Stratification of viral shedding patterns in saliva of COVID-19 patients

Coronavirus disease 2019 (COVID-19) vaccinations, which are effective in preventing infection by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and severe COVID-19 illness, have enabled the gradual and safe removal of COVID-19 restrictions on everyday life over the past year. However, we still face the emergence of variants of concern (VOCs), which is a major worry in the era of ‘living with COVID-19’. As a result, to prevent major outbreaks, basic public health responses such as testing, isolation, and quarantine are demanded and important. In particular, rapid and accurate diagnostic tests are essential for controlling ongoing transmission. Salivary diagnostic testing is a convenient tool for early and efficient diagnosis of COVID-19 because it is easy for health care professionals and patients to administer (Baghizadeh Fini, 2020; Wyllie et al., 2020).

The oral cavity is an important target for SARS-CoV-2 (Huang et al., 2021), and viral particles in the lower and upper respiratory tract can reach the oral cavity through liquid droplets (Baghizadeh Fini, 2020). Saliva droplets are thus a potential route of SARS-CoV-2 transmission (Baghizadeh Fini, 2020; Huang et al., 2021). Although SARS-CoV-2 infection dynamics within the upper respiratory tract are well characterized thanks to data from oropharyngeal and nasopharyngeal swabs (Kim et al., 2021; Ke et al., 2022; Jeong et al., 2022; Goyal et al., 2020; Marc et al., 2021; Néant et al., 2021), infection dynamics in saliva are poorly understood. One potential explanation is the practical challenges inherent in using saliva as a specimen type. Handling steps such as tube opening, pipetting, and vortexing can generate infectious aerosols, requiring strict biosafety precautions (e.g. work in a biosafety cabinet). Also, salivary RNases and the abundant oral microbiota may degrade viral RNA or increase background noise, particularly in low viral load samples.

Recent reports (Huang et al., 2021; Ke et al., 2022) have suggested that SARS-CoV-2 infection dynamics differ qualitatively across tissues. Salivary gland tissue (parotid, submandibular, and sublingual) and other tissues are highly compartmentalized, and saliva represents the corresponding biofluid of the oral compartment. However, although individual-level heterogeneity in virus dynamics (especially individual infectiousness) has been evaluated, it remains unknown how individual-level viral shedding patterns in saliva are stratified, and which factor(s) determine the patterns (Ke et al., 2022; Iwanami et al., 2021). Since saliva is a route of direct viral transmission, salivary viral load provides particularly valuable information for assessing a patient’s immediate infectiousness compared with measurements from other anatomical sites. Accordingly, to shape a country’s early response to future VOCs, such as through isolation and screening guidelines based on salivary diagnostic testing, it is critical to address these points with the use of a high quality and quantity of saliva specimens annotated with basic clinical patient data.

So far, our ability to understand and characterize whole SARS-CoV-2 infection dynamics has been hindered by several limitations of the clinical data that made it impossible to capture either the early or the late phase of infection or to annotate individual-level clinical information. To overcome these limitations, here we quantified and stratified longitudinal virus dynamics in saliva samples from 144 mildly symptomatic participants of two different but complementary cohorts (Ke et al., 2022; Hosogaya et al., 2021; Miyazaki et al., 2023). We successfully identified three groups with distinct viral shedding patterns. Notably, the groups showed clear differences in the duration of viral RNA detection (the mean durations were 11.5 days, 17.4 days, and 30.0 days, respectively). These findings imply a large inter-participant heterogeneity in virus infection dynamics. However, when we analyzed a total of 47 variables, including basic demographic information, daily clinical symptoms, results of blood tests, and vital signs, none explained the stratified grouping.

We also retrospectively explored whether salivary micro-RNAs were associated with the stratification by using stored residual saliva specimens. Micro-RNAs are non-coding RNAs that regulate numerous cellular processes by modulating protein levels through direct binding to mRNA (coding-RNA), thereby influencing translation efficiency or mRNA abundance. Various microRNAs are recognized for their impact on the viral replication ability and immune response in viruses like EBV, HCV, and HIV etc (Trobaugh and Klimstra, 2017). Many studies are underway to investigate micro-RNAs as potential targets for virus diagnosis and treatment. However, the relationship between micro-RNAs and the patterns of virus shedding of the SARS-CoV-2 virus in the body remains unknown. We quantified the expression levels of 92 micro-RNAs and found that no micro-RNA significantly explained the stratified groups, although the mir-1846 level may have been weakly correlated with the peak viral load. Our findings provide important insights into the complexities of viral shedding patterns in saliva and suggest that predicting the heterogeneity of viral dynamics using basic clinical and micro-RNA data may be a challenging task. These insights are critical for developing accurate diagnostic tools, effective treatments, and prevention strategies for COVID-19.

Being able to quickly and efficiently diagnose COVID-19 is essential in monitoring the pandemic. Because the sampling process for saliva is noninvasive, and because it is inexpensive and minimizes the risk for transmissions to health care workers (Baghizadeh Fini, 2020), saliva sampling has excellent potential and advantages over other sampling methods from biological specimens such as the lower and upper respiratory tract (Wyllie et al., 2020; To et al., 2020). Given the significant individual heterogeneity in the saliva viral shedding (Ke et al., 2022; Hay et al., 2022), identifying biomarker(s) for viral shedding patterns will be crucial for improving public health interventions in the era of living with COVID-19.

To improve our understanding of SARS-CoV-2 infection dynamics in saliva to enable application of saliva testing in the fight against COVID-19, we quantified and stratified longitudinal virus dynamics in saliva samples from 144 mildly symptomatic individuals from the cohorts of the NFV clinical trial (Hosogaya et al., 2021) and the University of Illinois at Urbana-Champaign (Ke et al., 2022), and we uniquely analyzed the relationships among viral dynamics, clinical data, and micro-RNAs. Our mathematical modeling analysis indicates that viral dynamics in saliva may exhibit distinct patterns compared to those in the upper respiratory tract. We estimated that viral replication in saliva is characterized by a relatively rapid early growth phase, with a mean (standard deviation) doubling time of 1.44 (0.49) hours. Compared with prior studies analyzing viral dynamics in the upper respiratory tract using similar models, which reported doubling times of 2–4 hr (Ke et al., 2022; Gunawardana et al., 2022; Iyaniwura et al., 2024), our findings suggest that viral replication in saliva proceeds faster than in the upper respiratory tract. Multiple previous studies have also shown that viral loads in saliva rise more rapidly than in the nasal cavity, are detected with higher sensitivity early in infection, and reach their peak earlier (Ke et al., 2022; Migueres et al., 2022; Puhach et al., 2023; Savela et al., 2022; Smith et al., 2021).

In addition to the large heterogeneity in virus infection dynamics, we identified three groups (i.e. G1, G2, and G3) with different viral shedding patterns (Figure 2D). Immunocompromised patients have been reported to have a prolonged duration of viral RNA detection, lasting over three months, underscoring the critical role of host immune responses in controlling viral infections (Leung et al., 2022; Niyonkuru et al., 2021; Nakajima et al., 2021; Wei et al., 2021). Although oral immune responses remain poorly understood, Huang et al. recently confirmed by using single-cell RNA sequencing of the human minor salivary glands and gingiva that SARS-CoV-2 infection can trigger sustained, localized immune responses in saliva (Huang et al., 2021). In this study, we observed significant differences in the down-slopes of viral shedding in saliva among participants in different groups, with a more rapid decline in G1. This decline is likely attributed to a stronger immune response to SARS-CoV-2 in G1 participants than in participants in G2 and G3, as reflected in the death rate of infected cells due to the immune response (Figure 2D). Lower levels of viral replication have also been observed among infected participants with high baseline levels of mucosal IgA (but not IgG), as reported elsewhere (Havervall et al., 2022). Recently, we demonstrated that rapid anti-spike secretory IgA antibody responses can contribute to reducing duration of viral RNA detection and amounts in nasopharyngeal mucosa (Miyamoto et al., 2023). These findings highlight the importance of biomarkers that directly reflect an individual’s immune response, such as virus-specific antibody induction and T cell levels etc., in predicting viral shedding patterns. Therefore, quantifying the time-series pattern of mucosal IgA and its correlation with saliva viral load may provide crucial insights into the stratification of SARS-CoV-2 infection dynamics.

For the purpose of predicting viral shedding patterns during the early stage of infection, we first explored the association of 39 basic clinical variables, 8 daily symptoms, and the levels of 92 micro-RNAs with the stratified groups. However, none of the factors were significant (Table 1, Figure 3A, Figure 4B, Supplementary file 1A and Supplementary file 1F). On the other hand, it is noteworthy that all infection cases were mild and that most participants had clinical indicators within normal ranges. This lack of clinical heterogeneity within the cohort may have limited the ability to fully capture the diversity of infection dynamics. Moreover, the clinical parameters analyzed in this study are, a priori, unlikely to exhibit strong correlations with virologic outcomes. In contrast, we showed that mir-1846, which is an exogenous micro-RNA that is specifically classified as an Oryza sativa micro-RNA (osa-microRNA; Rakhmetullina et al., 2020), may exhibit a weak negative correlation. Exogenous micro-RNAs enter the human body primarily through food and can affect human metabolism by interacting and binding with human genes. mir-1846 is reported to interact with two human genes (Rakhmetullina et al., 2020) that are known to be associated with the progression of melanoma, various cancers, and leukemia. This suggests that mir-1846 levels may be linked to human immunity. Few studies have investigated the role of mir-1846 in humans, but our findings suggest the need for further investigations into the impact of this micro-RNA level on human immunity. Our research sheds light on the intricate patterns of viral shedding in saliva.

Our approach has several limitations that must be considered in our next study: First, our analysis was limited to participants with symptomatic infection and excluded those with asymptomatic infection (22 asymptomatic individuals out of a total of 182 individuals, i.e. 12% of participants) because we integrated datasets with different time scales from different cohorts. Although our data do not include participants infected with omicron variants, others have reported that the omicron variant may cause a higher proportion of asymptomatic infection (Garrett et al., 2022). Thus, evaluating the effect of asymptomatic infection will be important to update our stratification, especially for recent (or future emerging) VOCs. Second, potential viral rebound was neither prespecified nor systematically assessed. A subset of participants exhibited patterns consistent with possible rebound (e.g. S01-16 and S01-43 in Figure 2—figure supplement 1), which could affect estimates of viral RNA detection duration. Future studies should predefine an operational criterion for viral rebound and explicitly incorporate it into the modeling framework to strengthen robustness. Since both models considered in the present study cannot account for viral rebound, a more complex model would be required to capture this phenomenon. Third, micro-RNAs participate in the post-transcriptional regulation of gene expression; however, they do not provide direct insights into immune cell dynamics. Given the reported association between the duration of viral RNA detection and mucosal immunity as discussed above, it appears imperative to analyze modalities that are directly linked to the immune response in the future. Fourth, some of our results may have limited relevance to the current COVID-19 situation, as most people have now either been infected or vaccinated. Nevertheless, investigating the relationship between viral shedding patterns in saliva and various clinical and microRNA data, and developing a method to do so, remains important. Such research can offer valuable insights into the early response to emerging infectious viruses in the future.

Another potential limitation of this study is the timing of saliva specimen sampling, although we took great care to select and compare specimens from G1, G2, and G3 without bias. As a result of our clinical trial design (jRCT2071200023 Hosogaya et al., 2021; Miyazaki et al., 2023), participants were enrolled after the onset of symptoms, thereby restricting saliva specimen collection exclusively to the post-symptom phase. Unfortunately, we lack samples from the pre-infection, pre-symptomatic, and early infection phases. Consequently, the absence of individual-level baseline values for micro-RNA means that inter-participant heterogeneity in micro-RNA levels may obscure signals related to distinct viral infection dynamics in saliva.

In conclusion, our study revealed that the dynamics of SARS-CoV-2 infection in saliva can be classified into three groups based mainly on the duration of viral RNA detection. However, accurately predicting the variability in viral dynamics remains a challenging task, because it requires a more comprehensive understanding of the complex shedding patterns in saliva, as well as detailed clinical and molecular data. The identification of a sensitive, simple, and rapid biomarker for saliva viral shedding will be imperative for future COVID-19 outbreak control.

The data generated and analyzed in this study include individual-level clinical, microRNA, and viral load data. All data are managed in accordance with the ethical standards and data management policies of the affiliated institutions. Due to the inclusion of detailed longitudinal and regional information, even de-identified versions of these datasets carry a non-negligible risk of participant re-identification, and the informed consent forms and institutional review board (IRB) approvals at the participating institutions do not permit public release of individual-level data. For these ethical and legal reasons, neither the raw nor de-identified clinical, microRNA, and viral load datasets can be publicly shared. However, the reconstructed viral load dynamics derived from the mathematical models described in this study are fully reproducible using the publicly available analysis code, and all scripts required to reproduce the model-based viral load dynamics and figures are available on Zenodo (https://doi.org/10.5281/zenodo.18125739). Researchers seeking access to the restricted individual-level datasets may contact the corresponding author, Taiga Miyazaki (taiga_miyazaki@med.miyazaki-u.ac.jp); access may be granted upon reasonable request and subject to approval by the relevant institutional review boards of all participating institutions.

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Author details

1. Hyeongki Park

   1. interdisciplinary Biology Laboratory (iBLab), Division of Natural Science, Graduate School of Science, Nagoya University, Nagoya, Japan
   2. School of Biomedical Convergence Engineering, Pusan National University, Yangsan, Republic of Korea

   Contribution

   Formal analysis, Visualization, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0009-0002-6507-0731

2. Yoshimura Raiki

   interdisciplinary Biology Laboratory (iBLab), Division of Natural Science, Graduate School of Science, Nagoya University, Nagoya, Japan

   Contribution

   Formal analysis, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

3. Shoya Iwanami

   interdisciplinary Biology Laboratory (iBLab), Division of Natural Science, Graduate School of Science, Nagoya University, Nagoya, Japan

   Contribution

   Formal analysis, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

4. Kwangsu Kim

   1. interdisciplinary Biology Laboratory (iBLab), Division of Natural Science, Graduate School of Science, Nagoya University, Nagoya, Japan
   2. Department of Science System Simulation, Pukyong National University, Busan, Republic of Korea
   3. Department of Mathematics, Pusan National University, Busan, Republic of Korea

   Contribution

   Formal analysis, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

5. Keisuke Ejima

   1. Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
   2. The Tokyo Foundation for Policy Research, Tokyo, Japan

   Contribution

   Formal analysis, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

6. Naotoshi Nakamura

   interdisciplinary Biology Laboratory (iBLab), Division of Natural Science, Graduate School of Science, Nagoya University, Nagoya, Japan

   Contribution

   Formal analysis, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4798-3660

7. Kazuyuki Aihara

   International Research Center for Neurointelligence, The University of Tokyo Institutes for Advanced Study, The University of Tokyo, Tokyo, Japan

   Contribution

   Formal analysis, Investigation, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4602-9816

8. Yoshitsugu Miyazaki

   Department of Chemotherapy and Mycoses, National Institute of Infectious Diseases, Tokyo, Japan

   Contribution

   Data curation, Investigation, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

9. Takashi Umeyama

   Department of Chemotherapy and Mycoses, National Institute of Infectious Diseases, Tokyo, Japan

   Contribution

   Data curation, Investigation, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

10. Ken Miyazawa

    Department of Chemotherapy and Mycoses, National Institute of Infectious Diseases, Tokyo, Japan

    Contribution

    Investigation, Writing – original draft, Writing – review and editing

    Competing interests

    No competing interests declared

11. Takeshi Morita

    Research Center for Drug and Vaccine Development, National Institute of Infectious Diseases, Tokyo, Japan

    Contribution

    Data curation, Investigation, Writing – original draft, Writing – review and editing

    Competing interests

    No competing interests declared

12. Koichi Watashi

    Research Center for Drug and Vaccine Development, National Institute of Infectious Diseases, Tokyo, Japan

    Contribution

    Data curation, Investigation, Writing – original draft, Writing – review and editing

    Competing interests

    No competing interests declared

13. Christopher B Brooke

    1. Department of Microbiology, University of Illinois at Urbana-Champaign, Urbana, United States
    2. Department of Statistics, University of Illinois at Urbana-Champaign, Urbana, United States

    Contribution

    Data curation, Investigation, Writing – original draft, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-6815-1193

14. Ruian Ke

    Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, United States

    Contribution

    Formal analysis, Writing – original draft, Writing – review and editing

    Competing interests

    No competing interests declared

15. Shingo Iwami

    1. interdisciplinary Biology Laboratory (iBLab), Division of Natural Science, Graduate School of Science, Nagoya University, Nagoya, Japan
    2. International Research Center for Neurointelligence, The University of Tokyo Institutes for Advanced Study, The University of Tokyo, Tokyo, Japan
    3. Institute of Mathematics for Industry, Kyushu University, Fukuoka, Japan
    4. Institute for the Advanced Study of Human Biology (ASHBi), Kyoto University, Kyoto, Japan
    5. Interdisciplinary Theoretical and Mathematical Sciences Program (iTHEMS), RIKEN, Saitama, Japan
    6. NEXT-Ganken Program, Japanese Foundation for Cancer Research (JFCR), Tokyo, Japan
    7. Science Groove Inc., Fukuoka, Japan

    Contribution

    Conceptualization, Formal analysis, Supervision, Methodology, Writing – original draft, Project administration, Writing – review and editing

    Contributed equally with

    Taiga Miyazaki

    For correspondence

    iwamishingo@gmail.com

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-1780-350X

16. Taiga Miyazaki

    Division of Respirology, Rheumatology, Infectious Diseases, and Neurology, Department of Internal Medicine, Faculty of Medicine, University of Miyazaki, Miyazaki, Japan

    Contribution

    Conceptualization, Data curation, Supervision, Funding acquisition, Writing – original draft, Project administration, Writing – review and editing

    Contributed equally with

    Shingo Iwami

    For correspondence

    taiga_miyazaki@med.miyazaki-u.ac.jp

    Competing interests

    No competing interests declared

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