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Salivary bacterial fingerprints in healthy adult individuals: A proof-of-concept study based on heat, dehydration, and antibiotic resistance
*Corresponding author E-mail address: gkhaled@ksu.edu.sa (J M.A. Khaled)
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Received: ,
Accepted: ,
Abstract
The human oral cavity harbors a diverse microbial ecosystem that plays a vital role in health. Although microbial fingerprints have been proposed for personal identification, limited attention has been given to the stability of salivary bacteria exhibiting resistance to heat, dehydration, and antibiotics. This study aimed to assess whether the salivary microbiota of healthy adults could serve as a reliable microbial fingerprint by focusing on the abundance and stress-resistance traits of bacterial isolates. In a cross-sectional, proof-of-concept study, saliva samples were collected from 28 healthy volunteers. Culturable bacteria were collected using standard techniques, focusing on strains that can survive heat and drying, and colony-forming units per milliliter (CFU/mL) were quantified. The identification and susceptibility testing were done using VITEK® MS and VITEK® 2 Compact. Six samples yielded no cultivable bacteria. The identified bacterial isolates (N=1452) belonged to the genera Staphylococcus and Streptococcus. These bacterial strains showed significant inter-individual variation based on microbial abundance and their resistance to heat, dehydration, and antibiotics. The results indicated that approximately 29.7% of the bacterial isolates were resistant to benzylpenicillin, 12% to vancomycin, and smaller percentages to other clinically relevant antibiotics (e.g., tetracycline, fluoroquinolones). In this work, no tigecycline- and linezolid-resistant bacteria strains were isolated. In this study, all dehydration-resistant S. epidermidis strains were found to be sensitive to vancomycin, while the heat-tolerant strains showed resistance. No resistance to any of the study’s antibiotics was observed in either dehydration-tolerant or dehydration-intolerant S. oralis strains. In contrast, some thermotolerant S. salivarius strains showed resistance to tetracycline and benzylpenicillin. Unlike previous fingerprinting studies that focused primarily on taxonomic profiles, the present study highlights the novelty of incorporating salivary microbial resistance traits, specifically tolerance to heat, dehydration, and antibiotics, as reliable identifiers of healthy individuals. Our findings indicate that specific bacterial strains, particularly those resistant to heat and dehydration stress, persist across individuals, differing in abundance and antibiotic response patterns. These collective features can distinguish individuals via salivary microbial profiling. Furthermore, identifying opportunistic bacterial pathogens with recorded resistance profiles suggests potential for early risk assessment and personalized antibiotic guidance. In conclusion, this study provides initial evidence that salivary bacterial fingerprints, defined by abundance and resistance, can differentiate healthy individuals. The employed methodology is simple, cost-effective, and scalable, laying the foundation for applications in forensic microbiology and personalized medicine. Larger, more diverse population studies are recommended for validation.
Keywords
Heat- and dehydration-resistant bacteria
Microbial fingerprints
Saliva
VITEK® MS
1. Introduction
The human mouth cavity hosts one of the most varied and sophisticated microbial ecosystems in the body, which includes a structured biofilm, bacteria, archaea, and fungi; this dynamic environment significantly supports both oral and systemic health. Called the oral microbiota, these microbes help with important functions like controlling the immune system, starting digestion, and fighting off harmful germs (Lamont et al., 2018; Yamashita and Takeshita, 2017).
Among the many habitats found in the human oral cavity are teeth, gingival sulcus, tongue, cheeks, solid and soft palates, and tonsils colonized by microorganisms (Dewhirst et al., 2010). The Human Oral Microbiome Database (eHOMD) offers trustworthy scientific information about different groups of bacteria that live in the human oral cavity. According to the data available, there are 834 taxa, 523 of which live in the oral cavity. Less than half of the oral taxa have been identified and named so far (eHOMD, 2007-2025). The complex microbial community in the human oral cavity includes, in addition to bacteria, other microbes, such as yeasts (especially Candida species), molds, and phages (Krom et al., 2014; Baker et al., 2017).
Understanding the oral environment and these microbes’ role in oral health has been the focus of most research investigations. Numerous diseases can result from an imbalance, according to research findings. For instance, Peng et al. (2022) have verified that the oral cavity acts as a gateway to the gastrointestinal and respiratory systems and is a vital interface between the internal and external environments. Oral microbiome dysbiosis has been connected to systemic conditions like diabetes, cardiovascular disease, and unfavorable pregnancy outcomes in addition to oral diseases (Georges and Seleem, 2022).
Apart from its function in health and disease, the human oral microbiota is becoming increasingly known as a distinct, stable microbial signature able to differentiate one person from another. Based on the observation that everyone has a highly unique microbial community molded by a complex interaction of genes, immune response, diet, oral hygiene, environment, and even cohabitation patterns, this concept is known as the oral microbial fingerprint (Stahringer et al., 2012, DeClercq et al., 2024).
High-throughput sequencing studies have revealed that, although some microbial taxa are routinely shared among individuals, the relative abundance, strain-level diversity, and spatial organization of these microbes are quite individualized. Oral microbial profiles actually show enough consistency over time to be regarded as microbial identifiers, much like digital or genetic fingerprints. Researchers have also investigated the fundamental concept of the microbiome and its known variations across different populations and regions. These findings led to the emergence of a scientific concept known as the community microbiome, which can be a biomarker for geographical origin inference (Li et al., 2014; Lei et al., 2025).
The personalized nature of the oral microbiota makes it a compelling tool in forensic science, particularly in cases where traditional biological evidence is limited. Microbial signatures recovered from personal items such as toothbrushes, dental prosthetics, or bite marks have shown potential for linking individuals to crime scenes (Moitas et al., 2022). This microbial approach offers a valuable complement, or in some contexts, an alternative to conventional DNA or fingerprint analyses, especially when those are compromised by degradation or sample insufficiency. However, while the prospects are promising, current evidence suggests that oral microbiota-based identification lacks the discriminatory power and standardization required for a stand-alone forensic application. As such, it is best viewed as an ancillary tool that augments existing methods, contributing to a more holistic biological profile of individuals in forensic investigations (Franceschetti et al., 2024).
Additionally, the unique bacteria found in our mouths can help identify diseases early, track how well treatments are working, or predict the risk of issues like cavities, gum disease, and other health problems. As research advances, the ability to interpret individual oral microbiomes may lead to more personalized interventions in dentistry and medicine, moving us closer to precision oral health care (Zhang et al., 2018).
Recent research is examining the potential to utilize the characteristics of bacteria present in human saliva as it is spat into the external environment. Scientific data indicate that microbes living in human saliva can survive on environmental surfaces after deposition, retaining characteristics that may hold scientific, therapeutic, or forensic significance. For instance, Wang et al. (2022) examined the feasibility of using the succession pattern of human salivary microbial communities to predict the time since deposition (TsD) in forensic investigations. Their study revealed substantial changes in salivary microbial abundance as TsD increased. This research demonstrated that microbial indicators could serve as a “biomarker” for TsD in human dry saliva samples. In 2024, Yadav and colleagues confirmed that the salivary microbiome contains valuable information for suspect identification by linking the influence of individual behaviors and habits to its profile. Researchers specifically found a direct link between the presence or absence of certain microbes and smoking, alcohol consumption, and dietary habits (Yadav et al., 2024).
Reports suggest that even low-biomass surfaces, such as inner surfaces of face masks, can retain individual microbial saliva signatures. This information is very useful to link the microbial profile found on a surface or at a crime scene back to a specific individual. Additionally, one could use it to evaluate the cleanliness and potential risk of pathogen transmission (Gund et al., 2024).
These studies suggest that salivary microbiota present on surfaces endure and retain distinctive microbial patterns. These patterns may function as supplementary bioindicators for forensic identification or environmental health monitoring. This work aims to explore the potential of utilizing heat- and drought-resistant bacteria in the saliva of healthy individuals as a unique microbial fingerprint, characterized by their features, abundance, and antibiotic resistance.
2. Materials and Methods
2.1 Experimental design
To achieve the primary objective of the study, which was to characterize the heat-, dehydration-, and antibiotic-resistant bacteria in the saliva of healthy adults under natural conditions, a cross-sectional study (one sample from each participant at a single time point) and an observational, non-interventional design were used. The inter-individual variability was evaluated. Standardized controls and criteria included collecting saliva samples at midday (to minimize time-related differences), not asking participants to follow any specific diet or health routines, using sterile plastic containers, and quickly storing samples at -80°C. Given the proof-of-concept nature of this study, a sample size of 28 participants was considered sufficient to generate preliminary evidence on the discriminatory potential of salivary microbial resistance traits, while maintaining feasibility for culture-based and phenotypic analyses. An example of this is the study conducted by Caselli et al., (2020), in which the number of participants was 20.
2.2 Research ethics and informed consent
The Research Ethics Committee at King Saud University approved this study (IRB Approval of Research Project No. E-25-9720). Individuals were given their consent and informed consent, which explained the procedures followed and the absence of any treatment that would cause them harm or pain. The informed consent also confirmed the confidentiality of the results and data contained therein, and the names (or characteristics) of the subjects that may identify them would never appear in the research results.
2.3 Saliva sample collection
Saliva samples were collected in sterile plastic containers with tight lids. The work was carried out in the Microbiology Laboratory of the Department of Botany and Microbiology, College of Science, King Saud University, under controlled conditions to minimize microbial contamination. After donors were asked to spit out as much saliva as possible into the container, the saliva sample was tightly sealed and immediately stored at -80°C until microbial experiments were performed. All samples were labeled with the donor’s code, age, gender, date, time, and location of collection, as well as the person who collected the sample. The collected data did not include any information identifying the donor.
2.4 Heat and dehydration specimen treatment
The sample was divided into three sections. The first section was heated at 60°C for 10 min, the second section was placed in a water bath at 37°C for 10 min, and the third section was placed in a sterile plastic Petri dish, sealed (lid only; parafilm or tape was not used), and left at room temperature for a full week. All work was carried out under conditions that prevented external contamination, and all samples were labeled with the necessary data. The sample size in all previous treatments was 1 mL.
2.5 Isolation and purification
The isolation and purification processes were carried out according to scientific protocols followed in microbiology laboratories, and at this stage, blood agar was used (Vandepitte et al., 2003; Reseco et al., 2024). Bacteria were cultivated on sterile plastic Petri dishes by introducing 20 mL of blood medium (at 45 to 50°C) while it remained in the liquid state. The dishes were allowed to solidify at room temperature. Samples were incubated in an aerobic incubator at 37°C for 24 h for mesophilic samples and at 50°C for 48 h for thermoduric samples. To identify dehydration-tolerant bacteria, 20 mL of liquid blood medium was added to the dishes, which had been kept for a week and contained 1 mL of saliva, as previously described. The dishes were allowed to solidify completely before being incubated in an aerobic incubator at 37°C for 24 h.
Aseptic technique was strictly followed for all procedures. Before the medium solidified, and while maintaining contact with the bench surface, the plates were gently swirled in a circular motion, then in a reverse circular motion, and finally in a figure-eight pattern. This careful manipulation ensured the sample was evenly dispersed throughout the solidifying medium without splashing onto the lid or out of the dish. The total number was estimated as a colony-forming unit per mL (CFU/mL) after incubation, and the colony characteristics were accurately recorded. All colonies that looked the same on the surface of the medium and had the same features under a microscope (after Gram staining) were classified as the same type of microbe (matching macroscopic and microscopic characteristics) and were assigned a specific code for easier identification later with Vitek-MS.
The isolates were purified using the triple-streak and sub-cultivation methods. Each isolate was subcultured at least three times to ensure pure cultures of a single bacterial species.
2.6 Identification of pure bacterial isolates
According to Richter et al. (2013) and Al Bulushi et al., (2021) the VITEK® MS (bioMérieux, France) is based on an advanced technology known as Matrix-Assisted Laser Desorption/Ionization-Time of Flight Mass Spectrometry (MALDI-TOF MS). A pure colony of the bacteria to be identified was taken, and a tiny amount of this colony was placed directly onto a special metal slide (called a “target slide”). The sample was covered with a special solution called a matrix, which efficiently absorbs the laser energy and transfers it to the microorganism’s molecules. The target slide, containing the sample and matrix, was placed inside the device. A laser pulse was shone on the mixture of sample and matrix. The matrix took in the laser energy, quickly turned into gas (desorption), and became charged, bringing the microorganism’s molecules (mainly the plentiful ribosomal proteins) into the air as charged particles. This process occurs relatively gently, ionizing large molecules (such as proteins) without breaking them into tiny fragments. An electric field accelerates ions formed in an evacuated flight tube to separate them by TOF. Because all ions have the same initial kinetic energy, their speed depends on their mass-to-charge ratio (m/z). Lighter ions (with a lower m/z ratio) reach the detector at the end of the flight tube faster than heavier ions (with a higher m/z ratio). The detector records the “time of flight” for each ion. To generate a mass spectrum (MS), the measured TOFs are converted into an MS. Each MS represents a unique signature of the proteins present in the microorganism, known as the organism’s “protein fingerprint.” This fingerprint contains a series of peaks, each representing an ion of a specific mass and intensity (representing its abundance). The MS obtained from the microscopic sample is compared with a database of known microorganism spectra. The VITEK® MS database has thousands of reference spectra for different bacterial strains, gathered from various places, samples, and growth conditions to ensure that there is a wide range and an accurate representation of species. A confidence value was calculated for the extent to which the sample spectrum matched the reference spectra in the database, and high values were used to identify the bacterial organism at the species level.
2.7 Susceptibility testing
Susceptibility testing for identified bacterial isolates was carried out using VITEK® 2 (bioMérieux, France). VITEK® 2 Compact AST Cards for Gram-positive bacteria were employed to ascertain the antimicrobial susceptibility profiles of the isolates. Each card comprises several microwells preloaded with successive dilutions of specific antibiotics, facilitating automated assessment of minimum inhibitory concentrations (MICs) and interpretation of susceptibility categories in accordance with CLSI criteria. This system complies with the guidelines of international organizations such as the CLSI and the EUCAST. This ensures that the concentration ranges tested reflect globally recognized clinical breakpoints (Colaninno, 2021).
2.8 Statistical analysis
The percentages of male and female participants, as well as their age distribution, were calculated to characterize the study population. To understand the relationships and patterns between individuals according to the characteristics of bacterial isolates obtained from saliva samples, hierarchical cluster analysis (dendrogram) and K-Means cluster analysis using OriginPro 2018. Hierarchical cluster analysis and K-means cluster analysis were employed to explore and validate the discriminatory potential of salivary microbiota profiles among individuals. Hierarchical cluster analysis was used to generate dendrograms that provide an overview of similar relationships between samples, allowing an exploratory visualization of natural groupings. K-means cluster was applied as a complementary approach to quantitatively partition the dataset into distinct clusters, thereby confirming the robustness and consistency of individual microbial fingerprints. The combined use of these clustering methods ensured both exploratory and confirmatory insights into the capacity of salivary microbial communities to serve as reliable individual-specific signatures. Python 3.13 was used to draw a unique barcode for each individual to determine their bacterial fingerprint, based on the characteristics of bacterial isolates, such as resistance to degradation, heat, abundance, and antibiotic resistance. Antimicrobial susceptibility results for seven consistently tested antibiotics (vancomycin, tetracycline, tigecycline, linezolid, levofloxacin, moxifloxacin, and benzylpenicillin) were selected for analysis. These antibiotics were common across all isolates, showing a response (susceptible, intermediate, or resistant) for every isolate.
3. Results
The study included 28 participants, including eight women (28.6%). Their ages ranged from 18 to 45, with the most represented age group being 26. The age group that appeared only once was 33 years (3.6%). The age distribution showed a wide dispersion with unequal frequencies. (Figs. 1 and 2).
- The percentage of healthy male and female adults (N=28) participating in this study from whom saliva samples were collected after obtaining their informed consent. The study did not impose any specific dietary or health behavior requirements on the participants, allowing them to lead normal lives. The total number of participants in the study was twenty-eight, from whom saliva samples were collected, six of whom did not have any microbes isolated under the conditions of this study. The black part indicates the proportion and number of females and the gray part indicates the proportion and number of males.
- Distribution of participants’ ages as a number (N) and percentage (%). The total number of participants in the study was 28, from whom saliva samples were collected, six of whom did not have any microbes isolated under the conditions of this study.
Initial analysis shows that the most represented group is 26-35 years old (42.9%), and the least represented group is 18-25 years old (25%), and that the distribution is relatively balanced, but with a clear bias towards the middle group (26-35 years old). Statistically, there is an uneven frequency across age groups, with some ages overrepresented (relative bias towards ages 26 and 30). The distribution also lacks asymmetry, with the absence of middle-aged groups (20–24 or 40–44).
Fig. 3 illustrates a hierarchical cluster analysis that classifies bacterial strains isolated from the saliva of 22 healthy individuals according to their similarities in the bacterial species, abundance, heat resistance, dehydration resistance, and antibiotic resistance. The resultant dendrogram visually depicts bacterial groupings, with branching patterns demonstrating their interrelationships. Color coding (red, green, blue, and purple) differentiates the bacterial groupings. The Y-axis represents the extent of similarity or dissimilarity among strains; increased vertical distances signify greater dissimilarity. The X-axis denotes distinct strains or their particular attributes. Branches that are closely clustered indicate a greater extent of shared traits.
- Hierarchical cluster analysis of bacterial strains from the saliva of 22 healthy individuals: based on bacterial isolate features. Twenty-eight people participated in this study, six of whom did not have any microbes isolated from their saliva under the conditions of this study.
Based on the characteristics of the bacterial strains isolated from their saliva, the results indicate the division of individuals into five groups. The first group included 22, 20, 19, 18, 17, 21, and 16; the second included only 13; the third included 15, 14, and 12; the fourth included 10, 4, 7, 9, 11, and 3; and the fifth included 8, 2, 6, 5, and 1.
Approximately 30% of study participants were assigned to the first group, and 27% to the fourth. The second group contained only one individual, indicating that the characteristics of microbes isolated from his saliva were highly distinct from the other groups.
Table 1 represents the heat- and dehydration-resistant bacterial isolates cultivated and identified from the saliva of healthy individuals (N=22).
| Individuals code | Bacterial strains** | Treatment*** | CFU/mL | Van | Tet | Tig | Lin | Lev | Mox | Benz |
|---|---|---|---|---|---|---|---|---|---|---|
| G6 | S. aureus | DR | 2.0 | S | S | S | S | R | R | S |
| G7 | S. epidermidis | DR | 14 | S | S | S | S | S | S | R |
| G8 | S. hominis | DR | 46 | S | S | S | S | S | S | R |
| G9 | S. epidermidis | DR | 53 | S | S | S | S | S | S | S |
| G14 | S. aureus | DR | 3 | S | S | S | S | S | S | S |
| G21 | S. epidermidis | DR | 5.0 | S | S | S | S | S | S | R |
| G23 | S. epidermidis | DR | 40.0 | S | S | S | S | S | S | S |
| G16 | S. oralis | DR | 12.0 | S | S | S | S | S | S | I |
| G17 | S. cristatus | DR | 44.0 | S | S | S | S | S | S | I |
| G20 | S. salivarius | DR | 53.0 | S | S | S | S | S | S | I |
| G22 | S. salivarius | DR | 48.0 | S | S | S | S | S | S | I |
| H13 | S. capitis | HR | 56.0 | S | S | S | S | S | S | R |
| H19 | S. aureus | HR | 2.0 | S | S | S | S | R | R | S |
| H18 | S. epidermidis | HR | 66.0 | R | S | S | S | S | S | R |
| H20 | S. salivarius | HR | 40.0 | S | R | S | S | I | S | I |
| S9 | S. oralis | 37°C | 130.0 | S | S | S | S | S | S | S |
| S11 | S. salivarius | 37°C | 142.0 | S | S | S | S | S | S | I |
| S17 | S. oralis | 37°C | 155.0 | S | S | S | S | S | S | S |
| S20 | S. oralis | 37°C | 148.0 | S | S | S | S | S | S | I |
| S18 | S. arasanguinis | 37°C | 150.0 | S | S | S | S | S | S | S |
| S10 | S. epidermidis | 37°C | 135.0 | S | S | S | S | S | S | R |
| S16 | S. epidermidis | 37°C | 110.0 | R | S | S | S | S | S | R |
*Twenty-eight individuals participated in this study, six of whom did not have any microbes isolated from their saliva under the conditions of this study.
**S. aureus = Staphylococcus aureus, S. hominis = Staphylococcus hominis, S. epidermidis = Staphylococcus epidermidis, S. oralis = Streptococcus oralis, S. cristatus = Streptococcus cristatus, S. salivarius = Streptococcus salivarius subsp. salivarius, S. capitis = Staphylococcus capitis, and S. arasanguinis = Streptococcus arasanguinis.
***DR=Dehydration-resistant, HR=Hear-resistant, 37°C=Treatment at 37°C for 10 min., CFU/ml=Microbial abundance, Van=Vancomycin, Tet=Tetracycline, Tig=Tigecycline, Lin=, Linezolid, Lev=Levofloxacin, Mox=Moxifloxacin, Benz=Benzylpenicillin.
Table 1 shows the prevalence of antibiotic resistance among different bacterial strains. It was found that the saliva of the individuals participating in the study carried bacterial strains resistant to vancomycin, tetracycline, levofloxacin, moxifloxacin, and benzylpenicillin. The table also showed that all bacterial strains isolated in this study were sensitive to tigecycline and linezolid. This phenomenon has been illustrated in Fig. 4, where approximately 30% (432/1454*100) and 12% (176/1454*100) of the bacterial isolates were resistant to benzylpenicillin and vancomycin, respectively.
- The percentage (%) of antibiotic-resistant bacterial strains isolated from the saliva of healthy individuals (N=22). These bacterial strains included heat- and dehydration-tolerant strains, and the total number of bacterial isolates was 1452. Van=Vancomycin, Tet=Tetracycline, Tig=Tigecycline, Lin=, Linezolid, Lev=Levofloxacin, Mox=Moxifloxacin, Benz=Benzylpenicillin.
Table 2 shows the clusters resulting from K-Means. Cluster 1 has a very low number of microbes (very high CFU/mL, average ≈ 6.33) and includes six samples, with the bacteria in these samples being sensitive to linezolid, levofloxacin, and moxifloxacin. Cluster 2 has three samples with a high number of bacterial cells (very high CFU/mL, average ≈ 151), and the bacteria in these samples are not resistant to linezolid and levofloxacin. Cluster 3 has three samples with a high number of bacterial cells (high CFU/mL, average ≈ 135.67), and the bacteria in this group are susceptible to linezolid and levofloxacin. Cluster 4 has one sample and has a microbial abundance of CFU/mL - Center ≈ 110; this bacterial strain is resistant to moxifloxacin. Cluster 5 is characterized by a moderate microbial abundance (median CFU - center ≈ 49.56) and contains nine samples. All bacteria in this cluster are sensitive to linezolid, levofloxacin, and moxifloxacin.
| CFU/mL** | Number of observations | Most common Lin. | Most common Lev. | Most common Mox. | |||
|---|---|---|---|---|---|---|---|
| Initial cluster center | Final cluster center | ||||||
| Cluster1 | 2 | 6.33333 | 6 | S | S | S | |
| Cluster2 | 155 | 151 | 3 | R | R | S | |
| Cluster3 | 135 | 135.66667 | 3 | R | R | S | |
| Cluster4 | 110 | 110 | 1 | S | S | R | |
| Cluster5 | 40 | 49.55556 | 9 | S | S | S | |
| Distance between final cluster centers | |||||||
| Cluster1 | Cluster2 | Cluster3 | Cluster4 | Cluster5 | |||
| Cluster1 | 0 | 144.66667 | 129.33333 | 103.66667 | 43.22222 | ||
| Cluster2 | 144.66667 | 0 | 15.33333 | 41 | 101.4444 | ||
| Cluster3 | 129.33333 | 15.33333 | 0 | 25.66667 | 86.11111 | ||
| Cluster4 | 103.66667 | 41 | 25.66667 | 0 | 60.44444 | ||
| Cluster5 | 43.22222 | 101.44444 | 86.11111 | 60.44444 | 0 | ||
| ANOVA | |||||||
| Cluster DF | Cluster SS | Error DF | Error SS | F Value | Prob>F | ||
| CFU/mL*** | 4 | 15491.39899 | 17 | 47.30719 | 327.464 | 7.77E-16 | |
*Twenty-eight people participated in this study, six of whom did not have any microbes isolated from their saliva under the conditions of this study.
**Microbial abundance (CFU/ml), Lin=Linezolid, Lev=Levofloxacin, Mox=Moxifloxacin,
***There are significant differences at P<0.05 between different clusters.
The ANOVA in Table 2 shows that there were significant differences (P<0.05) between the different clusters regarding the total number of bacteria (CFU/mL), as the F value was 327.464 and the p-value was 7.77E-16. Statistically, no significant differences (P<0.05) were recorded between the different clusters regarding their susceptibility testing using linezolid, levofloxacin, and moxifloxacin. Fig. 5 includes a barcode generated using Python based on the characteristics of bacteria isolated from the saliva of healthy individuals participating in this study. The results indicated that the bacteria’s traits, like how well they resist heat and drying out, how many there are, and how they react to common antibiotics, create a unique microbial fingerprint for each person, which is shown in the figure as a special barcode.
- A unique code for each individual (N=22) based on the traits of the bacterial strains isolated from their saliva, such as resistance to heat and dehydration, abundance, and sensitivity to standard antibiotics
4. Discussion
The current work was designed as a proof-of-concept investigation to explore the potential of salivary microbiota as a personalized microbial fingerprint for individual identification. The study aimed to isolate and study bacteria in saliva that can survive heat and drying, looking at how many of these bacteria are present and how resistant they are, which helps tell different types of bacteria apart. This cross-sectional study involved a total of 28 adult volunteers, but only 22 of them yielded recoverable microbial isolates. The sampling method was designed to be open, letting healthy adult men and women join freely without any restrictions regarding the characteristics of the study population, which created natural differences between individuals and helped prevent bias in the sampling. Although the sample size was limited and did not encompass all age groups, the authors consider it sufficient for a preliminary assessment, especially given the conceptual nature of the study. The differences in microbes and their resistance found in different individuals are an important first step in creating tools to identify humans and discover biomarkers.
Despite the current study comprising samples from just 28 individuals, this sample size is deemed adequate for proof-of-concept research focused on assessing the viability of microbiome-based individual identification. Schmedes et al. (2018) successfully demonstrated the use of skin microbiome profiles for human identification using samples from just 12 healthy individuals. Their study utilized supervised learning to classify skin microbiomes from 14 body sites with up to 100% accuracy at selected sites, highlighting the potential of stable microbial features as unique individual markers. These findings support the idea that even small sample sizes can be enough to meet the main goals of exploratory studies, especially when looking at how a person’s microbiome is compared to others, rather than trying to apply the results to a larger population. In this study, researchers applied a similar approach by examining the characteristics of saliva microbiota in healthy adults to provide basic evidence for future uses in microbial fingerprinting.
Most of the bacteria whose characteristics were used to generate the microbial fingerprint in this study are present in the saliva of healthy individuals as a normal part of the oral microbiome (normal oral flora) from an early age, often beginning soon after birth. Although S. aureus is known as a pathogenic bacterium, it can be found as part of the transient flora in the mouth and nasopharynx of several healthy individuals and may not cause any problems (Koukos et al., 2015). It can appear and disappear based on exposure and environmental factors. S. hominis is a very common bacterial species that is generally harmless as a commensal on human skin, particularly in areas containing sweat glands, including the head and face (Ohara-Nemoto et al., 2008; Corrêa et al., 2025). Therefore, it is likely in saliva because the mouth is near the skin. S. epidermidis is a permanent member of the normal human microbiome and is commonly found on the skin, mucous membranes, and saliva of healthy adults (Ohara-Nemoto et al., 2008). It begins to colonize immediately after birth and is normally present in saliva. Regarding S. oralis, this bacterial species is an essential part of the human oral microbiome and is considered one of the first colonizers of the oral cavity (Okahashi et al., 2022). It is typically present shortly after birth. S. cristatus is also part of the normal oral flora (Wang et al., 2024). Streptococcus species generally begin to colonize the mouth after birth and play a role in the formation of oral biofilms. S. salivarius subsp. Salivarius is known to be one of the first microbes to colonize the oral cavity, upper respiratory tract, and intestines within a few hours of birth (Palma et al., 2016). It is considered an important and dominant part of the normal oral flora. S. capitis is known to be part of the normal skin flora of the scalp, face, neck, and ears (O’Neill et al., 2020). Due to the proximity of these areas to the mouth, it is normally present in saliva. This bacterial species, S. arasanguinis, belongs to the genus Streptococcus, a genus that dominates the oral cavity (Baty et al., 2022). It is expected to be in saliva as part of the normal flora in the mouth from early childhood, like other oral streptococci.
While there’s increasing scientific evidence that some of these bacterial species found in the saliva of healthy adults can act as harmful opportunistic pathogens (e.g, Heath et al., 2023), our study focuses on their significance within the normal microbial makeup of the saliva microbiota in healthy individuals and uses them to create microbial signatures.
To the best of our knowledge, no previous study has specifically investigated the salivary bacterial flora of healthy adults to demonstrate its potential as a unique microbial fingerprint based on specific microbial traits, namely, tolerance to heat and desiccation, antibiotic resistance profiles, and microbial abundance. The results of this case study provide compelling preliminary evidence that these traits vary across individuals, supporting their potential use in microbial differentiation. A key strength of this study lies in its use of simple, cost-effective methods and its focus on resilient microbial strains, those capable of surviving heat and dehydration, which may remain detectable in saliva even under harsh environmental conditions.
In addition to its forensic applications, this study offers potential clinical benefits. Several of the isolated microbes used as microbial fingerprints were identified as opportunistic pathogens. While these organisms may not pose an immediate risk to healthy individuals, their presence in the salivary microbiota may serve as an early indicator of potential infection risk should the individual’s immune status become compromised. Immunosuppression can occur due to various causes, and having a baseline profile of resident opportunistic bacteria may allow for early clinical intervention. Furthermore, the recorded antibiotic resistance profiles of these isolates provide valuable predictive insight into effective antimicrobial therapy should such infections emerge. This highlights the broader utility of microbial fingerprinting in both preventive medicine and personalized healthcare.
The detection of antibiotic-resistant bacteria in the saliva of healthy individuals is a noteworthy public health concern, particularly when these isolates exhibit additional resilience traits such as heat and desiccation tolerance combined with high microbial abundance. In this study, approximately 29% of the isolates demonstrated resistance to benzylpenicillin, and around 12% were resistant to vancomycin. Resistance to other clinically relevant antibiotics, including tetracycline, levofloxacin, and moxifloxacin, was also observed. Although the presence of antibiotic-resistant bacteria in the saliva of healthy individuals has been documented in previous studies (e.g. Anderson et al., 2023; Chmielewski et al., 2024), to the best of our knowledge, this is the first report highlighting such resistance specifically in bacteria that are simultaneously tolerant to heat and desiccation. The findings of our research are consistent with longitudinal evidence that demonstrates that oral locations, which include saliva, present a microbiome that is both stable and unique to each individual. This evidence supports the idea that person-level discrimination is feasible. The recent documentation of a diverse oral resistome, notably in saliva, is congruent with the idea of using stress-tolerance phenotypes (heat, dehydration, and antibiotic resistance) as identifiers (Zhou et al., 2024; Dave and Tattar, 2025). These findings suggest that the oral cavity may serve as a reservoir of environmentally resilient, drug-resistant microbes that could pose an opportunistic threat, particularly in immunocompromised individuals or in situations where these bacteria are transmitted to vulnerable hosts.
5. Conclusions
In its conclusion, this proof-of-concept work provides evidence that the salivary microbiota of healthy humans, especially those who exhibit resilience to heat, dehydration, and antibiotics, are capable of providing distinct microbial fingerprints. In addition to the academic value that these findings provide, they also have practical applications in a variety of fields. In forensic science, they can be used for the purpose of individual identification. In personalized medicine, they can be used to monitor oral health and predict susceptibility to opportunistic infections. In public health, they can be used to track reservoirs of antimicrobial resistance. Future studies should increase the sample numbers used, include metagenomic and functional assays, and make use of machine learning tools to enhance discriminatory power and validate the robustness of salivary microbial fingerprints across a wide variety of populations and habitats.
Acknowledgments
The authors express their sincere appreciation to the Ongoing Research Funding program, (ORF-2025-679), King Saud University, Riyadh, Saudi Arabia.
CRediT authorship contribution statement
Jamal M. Khaled: Conceptualization, experiments, manuscript preparation, data analysis, reviewing, editing; Essa S. Alhussain: Experiments, data analysis, manuscript preparation; Ahmed S. Alobaidi: Experiments, manuscript preparation; Shine Kadaikunnan: Experiments, data analysis; Adel A. Abdulmanea: Experiments; Naiyf S. Alharbi: Conceptualization, reviewing, editing.
Declaration of competing interest
The authors declare that they have no competing financial interests or personal relationships that could have influenced the work presented in this paper
Declaration of Generative AI and AI-assisted technologies in the writing process
The authors confirm that there was no use of Artificial Intelligence (AI)-Assisted Technology for assisting in the writing or editing of the manuscript and no images were manipulated using AI.
Funding
This research was supported by the Deanship of Scientific Research, King Saud University, Saudi Arabia, under the Ongoing Research Funding program, grant number (ORF-2025-679).
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