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Detection of respiratory syncytial virus defective genomes in nasal secretions is associated with distinct clinical outcomes

Abstract

Respiratory syncytial virus (RSV) causes respiratory illness in children, immunosuppressed individuals and the elderly. However, the viral factors influencing the clinical outcome of RSV infections remain poorly defined. Defective viral genomes (DVGs) can suppress virus replication by competing for viral proteins and by stimulating antiviral immunity. We studied the association between detection of DVGs of the copy-back type and disease severity in three RSV A-confirmed cohorts. In hospitalized children, detection of DVGs in respiratory samples at or around the time of admission associated strongly with more severe disease, higher viral load and a stronger pro-inflammatory response. Interestingly, in experimentally infected adults, the presence of DVGs in respiratory secretions differentially associated with RSV disease severity depending on when DVGs were detected. Detection of DVGs early after infection associated with low viral loads and mild disease, whereas detection of DVGs late after infection, especially if DVGs were present for prolonged periods, associated with high viral loads and severe disease. Taken together, we demonstrate that the kinetics of DVG accumulation and duration could predict clinical outcome of RSV A infection in humans, and thus could be used as a prognostic tool to identify patients at risk of worse clinical disease.

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Fig. 1: cbDVG screening methods and sensitivity.
Fig. 2: cbDVG+ hospitalized paediatric patients have higher viral load and more severe clinical outcomes.
Fig. 3: cbDVG+ non-hospitalized paediatric patients do not have worse respiratory disease than cbDVG− patients.
Fig. 4: cbDVG appearance is independent of viral load.
Fig. 5: Early appearance of cbDVGs associates with lower viral loads and disease severity scores in experimentally infected subjects.
Fig. 6: Prolonged detection of cbDVGs associates with higher viral loads and worse clinical outcomes.

Data availability

All data associated with this study are present in the paper or the supplementary materials. Raw sequence data are deposited on SRA or GEO (Fig.1, PRJNA681672; Fig.2 and S1A, GSE146925 and Fig.6, GSE166161). Source data are provided with this paper.

Code availability

All R codes used to analyse RNA-seq data from clinical samples were indicated in Rmarkdown (Supplementary Information). VODKA is self-developed and is deposited in GitHub at https://github.com/itmat/VODKA.

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Acknowledgements

For the Cohort 1 study, we thank DBMI for pulling the data from the data warehouse and S. Worthen and S. Masters for IRB approval and grant support. We thank D. P. Beiting for the help of host transcriptome analysis. We also thank V. Bernhauerova for the help with statistics. Funding was received from NIH R01 AI137092 and R01 AI137062 for C.B.L., NIH U19 AI095227, UL1 RR024975 and K24 AI077930 for T.H., and the Medical Research Council (G0902266) and Wellcome Trust (087805/Z/08/Z) for C.C. C.C. is supported by the Biomedical Research Centre award to Imperial College Healthcare NHS Trust. Infrastructure support for C.C. was provided by the NIHR Imperial Biomedical Research Centre and the NIHR Imperial Clinical Research Facility. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care.

Author information

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Authors

Contributions

S.A.F., Y.S. and C.B.L. contributed in writing the original draft, review and editing. S.A.F. and Y.S. performed cbDVG screening, analysed data and validated the results. A.J., A.P., M.S.H. and C.C. participated in Cohort 3 sample collection and provided the participants’ clinical information. D.N. and M.C. performed and provided the RNA-seq data for Cohort 3. L.A. and T.V.H. participated in Cohort 2 sample collection and provided the patients’ clinical information. K.N.T. performed multiplex experiments for cytokine expression of Cohort 2. S.S., A.M.C. and K.A.F. participated in Cohort 1 sample collection and provided the patients’ clinical information. E.A. performed critical bioinformatics work. C.C., T.V.H. and C.B.L. provided resources and acquired funding. C.B.L. supervised the overall study.

Corresponding authors

Correspondence to Christopher Chiu or Carolina B. López.

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The authors declare no competing interests.

Additional information

Peer review information Nature Microbiology thanks Shirit Einav and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Confirmation of RT-PCR and RNA-seq/VODKA methods with paediatric patient samples.

a, Thirteen hospitalized paediatric patients from cohort 1 were divided into 2 groups based of PCR screening: DVG PCR+ (left) and DVG PCR- (right). DVG reads identified for each patient using the RNA-seq/VODKA pipeline were normalized to 108 total reads. b, Specific primers for unique cbDVG junction regions were designed based on cbDVG sequences identified by RNA-seq/VODKA in sample H75. The numbers in parenthesis indicate break and rejoin positions targeted by the primers in sample H75. Amplicons were of the expected sizes (left to right: 417bp, 804bp and 436bp). c, cbDVG band from sample H75 12776-13401 was gel extracted and confirmed by Sanger’s sequencing. P2 primer sequence is labeled in green and P1 primer sequence (spanning the junction region) is labeled in red and blue to show break/rejoin point. Source data

Extended Data Fig. 2 Representative DNA gel pictures of Cohorts 1, 2 and 3.

Positive PCR results were marked in green. H2O was added as a negative control for each PCR as shown in (a). a, same results were observed for all samples for two independent repeats. b, same results were observed for most samples for 4 independent repeats. T1, T2, T4 and T7 were positive 2, 3, 1 and 1 time out of 4 total repeats, respectively. c, same results were observed for most samples for two independent repeats. D3 and D7 were positive 1 time out of 4 total repeats. Source data

Extended Data Fig. 3 cbDVG+ patients in the non-hospitalized group were sampled earlier than that in the hospitalized group.

In Cohort 2, there are 73 non-hospitalized patients and 27 hospitalized patients. Days post the onset of symptoms were recorded at the time of sampling. Within cbDVG+ patients, days post the onset of symptoms was compared between non-hospitalized group and hospitalized group. Data are shown as mean±SEM. Significance upon two-tailed Mann-Whitney test. Source data

Extended Data Fig. 4 Cut off I and III for early appearance of cbDVGs were also associated with lower viral loads and disease severity scores in experimentally infected subjects.

Kinetics of viral load (a) and disease severity score (b) among negative (n=38), Early (cutoff I n=3; cutoff III n=11), and Late (cutoff I n=15; cutoff III n=7) groups were compared. Data are plotted over time and trendline represents mean±SEM. Significant P value for two-way ANOVA with Bonferroni post hoc test are indicated for Early vs Late groups. Best-fit curves for kinetics of disease severity scores from negative, Early, and Late groups were calculated using nonlinear Gaussian model (c). The best-fit values for amplitude of the negative, Early and Late group curves were compared. Source data

Extended Data Fig. 5 Viral load is not the sole viral factor that impact RSV disease severity.

a, Total viral load and total disease severity score were compared between cbDVG+ (n=18) and cbDVG- (n=38) individuals in Cohort 3. Data are shown as mean±s.e.m. b, Total viral load and total disease severity scores were correlated within cbDVG+ (n=17) and cbDVG- (n=37) group separately in Cohort 3. One outlier in each group was eliminated upon testing using Grubbs’ test in Prism. P value was tested from the correlation between total clinical scores and viral load using nonparametric spearman correlation. Dashed line represent the 95% confidence for the slopes. c, In Cohort 2, viral load was compared between non-hospitalized (n=73) and hospitalized (n=27) individuals. Data are shown as mean±s.e.m. No significance upon two-tailed Mann-Whitney test. Source data

Extended Data Fig. 6 Impact of sex and age on cbDVG generation in different cohorts.

In Cohort 3, a, the percentage of female and male patients within the cbDVG+ (11:7 ratio) and cbDVG- (10:28 ratio) groups were compared with the percentage of female subjects shown within the bars. (b-c) showing the comparison of viral load (b) and disease severity (c) between females (n=21) and males (n=35). Data are shown as mean±s.e.m. In Cohort 1, (d) the percentage of female and male patients within the cbDVG+ (52:48 ratio) and cbDVG- (6:16) groups were compared with the percentage of female subjects shown within the bars. (e-f) showing the comparison of viral load (e) and length of stay (f) between females (n=56, n=58) and males (n=62, n=64). Data are shown as mean±s.e.m. g, Age was compared between cbDVG+ (n=100) and cbDVG- (n=22) patients. Data are shown as mean±s.e.m. Significance upon Mann-Whitney test. Source data

Supplementary information

Source data

Source Data Fig. 1

Unprocessed PCR gel for Fig. 1b.

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Raw numerical data for Fig. 1c.

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Source Data Extended Data Fig. 1

Raw numerical data for Extended Data Fig. 1a.

Source Data Extended Data Fig. 1

Unprocessed PCR gels for Extended Data Fig. 1b.

Source Data Extended Data Fig. 2

Unprocessed PCR gels.

Source Data Extended Data Fig. 3

Raw numerical data.

Source Data Extended Data Fig. 4

Raw numerical data.

Source Data Extended Data Fig. 5

Raw numerical data.

Source Data Extended Data Fig. 6

Raw numerical data.

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Felt, S.A., Sun, Y., Jozwik, A. et al. Detection of respiratory syncytial virus defective genomes in nasal secretions is associated with distinct clinical outcomes. Nat Microbiol 6, 672–681 (2021). https://doi.org/10.1038/s41564-021-00882-3

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