Employing Metagenomic Next-Generation Sequencing for Enhanced Surveillance of Respiratory Viruses in Fever Clinics: A Comparative Analysis in the Context of the COVID-19 Pandemic in a General Hospital

doi:10.21203/rs.3.rs-4937704/v1

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Research Article

Employing Metagenomic Next-Generation Sequencing for Enhanced Surveillance of Respiratory Viruses in Fever Clinics: A Comparative Analysis in the Context of the COVID-19 Pandemic in a General Hospital

https://doi.org/10.21203/rs.3.rs-4937704/v1

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Background: Clinical surveillance in hospitals, especially in fever clinic, plays a critical role in detecting and managing emerging infectious diseases, serving as an initial alert system for public health epidemics. However, current clinical surveillance networks lack effective methods for comprehensive viral monitoring.

Aim and Methods: This study aimed to establish a pathogen monitoring method using metagenomic next-generation sequencing (mNGS) for pooled specimens in fever clinics, enhancing the capacity for broad-spectrum viral surveillance. We randomly selected oropharyngeal swab specimens from patients at the Fever Clinic who underwent SARS-CoV-2 nucleic acid testing between June 2022 and June 2023. These specimens were pooled, nucleic acids were extracted following standardized protocols, and pathogens were identified through the mNGS technique.

Results: Our results indicated that mNGS for pooled samples exhibited RNA viral detection efficiency equivalent to that of targeted next-generation sequencing (tNGS) for individual samples. Data obtained from mNGS demonstrated a strong correlation with influenza surveillance data from the Guangzhou CDC. Our surveillance method adeptly tracked the progression of the H3N2 epidemic through June 2023 and pinpointed the spread of COVID-19 by late November 2023. Additionally, our analysis exposed notable variations in the respiratory viral spectrum among patients before and after the COVID-19 outbreak.

Conclusion: Utilizing mNGS for mixed-sample detection in fever clinics proved effective and feasible for pathogen surveillance. The approach enhanced understanding of respiratory virus epidemiology, supporting reduced circulation of non-SARS-CoV-2 respiratory viruses during and after the COVID-19 pandemic. This innovative method strengthens public health surveillance by enabling timely detection and response to respiratory virus outbreaks.

Virus Surveillance

Metagenomic Next-Generation Sequencing

SARS-CoV-2

COVID-19 Pandemic

Acute respiratory infections (ARIs), which can vary from common colds to severe pneumonia, result in substantial disease burden worldwide^1–3.

Acute respiratory infectious diseases can rapidly spread from a single country or region to the entire globe in a short period, leading to pandemics⁴. ARI particularly those caused by respiratory viruses, can rapidly spread in a short period of time, quickly disseminating from one location to another and causing epidemics or even pandemics. Since its identification at the end of 2019, SARS-CoV-2 has continued to ravage the world due to its strong transmissibility and antibody evasion capabilities^5–8. Moreover, in the post-COVID-19 world, influenza and respiratory syncytial virus (RSV) drove most spikes in illness and hospitalizations in many countries⁹. ARIs stand as the most prevalent diseases globally across all age groups¹⁰. Individuals frequently present at hospitals and emergency departments with respiratory symptoms as their chief complaint¹¹. During peaks in the prevalence of respiratory pathogens, the surge in healthcare-seeking behavior can easily trigger serious issues, including nosocomial transmission and medical congestion¹². Consequently, the establishment and advancement of monitoring and early warning technologies for respiratory infectious diseases become imperative. Enhancing the capabilities of acute respiratory virus surveillance and early warning systems is an urgent necessity to address these challenges effectively.

Fever clinics play a crucial role in triaging patients during the prevention, control, and treatment of newly emerging and sudden infectious diseases¹³. Accurate and rapid detection and diagnosis of pathogens on the fever clinic and emergency room can assist public health agencies and healthcare workers in understanding the incidence of the infectious disease, predicting epidemic trends, and formulating timely treatment plans¹⁴. However, the detection and surveillance of respiratory pathogens in fever clinics still rely on traditional PCR for individual specimen testing. In the realm of pathogen detection, traditional PCR methods encounter challenges related to low sensitivity and limited coverage. Moreover, for newly discovered viruses or highly variable viruses, traditional detection methods could not be effective. Metagenomic next-generation sequencing (mNGS) is a comprehensive method based on sequence identification of pathogens in clinical specimens, especially viruses^15,16. Previous studies have shown that mNGS can be used for virus monitoring and identification of novel viruses circulating in blood and nasopharyngeal swabs¹⁷. However, the anticipated increase in costs associated with expanding mNGS testing to encompass all fever patients restricts its feasibility for large-scale universal respiratory virus surveillance in its current form. This necessitates the development of a more efficient surveillance strategy. One promising approach to enhance screening efficiency while conserving resources is the implementation of sample pooling^18,19. During the COVID-19 pandemic, nasopharyngeal swab pooling strategy were used to screen large populations for SARS-CoV-2 infections in China, particularly in countries and regions with limited medical resources during the peak of the outbreak, suggesting its potential of clinical applications²⁰.

In this study, we conducted mNGS detections on oropharyngeal swab specimens from fever clinic patients, employing sample pooling strategies to delineate the spectrum of pathogen distribution. Our findings suggest that mNGS detection on pooled samples serves as an efficient tool for simultaneously identifying multiple respiratory viruses in fever patients, enhancing the precision of epidemic monitoring and control initiatives.

Specimen collection and pooling samples

Our investigation involved a randomized selection of oropharyngeal swab specimens obtained from individuals seeking medical attention at the Fever Clinic of the First Affiliated Hospital of Sun Yat-sen University, who underwent SARS-CoV-2 nucleic acid testing between June 2022 and June 2023. Fifty samples were randomly selected from fever patients and divided evenly into two groups. Each group of the specimens were homogenized, and then nucleic acids were extracted, followed by pathogen detection using the mNGS detection process for routine pathogen testing. Ultimately, to comprehensively cover viral pathogens, the study encompassed 5100 samples, with 204 pools undergoing dual-process mNGS for Viral RNA and DNA detection. Except for November 2022, the monitoring frequency ranged from 2–3 times per week between June 2022 and January 2023. Due to a decrease of fever clinic visits, monitoring frequency was reduced to once a week from February to June 2023. In addition, specimen testing was suspended from November 13th to November 28th, 2022, due to the infection of laboratory personnel with SARS-Cov-2.

The study was approved by the Institutional Review Board and the Ethics Committee of The First Affiliated Hospital of Sun Yat-sen University. Given the retrospective nature of the study, the need for informed consent was waived.

Our investigation involved a randomized selection of oropharyngeal swab specimens obtained from individuals seeking medical attention at the Fever Clinic of the First Affiliated Hospital of Sun Yat-sen University, who underwent SARS-CoV-2 nucleic acid testing between June 2022 and June 2023. The experimental workflow is illustrated in Fig. 1. Fifty specimens were randomly chosen during each time frame and homogenized into two pools, subsequently subjected to metagenomic next-generation sequencing (mNGS) analysis. Ultimately, to comprehensively cover viral pathogens, the study encompassed 5100 samples, with 204 pools undergoing dual-process mNGS for RNA and DNA detection. Except for November 2022, the monitoring frequency ranged from 2–3 times per week between June 2022 and January 2023. Due to a decrease in the number of Fever Clinic visits, monitoring frequency was reduced to once a week from February to June 2023. In addition, specimen testing was suspended from November 13th to November 28th, 2022, due to the infection of laboratory personnel with SARS-Cov-2.

Nuclear acid extraction, library preparation, and sequencing

DNA was extracted from all samples using a QIAamp® UCP Pathogen DNA Kit (Qiagen) following the manufacturer’s instructions. Human DNA was removed using Benzonase (Qiagen) and Tween20 (Sigma)²¹. Total RNA was extracted with a QIAamp® Viral RNA Kit (Qiagen) and ribosomal RNA was removed by a Ribo-Zero rRNA Removal Kit (Illumina). cDNA was generated using reverse transcriptase and dNTPs (Thermo Fisher). Libraries were constructed for the DNA and cDNA samples using a Nextera XT DNA Library Prep Kit (Illumina, San Diego, CA)². Library was quality assessed by Qubit dsDNA HS Assay kit followed by High Sensitivity DNA kit (Agilent) on an Agilent 2100 Bioanalyzer. Library pools were then loaded onto an Illumina Nextseq CN500 sequencer for 75 cycles of single-end sequencing to generate approximately 20 million reads for each library. Internal DNA and RNA controls, consisting of a DNA phage (Escherichia coli bacteriophage T1, ATCC 11303-B1) and RNA phage (Escherichia coli bacteriophage MS2, ATCC 15597-B1), were then added into the supernatant at a concentration both of 10⁴ copies/mL, levels which resulted in 10 RPM or above in clinical samples which host cell ranged from 10³ copies/mL to 10⁷ copies/mL. For negative controls, we also prepared Hela cell with 10⁵ cells/mL with sterile deionized water in parallel with each batch, using the same protocol, and sterile deionized water was extracted alongside the specimens to serve as non-template controls (NTC)^22,23.

Bioinformatics Analyses

Trimmomatic²⁴ was used to remove low quality reads, adapter contamination, and duplicate reads, as well as those shorter than 50 bp. Low complexity reads were removed by Kcomplexity with default parameters²⁵. Human sequence data were identified and excluded by mapping to a human reference genome (hg38) using Burrows-Wheeler Aligner software²⁶. We designed a set of criteria similar to the National Center for Biotechnology Information (NCBI) criteria for selecting representative assembly for microorganisms (bacteria, viruses, fungi, protozoa, and other multicellular eukaryotic pathogens) from the NCBI Nucleotide and Genome databases⁷. Pathogen lists was selected according to three references: 1) Johns Hopkins ABX Guide (https://www.hopkinsguides.com/hopkins/index/Johns_Hopkins_ABX_Guide/Pathogens), 2) Manual of Clinical Microbiology²⁷, and 3) clinical case reports or research articles published in current peer-reviewed journals⁹. The final database consisted of about 13000 genomes. Microbial reads were aligned to database with SNAP v1.0beta.18¹⁰. Virus-positive detection results (DNA or RNA viruses) were defined as the coverage of three or more non-overlapping regions on the genome. A positive detection was reported for a given species or genus if the reads per million (RPM) ratio, or RPM-r was ≥ 5, where the RPM-r was defined as the RPM_sample / RPM_NC (i.e., the RPM corresponding to a given species or genus in the clinical sample divided by the RPM in the NC/negative control)²². In addition, to minimize cross-species misalignments among closely related microorganisms, we penalized (reduced) the RPM of microorganisms sharing a genus or family designation, if the species or genus appeared in non-template controls. A penalty of 5% was used for species²⁸.

tNGS process

For targeted next-generation sequencing (tNGS), this study employed a method focusing on specific genomic regions of interest rather than sequencing the entire genome. Briefly, total nucleic acids were extracted from clinical samples using standard protocols, followed by library preparation using target-specific primers designed to amplify regions of interest. PCR amplification consisted of two rounds: the first round utilized pathogen-specific primers (VMRS0086-50, Vision Medicals), and the second round employed primers from the IDseq Focus library (VMRSO085-20A, Vision Medicals). The amplified libraries were then subjected to high-throughput sequencing using the Illumina platform. Subsequent bioinformatics analysis involved mapping the sequencing reads to reference genomes, variant calling, and consensus sequence generation, enabling accurate identification and characterization of targeted pathogens.

Phylogenetic tree construction

The method for constructing the phylogenetic tree is as follows: 1. Raw fastq files undergo quality control using fastp. Corresponding virus whole-genome nucleotide data are downloaded from NCBI to establish references, which are then aligned to the reference genome using bwa. The sequences aligned to the reference genome are assembled using Unicycler. RagTag is employed to compare the assembled sequences with the reference genome, correcting alignment positions and linking dispersed contigs while discarding samples with insufficient coverage (retaining criteria: at least 60% of bases aligned to the reference genome). Virus nucleotide genome sequences are downloaded from NCBI, and approximately 30 strains are randomly selected and aligned with the assembled sequences of samples using muscle for input into phylogenetic tree construction. Conservative bases are filtered: at least half of the experimental samples (non-NCBI strains) must contain the base. The nucleotide phylogenetic tree is constructed using FastTree, with the output in Newick format, and the evolutionary tree graph is drawn using the R package ggtree.

1. Demographic of the Study Cohort

A total of 5100 patients were enrolled in our study between Jun 2021 and Jun 2022. Their ages ranged from 3 months to 88 years old and the median age was 27-year-old. The patients were divided into five age groups based on age ranges and developmental stages: younger than 5-year-old (infants and preschooler), 6–18 year-old (schoolchild and teenager), 19–44 year-old (young adults), 45–59 year-old (middle-aged adults), older than 60 year (elderly people). The age distribution of the enrolled population was as follows: the largest group was young adults (1783, 35%), followed by school children and teenagers (1329, 26%), children younger than 5 years old (923, 18%), middle-aged adults (560, 11%), and elderly people (505, 10%). Among the rolled patients, 2782 (54.5%) were males and 2318 (45.5%) were female.

2. High Concordance of RNA virus Detection between pooling strategy and individual testing

To assess whether the utilization of sample pooling strategies in mNGS affects the sensitivity and accuracy of virus detection, we employed targeted Next-Generation Sequencing (tNGS) to individually analyze 200 samples from a total of 8 pooled specimens collected between June 10 and 19, 2022. The pathogen spectrum covered by tNGS in this study included all viruses detected by mNGS. We compared the results of tNGS for individual samples with the results of mNGS obtained from pooled samples. As illustrated in Fig. 2A, both methods detected a range of RNA viruses, including H3N2, EV-D68, HPIV-1, HPIV-2, HPIV-3, NL63, HMPV, and RSVA in their respective specimens. Notably, mNGS of pooled samples revealed additional viruses, HRV and CVA6, compared to tNGS analysis of individual samples. Conversely, tNGS identified HMPV and RSVB in specimen 20220613-1, which were not detected by mNGS. Regarding DNA virus detection, both methods successfully identified HSV1, EBV, HCMV, HHV-6B, and HHV-7. mNGS additionally detected HAdV, while tNGS revealed extra instances of HSV-1, HCMV, EBV, HCMV, HHV-6B, HHV-7, HBoV in specimens which illustrated in Fig. 2B. In summary, the mixed-sample strategy employed by mNGS provides a certain degree of insight into the detection of RNA viruses in samples before pooling. However, it may not comprehensively reflect the real presence of DNA viruses in the samples.

As shown in Fig. 2C, the sample pooling strategy employed by mNGS provides a certain degree of insight into the detection of RNA viruses in all samples before pooling, achieving a consistency rate of 80.5%. However, the sensitivity of mNGS pooling for DNA detection is not particularly high, with a concordance rate of only 58.8% compared to the tNGS of individual samples. Moreover, tNGS additionally reported a 32.4% higher positivity rate, indicating that the use of pooled samples for mNGS may not comprehensively reflect the status of DNA viruses in each individual sample.

However, the sensitivity of mNGS pooling for DNA virus detection is comparatively lower, with a concordance rate of 58.8% relative to the targeted next-generation sequencing (tNGS) of individual samples. These data indicate that the use of pooled samples for mNGS may not comprehensively reflect the status of DNA viruses in each individual sample. Therefore, in the following analyses, we primarily focused on the detection results of RNA viruses.

3. High Consistency of mNGS Surveillance Data with CDC Influenza Monitoring Data

During the period of June to July 2022, as depicted in Fig. 3, a significant abundance of H3N2 virus reads was observed in throat swab specimens obtained from febrile patients. This observation precisely correlates with the influenza outbreak in southern China during that timeframe. To evaluate the effectiveness of this surveillance strategy, we compared our results with monitoring data from Guangzhou Center for Disease Control and Prevention (CDC). The prevalence of H3N2 virus detected in oropharyngeal swabs collected from patients in the fever clinic from June to September 2022 showed a coherent trend with the proportion of influenza-like illness cases reported by the Guangzhou CDC during the same period (Fig. 4A). Moreover, a positive correlation between virus abundance and the influenza-like illness cases two indicated the potential monitoring efficacy of virus abundance in our dataset. (Fig. 4B).

4. Enhanced Viral Subtype Identification through mNGS Surveillance

After performing mNGS on the pooled samples collected from fever patients throughout a one-year period from June 2022 to June 2023, a comprehensive virome curation was meticulously undertaken. The process ensured the exclusion of any artifacts or contaminants, leading to the acquisition of distribution heat maps for both RNA viruses (Fig. 3) and DNA viruses (Fig. 5).

In early June, we detected the prevalence of H3N2, EV-D68 and HMPV from pooling samples. In July and August 2022, various enteric viruses such as CVA5, CVA6 were detected in mixed samples. In September and October of the same year, respiratory specimens from febrile patients yielded multiple pathogens including NL63, RSV, and HPIV.

As shown in Fig. 5, our analysis covered a broad spectrum of herpesviruses, with EBV, HHV-7, HSV-1 and HCMV being the most common herpesviruses among these patients. Additionally, we observed an adenovirus outbreak from June to November 2022. In addition, we also detected other respiratory pathogenic viruses, such as Human Bocavirus 1 (HBoV1), as well as common commensal viruses in upper respiratory specimens, torque teno virus (TTV).

To comprehensively understand the genetic characteristics of respiratory infections in fever patients with ARIs, phylogenetic tree construction and analysis were conducted for 4 H3N2 strains, 4 NL63 strains, 5 SARS-CoV-2 strains, 4 RSV strains, and 5 HMPV strain detected in this study, each compared to their respective reference sequences. Results from Figs. 6A and B revealed that the HA and NA genes of the 4 influenza virus strains detected in June 2022 belonged to the H3 and N2 subtypes, respectively. Evolutionary tree analysis based on the S gene of coronavirus NL63 showed that the strain detected on August 27, 2022, belonged to the C3 subtype, while the 3 strains detected in September belonged to the B subtype, indicating subtype variations in NL63 transmission during this period²⁹ (Fig. 6C). The subtyping of SARS-CoV-2 is of paramount importance for vaccination and disease control. This study identified that the 2 strains detected in December 2022 belonged to the BA5.2 subtype, while those detected in May and June 2023 from fever clinic cases were of the XBB.1 subtype, both consistent with the prevalent subtypes of SARS-CoV-2 circulating in Guangzhou at the time (Fig. 6D). Additionally, genetic typing analysis was conducted for detected RSV and HMPV strains^30,31. Evolutionary tree results showed that the RSV-A strains detected on June 19 and October 5, 2022, belonged to the ON1 subtype, while one strain could not be typed due to insufficient sequencing depth. Furthermore, the RSV-B strain detected on October 5, 2022, belonged to the BA9 subtype. HMPV subtype analysis indicated that the strain detected on June 16, 2022, belonged to the B2 subtype, while subtype classification was not possible for other 4 strains due to insufficient sequencing depth.

5. Disruption of respiratory virus circulation during the COVID-19 pandemic

In the time window of this study, beginning in late November 2022, Guangzhou lifted temporary control measures and subsequently experienced a COVID-19 pandemic. In the meantime, SARS-CoV-2 were detected in pharyngeal swab specimens collected from patients presenting to our fever clinic (which had not been detected previously). Notably, the virus activity of influenza and other respiratory viruses showed a notable reduction (Fig. 3C). From December 2022 to March 2023, except for a few mixed samples where low levels of influenza virus, rhinovirus, or parainfluenza virus were detectable, other non-SARS-CoV-2 respiratory viruses remained undetected (Fig. 3). It was not until April 2023 that this situation began to slightly change. By June 2023, although there was an increase in the detection of respiratory virus types from mixed samples compared to the previous quarter, the variety of detectable respiratory viruses remained significantly lower than in June 2022 (Fig. 3). Furthermore, subsequent to the COVID-19 pandemic, adenovirus was also not detectable in respiratory samples obtained from febrile patients (Fig. 5). Overall, the transmission of non-SARS-CoV-2 respiratory viruses was significantly disrupted during the COVID-19 pandemic, with the timing and duration of this effect varying among different viral pathogens.

Fever and respiratory tract infections are among the leading causes of acute infectious diseases and mortality worldwide, with viral infections being the primary cause^1,2. In recent years, a number of viral agents associated with fever and respiratory tract infections have emerged, including H5N1³²,SARS-CoV³³, MERS³⁴, H7N9³⁵ and SARS-Cov-2⁵, imposing significant economic burdens and undermining public health globally. Fever clinics play a crucial role in triaging patients during the prevention, control, and treatment of emerging infectious diseases¹³. However, the detection and surveillance of respiratory pathogens in clinical laboratories still rely on traditional PCR for individual specimen testing³⁶. In this study, we conducted mNGS detections on oropharyngeal swab specimens from fever clinic patients, employing sample pooling strategies to delineate the spectrum of pathogen distribution.

To assess the efficiency of pooled samples on virus detection rates, we employed tNGS to individually test 200 samples and compared the results with those obtained through mNGS. tNGS is a sequencing technology that employs target enrichment to selectively capture regions of interest (ROIs), enabling the successful identification of target pathogens^37,38. Through target enrichment, tNGS enhances the proportion of microbial nucleic acids without the need to remove host material³⁸. Particularly suitable for assessing viral distribution in samples, tNGS could specifically amplifies the load and coverage of fastidious and low-abundance pathogens, thereby increasing the credibility of detection³⁹. Despite the mixing of 25 samples into one, the pooling results of mNGS exhibited a high concordance rate of around 80% with tNGS for RNA viruses, while the concordance rate for DNA viruses was less than 60%. Given that tNGS can detect low-abundance pathogens, its methodological advantage is highlighted in DNA detection from individual samples compared to mNGS, which involves DNA detection in pooled specimens with an extremely high host rate. This distinction may introduce a degree of bias in detection efficiency of DNA virus.

When examining the practical application of pooled sample testing in a clinical setting, it is crucial to consider the potential compromise in sensitivity that may occur due to dilution, particularly impacting samples with low viral loads¹⁹. Nevertheless, for respiratory virus surveillance, characterized by high viral loads in nasopharynx, this apprehension may be mitigated. Nonetheless, it is important to recognize that this approach may not be universally suitable for infections with potential low viral loads, prompting exploration of supplementary methodologies. In scenarios marked by potentially low viral loads, alternative strategies warrant consideration, as the current approach may not be optimally suited⁴⁰. Although mixed-sample testing has significantly reduced the costs associated with the mNGS experimental process, it is still necessary to evaluate, from the perspectives of monitoring sustainability and effectiveness, how many samples can be pooled to achieve an optimal balance between improved accuracy and reduced detection costs.

Our study utilized the mNGS mixed detection method to monitor the prevalence of pathogens in cases of acute respiratory infections treated in the fever clinic from June 2022 to June 2023. We detected the circulation of different viruses in fever patients during this period. Influenza virus was found to be the most important pathogen causing fever and respiratory infections⁴¹. In June to early July 2022, we observed a high level of H3N2 presence in the pharyngeal swab specimens of patients, indicating its circulation and transmission within the population. Our data suggest a positive relation between the data from the Center for Disease Control revealed that the outbreak in Guangzhou was consistent with our monitoring results. This data suggests that our research strategy may be suitable as a viral monitoring scheme for both CDC and sentinel hospitals.

Respiratory viruses are a group of infectious agents transmitted through the respiratory tract, primarily causing illnesses such as the common cold, influenza, bronchitis, and pneumonia⁴². Common respiratory viruses include rhinoviruses, influenza viruses, coronaviruses, adenoviruses, among others^42,43. Most clinical symptoms induced by respiratory viruses are asymptomatic or mild and have not received much attention in epidemic prevention and control in many countries. Consequently, there is a scarcity of molecular epidemiological data for respiratory viruses in these regions. It is imperative to conduct continuous monitoring of the epidemic trends and phylogenetic aspects of these viruses, as this holds significant importance for disease prevention and control strategies⁴⁴.This study identified pathogen sequences in acute febrile respiratory specimens, detecting sequences of H3N2 influenza virus, SARS-CoV-2, NL63, RSV and HMPV. Both RSV and HMPV are the most common cause of acute respiratory infections for infants and young children. Given the lack of effective therapeutic drugs and vaccines in China, continuous monitoring of RSV and HMPV is crucial for its prevention and control. RSV is primarily categorized into subtypes A and B, with alternating predominance in recent years in the southern region of China. To investigate the sequence typing of RSV in this study, evolutionary trees were constructed for four RSV sequences, revealing that two RSV-A viruses belonged to the ON1 subtype, while one RSV-B virus belonged to the BA9 subtype. These results suggest that the surveillance method proposed in this study, compared to traditional PCR, can simultaneously provide molecular typing results of viruses. This offers greater assistance for subsequent epidemic monitoring and vaccine deployment.

To control the pandemic of COVID-19, China has taken a series of powerful non-pharmaceutical interventions (NPIs) against COVID-19 in accordance with state and community instructions, such as face mask mandates in public spaces, stay-at-home and lockdown, health screening at points of entry/exit, zero COVID-19 policy. These multifaceted NPIs were highly effective in limiting the spread of the SARS-CoV-2 among the population. However, it is unclear how the non-SARS-CoV-2 respiratory viral etiology and epidemiology changed among fever clinic patients during and after the COVID-19 pandemic. Based on global surveillance data for respiratory viruses such as influenza and RSV, during the COVID-19 pandemic, there has been a significant decrease in the prevalence levels of these non-SARS-CoV-2 respiratory viruses^45,46. In our research findings, we observed a significant reduction in the diversity of respiratory viruses among patients attending the fever clinic during the COVID-19 pandemic in Guangzhou. Given that NPIs were designed to mitigate respiratory virus transmission, there have been notable disruptions to the typical seasonal circulation patterns of common respiratory virus infections, including by influenza virus and RSV. The decrease in community prevalence rates of non-SARS-CoV-2 respiratory viruses is undoubtedly influenced by multiple factors, including the collective implementation and sustained use of NPIs, reduced travel, changes in testing priorities and surveillance systems, among others^47,48. These changes in the prevalence of other respiratory viruses cannot be fully explained, as in several tests from Dec 2022 to Mar 2023, the SDSMRN of SARS-CoV-2 were not notably high and, in some instances, undetectable. Indeed, studies have indicated that during the COVID-19 pandemic, only 2%-3% of COVID-19-positive patients experience co-infections with other respiratory viruses, whereas during the same period, 13.1% of patients infected with non-SARS-CoV-2 respiratory viruses exhibit co-infections⁴⁹. These characterize this phenomenon, suggesting that coinfection with other respiratory viruses appears to be uncommon in patients with SARS-CoV‐2 infection. In fact, interactions between different viruses, such as virus interference, are also recognized as a factor that can affect the spread of respiratory viruses in the community⁵⁰.However, the underlying reasons for this are currently unclear, with one potential explanation being that the immune response induced by SARS-CoV-2 and its interaction with the host may confer a competitive advantage over other respiratory viruses⁴⁹. While the observed diversity changes in respiratory viruses using mNGS in mixed samples may differ from the concept of viral co-infection, our research results characterize significant differences in the respiratory virus composition among fever patients before and after the COVID-19 pandemic. This provides a novel understanding for discerning whether respiratory virus co-transmission entails antagonism or promotion.

This study has several limitations. Firstly, due to methodological constraints, we were unable to differentiate pathogens in each specimen, thus impeding our ability to discern the infection rates of various pathogens within this cohort of patients. Secondly, amid pandemic control measures, patients of all age groups sought treatment at the fever clinic, yet our experimental design lacked categorization of the included patients. This limitation hindered our understanding of the viral pathogen spectrum across different age groups, thereby compromising the effectiveness of pathogen surveillance. Subsequent research initiatives should refine grouping strategies to unveil respiratory virus profiles in febrile patients with distinct characteristics and features. This enhancement will contribute to more precise epidemic monitoring and control efforts. Moreover, as the pathogen detection approach utilized in this study involves mixed-sample sequencing using mNGS technology, it presents challenges in distinguishing the specific viral strain sequences of individual cases within mixed samples. Furthermore, issues such as sequencing depth and accuracy of species identification may potentially help to improve the accuracy of subtyping for certain viruses.

There are several aspects of the surveillance method utilized in this study that warrant further expansion in the future. It is widely recognized that mNGS not only facilitates the detection of viral pathogens but also enables the identification of bacterial and fungal pathogens, including their resistance and virulence genes^38,51. Our study focused primarily on the preliminary analysis of viral pathogens and demonstrated the efficacy of this surveillance method in pathogen monitoring. We believe that, with further optimization, this mNGS-based surveillance strategy could significantly enhance the monitoring of bacteria or fungi, as well as drug-resistant strains, in future applications. Another noteworthy consideration is the distinct seasonal preferences exhibited by these respiratory viruses ^52,53. For instance, in the southern region of China, influenza virus primarily peaks during the summer and winter seasons, while RSV predominantly circulates during the winter and spring⁵². HMPV, on the other hand, experiences heightened prevalence in the spring months of March and April. Furthermore, HPIV, and ADV were prevalent throughout the year⁵². Due to the limited timeframe encompassed by the study, the seasonal characteristics of these respiratory viruses were not adequately reflected. We aim to continue monitoring these seasonal changes in viruses through extended surveillance efforts. This will enable us to observe how the seasonal patterns of different respiratory viruses have been disrupted following the COVID-19 pandemic and identify the points at which these seasonal variations may eventually return to normalcy.

In conclusion, heightened surveillance of viruses is indispensable for predicting significant epidemic threats and comprehending the spatiotemporal dynamics of these pathogens. The monitoring strategy outlined in this study stands poised to enrich our understanding of epidemiology, outbreak patterns, and viral subtyping of respiratory viruses, thereby enhancing the capacity for timely detection and response to respiratory virus outbreaks, significantly improving viral surveillance.

Ethics approval and consent to participate

All experimental protocols in this study involving human subjects were performed in accordance with the ethical standards of the First Affiliated Hospital of Sun Yat-sen University and national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. The study affirms the preservation of anonymity and confidentiality for both the participants and their associated data.

Consent for publication

Given the retrospective nature of the study, the requirement for informed consent was waived.

Availability of data and materials

The data that support the findings of this study are available on request from the corresponding author upon reasonable request.

Funding

This work was funded by grants from the National Natural Science Foundation of China 82101775, the Major Program of Guangzhou National Laboratory GZNL2024A01002, the Key-Area Research and Development Program of Guangdong Province 2022B1111020006, Guangdong Provincial Key Laboratory of Pathogen Detection for Emerging Infectious Disease Response 2023B1212010010. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

Conceptualisation: Peisong Chen, Tao Ding, Xiangjun Du, Yan Xiong; data curation: Shu An, Pengqiang Zhong, Longting Du, Juhua Yang, Huifang Liu; investigation: qiangqiang Liu, Yan Xiong; funding acquisition: Shu An, Xiangjun Du, Baisheng Li; methodology: Peisong Chen, Tao Ding, Xiangjun Du, Shu An, Juhua Yang; project administration:Peisong Chen, Shu An; resources: Peisong Chen, Yan Xiong, Baisheng Li; validation: Pengqiang Zhong, Longting Du; writing – original draft:Shu An, Pengqiang Zhong; writing– review & editing:Peisong Chen, Tao Ding, Xiangjun Du.

Acknowledgements

not applicable.

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No competing interests reported.

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Employing Metagenomic Next-Generation Sequencing for Enhanced Surveillance of Respiratory Viruses in Fever Clinics: A Comparative Analysis in the Context of the COVID-19 Pandemic in a General Hospital

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Abstract

Figures

Introduction

Methods

Specimen collection and pooling samples

Nuclear acid extraction, library preparation, and sequencing

Bioinformatics Analyses

tNGS process

Phylogenetic tree construction

Results

1. Demographic of the Study Cohort

2. High Concordance of RNA virus Detection between pooling strategy and individual testing

3. High Consistency of mNGS Surveillance Data with CDC Influenza Monitoring Data

4. Enhanced Viral Subtype Identification through mNGS Surveillance

5. Disruption of respiratory virus circulation during the COVID-19 pandemic

Discussion

Declarations

References

Additional Declarations

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