment results.6

The molecular and biological responses to mechanical stress of patients undergoing orthodontic treatment has been better understood by identifying specific secretory proteins in GCF.7 Extensive structural and biochemical changes occur in the periodontal tissue following application of orthodontic forces. These changes appear to be initiated in the periodontal ligament space.8,9 Many inflammatory-like responses including vascular modifications and leukocyte passage out of the capillaries of the PDL take place during the earlier stages or orthodontic tooth movement.10 Following application of mechanical stress from orthodontic appliances, cells from the underlying tissues become activated and secrete biologically active molecules, including enzymes and cytokines, into the periodontum. These cells are responsible for connective tissue remodeling and osteoclast activation.11,12

Previous studies have shown that some of these molecules can be found in GCF and presumably in saliva as well. In recent years, efforts have been made to recognize alterations in protein composition of oral fluids in order to monitor underlying tissue modifications following appliance activation.7 The presence of numerous growth factors, enzymes, proteoglycans and cytokines in GCF has been examined in efforts to non- invasively monitor orthodontic tooth movement.12

An aspect of growing interest in orthodontics has been the study of external root resorption. Root resorption is a side effect of orthodontic treatment that needs to be considered. It is a physiologic process in deciduous teeth but it is considered to be a pathologic phenomenon in permanent teeth.13 The process involves the breakdown of cementum and can advance to involve the underlying dentin leading to mobility and eventual tooth loss.14 Various studies have indicated that the incidence of root resorption in patients that have undergone orthodontic treatment ranges between 20-

100%. It is usually mild to moderate but in some cases it can be severe.15 Many etiological factors have been linked to the development of root resorption including impaction of teeth, pulpal pathosis, trauma, and mechanical stress from orthodontic appliances.16

The frequency and degree of the occurrence of root resorption associated with orthodontic treatment varies among individuals and can complicate treatment.17

Therefore, early diagnosis should be made. Traditional radiographs and CT scans are the main methods currently available to diagnose and monitor the progression of root resorption. Nevertheless, these techniques lack the sensitivity to make an early diagnosis.18 In addition, other recognized challenges to the use of radiographic diagnosis include inconsistencies between the histologic and radiographic presentation of the condition, as well as an inability to differentiate between active and inactive forms.19 New methods with increased safety, accuracy and prognostic value for early diagnosis of root resorption in patients undergoing orthodontic treatment need to be developed.14

Biomarkers in Orthodontics

Osteoclastogenesis Related Factors (RANKL/OPG)

The RANK-RANKL-OPG system serve as a paracrine modulator of bone metabolism and osteoclastogenesis for orthodontic tooth movement. Receptor activator of nuclear factor–KB ligand (RANKL) induces bone resorption by promoting osteoclastogenesis. When RANKL binds to RANK on the cell surface of osteoclast precursors its biological function is expressed. On the other hand, osteoblasts secrete

Osteoprotegerin (OPG), which has mainly an inhibitory action on osteoclasts. It hinders osteoclast differentiation, suppresses activated osteoclasts and promotes osteoclast apoptosis. OPG binds RANKL and competitively inhibits its binding to RANK.20

Mechanical stimuli from orthodontic appliances causes the periodontal ligament and underlying alveolar bone to remodel leading to orthodontic tooth movement.

Remodeling of the surrounding tissues occurs as the equilibrium between bone apposition and bone resorption is disrupted.21 Bone is resorbed on the pressure side and formed on the tension side.22 Following compression, increased levels of RANKL and decreased levels of OPG are found in GCF as observed by Nishijima et al. in an in- vivo study. They concluded that the change in composition of GCF related to the amount of RANKL and OPG detected is involved in the bone resorptive process following compressive forces.11 Despite their limited sample size of only 10 patients, the findings of a simultaneously conducted in-vitro experiment corroborated their results, which in turn support previous research. In addition, studies have also shown that the

RANKL/RANK/OPG system is involved in both physiologic and pathologic root resorption.20

Biomarkers of Inflammation

During orthodontic treatment, the microenvironment of the underlying periodontium can be monitored by measuring the levels of cytokines in GCF. These cytokines, secreted by cells of the periodontal tissue such as fibroblasts and osteoblasts, are involved in bone metabolism.6 In numerous studies, increased concentrations of specific cytokines have been found in GCF during orthodontic tooth movement. Among the most studied ones are Interleukin-1훽, Interleukin-6 and TNF-훼.23-

During the beginning stages of orthodontic treatment, osteoclasts secrete

Interleukin-1훽 (IL-1훽) as a response to mechanical forces from orthodontic appliances.

Later on, macrophages concentrated in areas of compression, also secrete IL-1 which can be found in increased amounts in periodontal tissues. In addition to IL-1훽’s role in stimulating bone resorption and inhibiting bone formation, it has also shown to induce the production of Interleukin-6 (IL-6).26 Although well recognized as an important modulator of bone metabolism, the precise role of IL-6 in bone pathophysiology is still not well understood since it appears to exhibit both osteoblastic and osteoclastic functions.25

Another inflammatory cytokine associated with orthodontic tooth movement is

TNF-훼. In the presence of macrophage colony stimulating factor, it elicits an inflammatory response which may enhance osteoclast differentiation and induce bone resorption.26 However, the presence of RANKL is necessary in this process.

Extracellular Matrix Degradation Factors (MMPs)

Other biomarkers involved in bone metabolism that have been extensively studied in recent years are the Matrix Metalloproteinases (MMPs), and their counterpart,

the tissue inhibitors of metalloproteinases (TIMPs).27,28 MMPs and TIMPs have been isolated from numerous body fluids including urine, serum, saliva and GCF. They are secreted by different types of cells under both physiological and pathological conditions.27 There are many subsets of MMPs with different functions. Some MMPs have an important function in physiologic bone remodeling by degrading most of the proteins present in the extracellular matrix.27 For example, MMP-1 and MMP-13 are involved in the activation of osteoclasts. In turn, there are also at least four different types of TIMPs.21 Some counteract the action of MMPs thereby inhibiting extracellular matrix degradation.27,28

The role of MMPs and TIMPs in tissue remodeling as a result of mechanical forces from orthodontic treatment has also been recognized.12 An increase in MMPs and TIMPs during orthodontic tooth movement has been found in both bone apposition and resorption sides.28 In an animal study, Holliday et al. demonstrated that by blocking the activity of MMPs, bone resorption was inhibited thereby limiting Orthodontic tooth movement.21

Dentin Breakdown Products

The most abundant non-collagenous proteins found in dentine are dentine sialoprotein (DSP) and dentine phosphoproteins (DPP). Previous studies have revealed that DSP is found in odontoblasts, the dental pulp, predentin and dentine. They have also shown that they are not present in other cells or tissues like ameloblasts, bone, cartilage or soft tissues of the oral cavity. This strongly suggests that DSP is highly specific to dentin and is therefore a suitable biomarker to diagnose root resorption.29

Several studies have successfully identified dentin breakdown products in GCF in the presence of root resorption. With the use of biochemical essays, Mah and Prasad

detected dentine phosphoproteins (DPP) associated with teeth undergoing root resorption.30

Balducci et al. further explored the presence of dentine sialoprotein (DSP), dentine phosphoprotein (DPP), and dentine matrix protein-1 (DMP-1) in the GCF of patients undergoing orthodontic treatment who were radiographically diagnosed with mild and severe root resorption. The treated group had been in treatment for at least one year with fixed orthodontic appliances and was compared with untreated controls.

Results of this study indicate that there is a marked increase of DSP, DPP and DMP in

GCF of teeth undergoing root resorption. However, the presence of DSP and DPP in the control group was an unexpected finding. Authors postulated that undetected physiologic root resorption of control teeth might have caused this occurrence. The findings of this study supported the idea that DSP and DPP can be regarded as suitable biomarkers to diagnose and monitor the progression of root resorption during active orthodontic treatment.31 The use of traditional radiographs, as opposed to CBCT imaging, to detect the presence or absence of root resorption was a significant limitation of this study, which might have caused root resorption in control teeth to be misdiagnosed thereby altering the results.

In a study by Kereshanan et al. aimed at further investigating DSP as a potential biomarker for root resorption, levels of DSP in GCF were measured in primary teeth undergoing physiological root resorption, in non-resorbing mandibular second premolars, and in teeth prior and during orthodontic treatment. The results corroborated that DSP can be used as an analytical biomarker in GCF for root resorption.29 A drawback of this and other studies that use primary teeth to evaluate root resorption is

that it is assumed that the type of proteins released by primary and permanent teeth undergoing physiologic and pathologic root resorption respectively is the same, but both processes might differ.

Extracellular Vesicles (EVs)

Extracellular vesicles (EVs), which include exosomes and microvesicles, have been extensively studied in recent years due to their diagnostic potential as well as their possible use in therapeutics.32 EVs are small nanovesicles derived from endosomal membrane compartments that fuse with the plasma membrane of activated cells and are released. Microvesicles bud directly from the plasma membrane. Most cell types release EVs including salivary glands and epithelial cells, dendritic cells, macrophages and lymphocytes as well as tumor cells.33,34 EVs are also naturally present in physiologic fluids such as blood, saliva, urine, and breast milk.35

EVs contain proteins and lipids, as well DNA, mRNA, microRNA, and noncoding

RNA. They are used by the cell of origin as a disposal route of proteins and harmful

RNA.34 In a recent study, mass spectrometry was used to analyze if there was a difference in the GCF composition of teeth undergoing physiologic root resorption compared to non-resorbing permanent teeth.14 A split mouth design was used and a deciduous second molar with radiographic evidence of root resorption was selected as the experimental site. The contralateral permanent first molar as the control site. They were able to identify a number of proteins in GCF. Data shows that more than half of the proteins identified have been previously detected in EVs. A number of these proteins were upregulated or downregulated in the GCF of teeth undergoing root resorption.14

These findings indicate that osteoclasts, osteoblasts or any other type of cell associated with tooth or bone metabolism may release EVs.14

The role of RANKL derived from osteoblasts in the regulation of osteoclast formation and differentiation, osteoclast survival and osteoclast function has been extensively researched.36 Deng et al. demonstrated that EVs containing RANKL, which originated from osteoblasts, induced osteoclastogenesis. RANKL released from osteoblasts stimulated the RANKL-RANK system by engaging RANK in target cells.37

To further investigate the role of EVs in bone-cell communication and regulation,

Cappariello et al. isolated EVs from primary mouse osteoblasts. Red fluorescent dye labeled osteoblasts were placed in the upper chamber of a transwell dish with pores that allow the passage of EVs. In the bottom chamber they placed unstained osteoblasts. After 48 hours, they observed the appearance of red fluorescence in the osteoblasts of the lower chamber indicating that the EVs from the donor cells integrated with the target cells. With the use of Fluorescence-activated cell sorting (FACS) analysis, they confirmed that osteoblast-derived EVs from the donor cells actually integrate into their target cells. FACS analysis revealed that 53.95 % of osteoblast EVs were positive for RANKL. EVs were also isolated from osteoblasts treated with PTH, known to regulate osteoblast activity. Not only the total number of osteoblast derived

EVs increased in the PTH treated sample but also the percentage of RANKL-positive

EVs increased to 63.6%. Subsequent semiquantitative RT-PCR analysis performed on osteoblasts and osteoclasts treated with osteoblast EVs showed that osteoblast genes are intrinsically regulated by their own EVs. Also, osteoclast size and number increased.

They also evaluated whether osteoblast-derived EVs were able to become integrated into bone. To this purpose, they injected osteoblast RANKL positive EVs into RANKL negative mice and detected the presence of osteoclasts, confirming EV integration into

bone tissue. These findings corroborate the previously studied hypothesis that RANKL- rich EVs derived from osteoblasts regulate bone metabolism through osteoblast- osteoclast crosstalk.36

Communication between bone cells using EVs were also previously studied by

Huynh et al. EVs were isolated from primary mouse marrow osteoclast enriched cultures and osteoclast precursors. They characterized these vesicles using transmission electron microscopy. They found that their size ranged from 25 to 120 nm in diameter, with a mean of 40 nm. After characterizing the vesicles, they further evaluated whether osteoclast-derived EVs have the ability to regulate osteoclastogenesis. EVs isolated from osteoclast precursors or osteoclasts were added to calcitriol-stimulated mouse marrow and after 6 days, the cultures were fixed and stained for TRAP activity to detect osteoclasts. Cells treated with EVs from osteoclast precursors significantly stimulated osteoclastogenesis. In contrast, EVs from osteoclasts significantly reduced the number of osteoclasts formed. These data suggest that the presence of mature osteoclasts possibly inhibits osteoclastogenesis through their production of EVs. These results indicate that not only are EVs secreted by osteoclasts, but they are also biologically active as paracrine regulators of osteoclastogenesis.

Furthermore, Western blots were performed on EVs from osteoclasts or their precursors to examine the presence of RANK. RANK was detected at low levels in the precursors but at much higher levels in osteoclasts. To determine if RANK was on the surface of

EVs, osteoclast-derived EVs were examined by immunoelectron microscopy after staining with anti-RANK antibodies. Immunogold labeling showed that a subset of EVs was significantly labeled with anti-RANK. They next examined whether RANK-rich EVs

contributed to the ability of osteoclast-derived EVs to inhibit osteoclastogenesis. The results suggest that RANK-containing EV’s may inhibit osteoclastogenesis by binding

RANKL. It can be inferred from these results that EVs play a crucial role in the regulation of bone metabolism.38 The results of this study are very valuable to the current body of literature as for the first time it was demonstrated that osteoclasts release regulatory EVs that contain RANK. These findings suggest that RANK and

RANKL contained in EV’s may be important contributors to the regulation of bone metabolism.38

Of particular interest and complexity is the study of EVs as mediators in intercellular communication.38 The function of the target cells in physiologic and in diseased conditions is altered when important signals are carried from EVs.34 Several mechanisms are involved in cell-to-cell communication. Receptors on the surface of

EV’s can interact with receptors on the plasma membrane of target cells to stimulate them. EV’s can also fuse with recipient cells to transfer active proteins, lipids, small molecules and RNAs into their cytosol inducing various biologic responses.37 A recent study by Holliday et al. described the bone regulatory activity of EVs derived from osteoclasts and osteoclasts precursors on calcitriol-stimulated mouse marrow. They reviewed that EV’s derived from osteoclasts precursors stimulate osteoclastogenesis. In contrast, RANK-rich EV’s derived from osteoclasts have a negative feedback regulatory mechanism inhibiting osteoclast differentiation. These findings suggest that EVs derived from osteoclasts and osteoclast precursors provide a mechanism by which bone remodeling is tightly regulated.32

Furthermore, the role of RANK-EVs released by osteoclasts in bone remodeling was documented by Ikebuchi et al. in a study recently released in Nature. They found that osteoclast EVs rich in RANK bind to RANKL on the surface of osteoblasts and initiate the transition from bone resorption to bone formation. The authors stimulated osteoblasts with osteoclast EVs and observed an upregulation of Runx2, which promotes osteoblast differentiation. They also concluded that the activation of osteoblasts was triggered by vesicular RANK. Moreover, by administering anti-RANKL antibodies the formation of osteoclasts from osteoclast precursors was reduced. They observed that the binding of these antibodies also activated osteoblasts. This offers the promise of a next generation of therapeutic agents for the treatment of osteoporosis and other bone diseases that are bifunctional; both blocking bone resorption (like denosumab) but also stimulating bone formation. These results indicate that osteoclast

EVs play an important role in the coupling of bone resorption and bone formation.39

Other important biomarkers involved in the regulation of bone metabolism that have been extensively studied in recent years are secretory microRNAs (miRNAs).

MiRNAs are small non-coding RNA molecules important in gene regulation. They have been shown to have a regulatory function in bone remodeling by controlling osteoblast and osteoclast differentiation and function.40 A recent study by Atsawasuwan et al. investigated the presence of secretory microRNAs in GCF both freely circulating as well as encapsulated in EVs. They also studied the expression profile of miRNA-29 in patients undergoing orthodontic tooth movement. Four healthy adults served as their control group, and fifteen orthodontic patients undergoing canine retraction was their study group. Using a bioanalyzer, realtime PCR and Western Blot analysis, they

demonstrated the presence of miRNA in GCF, with the highest concentration present in the EV-associated fraction of GCF. In the orthodontic tooth movement GCF samples, they confirmed for the first time an increased expression of the microRNA-29 family during canine retraction, suggesting a correlation with osteoclast function.40 These findings support the use of microRNAs as potential GCF biomarkers in orthodontic tooth movement.

A preliminary study has been recently conducted by Dr. Wellington Rody from the

University of Florida using Nanoparticle Tracking Analysis (NTA) with the Malvern

NanoSight NS300. With the use of a laser light scattering and a charge-coupled device camera, the Nanoparticle tracking device allows visualization and recording of vesicles ranging in size from 30 to 1000 nm in diameter at a concentration range of 108–109. The

NTA software (version 2.3, NanoSight), identifies and tracks particles moving under

Brownian motion. The velocity of the particle movement is used to calculate particle size and concentration by applying the two-dimensional Stokes-Einstein equation.41,42

In this study, they collected GCF from one patient with localized aggressive periodontitis using a PerioPaper strip. Figure 1-1 shows the size distribution and concentration of nanoparticles recovered from the sample. Particles sized 30 to 150 nm appear in the red box and are consistent with EV size range. Figure 1-1A shows 400- nm control beads used to calibrate the device. Figure 1-1B and 1-1C show particles recovered from the dilution media alone and an empty paper strip with dilution media respectively. It can be observed that some nanoparticles in the size range of EVs were recovered from dilution media alone and the paper strip itself. Finally, Figure 1-1D shows the paper strip with the sampled GCF of a patient with localized aggressive

periodontitis. It can be seen that the concentration of nanoparticles recovered from the sample was 2-3 times higher than that seen in the previous figures. This suggests the presence of EVs in the collected sample.

Biomarkers in Oral Fluids

The use of saliva and gingival crevicular fluid (GCF) for the detection of biologic biomarkers to diagnose oral and systemic diseases has been extensively studied in the past 10 years.2

Saliva

The main constituents of saliva are water (97%), proteins, immunoglobulins, enzymes and electrolytes.43 The components of saliva arise from an array of sources which include secretions from salivary glands, blood, bronchial and nasal secretions, microorganism, as well as shedding cells from the inner mucosa or the oral cavity.5 The protein composition in saliva is a reflection of the local and, to a certain extent, the systemic state of an individual.44 Changes in saliva content can be studied to identify individuals at risk of certain diseases and to diagnose and monitor their progression. It can therefore be used to discover novel diagnostic biomarkers.5

A study by Guo et al. identified more than 1000 distinct proteins in whole saliva.

Small proteins such as PRPs and antimicrobial proteins, different cystatin precursors and lysozymes were identified. In addition, previously identified proteins present in bacterial genomes were detected in this study suggesting the presence of numerous bacterial species in saliva. This highlights the potential to use salivary diagnostic tests to detect exposure to frequently encountered pathogens.5 Other clinical applications of saliva-based tests currently employed are the diagnosis of autoimmune, cardiovascular and infectious disease, as well as monitoring drug usage.2

Gingival Crevicular Fluid (GCF)

Another fluid that has gained increased recognition for biomarker discovery is gingival crevicular fluid. GCF is a serum transudate of interstitial fluid or inflammatory exudates found in the gingival sulcus, which originates from the blood vessels in the gingival corium.2,45 The components contributing to the formation of GCF include serum derived factors, host cells, and biofilm microorganism.6

The composition of GCF is a reflection of the underlying periodontum due to the presence of several cellular and biochemical factors.29 As opposed to saliva, the site- specific nature of GCF make it better suited to evaluate the local cellular metabolism during orthodontic treatment. The molecular analysis of GCF could be of great diagnostic value in monitoring the level of local inflammatory reactions and status of bone turnover before, during and after orthodontic treatment, as well as to detect early signs of external root resorption.6,46 So far only a few diagnostic markers of root resorption have been identified. Recent research has mainly focused on the detection of dentin breakdown products in GCF. Formerly available technologies made separation, identification, and testing of single proteins necessary. This approach made the success rate for biomarker identification very low. With the advents in the field of proteomic, efforts are being made to develop a reliable biomarker panel for the diagnosis of root resorption.14

GCF Collection

When sampling body fluids, an appropriate collection method must be used to prevent the sample from becoming contaminated and to ensure that the sampling is effective.7 Numerous GCF collection methods have been described in the literature involving various techniques. They each have their indications, advantages and

disadvantages. Griffiths described three major sampling approaches. They include the gingival washing method, capillary micropipettes, and paper strips.47 Furthermore, the use of a Durapore filter membrane has been proposed by several authors

Gingival Washing Method

In this collection method, a standardized volume of an isotonic fluid, like Hanks balanced salt solution, is flushed into the gingival sulcus. This technique is especially valuable when there exists the indication for harvesting cells in addition to fluid, as the sampled fluid contains cells and other soluble components like plasma proteins in addition to diluted GCF.47

Capillary Tubing or Micropipettes

This sampling approach consists of positioning micropipettes at the entrance of the gingival sulcus after careful drying and isolation of the selected site, and allowing

GCF to migrate through capillary action into the tube. The internal diameter of the capillary tube must be known and must be consistent in order to allow an accurate calculation of the amount of fluid that is collected at each site, by measuring the distance that the fluid has traveled inside the tube.47

The advantage of this technique over other approaches is that the collected sample is undiluted and its volume can be measured precisely. Nevertheless, extracting the whole sample from the micropipette is challenging. Generally, the sample is removed from the tube by centrifugation, but other systems have been used such as passing a diluting solution through the micropipette or forcing the liquid out with air. A major drawback of this sampling method is that it is difficult to obtain a large volume of

GCF in the presence of healthy periodontal tissues without exposing the patient to extensive collection times which may surpass 30 minutes. When holding the

micropipettes at the entrance of the sulcus for prolonged times, it is difficult to ensure that the tissues will not be traumatized and contaminate the sample.47

A variety of micropipette diameters over longer or shorter collection times have been employed by different authors. Giannobile et al. followed the method described by

Last et al. to collect GCF. As described in this method, sterile 2 μl micropipettes are placed at the gingival margin for 5 minutes. Samples are then transported on ice to the laboratory within 3 hours after collection.48,49

Kereshanan et al. collected GCF using 5 μl micropipettes (Drummond Scientific

Co., Broomall, Pennsylvania, USA) over a 10-minute period. On the other hand, Bildt et al. utilized 1 μl microsyringe (Hamilton, Reno, Nevada, USA) to collect GCF. His method involved taking three samples per site at two-minute intervals. Both authors stored the collected samples at −80°C prior to laboratory analysis.28,29

Following the methods described by Giannobile et al..48, Ngo et al. collected GCF using 2 μl glass micropipettes (Drummond Scientific Co., Broomall, Pennsylvania,

USA). This method was preferred to the use of paperpoints because of the significant dilution and poor recovery with paperpoints. Cotton rolls were placed for isolation and a sterile curette was used to remove any supragingival plaque present. The glass micropipette was positioned at the entrance of the gingival sulcus and left in place for 30 seconds. GCF (0.2-1.5 μl) entered the tube through capillary action. The final samples were placed into microcentrifuge tubes and placed on ice, and finally stored at

−70°C.50,51

Absorbent Filter Paper Strips

When properly used, this technique appears to be the most atraumatic collection method for GCF. It is fast, not very technique sensitive, and can be used at individual

sites.47 The collection site is isolated with cotton rolls, air dried, and two filter paper strips are placed into the gingival crevice for 3 minutes.12

In various studies, the method of Offenbacher et al. was used to collect GCF using paper strips. As described by Nishijima et al. the surface of each tooth is washed with water, isolated and air dried. Paper strips (PerioPaper, Harco, Tustin, CA, USA) are carefully placed 1 mm into the sulcus of each tooth to be tested, and left in place for 1 minute. Subsequently, a second paper strip is inserted after 1 minute and allowed to remain in place for the same length of time. The procedure is performed as atraumatically as possible to avoid mechanically injuring the site.11,52

Rody et al. described a protocol for GCF collection. In their studies, the surface of each tooth selected was cleaned with cotton pellets to remove all gingival plaque, dried for 5 seconds using an air syringe, and isolated with cotton rolls. Absorbent paper strips (PerioPaper; Oraflow, Plainview, NY) were placed in the lingual gingival crevice of each tooth until mild resistance was felt, and allowed to remain in place for 40 seconds.

They discarded any paper strips that seemed visually contaminated. Samples were kept in sealed tubes at −80°C until ready for analysis.14,53

Durapore Filter Membranes

In this technique, Durapore filter membranes (pore size = 0.22 mm; Millipore

Corp., Bedford, Mass., USA) are used for GCF collection. Teeth selected for sampling are isolated; the strip is placed in the gingival sulcus and allowed to remain in place for

15 seconds. A second strip is then inserted at the same testing site after a 3-minute interval and is left in place for another 15 seconds. The samples are subsequently centrifuged and frozen at −20°C until ready for processing.54

Specific Objectives or Hypotheses

Objective

The objective of this study is to identify the GCF collection method best suited for

EV recovery. This study intends to be a pilot trial which may provide the basis for the development of a safer, more sensitive and less expensive test to diagnose and monitor the progression of certain oral diseases including root resorption. Data obtained from this study will allow us to further investigate the potential use of oral-fluid derived EVs for diagnostic purposes.

Hypotheses

Null Hypothesis #1: There will be no difference in the recovery of EVs in GCF between the PerioPaper, micropipettes or Durapore filter membrane samples and their corresponding control samples.

Null Hypothesis #2: There will be no difference between methods in the recovery of EVs in GCF using PerioPaper, micropipettes or Durapore filter membrane.

Figure 1-1. Size distribution and concentration of nanoparticles recovered from control and GCF samples. Analysis performed on the NanoSight instrument available at UF-ICBR

CHAPTER 2 MATERIALS AND METHODS

Participants, Eligibility Criteria

GCF from 19 adult volunteers was collected. Patient selection was performed using set inclusion and exclusion criteria outlined in Table 2-1. Approval for the study was granted from the University of Florida Institutional Review Board for the Protection of Human Subjects (IRB-01, approval # 201600476). Patient population reflected that of the patient pool of UFCD as patients of all races, genders and ethnicities were included in the study.

Study Design

This was a cross-sectional study. Collection of all samples was carried out in one single visit. GCF was collected from each subject. Prior to sample collection, all patients provided written informed consent at the time of enrollment. Three methods were used for GCF collection: PerioPaper, Durapore filter membrane and Microcapillary tubes. The oral fluid samples were categorized in three groups based on the collection method as follows:

1. EVs from GCF collected with PerioPaper (GCF-P) 2. EVs from GCF collected with Durapore filter membrane (GCF-D) 3. EVs from GCF collected with micropipette (GCF-M)

Sample Collection

GCF was collected following previously established protocols as shown in

Figures 2-1 to 2-4. GCF collection was carried out at the buccal side of the upper central incisors. Additional sites were used if necessary. The sites for GCF collection were gently air-dried and isolated with cotton rolls. Any supragingival plaque was removed with a sterile curette. Samples visually contaminated with blood were

discarded. Three major approaches for GCF collection were tested: PerioPaper,

Durapore filter membrane and Microcapillary tubes.

PerioPaper Strip

As shown in Figure 2-1, a PerioPaper® GCF collection strip (Oraflow Inc.,

Plainview, NY) was inserted 1-2 mm into the sulcus of the selected teeth and left in place for 60 seconds.14 Prior collection, the sampling site was dried by air and cotton pellets for 1 min. and isolated with cotton rolls.55 The volume of GCF collected was determined by positioning the strips between the upper and the lower counterparts of the pre-calibrated Periotron 8000® (Oraflow, Plainview, NY, USA), as can be observed in Figure 2-2. The volume was measured in Periotron units. The strips were placed in

Eppendorf tubes containing 100 μl of phosphate-buffered saline (PBS).

Durapore Filter Membrane

GCF was collected using 2x8 mm strips of Durapore filter membranes (pore size = 0.22 mm; Millipore Corp., Bedford, Mass., USA), as illustrated in Figure 2-3. The sampling site was dried by air and cotton pellets for 1 min. before sample collection and isolated with cotton rolls.55 The strips were inserted into the sulcus of the selected teeth until mild resistance was felt and left in place for 60 seconds.54 Samples contaminated with blood were discarded. The membrane was placed in Eppendorf tubes containing

100 μl of phosphate-buffered saline (PBS).

Microcapillary Tube

As shown in Figure 2-4, a 1 µl micropipette (Drummond Scientific Co., Broomall,

Pennsylvania, USA) was placed at the entrance of the gingival sulcus and left in place for 5 minutes. GCF was drawn into the glass tube through capillary action. The fluid was

dispensed from the microcapillary tube by means of gentle air pressure from one end of the tube, via a bulb.29,50

Control Samples Collection

Four control samples of each collection method were prepared. For each method, a sterile PerioPaper strip, Durapore membrane strip or micropipette was deposited in

Eppendorf tubes containing 100 μl of phosphate-buffered saline (PBS).

All collected samples were sealed and frozen at −80°C until further analysis.49

The tubes were labeled with unique identifiers only used for the purpose of tracking them and tracking the related lab results.

Sample Preparation

Collected samples were vortexed for 5 minutes. GCF was eluted by centrifugation at 15,000 g for 5 min. They were then spun at 5,000 xg for 10 minutes at

4 o C to remove large cell debris and residual organelles. The supernatant was transferred to another ultracentrifuge tube and subjected to a 3 hour spin at 200,000 xg in a Beckman Airfuge (Beckman Coulter, Brea, California), which allows centrifugation of very small volumes. The final EV pellet was resuspended in 100 μl of phosphate- buffered saline (PBS). For a final dilution of 16.67X, 30 μl were suspended in 500 μl of phosphate-buffered saline (PBS). Samples were kept at -800C until assayed.

EV Isolation with Nanoparticle Tracking Analysis (NTA)

Real-time characterization of the isolated EVs was done with NTA using a

NanoSight NS300 (Malvern Instruments, United Kingdom), which is equipped with fast video capture and particle-tracking software. Samples were loaded into the chamber of the NanoSight instrument unit using a 1 ml disposable syringe, which was directly connected to a luer port on the top-plate as shown in Figure 2-5 to 2-7. EVs were

visualized by laser light scattering, and Brownian motion of these vesicles were captured on video. Recorded videos were then analyzed with NTA software (version

2.3, NanoSight), which provides high-resolution particle size distribution profiles and concentration measurements of the EVs in solution. The velocity of the particle movement is used to calculate particle size by applying the two-dimensional Stokes-

Einstein equation. The smallest detectable size using the NTA system is approximately

50 nm. NTA post-acquisition settings were optimized and kept constant between samples.56 The following settings were used: camera at 30 frames per second, camera level at 16, temperature between 21–25 °C and video recording time at 60 seconds.

Five videos were recorded per sample. NanoSight NTA Software analyzed raw data videos to give the mean, mode and median vesicle size, together with an estimate of concentration. The results were plotted in Microsoft Excel and PDF datasheets.

Statistical Analysis

Two statistical approaches were used to assess if the EV concentrations differ, depending on the method of collection: analysis of variance, and Friedman’s test. While the first method is affected by outliers, the second method is based on ranks of the data, and hence not influenced by extreme values. If significant differences were detected, pairwise comparisons (paired t-test and Wilcoxon signed rank test) were made to identify which methods differed. In addition, a one-sample t test was done to evaluate if there was a significant difference between the samples and the control values. In addition, we evaluated if there was a correlation between the GCF volume collected with PerioPaper and the concentration of nanoparticles observed.

Table 2-1. Outline of Inclusion and Exclusion Criteria Inclusion 1. Age range from 21-45 years old Criteria 2. All six maxillary and mandibular anterior teeth present

Exclusion 1. Smokers and tobacco users in the past year Criteria 2. Extremely poor oral hygiene

Figure 2-1. PerioPaper, 2018. Courtesy of Dr. Estela Truzman. PerioPaper collection method on tooth #8

Figure 2-2. Periotron 8000®, 2018. Courtesy of Dr. Estela Truzman GCF volume determination with the Periotron 8000® (Oraflow, Plainview, NY, USA)

Figure 2-3. Durapore, 2018. Courtesy of Dr. Estela Truzman Volume of GCF collected with PerioPaper and concentration of nanoparticles

Figure 2-4. Microcapillary tube, 2018. Courtesy of Dr. Estela Truzman Microcapillary tube collection method on tooth #8

Figure 2-5. Leuer port, 2018. Courtesy of Dr. Estela Truzman Installation of the leuer port on the NanoSight NS300

Figure 2-6. Leuer Port Connection, 2018. Courtesy of Dr. Estela Truzman Connection of the leuer port into the top-plate of the NanoSight NS300.

Figure 2-7. Top-plate, 2018. Courtesy of Dr. Estela Truzman 1 ml disposable syringe connected to a luer port on the top-plate of the NanoSight NS300.

CHAPTER 3 RESULTS

Subjects were recruited from October 2017 to March 2018. Samples of 19 participants were collected using the three collection methods described above:

PerioPaper, Durapore filter membrane, and micropipettes. They were then evaluated with NTA and raw data obtained from the NTA software was obtained. Table 3-1 and

Figure 3-1 contain the average nanoparticle concentration of the samples for each collection method. Graph representations of a sample taken with each one of the methods obtained from the NanoSight software are shown in Figures 3-2 to 3-4. The figures show the nanoparticle size and the concentration of particles per ml. It can be observed that most of the nanoparticles were in the size range of 30-400 nm, consistent with EV size range. Of interest is to note that there was a very small volume of GCF collected for P3, P16 with the micropipette method. As would be expected, the average nanoparticle concentration pertaining to those samples was significantly lower than that observed in the other samples. This leads us to believe that the lower the volume of

GCF collected, the lower the concentration of nanoparticles present in the sample. The only available values we had at our disposable to corroborate these findings, was the volume of GCF collected with PerioPaper, as measured by the Periotron 8000®

(Oraflow, Plainview, NY, USA). These values can be found in Table 3-2. No significant correlation was detected. The Pearson correlation coefficient estimate, -0.02, was not significant (p=0.94), nor was the rank-based Spearman correlation coefficient estimate,

0.07 (p=0.77).

Table 3-3 shows the average nanoparticle concentration of each control sample prepared. A one-sample t-test and a Wilcoxon signed rank test were conducted to assess whether there was a significant difference between the control values and the samples for each method. As observed in Table 3-4, significant differences from the control values were detected by all three methods. Thus, we reject the previously stated null hypothesis #1 that there will be no difference in the recovery of EVs in GCF between the PerioPaper, micropipettes or Durapore filter membrane samples and their corresponding control samples.

The average control values were subtracted from the raw data for each sample and this number was multiplied by 15.7, which was the dilution media. Summary statistics (mean, median and standard deviation) were calculated for each method and are listed in Table 3-5 and depicted in Figure 3-5. These values were used in the final statistical analysis for our study. It can be observed that the highest number of nanoparticles in the size range of EVs were recovered using the Durapore membrane strips, followed closely by the PerioPaper strips. As shown in Table 3-6, the Wilcoxon signed rank test indicates that PerioPaper and micropipette differ (borderline significance), and also that Durapore and micropipette differ significantly with P values of 0.05 and 0.01 respectively. However, PerioPaper and Durapore do not differ significantly. Thus, we reject our null hypothesis #2 that there will be no difference in the recovery of EVs in GCF using PerioPaper, micropipettes or Durapore filter membrane

Lastly, we evaluated the correlation between the three methods of collection. The

Spearman correlation coefficient, as well as the Pearson correlation coefficient were calculated and are shown in Table 3-7 and plotted in Figure 3-7 to 3-8. A low correlation

was observed between PerioPaper and micropipette with a Spearman correlation of

0.06. Higher correlations were found between PerioPaper and Durapore and between

Durapore and micropipette.

Table 3-1. Raw Data with Average Concentration Obtained from the NanoSight Software (version 2.3, NanoSight) Patient PerioPaper Durapore Micropipettes P1 159,000,000 408,000,000 293,000,000 P2 339,000,000 631,000,000 62,000,000 P3 106,000,000 62,400,000 12,800,000 P4 749,000,000 568,000,000 119,000,000 P5 476,000,000 156,000,000 127,000,000 P6 67,600,000 113,000,000 21,300,000 P7 204,000,000 462,000,000 817,000,000 P8 189,000,000 443,000,000 23,200,000 P9 279,000,000 271,000,000 32,600,000 P10 338,000,000 526,000,000 311,000,000 P11 475,000,000 716,000,000 406,000,000 P12 89,900,000 206,000,000 258,000,000 P13 562,000,000 729,000,000 369,000,000 P14 419,000,000 115,000,000 140,000,000 P15 19,400,000 478,000,000 422,000,000 P16 327,000,000 246,000,000 12,900,000 P17 175,000,000 60,800,000 167,000,000 P18 564,000,000 329,000,000 128,000,000 P19 257,000,000 464,000,000 0 Average 304,994,737 367,589,474 195,884,211 Median 279,000,000 408,000,000 128,000,000 SD 196183039 215603932 206431867

Table 3-2. Volume of GCF Collected with PerioPaper and Average Concentration of Nanoparticles Concentration PerioPaper Periotron Units P1 159,000,000 52 P2 339,000,000 68 P3 106,000,000 41 P4 749,000,000 23 P5 476,000,000 71 P6 67,600,000 16 P7 204,000,000 19 P8 189,000,000 56 P9 279,000,000 60 P10 338,000,000 34 P11 475,000,000 15 P12 89,900,000 27 P13 562,000,000 38 P14 419,000,000 27 P15 19,400,000 16 P16 327,000,000 12 P17 175,000,000 51 P18 564,000,000 18 P19 257,000,000 17 Median 27 Mean 34.789 Std Dev 19.484 Minimum 12 Maximum 71

Table 3-3. Average Nanoparticle Concentration of Control Samples Control PerioPaper Durapore Micropipettes Average 57375000 96700000 67850000

Table 3-4. Comparison of Raw Data to Control Values Method One-sample t test (p) Wilcoxon signed rank test (p) PerioPaper <.0001 <.0001 Durapore <.0001 <.0001 Micropipette 0.01 0.01

Table 3-5. Adjusted Data- Average Control Values Subtracted from the Raw Data Patient PerioPaper Durapore Micropipettes P1 1,595,512,500 4,887,410,000 3,534,855,000 P2 4,421,512,500 8,388,510,000 -91,845,000 P3 763,412,500 -538,510,000 -864,285,000 P4 10,858,512,500 7,399,410,000 803,055,000 P5 6,572,412,500 931,010,000 928,655,000 P6 160,532,500 255,910,000 -730,835,000 P7 2,302,012,500 5,735,210,000 11,761,655,000 P8 2,066,512,500 5,436,910,000 -701,005,000 P9 3,479,512,500 2,736,510,000 -553,425,000 P10 4,405,812,500 6,740,010,000 3,817,455,000 P11 6,556,712,500 9,723,010,000 5,308,955,000 P12 510,642,500 1,716,010,000 2,985,355,000 P13 7,922,612,500 9,927,110,000 4,728,055,000 P14 5,677,512,500 287,310,000 1,132,755,000 P15 -596,207,500 5,986,410,000 5,560,155,000 P16 4,233,112,500 2,344,010,000 -862,715,000 P17 1,846,712,500 -563,630,000 1,556,655,000 P18 7,954,012,500 3,647,110,000 944,355,000 P19 3,134,112,500 5,766,610,000 -1,065,245,000

Average 3,887,629,868 4,252,964,737 2,010,137,105 Median 3,479,512,500 4,887,410,000 944,355,000 SD 2,997,923,616 3,294,699,281 3,154,538,597

Table 3-6. Concentration Difference between the three Collection Methods Variable N Median Mean Std Dev Std Minimum Maximum p- Error value cdiffpd 19 -1.21E9 -3.65E8 3.507E9 8.046E8 -6.58E9 5.641E9 0.68 cdiffpm 19 2.768E9 1.877E9 4.574E9 1.049E9 -9.46E9 1.01E10 0.05 cdiffdm 19 2.703E9 2.243E9 3.605E9 8.27E8 -6.03E9 8.48E9 0.01

Table 3-7. Correlation between the adjusted values of the three methods Pearson (p) Spearman (p) Cperiop and cdurapor 0.41 (0.08) 0.43 (0.07) Cperiop and cmicrop -0.05 (0.85) 0.06 (0.82) Cdurapor and cmicrop 0.41 (0.08) 0.39 (0.10)

Raw Data from Nanosight 700000000

600000000

500000000

400000000

300000000

200000000

100000000

0 Periopaper Durapore Micropipettes

Figure 3-1. Bar graph with raw data obtained from the NanoSight software. The average concentration of each method including the Standard deviation are shown

Figure 3-2. Graph from the NanoSight showing the average nanoparticle concentration of a PerioPaper sample

Figure 3-3. Graph from the NanoSight showing the average nanoparticle concentration of a Durapore membrane sample

Figure 3-4. Graph from the NanoSight showing the average nanoparticle concentration of a micropipette

Figure 3-5. Bar graph with adjusted data. The average control values were subtracted from the raw data for each sample. This number was multiplied by 15.7, which was the dilution media

Figure 3-6. PerioPaper vs. Durapore Spearman correlation coefficient

Figure 3-7. PerioPaper vs. micropipette Spearman correlation coefficient

Figure 3-8. Durapore vs. micropipette Spearman correlation coefficient

CHAPTER 4 DISCUSSION

Main Findings

Currently, the diagnosis of several conditions including external root resorption and periodontal disease relies on radiographic examination. Unfortunately, the low sensitivity of these methods delay the diagnosis of the problem until it is at an advanced stage. Thus, the study of biomarkers for the early detection of certain conditions has gained significant popularity in the recent years. The discovery of new biologic markers of mineralized tissue resorption provides a tool for the early diagnosis of several conditions including external root resorption, among others. External apical root resorption is a widespread condition affecting up to 80% of orthodontic patients.57

Fortunately, only 1-5% of those affected exhibit severe root resorption. A meta-analysis of the treatment-related factors associated with this phenomenon concluded that apical displacement, and total treatment duration showed significant correlation with mean apical root resorption.58 Should the identification of pre-disposed individuals be done early, possibly through non-invasive methods such as biomarker analysis in GCF, treatment duration should be minimized and certain tooth moments totally avoided.

The basic components of GCF include local breakdown products, inflammatory cytokines, serum transudate, plaque, extracellular proteins, and cells.6 GCF’s composition reflects the local environment more closely than other fluids such as saliva, making it a promising non-invasive tool to evaluate mineralized tissue resorption during orthodontic treatment. Using liquid chromatography-mass spectrometry, Rody Jr et al. examined the protein composition in GCF of resorbing and non-resorbing teeth. They were able to identify 2789 proteins in the control group and 2421 proteins in the study

group, some of which were upregulated or downregulated in the resorption sample.

Because many of these proteins are typically found in extracellular vesicles, the presence of EVs can be inferred. Nevertheless, EVs were not directly observed.14 More recently, Atsawasuwan et al. conducted a GCF study to examine secretory miRNA contained in EVs and if miRNA-29 was upregulated during orthodontic tooth movement.

Using a bioanalyzer, real-time PCR and Western blot analysis, they were able to demonstrate the presence of miRNA in the GCF sample. Moreover, they showed that the expression of miRNA-29 was increased during orthodontic tooth movement.40

One of the major drawbacks of GCF analysis is the difficulty in obtaining adequate amounts of sample. The volume of GCF is generally very low, especially in individuals with healthy periodontium. Multiple methods have been employed to collect

GCF including the use of micropipettes and absorbent filter paper strips. As there are advantages and disadvantages with each one of these techniques, we conducted this pilot study to determine which is the most reliable and efficient method of GCF collection for EV isolation.

NTA with the NanoSight NS300 (Malvern Instruments, United Kingdom) was the method employed to detect and quantify the concentration of EVs present in our sample. The analysis revealed a concentration range of nanoparticle recovery of

2,010,137,105- 4,252,964,737 nanoparticles per ml. Although NTA has become the gold standard for EV isolation in recent years, there are some inherent challenges with this technology. Most notably, the particles released by the sampling tool and the dilution media itself are observed as background, as many of these particles are in the size range of EVs. Thus, they contribute to the overall nanoparticle concentration of the

sample. To account for this drawback, control samples for each one of our methods were prepared and examined, and these values were subtracted from our data. As seen in Table 3-4, there was a statistically significant difference between our samples and the controls for all three methods. This leads us to the conclusion that the remaining nanoparticles after the control values are subtracted are extracellular vesicles present in our samples. These results confirm the preliminary findings obtained by Dr. Rody at the

University of Florida using NTA.

As previously discussed, the micropipette collection method involves holding a capillary tube at the entrance of the gingival sulcus and allowing GCF to flow in through capillary action. This technique should allow for the purest sample to be collected. The number of nanoparticles expected to be released from a glass tube itself is less than that of paper strips and Durapore membranes. As observed in Table 3-4, the opposite was actually true as the difference between the samples and controls for micropipette was less significant (but still statistically significant) than with the other two methods. In addition, another expected advantage of sampling with micropipettes was the ability to measure the volume of fluid collected. This notion proved to be wrong for a number of reasons. First, extracting the full volume of GCF collected from the micropipette with bulb air pressure was very technique sensitive. On the other hand, even though we used a 1ul micropipette and kept it in place for 5 minutes, we rarely observed a complete fill of the tube. Therefore, it was not possible to quantify the total volume collected. We noted that not only the amount of GCF produced by the patient but also the quality of the fluid influenced how much the capillary tube was filled. We observed that thicker and more viscous fluids did not flow into the tube as readily as thinner ones.

Micropipette GCF collection was also more time consuming and more physically demanding for both the operator and the patient. GCF sampling with micropipettes poses many technical difficulties, which may result in inaccurate and inconsistent sampling.

Collection of GCF with the use of PerioPaper and Durapore membrane is accomplished by inserting a sterile strip 1-2 mm into the gingival sulcus until resistance is felt.59 In contrast to micropipettes, these collection methods are much faster and seem to be more reliable. While Durapore is much more economic than PerioPaper,

PerioPaper is more widely used. In addition, the volume of GCF collected with

PerioPaper can be measured using a pre-calibrated Periotron 8000® (Oraflow,

Plainview, NY, USA). However, as seen in Table 3-2 there was no correlation between the volume collected and the concentration of nanoparticles observed. A major drawback is that complete elution of the collected sample can be very challenging. In addition, the PerioPaper and Durapore membranes themselves release a large number of nanoparticles in the size range of extracellular vesicles, which are recorded by NTA.

Nevertheless, our analysis revealed significant differences between the control samples and the study samples confirming the recovery of EV sized nanoparticles from our collected samples.

Our results are consistent with the data obtained in a recent cross-sectional study conducted by our group at the University of Florida.60 Three different GCF collection methods were tested to evaluate which was the most efficient one at capturing GCF derived proteins. They observed that the use of Durapore filter membrane (Millipore Corp., Bedford, MA) and PerioPaper (Oraflow Inc., Plainview, NY)

had a significant advantage over the use of micropipettes at recovering proteins. The difference between PerioPaper and Durapore, however, was not statistically significant.

Based on the average concentration of nanoparticles recovered from our samples, Durapore membranes seem to facilitate the highest concentration of EV recovery, followed closely by PerioPaper. The average concentration of nanoparticles observed in the samples collected with Durapore was 4,168,873,333 particles/ml, compared with 3,929,491,944 particles/ml observed with PerioPaper. Nevertheless, as seen in Table 3-6, there is no statistically significant difference in the recovery of EVs between the two methods. We conclude that Durapore and PerioPaper are the most efficient and reliable mean of collecting GCF for EV isolation, Also, Durapore and

PerioPaper are more economic, faster and more comfortable for both the patient and the operator than micropipettes.

Limitations

Limitations of our study include the small sample size, as well as the homogeneity of the participant population. All of our subjects were in the age range of

22 to 37 years old and had a healthy periodontium. It cannot be ruled out that in the presence of periodontal disease or other systemic factors, the recovery of EVs using different methods might differ.

Another significant limitation of our study lies in the standardized sequence in which the sample collection was carried out. We collected GCF with micropipettes first, followed by PerioPaper and Durapore. In addition, no time was allowed between samples. It has been postulated that GCF needs time to replenish its volume, as well as its EV content following GCF collection. Thus, the GCF environment could have differed

between samples. Nevertheless, this does not seem to have influenced our results as micropipette samples consistently yielded a lower concentration of nanoparticles.

CHAPTER 5 CONCLUSIONS

Orthodontic tooth movement is carried out by a sequence of cellular events that control the rate, direction and amount of bone remodeling. Unfortunately, these events sometimes extend beyond the confines of bone to effect mineralized tooth tissues causing root resorption. Through non-invasive GCF collection, biomarker levels of root resorption before, during and after orthodontic treatment could be studied to improve our ability to specifically identify cellular events leading to root resorption.

The objective of investigating which method of GCF collection is most accurate and efficient for EV recovery was achieved. This study concluded that there was a significant benefit in the use of PerioPaper and Durapore membranes compared to the use of microcapillary tubes.

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BIOGRAPHICAL SKETCH

Estela L Truzman was born and raised in Caracas, Venezuela by her parents

Perla and Jose Truzman. She completed her dental school training in Caracas,

Venezuela. She and her family relocated to South Florida seeking brighter opportunities for Estela and her siblings. Once in South Florida, Estela completed a 2-year Advanced

Education in General Dentistry program (EAGD) at the University of Florida. She was then accepted into the University of Florida Graduate Orthodontic Program to continue her training. In December 2018, Estela married her wonderful husband Arne

Grundmann. Her husband and her family have supported her unconditionally through all of her endeavors. Estela plans to practice orthodontics in South Florida upon completion of orthodontics residency in May 2019.