Interactions among 17 respiratory pathogens: a cross-sectional study using clinical and community surveillance data

  1. Roy Burstein
  2. Benjamin M. Althouse
  3. Amanda Adler
  4. Adam Akullian
  5. Elizabeth Brandstetter
  6. Shari Cho
  7. Anne Emanuels
  8. Kairsten Fay
  9. Luis Gamboa
  10. Peter Han
  11. Kristen Huden
  12. Misja Ilcisin
  13. Mandy Izzo
  14. Michael L. Jackson
  15. Ashley E. Kim
  16. Louise Kimball
  17. Kirsten Lacombe
  18. Jover Lee
  19. Jennifer K. Logue
  20. Julia Rogers
  21. Erin Chung
  22. Thomas R. Sibley
  23. Katrina Van Raay
  24. Edward Wenger
  25. Caitlin R. Wolf
  26. Michael Boeckh
  27. Helen Chu
  28. Jeff Duchin
  29. Mark Rieder
  30. Jay Shendure
  31. Lea M. Starita
  32. Cecile Viboud
  33. Trevor Bedford
  34. Janet A. Englund
  35. Michael Famulare

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Abstract

Background

Co-circulating respiratory pathogens can interfere with or promote each other, leading to important effects on disease epidemiology. Estimating the magnitude of pathogen-pathogen interactions from clinical specimens is challenging because sampling from symptomatic individuals can create biased estimates.

Methods

We conducted an observational, cross-sectional study using samples collected by the Seattle Flu Study between 11 November 2018 and 20 August 2021. Samples that tested positive via RT-qPCR for at least one of 17 potential respiratory pathogens were included in this study. Semi-quantitative cycle threshold (Ct) values were used to measure pathogen load. Differences in pathogen load between monoinfected and coinfected samples were assessed using linear regression adjusting for age, season, and recruitment channel.

Results

21,686 samples were positive for at least one potential pathogen. Most prevalent were rhinovirus (33·5%), Streptococcus pneumoniae ( SPn , 29·0%), SARS-CoV-2 (13.8%) and influenza A/H1N1 (9·6%). 140 potential pathogen pairs were included for analysis, and 56 (40%) pairs yielded significant Ct differences (p < 0.01) between monoinfected and co-infected samples. We observed no virus-virus pairs showing evidence of significant facilitating interactions, and found significant viral load decrease among 37 of 108 (34%) assessed pairs. Samples positive with SPn and a virus were consistently associated with increased SPn load.

Conclusions

Viral load data can be used to overcome sampling bias in studies of pathogen-pathogen interactions. When applied to respiratory pathogens, we found evidence of viral- SPn facilitation and several examples of viral-viral interference. Multipathogen surveillance is a cost-efficient data collection approach, with added clinical and epidemiological informational value over single-pathogen testing, but requires careful analysis to mitigate selection bias.

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  1. ScreenIT

    SciScore for 10.1101/2022.02.04.22270474: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    EthicsField Sample Permit: Residual specimens from patients tested for suspected respiratory infection at four regional hospitals were also collected.
    IRB: Analysis code to be posted at https://github.com/InstituteforDiseaseModeling/SFS-coinfection-interactions Ethics approval statement: This study was approved by the University of Washington Institutional Review Board, with reliance from Fred Hutchinson and Seattle Children’s Hospital.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Like all studies, ours is subject to several limitations. First, for multiplex PCR assays that target more than one distinct pathogen, strain, or serotype (e.g. CoVs, AdV, RV, hPIVs, EV, SPn), our analysis could average over important heterogeneities in how each interacts with the other pathogens. RV is of particular concern as it comprises 3 species representing over 150 genotypes, with varying qPCR amplification efficiencies, resulting in inconsistent quantification of RV RNAs.57 As such, our results for RV could be sensitive to the genotypes circulating during this study. Second, several swab types were used over the study period without this data always collected and thus could not be directly adjusted in the analysis. We expect swab type leads to extra unexplained variation in Ct values but is unlikely to bias results as it is not associated with the exposure or outcome of interest. Third, infection and test timing, sequence, and inoculum size, are critical components of interaction dynamics,14,58,59 none of which we could observe or control for in this analysis; the lack of sequential sampling in our study also prohibits analysis of Ct changes in individuals over time. Future experimental, longitudinal, and modeling studies should be conducted to elucidate these factors. Fourth, while we included an extensive panel of 17 pathogens, it is possible that monoinfected individuals in our study are infected with unmeasured pathogens. Fifth, to the extent that pathogen load is...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

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  2. Version published to 10.1101/2022.02.04.22270474 on medRxiv