SalivaPrint as a Non-invasive Diagnostic Tool

Eduardo Esteves

, Igor Cruz

, Ana Cristina Esteves

, Marlene Barros

and Nuno Rosa

Universidade Católica Portuguesa, Faculty of Dental Medicine,

Center for Interdisciplinary Research in Health (CIIS), Portugal

Keywords: Saliva Diagnostic, Saliva Protein Profile, Machine Learning Strategies, Health Diagnostic.

Abstract: Currently, the molecular diagnosis is based on the quantification of RNA, proteins and metabolites because

they present changes in their quantity related to clinical situations. The same molecules are not generally

suitable for early diagnosis or to follow clinical evolution, making necessary strategies to evaluate the

complete molecular scenario. There are already experimental strategies that allow the determination of total

protein profiles from saliva samples (the SalivaPrint). The goal of this work is to identify a profile of saliva

proteins (similar to a fingerprint) and, using computational methods, identify how this profiles changes with

age and gender. So far it has been possible to collect 79 samples as well as the metadata associated with each

sample using an electronic questionnaire developed by us. A total protein profile was obtained and their

association with gender was verified using statistical methods. Currently we are developing the Python scripts

for automatic data acquiring and normalization. Total protein profiles annotation on a database

(SalivaPrintDB) and their integration with the factors that affects them using machine learning strategies can

empower the use of the approach proposed on this work as a tool for monitoring the individual's health status.

1 INTRODUCTION

In the last decade, saliva has been studied as a

noninvasive, easily obtainable fluid, with diagnosis

potential (Loo et al., 2010).

Saliva reflects the secretions of the 3 largest

salivary glands (parotid, submandibular and

sublingual), smaller salivary glands, crevicular fluid

and also contains serum components, transported by

blood capillaries and subsequently transferred by

diffusion, transport and/or ultrafiltration. The

presence of serum proteins in saliva enhances its use

as a systemic health status monitoring tool

(Castagnola et al., 2017; Kaczor-Urbanowicz et al.,

2017; Kaushik and Mujawar, 2018; Wang, Kaczor-

Urbanowicz, and Wong, 2017).

We recently described an automated capillary

electrophoresis-based strategy that allows to obtain a

salivary protein profile – the SalivaPrint Toolkit

(Cruz et al., 2018).

https://orcid.org/0000-0001-5458-4978

https://orcid.org/0000-0002-7082-297X

https://orcid.org/0000-0003-2239-2976

https://orcid.org/0000-0003-0631-4062

https://orcid.org/0000-0003-4604-0780

Since proteins are separated according to their

molecular mass, changes in peak morphology or

fluorescence intensity (translated by changes in peak

height) correspond to changes in the amount of

proteins or in the type of proteins being expressed.

The association of clinical and personal data to saliva

protein signatures, the SalivaPrint, to alterations in

different health/disease situations, will allow to build

a powerful framework for the creation of noninvasive

diagnostic strategies.

Preliminary results of data extraction

(SalivaPrint-Toolkit, Cruz et al., 2018) from capillary

electrophoresis allowed to identify the potential for

the creation of a SalivaPrint database.

It is known that some factors such as age, sex, or

circadian rhythm (Castagnola et al., 2011, 2017; Murr

et al., 2017), contribute to altering the expression of

salivary proteins . It is essential to define the signature

of proteins correspondent to the "healthy individual",

and simultaneously consider the intra-individual

variations.

Esteves, E., Cruz, I., Esteves, A., Barros, M. and Rosa, N.

SalivaPrint as a Non-invasive Diagnostic Tool.

DOI: 10.5220/0009163506770682

In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 5: HEALTHINF, pages 677-682

ISBN: 978-989-758-398-8; ISSN: 2184-4305

677

Additionally, will be useful to identify potential

proteins that could reflect the changes in the

SalivaPrint profiles characteristic of different

situations. Data on proteins in saliva associated with

disease and health is already extensive. Our group

compilated this information in SalivaTecDB database

(http://salivatec.viseu.ucp.pt/salivatec-db, Arrais et

al., 2013; Rosa et al., 2012), which is an important

support for the identification of proteins that may

potentially be associated with certain signatures.

SalivaTecDB has currently stored more than 4000

human salivary proteins and is constantly being

updated.

The profile of salivary proteins, expressed in

different situations, has the potential of being used for

noninvasive diagnosis. The objectives of this work

are:

1- Establish the Health-SalivaPrint(s). The factors

that influence the protein profile of saliva in healthy

individuals will be considered.

2- Build a SalivaPrint database. These profiles

will posteriorly be used for the development of a tool

for heath monitoring using machine learning

strategies.

2 MOTIVATION

Nowadays, molecular diagnosis is based on the

quantification of RNA, proteins or metabolites whose

concentration can be correlated to clinical situations.

Usually, these molecules are not suitable for early

diagnosis or to follow clinical evolution. Therefore,

strategies to evaluate the complete molecular scenario

– early diagnosis, diagnosis and clinical evolution –

are necessary.

The potential of proteins for a large-scale

diagnosis depends on cheap and preferably

noninvasive strategies for screening. Bioinformatics

strategies and solutions to work on different types of

data: from biological related data to personal and

clinical information’s, is the best approach. Data

integration by these methods is an asset to predict the

pathological status before clinical outcomes.

This work explores the potential of saliva protein

profile obtained by capillary electrophoresis – the

SalivaPrint - as a strategy to obtain signatures of

health/disease states. The application of

computational methods (machine learning strategies)

will allow the establishment of a signature that

characterizes the healthy individual and how it varies

with diverse factors.

At this time, we are only interested in establishing

the entire SalivaPrint methodology, from capillary

electrophoresis to determining which factors

influence the protein profile (of healthy individuals)

including the creation of the database that stores all

this information.

3 METHODOLOGY

This study was approved by the Ethics Committee of

the Portuguese Catholic University (UCP). In

addition, donors will be asked, prior to any collection,

the consent for samples’ collection. All work will be

carried out in accordance with the principles of the

Helsinki declaration, as reviewed in 2008.

In order to achieve the proposed objectives, the

strategy presented in figure 1 will be followed:

Figure 1: Flowchart of proposed strategy plan.

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3.1 Questionnaire Preparation to

Characterize the Biological Sample

An online filling questionnaire will be created using

Qualtrics® software, for the collection of clinical and

personal information.

Questions will be defined to address the

individual's personal data and clinical status

(SalivaTec Donor Form). Each donor will be

observed by a dentist (collaboration with the Dental

Clinic of UCP), for oral health status evaluation,

taking into account the PSR index (Periodontal

Screening Recording) and DMFT (Decayed, Missing,

Filled Teeth) (Anón, 2002; Schuller and Holst, 2001).

3.2 Collection and Processing of Saliva

Samples

To define the protein profile of healthy individuals,

samples of non-stimulated saliva will be collected,

using an established protocol (Rosa et al., 2016), from

patients from the dental clinic of the Portuguese

Catholic University and from seniors in the Senior

Activity program from the municipality of Viseu. The

criteria for exclusion of healthy donors were defined

according to the literature: patients with oral or

systemic pathology; intake of regular or punctual

medication in the last 30 days; antibiotic

administration in the last 3 months; never subjected

to radiological or chemical treatment (Mozaffari et

al., 2019; Murr et al., 2017; Wang et al., 2015).

Samples will be categorized into groups of different

ages and genders.

These collections will be made under partnerships

already established with each institution.

Total protein concentration, pH and volume of

each sample will be determined.

3.3 Determination of the Total Profile

of Proteins by Capillary

Electrophoresis

Saliva protein profile (SalivaPrint) will be determined

by capillary electrophoresis, using the Experion™

Automated Electrophoresis System (BioRad) in

standard protein chips (Experion™ Pro260 Analysis

Kit). The samples will be analyzed according to the

technical specifications provided by BioRad.

Protein profile and the quantification of bands

will be obtained using the Experion™ Software,

version 3.20.

3.4 Determination of Factors

Influencing Health-SalivaPrint

The clustering of profiles will be tested according to

potentially influencing factors of salivary proteome

[age, gender and circadian rhythm (Castagnola et al.,

2017), among others].

For this, a variety of statistical analysis models

(e.g. Kruskall-Wallis) will be applied for comparing

each profile between influence groups created. Also,

data classification techniques will be implemented

with high robustness and effectiveness, such as

Random Forest, as well as approaches based on

unsupervised learning models for clustering analysis.

The performance of the different existing techniques,

the pertinence ratio of the data involved, the

effectiveness of the quality of the results obtained,

analyzing accuracy and tests of positive and negative

diagnoses, among others, will be evaluated.

Parallelly a distance profile will be determined

between the mean profile of healthy individuals’

groups formed according impact factors on protein

profile. This approach will allow the identification of

the molecular mass corresponding to the peaks that

demonstrate to be different. A threshold will be set at

half peak height to identify these intervals. This step

will allow the association of potential proteins in

these ranges using saliva protein database

SalivaTecDB (http://salivatec.viseu.ucp.pt/salivatec-

db). The area of each peak in each profile will be

compared between all individual profiles, using a t-

test, in order to identify peaks characteristic of each

factor.

From these analyses will result intervals of

molecular masses corresponding to proteins of

interest (later confirmed by mass spectrometry).

3.5 Construction of the Database for

the Storage of SalivaPrints

Protein profiles representing different conditions will

result from this work and will be gathered in

SalivaPrint database. Each SalivaPrint will be linked

to the metadata collected in the questionnaires

developed in 3.1. Tools will be developed to

categorize, edit, visualize and export the data

obtained to different formats, creating the necessary

conditions for its use in the development of

computational strategies for analysis and comparison

of saliva protein profiles for diagnostic purposes.

SalivaPrint as a Non-invasive Diagnostic Tool

679

4 PRELIMINARY RESULTS

The implementation of the proposed strategy (Figure

1) has so far yielded the results described below.

So far it has been possible to collect 79 samples

meeting the defined inclusion criteria (Table 1) and

the metadata associated with each sample using the

questionnaire developed by us.

Table 1: Number of saliva samples collected form healthy

individuals after applying the selection criteria.

Gender

Total

Female Male

Age Group

(years)

<13 12 4 24

13-24 14 10 39

25-50 22 17 16

Total 48 31 79

A protein profile for each gender (Female n=48;

Male n=31) was built (Figure 2 a), according to the

methodology described in the section 3.3.

In order to determine how gender impact, the

saliva total protein profile in health individuals

(Health-SalivaPrint) a Kruskal-Wallis hypothesis test

was applied (Figure 2). In top graph we could see the

protein profiles from Female (red) and Male (blue)

individuals. When Kruskal-Wallis test is applied, on

lower graph the higher peak (green) with lower p

value (yellow) corresponds to molecular weight

where it exists a significant difference between

genders, so the gender characteristic signature.

A distance profile for each gender was built

according to the methodology described earlier. The

comparison of the mean-profile obtained from saliva

of Female and Male individuals was used to establish

a distance-profile (Figure 3). This distance profile

reflects the molecular weight (MW) range

corresponding to the proteins that are altered between

genders. The creation of the distance profile allows

the identification of the specific molecular mass

according to different expressed peaks on each group

(Figure 3).

The t-test applied on half-peak areas of all profiles

showed the main differences were found in 2 of the

typical 6 peaks of the saliva protein profile. Within

these peaks, the MW ranges of

peak 5 (58.5 - 63.2

kDa) and peak 6 (66.8 - 74.2 kDa).

Figure 2: Application of the Kruskal-Wallis hypotheses test in determining profile signatures for gender identification. In a),

red lines correspond to the profiles of female individuals and blue lines to the profiles of male individuals. In b) the green line

corresponds to the test value and the yellow line to the p value. Higher hypothesis test value (green) and lower p value (yellow)

correspond to the molecular weight were exist a clear difference on fluorescence between genders.

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Figure 3: Protein profile distribution in Female (----) and Male (– – –) individuals. a) Female and Male mean-profile

corresponding to the average of fluorescence per molecular weight. The standard deviations are represented by the vertical

error lines. b) Protein profile for Female (----) and Male (– – –) and the distance-profile (–). Shaded areas correspond to the

MW range of the proteins most altered between genders.

Once the distance profile and the respective MW

ranges are established, it is possible, use

SalivaTecDB (Figure 4), to associate potential

proteins with molecular mass within the defined

ranges and after that confirmed by mass

spectrometry.

Figure 4: SalivaTecDB homepage. SalivaTecDB is

dedicated to the annotation and characterization of proteins,

miRNAs and Microorganisms present in the oral cavity,

associated with oral or systemic mechanisms. This database

was developed as a tool to be used by researchers working

in Salivary Diagnostics.

5 FUTURE WORK

Currently we are developing the Python scripts for:

data acquiring (from electrophoresis system) and

treatment (profile alignment, peak determination and

peak area calculation). These scripts will allow the

total protein profile comparing between individuals

and groups, with peak areas for statistical analysis, as

well as analysis with machine learning strategies. The

application of machine learning, for example Random

Forest based on unsupervised models, allow the

clusterization according to impact factors on profile

(if it’s the case) and thus define a characteristic

Health-SalivaPrint.

6 CONCLUSIONS

This paper presents the SalivaPrint strategy and

preliminary results already obtained with this

approach. This work already allowed us to develop

the methodologies that permits to obtain the total

protein profile of health individuals (in a convenience

sample). Through the analysis of these profiles it is

possible to associate the potential proteins more

representative of the differences between them using

SalivaTecDB. The application of the Kruskal-Wallis

test is a first approach on the identification of

signatures, although there still need for other analysis

to confirm the results.

Future work will be directed to python scripts

development to acquire aligned protein profiles and

determine the factors that influence the profile, as

well as establish the machine learning strategy for

cluster determination and its association to database

(SalivaPrintDB).

It is expected with this work to use the database

with SalivaPrints associated with an unsupervised

machine learning algorithm that allows protein

SalivaPrint as a Non-invasive Diagnostic Tool

681

patterns recognition through profiles and personal

data, without defined diagnosis, pathology or health

situations. This approach will consider the

morphology of the protein profile obtained with

capillary electrophoresis in an automated manner and

the influencing factors of saliva proteome, namely,

the total protein concentration, age, gender and inter-

individual and intra-individual variability. These

variables will be analyzed through the application of

supervised analysis models in a first phase to identify

the influence of each factor in the profile. Later with

a higher number of individuals we resorted to

unsupervised analysis models.

For this strategy to be used for diagnosis

purposes, it is necessary to compare Health-

SalivaPrint with a Disease-SalivaPrint. Since

differences in saliva protein profiles have been

observed in healthy people, depending on age and

gender, it is important to take this into account in

order to isolate the effect of the disease from the

effects of these parameters.

This type of work is essential to find less invasive

forms of diagnosis that take into account all the

molecular and physiological variability of the

individual.

ACKNOWLEDGEMENTS

Thanks are due to FCT/MCTES, for the financial

support of Centre for Interdisciplinary Research in

Health (UID/MULTI/4279/2019). Thanks, are also

due to FCT and UCP for the CEEC institutional

financing of AC Esteves.

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