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. 2026 Feb 1;41(1):48–57. doi: 10.1016/j.virs.2026.01.006

Genomic evidence of HMPV resurgence in Beijing: Clade B2 triggers the 2024 winter epidemic peak

Lu Kang a,b,c,1, Fang Huang a,b,c,1, Yi-Mo Deng d,e,1, Geng Hu a,b,c, Yiting Wang a,b,c, Aihua Li a,b,c, Hui Xie a,b,c, Xiaofeng Wei a,b, Yuling Han a,b, Ming Luo a,b,c, Ian G Barr d,f, George F Gao g,⁎, Liang Wang g,⁎, Quanyi Wang a,b,c,⁎
PMCID: PMC13007308  PMID: 41633435

Abstract

With an unexpected increase of human metapneumovirus (hMPV) cases in northern China since late 2024, concerns arose whether novel hMPV variants triggered this epidemic. Utilizing the Beijing Respiratory Pathogen Surveillance System (RPSS), we conducted a genomic evolutionary analysis spanning 2014–2024 and revealed genetic information for the strains that caused the high rates of hMPV outbreaks during this period. To clarify the epidemic drivers and evolutionary characteristics of the hMPV strains circulating in Beijing, phylogenetic, population dynamic and mutation analyses were performed using high-quality complete sequences from both this study and publicly available data. A total of 348 high-quality hMPV genomes were obtained by next-generation sequencing (NGS), all of which belonged to four known clades: A2b1, A2b2, B1, and B2. Before 2024, A2b2 predominated in Beijing; however, a shift to clade B2 was observed starting in late 2024. In addition, a phylogenetically independent lineage Ⅰ was identified in this study, accounting for 93.1% of B2 genomes collected since late 2024. Furthermore, we identified several unique nonsynonymous mutations in viruses within lineage I that may have phenotypic implications. Our findings indicate that lineage I of clade B2 was the major cause of the unusual increase in hMPV outbreaks in Beijing in late 2024, with no evidence of an emerging novel variant. Although our data were only restricted to samples from Beijing, the findings are likely representative of the hMPV surge across northern China in 2024, given city's high population density and mobility.

Keywords: Human metapneumovirus (hMPV), Genomics, Acute respiratory tract infections (ARTIs), Next-generation sequencing (NGS)

Highlights

  • •

    An unexpected increase in the incidence of hMPV has been observed in northern China since late 2024.

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    Clade A2b2 predominated in Beijing before 2024, with a shift to clade B2 since late 2024.

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    Lineage Ⅰ of clade B2 contributes to the increase in hMPV incidence in Beijing since late 2024.

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    No novel hMPV variants were found in Beijing.

Introduction

Human metapneumovirus (hMPV), a member of the Pneumoviridae family, commonly causes mild respiratory tract infections in children (Van Den Hoogen et al., 2001). However, hMPV infections can also lead to severe disease and even cause death in critically ill patients (Hashemi et al., 2021). hMPV circulation is not routinely monitored in many countries and regions (WHO, 2025). Nationwide hMPV surveillance has been implemented across all provinces in China since 2024. Currently, there are no antiviral agents or effective vaccines available for hMPV. Surveillance data from the Chinese Center for Disease Control and Prevention (https://www.chinacdc.cn) revealed a gradual increase in influenza-like illness (ILI) cases beginning in November 2024. By December 2024, hMPV ranked among the three most prevalent respiratory pathogens detected in ILI outpatients in both southern and northern China. Notably, data from Beijing specifically showed a significant increase in hMPV cases in December 2024, reaching an unprecedented peak compared to the past decade (Li et al., 2025). This unexpected increase in hMPV incidence subsequently attracted worldwide attention (Murphy, 2025), raising concerns that it might be driven by the emergence and spread of a novel variant. This highlights not only the importance of long-term genomic surveillance of hMPV, but also the urgent need for enhanced global attention, response and further collaboration (Gao et al., 2023; Lobato et al., 2024; Liu et al., 2025,b).

Studying the genetics and evolution of hMPV is crucial for its improved management and control. hMPV is a negative-sense, single-stranded RNA virus that contains eight major genes (N, P, M, F, M2, SH, G and L) encoding nine proteins (Van Den Hoogen et al., 2004). The fusion (F) and attachment (G) proteins are two primary hMPV surface glycoproteins that play crucial roles in viral replication and the host immune response (Van Den Hoogen et al., 2004). Phylogenetic analysis of the whole-genome sequences has revealed that hMPV can be categorized into two major genetic groups (A and B), which can then be further subdivided into lineages A1, A2a, A2b1, A2b2, B1, and B2 (Nao et al., 2020). These features provide the foundation for elucidating hMPV genome structure, function and genetic variation patterns. Long-term and sustained genomic surveillance is therefore essential (Liu et al., 2025,b). From a public health perspective, genomic surveillance and research enable the identification of emerging lineages and variants, the tracking of transmission chains, and the tracing of outbreak origins, thereby providing early warning for epidemic prevention and control (Rambaut et al., 2008). Structural characterization of key conserved genomic regions can also facilitate the development of targeted antiviral drugs and vaccines (Rappazzo et al., 2022). However, genomic research on hMPV has been limited compared to other respiratory viruses such as SARS-CoV-2, influenza, and respiratory syncytial virus (RSV), which severely hampers our understanding of its genomic diversity and the development of effective prevention and control strategies.

The Beijing Respiratory Pathogen Surveillance System (RPSS) is a year-round regional surveillance program that has been monitoring the prevalence of respiratory pathogens associated with acute respiratory tract infections (ARTIs), including hMPV, across 35 sentinel hospitals since its establishment in 2014. RPSS has been proven to be both efficient and effective in tracking epidemiological dynamics at the genomic level during multiple epidemic waves caused by various types of respiratory pathogens (Li et al., 2023; Wei et al., 2024). Here, we used data from RPSS to describe the dynamics of hMPV genomic epidemiology between 2014 and 2024 in Beijing. As one of the largest cities in China, the genomic epidemiological trends of hMPV in Beijing provide a crucial reference for understanding its spread to other regions of China as there are approximately two million people arriving and departing every day from Beijing to greater China.

Results

Enrolled patients and epidemiological analyses

A total of 79,793 ARTI cases were enrolled between September 2014 and December 2024. To explore the genomic epidemiology of hMPV, we performed whole-genome sequencing (WGS) on a total of 1245 hMPV-positive samples. WGS data were obtained from 348 hMPV positive samples (27.95%). The remaining 897 hMPV-positive samples were not sequenced, mainly because of low viral loads, insufficient sample volume, or missed samples during the surveillance.

To assess whether the 348 sequenced samples are representative of the entire set of 1245 positive cases, we compared key epidemiological characteristics between the two groups. We found that there was a significant difference in age distribution (Chi-square test, P = 0.004, Supplementary Fig. S1A). Specifically, the proportion of children under 5 years was higher in the sequenced subset (42.9% vs. 32.7%), while adults aged 60 or older were underrepresented (19.7% vs. 25.4%). This discrepancy may result from technical requirements of high viral loads for WGS. Ct values were significantly lower in children under 5 compared to those aged 60 or older (Wilcoxon test, P = 0.013, Supplementary Fig. S1B), indicating higher viral loads in younger children. In contrast, there was no significant difference (Chi-square test, P = 0.39, Supplementary Fig. S1C) in disease type distribution (e.g., upper respiratory tract infection (URTI), severe community-acquired pneumonia (sCAP), nonsevere CAP (nsCAP), and others).

The sequenced samples exhibited comprehensive temporal coverage, encompassing all epidemiologically active months from September 2014 to December 2024. The hMPV epidemics showed distinct seasonality, primarily from December to April the next year, with an overall sequencing detection rate of 0.44%. The sequencing detection rate of the pre-pandemic phase, the pandemic phase, the post-pandemic I phase and post-pandemic II phase was 0.50% (202/40, 407), 0.03% (5/15, 216), 0.43% (81/18, 860) and 1.13% (60/5310), respectively (Fig. 1). A significant increase in hMPV cases was identified during the post-pandemic II phase (Chi-square test, P < 0.0001), with the detection rate rising to 0.96% (13/1355) in November 2024 and peaking at 3.08% (39/1267) in December 2024, marking the highest levels observed in the last decade (Fig. 1, Supplementary Table S1).

Fig. 1.

Fig. 1

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Monthly distributions of hMPV-positive cases and sequenced cases (bars), and sequencing detection rate (line) in Beijing from September 2014 to December 2024. The monthly sampling size remained relatively consistent throughout this period except during the pandemic period. The bars represent the number of hMPV-positive cases (light blue bars) and hMPV-sequenced cases (dark blue bars) included in this study. All bars start counting from zero on the y-axis. The top panels of the figure represent the time periods of hMPV surveillance from September 2014 to December 2024.

Among the 348 samples, the male-to-female ratio was 1.08. The age of the patients ranged from 4 months to 96 years old, with a median age of 5 years (interquartile range (IQR), 3–51). The proportions of URTI, nsCAP, sCAP, and others (such as bronchitis and tonsillitis) were 50.0%, 40.2%, 5.5% and 4.3%, respectively. Among these patients, 33.9% required hospitalization, with 6 (5.1%, 6/118) patients admitted to the ICU (Supplementary Table S2). In terms of clinical symptoms, 93.2% (278/298) presented with cough, and 86.0% (246/286) of the patients presented with fever. Additionally, among the 243 cases with available outcome data, one fatal case occurred.

Temporal dynamics and clinical characteristics of hMPV clades

All hMPV genomes obtained from this study belonged to four phylogenetic clades: A2b2 (n = 176, 50.6%), B2 (n = 102, 29.3%), B1 (n = 48, 13.8%), and A2b1 (n = 22, 6.3%) (Fig. 2A and B). No significant evidence for recombination events (supported by at least three methods) was found in the dataset. Clade A2b2 was predominant in Beijing for several years, with exceptions where B1 was dominant in 2016 and 2017, and B2 predominated in 2020, 2021 and 2024. Furthermore, the epidemic trends of hMPV in Beijing showed a potential “AABB” switching pattern (Fig. 2B). Global clade distribution during the same period were: A2b2 (n = 405, 47.3%), B2 (n = 245, 28.6%), B1 (n = 172, 20.1%), A2b1 (n = 30, 3.5%), and A2a (n = 4, 0.5%) (Fig. 2C). Globally, A2b2 prevailed in most years, except 2014 and 2022 when B1 predominated. Global hMPV exhibited higher genomic diversity, with 3–4 clades coexisting each year compared to Beijing. The only clade not detected in Beijing (2014–2024) in this study was A2a, which was only detected in 2016 (n = 2) in the USA and the Netherlands, and in 2017 (n = 2) in Norway. Compared with the global pattern, the clade dynamics in Beijing exhibited phase-specific characteristics: specifically, clade A2b2 predominated in Beijing before the post-pandemic II phase, except for the pandemic phase (Fig. 2B). However, clade B2 (58/60, 96.7%) replaced A2b2 (1/60, 1.7%) as the dominant clade in Beijing since the post-pandemic II phase (Fig. 2B).

Fig. 2.

Fig. 2

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Phylogeny and clade composition dynamics of hMPV genomes detected in Beijing between 2014 and 2024. A Phylogenetic tree of high-quality hMPV genomes obtained in this study. The phylogenetic tree reconstructed by auto-pipeline in Nextclade and then was visualized via the Auspice online tool. The tips with circles and bold branch indicate the genomic sequences generated in this study (accession numbers are provided in Supplementary Table S2). The others are reference sequences (accession numbers are provided in Supplementary Table S4). The color of the circle corresponds to different clades. B The composition of hMPV clades circulating in Beijing during different phases. C The composition of hMPV clades circulating in Beijing and global from 2014 to 2024. Above the x-axis is the clade distribution of Beijing hMPV sequences generated in this study, while below the x-axis is that of high-quality global hMPV sequences.

The proportion of A2b2 clade among patients with URTI, nsCAP, sCAP and others were 33.5%, 54.8%, 7.8%, and 3.9%, respectively. For the B2 clade, the distribution across these categories was as follows: 44.6% in URTI, 46.5% in nsCAP, 2.0% in sCAP, and 6.9% in others (Supplementary Fig. S2A). Notably, the prevalent hMPV clade B2 in winter 2024 was associated with a CAP proportion of 31.0% (18/58), which was significantly lower than that of A2b2 (62.6%, 112/179), B1 (61.7%, 29/47), and previous B2 (72.1%, 31/43) (Supplementary Fig. S2A, P < 0.01, Fisher's Exact Test), but higher than that of clade A2b1 (14.3%, 3/21), with no sCAP cases observed. The hospitalization rate for the prevalent hMPV clade B2 in winter 2024 was 12.1% (7/58), also significantly lower than that of A2b2 (40.8%, 73/179), B1 (38.3%, 18/47), and previous B2 (39.5%, 17/43) (P < 0.01, Fisher's Exact Test), while showing no significant difference from clade A2b1 (14.3%, 3/21, Supplementary Fig. S2B, P = 0.07, Fisher's Exact Test).

Phylodynamic and phylogenetic analysis

In order to explore the reasons for the high incidence of hMPV at the end of 2024, we focused on the clade B2 hMPV in Beijing. Linear regression between the root-to-tip divergence and sampling dates for the genomic data supported the inference of the population dynamics over time, as a sufficient temporal signal was detected within the dataset (Fig. 3A). Since the root-to-tip regression assumed a strict molecular clock and a high correlation was found, we used a strict molecular clock for phylogenomic analysis. Phylogenomic analysis indicated that the effective population size (Ne) for clade B2 experienced dynamic changes in Beijing since 2015 (Fig. 3B). Before mid-November 2022, when China optimized the prevention and control strategies for dynamic zero-COVID policy, it did not change substantially, but a marked decline was subsequently observed. Furthermore, a significant increase in the effective population size during the initial phase of both post-pandemic I and II was also observed for clade B2, which was in line with both the increase in incidence during the corresponding phase and the seasonality of hMPV.

Fig. 3.

Fig. 3

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Temporal population dynamics of clade B2 in Beijing between 2014 and 2024. A The regression of root-to-tip divergence and sampling date for B2 genomic sequences used in this study. A linear model was used to fit the data. Red line represents the regression line. P value was determined by two-sided Pearson correlation test. B The dynamics of the effective population size (Ne) during the same period for B2 in Beijing. The blue dashed lines indicate the phase. The black dashed line indicates the time at which China optimized the dynamic zero-COVID policy (December 3, 2022). The red line and area indicate the mean and the 95% confidence interval of Ne, respectively.

When combined with all publicly available hMPV clade B2 genomes, clade B2 genomes from post-pandemic II in Beijing were mainly from lineage I, a phylogenetically independent lineage, while others collected from Beijing were distributed across the phylogenetic tree without obvious concentration, indicating the potential import with limited onward transmission (Fig. 4A). It was found that 93.1% (54 out of 58) of all clade B2 genomes collected during post-pandemic II belonged to lineage I, whereas its frequency was only 22.73% (10 out of 44) for all clade B2 genomes collected during other phases (Fig. 4B). The significant difference in the frequency of lineage I between the post-pandemic II phase and other phases (Chi-square test, P < 0.0001) suggested that the increase in hMPV during the post-pandemic II phase in Beijing could be mainly attributed to this lineage.

Fig. 4.

Fig. 4

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Identification of phylogenetically independent lineage I within clade B2. A The phylogeny of clade B2 was constructed using all B2 genomes from both this study and from NCBI (accession numbers are provided in Supplementary Table S6). The outgroup was discarded from visualization. Only SH-aLRT ≥80% and UFboot ≥95% were visualized as purple circles in the middle of the branch. The genomic sequences obtained from this study are marked with a red circle at the end of the branch. B The composition of lineage I and other lineages within B2 in different phases. The number of genomes used in each phase was also shown in the figure.

Amino acid variation analysis

So far, lineage I has been detected in three countries, USA (first detected in 2015), Australia (first detected in 2016), and China (first detected in 2017) (Fig. 5A). Before 2024, only small number of the lineage I genomes was detected in China, and most lineage I genomes came from USA, peaking at 2020. Lineage I continued to be detected in both USA and China, however, it was not detected in Australia since 2021. We also identified eight nonsynonymous mutations only associated with lineage I from four genes (n = 2, 3, 2 and 1 in F, SH, G, and L, respectively) (Fig. 5B). In particularly, L19I in protein encoded by SH and P70Q in protein encoded by G gene were found in all the genomes from lineage I (n = 105); however, they were rarely detected in other clade B2 hMPV (4 and 1 out of 283, respectively). Notably, P70Q in G was not detected in any hMPV clade A genome, P1604T in L gene was also dominant in lineage I (96.19%, n = 101), but not in any other genomes from either clades A or B. Furthermore, we performed a positive selection analysis on lineage I using FUBAR, a method particularly powerful for identifying selection signals. The results identified 20 positively selected sites (Supplementary Table S3), with the following distribution: one in the F protein, four in the SH protein, and fifteen in the G protein. Taken together, lineage I has its characteristic genetic feature. So far, the functional implication of most characteristic genetic feature is unknown, but it is worth further study.

Fig. 5.

Fig. 5

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The characteristics of lineage I of clade B2. A Number of genomes belonging to lineage I of clade B2 over time. B The characteristic nonsynonymous mutation of lineage I. The Y-axis represents the proportion of nonsynonymous mutations within lineage I and the x-axis represents the hMPV genome. Different genes are represented by different colors, and the color of the mutation corresponds to the color of the gene in which it is located.

Discussion

Emerging and re-emerging infectious diseases have posed enormous threats to public health throughout human history (Gao, G. F., 2018; Wang and Gao, 2025; Shen et al., 2025; Xiao and Nishijima, 2024). Moreover, infectious diseases could spread more rapidly, particularly with the intensified globalization driven by frequent travel and trade worldwide. Since the COVID-19 pandemic, there have been successive abnormal outbreaks of respiratory viruses (Shen et al., 2024). In Western Australia, the incidence of hMPV infection in 2021 was 4-fold that from 2017 to 2019 (Foley et al., 2022), while in the United States, hMPV cases surged to an all-time high in spring 2023 (https://www.cdc.gov/surveillance/nrevss/hmpv/region.html). Similarly, in China, hMPV triggered a large wave in late 2024, raising global concern. Long-term genomic surveillance of diverse pathogens, together with prompt data sharing, is essential for improving responses to epidemics and pandemics (Halabi et al., 2023; Wang and Gao, 2024). Here we reported the phylodynamics of hMPV between 2014 and 2024 via NGS of 348 hMPV WGS obtained from samples collected by the Beijing RPSS.

We found that the hMPV had epidemic peaks from December to April during the pre- and post-pandemic periods, with dominant genotypes following a potential “AABB” switching pattern. This pattern observed in Beijing likely reflects periodic shifts in local hMPV clade dominance, whereas global data indicate higher genomic diversity and multi-clade co-circulation. Although this pattern partially aligns with global epidemic trends, it also exhibits distinct regional specificity. This suggests that local transmission dynamics may be influenced by a combination of population immune pressure, climatic factors, and viral introduction events. Future studies should integrate phylogeographic analysis to explore the underlying drivers of these transmission dynamics. Our previous study integrated epidemiological data with Sanger sequencing of the F and G genes of 1245 samples (64.8% success rate) (Li et al., 2025). This prior study revealed that 18.6% (231/1245) of patients had co-infections, most commonly with seasonal influenza A virus (22.1%), RSV (19.5%), and human rhinovirus (18.2%), and yielded the genotype distribution consistent with the WGS data generated in this study (Chi-square test, P = 0.21), thus verifying the reliability of the genomic data (Li et al., 2025). Building on expanded genomic evidence, we elucidated drivers of these incidence dynamics. In the current study, we have far more WGS data which helped us to study viral diversity and evolution patterns in detail.

Specifically, Clade A2b2 predominated in Beijing before post-pandemic phase II, which aligns with multicenter findings from China during 2017–2019 (Zhao et al., 2022). However, a shift to clade B2 was observed thereafter, coinciding with a marked increase in hMPV incidence. This pattern was further supported by the exponential rise in the effective population size, a feature typically occurring during the early phase of outbreaks of B2 since the initial stage of post-pandemic phase II. Despite first being detected in Beijing in 2014, clade B2 had a low prevalence, with minimal changes in its effective population size before late 2022, when China adopted a dynamic zero-COVID policy. After that, the effective population size declined significantly. This phenomenon was in line with the increase in COVID-19 in Beijing during the corresponding phase (Pan et al., 2023), which suppressed the transmission of all other respiratory pathogens and subsequently reduced their genomic diversity. During the pandemic II phase, while no novel hMPV variant was found in Beijing, the increase in hMPV incidence appeared to be driven by a phylogenetically independent lineage within clade B2, termed lineage I. Most hMPV genomes were collected from the post-pandemic phase II. Given the historically low prevalence of clade B in Beijing, the accumulation of susceptible populations due to the low incidence during the pandemic phase and the post-pandemic phase I, as well as the varying degree of cross-neutralization efficiency between clades based on serology (Matsuzaki et al., 2008), the unexpected increase of hMPV incidence in Beijing during the post-pandemic phase II could be attributed to the expected superimposition of all the above factors.

Further analysis of disease burden and severity revealed that the 2024 winter-dominant B2 clade was associated with significantly lower CAP and hospitalization rates than the A2b2, B1, and previously circulating B2 clades. These results indicate that infections caused by the 2024 B2 clade were relatively milder than those from prior clades. Notable, one fatal case was recorded in our study: a 72-year-old male admitted with nsCAP in December 2019, with severe comorbidities and co-infection with non-lineage I hMPV B2 clade, RSV, and Streptococcus pneumoniae. His condition worsened rapidly, resulting in ICU transfer, heart failure and other complications, and death 12 days later. The death may have resulted from the combined effects of his severe comorbidities and co-infection.

Lineage I, as defined in this study, is characterized by several nonsynonymous mutations. Notably, some of these are derived from two genes (F and G), which both play critical roles in virus entry into the host cell and are therefore considered important targets for the development of vaccines and antiviral drugs, similar to the targeting of the pre-F protein of RSV. Of particular significance, L36 in the F protein is one of the conserved and critical residues for antigen recognition by antibody (Wen et al., 2023). Therefore, how the L36H mutation affects antigen recognition should be further tested. In addition, the residue at position 179 in the F protein is located within the antigenic site Ø and directly interacts with neutralizing antibodies such as ADI-61026 (Rappazzo et al., 2022). Given the ongoing development of multiple neutralizing antibodies targeting the hMPV F protein (Guo et al., 2023), the potential impact of these mutations on their efficacy warrants further investigation. A similar phenomenon had been observed in SARS-CoV-2 via the accumulation of mutations during the persistent circulation in the human populations (Tan et al., 2023; Tan and Wang, 2024). Additionally, P1604T in L is another identified nonsynonymous mutation that is exclusively detected in lineage I. As the L gene encodes an RNA-dependent RNA polymerase, further studies are needed to elucidate its effects on viral replication in host cells.

This study represents the most extensive hMPV genomic surveillance study showing phylodynamics from the genomic epidemiological perspective in Beijing. However, the global availability of hMPV genomic sequences remains limited, with only approximately 1000 whole genomes available to date. This scarcity of genomic data seriously hinders the timely detection of novel variants and impedes real-time assessing of the prevalence of different clades around the world. Large-scale hMPV genomic data provide critical support for future vaccine development, particularly in terms of candidate strain selection, given the global absence of any approved vaccines (Cao et al., 2025). With the continuous decrease in cost, genomic sequencing becomes widely used in public health (Armstrong et al., 2019), particularly since the COVID-19 pandemic. In recognition of the critical role of pathogen genomics, the World Health Organization (WHO) has issued guiding principles to promote rapid sharing of pathogen genome data by the end of 2022 (WHO, 2022). Consequently, expanding routine genomic surveillance to include hMPV and other common respiratory pathogens, alongside the timely sharing of genomic data, should be encouraged, where it is feasible. This information is also crucial for formulating timely, evidence-based public health intervention policies and thereby ultimately mitigating both the economic and health burdens associated with epidemics caused by novel respiratory virus variants.

Our study has several limitations. First, this study only collected and analyzed genomic data from Beijing during 2014 and 2024 and did not include data from other regions of China. Therefore, the conclusions drawn from this study are applicable mainly to Beijing. However, as the capital city of China, Beijing receives a large number of patients from other provinces, so the findings partially reflect the national situation. The extrapolation of the current findings to China should be performed with caution, but the findings are a critical reference for further studies on future hMPV surge in northern China. Secondly, hMPV genomes were only obtained from a subset of samples for technical reasons, with a bias towards children under 5 years, associated with higher viral loads in this group. This aligns with studies on RSV, which shares genetic features and a similar primary affected population with hMPV and also demonstrates higher viral loads in young children (Gómez-Novo et al., 2018). Despite this, the sequenced samples remain representative in terms of disease severity and genotype distribution, accurately reflecting the overall epidemiological characteristics. Thirdly, while our study compared the clade distributions of Beijing with those globally, the global sequence data are limited and subject to sampling bias. Thus, comparisons with global data require caution, which emphasizes the need for more extensive global hMPV genomic surveillance. Fourthly, a strict molecular clock was assumed and used in the phylodynamic analysis, which might have overlooked the potential heterogeneity of evolutionary rates among branches.

Conclusions

In conclusion, we demonstrated that clade B2 lineage I was the driving force for the large hMPV wave seen in Beijing from November to December 2024, but no novel variants was observed during this period. Although the data in this study was obtained solely from Beijing, this may be considered more representative of northern China. As the hub with the largest population flow in the country, Beijing can be both a major recipient of infectious diseases introduced from other regions and a potential source of further spread across China via frequent travel. Given that northern China shares a similar climate with Beijing, the epidemic patterns of infectious diseases, including hMPV, are also likely to be comparable. Therefore, this study provides a valuable reference for understanding hMPV prevalence in northern China in the period 2014–2024. This study underscores the critical need for ongoing genomic monitoring and research of respiratory viruses, including hMPV to further strengthen preparedness for future public health challenges.

Materials and methods

Study design and data sources

Between September 1, 2014, and December 31, 2024, we collected respiratory specimens including throat swabs, nasopharyngeal swabs, bronchoalveolar lavage fluid, and sputum from inpatients and outpatients with ARTIs from 35 sentinel hospitals across all 16 districts within RPSS in Beijing. The inclusion criteria were as follows: 1) individuals of all age groups, 2) residents of any of the 16 districts, and 3) meeting the criteria for a diagnosis of URTI or CAP. URTI was defined as fever (body temperature ≥38 °C), along with respiratory symptoms such as cough, sore throat, nasal congestion, runny nose, or sputum production. CAP was defined according to the corresponding guidelines issued in China (Qu and Cao, 2016, Subspecialty Group of Respiratory Diseases, T.S.O.P., Chinese Medical Association, 2013) and was further categorized into sCAP and nsCAP (Gong et al., 2018). Notably, the monthly sampling size remained relatively consistent throughout the surveillance period at approximately 643 per month. All samples were sent to the collaborating laboratories every week for laboratory testing. We also downloaded all hMPV genomes with lengths >10,000 bp from NCBI on August 5, 2025 for further analysis (n = 1299).

Division of the hMPV season and pandemic phases

Given the key definitions by the WHO regarding the global transmission phases of COVID-19 (https://www.who.int/europe/emergencies/situations/covid-19), the natural seasonality of hMPV incidence in Beijing (Zhu et al., 2020), and the impact of COVID-19 prevention and control measures on its prevalence (Dong et al., 2021), we divided the survey period into four phases: the pre-pandemic phase (September 1, 2014 to March 10, 2020), the pandemic phase (March 11, 2020 to May 4, 2023), the post-pandemic I phase (May 5, 2023 to August 31, 2024), and the post-pandemic II phase (September 1, 2024 to December 31, 2024).

hMPV detection and whole genome sequencing

Total nucleic acids were extracted from the samples using Bokun Nucleic Acid Extraction Kit (Changchun Bokun Bio-Technology Co., Ltd., China) with the KingFisher™ Flex Magnetic Particle Processors (Thermo Fisher). A multiple RT-PCR assay was performed using the Respiratory Pathogen Multiplex Nucleic Acid Detection Kit (Jiangsu Uninovo Biological Technology Co. Ltd., China) to detect for 20 common respiratory pathogens, including hMPV, human coronavirus (HCoV: NL63, OC43, 229E, and HKU1), influenza virus A of H1N1 and H3N2, influenza virus B, parainfluenza virus 1 to 4, SARS-CoV-2, RSV, human rhinovirus, human adenovirus, human enterovirus, human bocavirus, Mycoplasma pneumoniae, and Chlamydia pneumoniae. Thirteen microliters of the RNA extract were used for library preparation (IGT™ Fast Stranded RNA Library Prep Kit v2.0). An aliquot of the 750 ng library from each sample was used for hybrid capture-based enrichment (Xu et al., 2020; Metsky et al., 2017) of hMPV using the TargetSeq One®, Hyb & Wash Kit (v2.0) (Aijitaikang Biotechnology Co., Ltd., China) with one or two rounds of hybridization. Sequencing was performed on Illumina NovaSeq 6000 platform.

The quality control processes involved adapter trimming, removal of low-quality reads, and short reads with Fastp v0.20.0.20 (Chen et al., 2018). Human-derived reads were filtered out by mapping to the UCSC hg19 (2009) genome. The cleaned reads were subsequently assembled via MEGAHIT v1.2.9 (Li et al., 2015). The assembled contigs were subjected to BLAST with the hMPV genomes downloaded from NCBI. The genome with the highest identity was selected as the reference genome for each sample, and the final genome was generated through iVar-1.3.1(Grubaugh et al., 2019).

Phylogenetic, phylogenomic, mutation and selection analyses

The metadata for all global hMPV sequences analyzed in this study (accession numbers, collection dates, and locations) can be found in Supplementary Table S4. Nextclade version 3.9.1 (Aksamentov et al., 2021) was used to perform the following analyses: evaluation of the quality of hMPV genomes, mutation annotation, clade assignment and the construction of a phylogenetic tree. Phylogenomic analysis was conducted for all the hMPV clade B2 genomes collected in this study. RDP4 (Martin et al., 2015) was used to detect the potential recombination events. Modeltest-ng version 0.1.7 was then used to find the best substitution model according to the Bayesian information criterion (Darriba et al., 2020; Flouri et al., 2015). The best substitution model was GTR + I + G. This model was then used to reconstruct the phylogenetic tree via IQ-TREE version 2.3.6 (Minh et al., 2020), with 1000 ultrafast bootstrap replicates (Hoang et al., 2018). Linear regression between the root-to-tip divergence and sampling dates for the genomic data was performed via TempEst version 1.5.3 (Rambaut et al., 2016). The Bayesian Markov chain Monte Carlo (MCMC) approach implemented in BEAST version 1.10.4 (Suchard et al., 2018) was then used to infer the population dynamics for the B2 (n = 102) dataset. Three types of coalescent tree priors (constant-size population, exponential growth population, and Bayesian skyline) were used and the best-fit tree prior was then evaluated via marginal likelihood estimated by path sampling and stepping-stone sampling strategies. The Bayesian skyline tree prior (Drummond et al., 2005) was the best-fit for the dataset (Supplementary Table S5). Three replicate runs for each of the 10 million MCMC steps, sampling parameters, and trees every 1000 steps were performed for B2. We then used Tracer version 1.7.1 to check the convergence of the MCMC chains (effective sample size >200) and computed the marginal posterior distributions of the parameters, after discarding the first 10% of the MCMC chain as burn-in. TreeAnnotator was then used to summarize the maximum clade credibility tree based on the posterior distribution of trees (Rambaut et al., 2018).

We also combined all the B2 genomes obtained from this study and the publicly available high quality hMPV genomes belonging to the B2 clade to conduct the phylogenetic analysis. IQ-TREE version 2.3.6 (Minh et al., 2020) was used to reconstruct the phylogenetic tree with 1000 ultrafast bootstrap replicates (Hoang et al., 2018) and the best-fit substitution model from the previous analysis, and NC_039199.1 was used as the outgroup. Nonsynonymous mutation (discarding B2-specific mutations, which are defined as mutations with a frequency ≥0.9 within clade B2), meeting all of the following conditions, was defined as lineage I-specific nonsynonymous mutation: 1). having a frequency ≥0.6 within lineage I; 2). having a frequency <0.1 in all other clade B2 genomes.

We performed a selection analysis on the novel lineage (lineage I from B2) defined in this study using the unconstrained Bayesian approximation (FUBAR) (Murrell et al., 2013) method in HyPhy 2.5.14 (Kosakovsky Pond et al., 2020). The subtree of lineage I was extracted from the maximum clade credibility tree (MCC) tree of clade B2 and then used as an input phylogeny. Sites with a posterior probability (dN/dS > 1) greater than 0.9 were considered to be under positive selection.

Statistical analysis

Statistical significance was set at P < 0.05. Statistical analysis was performed via R (version 4.1.3) software.

Data availability

The genomic sequences have been deposited at NCBI under the following accession number: PV217842–PV218189 (Supplementary Table S2). Additionally, the corresponding data have been uploaded to ScienceDB, with the access link available at: https://doi.org/10.57760/sciencedb.28978 as well.

Ethics statement

This study was approved by the Ethics Committee of the Beijing Center for Disease Prevention and Control [2018(2)]. Written informed consent was obtained from each included patient, or from their legal guardians.

Author contributions

Lu Kang: investigation, visualization, formal analysis, writing–original draft, writing–review & editing. Fang Huang: conceptualization, writing–review & editing, resources, supervision, funding acquisition. Yi-Mo Deng: validation, writing–original draft. Geng Hu: investigation. Yiting Wang: investigation. Aihua Li: investigation, funding acquisition. Hui Xie: investigation. Xiaofeng Wei: investigation. Yuling Han: investigation. Ming Luo: investigation. Ian G. Barr: writing–original draft. George F. Gao: conceptualization, writing-original draft. Liang Wang: validation, formal analysis, visualization, writing–original draft, writing–review & editing. Quanyi Wang: conceptualization, resources, writing–original draft, supervision, project administration.

Conflict of interest

We declare no competing interests.

Acknowledgements

This work was supported by the National Key R&D Program of China (2022YFF1203203), the Capital's Funds for Health Improvement and Research (grant number 2024-1G-3015), the Beijing Municipal Health Commission’s Funds for the High-qualified Public Health Professionals Development Project (Leading Professionals 01–10), the Beijing Natural Science Foundation (grant number L242051) and the Science & Technology Fundamental Resources Investigation Program (2022FY100903).

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.virs.2026.01.006.

Contributor Information

George F. Gao, Email: gaof@im.ac.cn.

Liang Wang, Email: wangliang@im.ac.cn.

Quanyi Wang, Email: wangqy@bjcdc.org.

Appendix A. Supplementary data

The following are the Supplementary data to this article:

Supplementary Material
mmc1.docx (214.2KB, docx)

Supplementary Figure S1.

Supplementary Figure S1

Open in a new tab

Epidemiological and virological comparisons between total hMPV-positive cases and hMPV sequenced subset. A. Age distribution of all hMPV-positive cases and the sequenced subset. B. Comparison of viral Ct values across all age groups. C. Disease type distribution of all hMPV-positive cases and the sequenced subset.

Supplementary Figure S2.

Supplementary Figure S2

Open in a new tab

Disease type distribution (A) and hospitalization rates (B) of hMPV clades. B2 (previous) represents B2 clade samples collected from September 1, 2014 to August 31, 2024; B2 (post-pandemic II phase) represents B2 clade samples collected from September 1, 2024 to December 31, 2024.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material
mmc1.docx (214.2KB, docx)

Data Availability Statement

The genomic sequences have been deposited at NCBI under the following accession number: PV217842–PV218189 (Supplementary Table S2). Additionally, the corresponding data have been uploaded to ScienceDB, with the access link available at: https://doi.org/10.57760/sciencedb.28978 as well.

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