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Detection and prevalence of SARS-CoV-2 co-infections during the Omicron variant circulation in France

Medicine and Health

Detection and prevalence of SARS-CoV-2 co-infections during the Omicron variant circulation in France

A. Bal, B. Simon, et al.

A groundbreaking study by Antonin Bal and colleagues reveals the rare occurrence of SARS-CoV-2 co-infections amidst an unprecedented wave of Delta and Omicron variants. By analyzing over 21,000 samples, they provide crucial insights into the implications for ICU admission rates and the clinical impact of these co-infections, emphasizing the need for robust detection methods.

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Playback language: English
Introduction
The emergence of SARS-CoV-2 variants of concern (VOCs), characterized by increased transmissibility and immune evasion, has posed significant challenges to global pandemic management. Omicron (B.1.1.529), the latest VOC at the time of this study, rapidly outcompeted the previously dominant Delta (B.1.617.2) variant. The co-circulation of these genetically distinct variants, particularly in France during the fifth wave (November 2021-January 2022), raised concerns about the possibility of co-infections and the potential for novel recombinant strains. While some cases of Delta/Omicron co-infection had been reported, a large-scale assessment of their prevalence, including co-infections involving Omicron sub-lineages B.A.1 and B.A.2, was lacking. Furthermore, the clinical implications of such co-infections, given the differing severities associated with Delta and Omicron, remained unclear. This research aimed to systematically assess the prevalence of SARS-CoV-2 co-infections during the period of intense variant co-circulation in France and to investigate the clinical characteristics of these cases. The study's importance stems from the need to understand the dynamics of co-infection to better predict and manage future pandemic waves and the potential emergence of new, more dangerous variants.
Literature Review
Prior to this study, reports on SARS-CoV-2 co-infections were limited and often based on manual inspection of mutation frequencies, which lacked robustness and standardization. Methods for detecting co-infections using whole-genome sequencing (WGS) faced several challenges: primer bias in PCR amplification could lead to unequal representation of genomic regions, potentially obscuring the presence of a less abundant variant; sample contamination during sequencing could lead to false positives; and the lack of validated bioinformatics tools hampered accurate detection. Existing methods, such as de novo assembly, were often computationally demanding and impractical for large-scale analyses. This study sought to address these limitations by developing a more robust and unbiased bioinformatic approach, coupled with careful experimental validation using defined mixtures of Delta and Omicron isolates.
Methodology
The study comprised two main phases: experimental validation and large-scale prevalence assessment. In the first phase, eleven mixtures of Delta and Omicron cell culture isolates were created with varying ratios (0:100 to 100:0). These mixes were sequenced using four different primer sets (Artic V4 and V1, Midnight V1 and V2) to assess their performance in detecting co-infections. The impact of PCR amplification bias on co-infection detection was evaluated. A novel bioinformatic approach was developed to detect co-infections based on the identification of secondary lineages, independent of pre-defined mutation lists, thus minimizing bias. This method focused on identifying minor sequence branches exceeding a defined frequency threshold. The second phase involved analyzing 21,387 WGS samples collected from France between December 6, 2021, and February 27, 2022. Samples were derived from both random genomic surveillance (Flash surveys) and systematic sequencing of hospitalized patients and healthcare workers. The developed bioinformatic pipeline was used to identify co-infections within these samples. Clinical data such as hospitalization status, ICU admission, and vaccination status were collected where available to analyze the clinical impact of co-infections. Statistical analyses were conducted using appropriate methods for comparing continuous and categorical variables, with binomial logistic regression used to assess the association between ICU admission and infection type (Delta, Omicron, or co-infection).
Key Findings
The experimental validation demonstrated that different primer sets exhibited varying degrees of bias, preferentially amplifying either Delta or Omicron. The newly developed bioinformatic pipeline successfully identified co-infections in all experimental mixes, even those with low proportions of one variant. In the large-scale analysis, 53 co-infections were detected among the 21,387 samples. The prevalence of Delta/Omicron (B.A.1) co-infection was estimated at 0.18% (95% CI: 0.12–0.26%), and the prevalence of B.A.1/B.A.2 co-infection was 0.26% (95% CI: 0.17–0.39%). Among hospitalized patients, ICU admission rates were significantly higher for patients with Delta/Omicron co-infections (15.38%) compared to those with Omicron (1.64%) or Delta (4.81%) infections alone. Notably, no B.A.1/B.A.2 co-infections were observed among ICU patients. A substantial proportion of co-infected patients (39.6%) were unvaccinated. While the overall prevalence of co-infections was low, the findings highlight the potential clinical impact of such infections and the importance of robust detection methods. Three potential recombinant events were identified among the co-infected samples.
Discussion
This study provides crucial insights into the prevalence and clinical significance of SARS-CoV-2 co-infections during a period of significant viral diversity. The low overall prevalence suggests that co-infection was not a major driver of the epidemic during the fifth wave in France. However, the significantly higher ICU admission rate in patients with Delta/Omicron co-infections compared to single infections underscores the potential severity of these cases. The higher rate of co-infection among unvaccinated individuals further emphasizes the importance of vaccination in preventing severe COVID-19 outcomes. The observation of potential recombinant events highlights the risk of emerging variants generated through co-infection. The development and validation of the unbiased bioinformatic pipeline is a key contribution of this study. This pipeline can be adapted to other pathogens, making it a valuable resource for genomic surveillance and understanding co-infection dynamics in infectious diseases.
Conclusion
This study demonstrated a low but significant prevalence of SARS-CoV-2 co-infections during the Omicron wave in France, with higher ICU admission rates observed among patients with Delta/Omicron co-infections. The development of a novel bioinformatic pipeline for detecting co-infections, validated through experimental mixes, is a significant contribution to the field. Future research should focus on the comprehensive investigation of factors influencing co-infection, explore the clinical course of co-infected patients, and improve detection methods for identifying co-infections with minor lineage representation.
Limitations
The study has some limitations. Co-infections with minor lineages (<5%) or genetically closely related lineages might not be detected. The short follow-up period might underestimate the prevalence of B.A.1/B.A.2 co-infections. Missing clinical data for some samples limited the clinical characterization of co-infected patients. Finally, other sequencing methods were not tested.
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