Human metapneumovirus prevalence and patterns of genotype persistence identified through surveillance of pediatric pneumonia hospital admissions in coastal Kenya, 2007-2016

doi:10.21203/rs.2.9147/v1

Download PDF

Research article

Human metapneumovirus prevalence and patterns of genotype persistence identified through surveillance of pediatric pneumonia hospital admissions in coastal Kenya, 2007-2016

https://doi.org/10.21203/rs.2.9147/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 30 Aug, 2019

Read the published version in BMC Infectious Diseases →

You are reading this older preprint version

Read the latest preprint version →

Background

Human metapneumovirus (HMPV) is an important respiratory pathogen that causes seasonal epidemics of acute respiratory illness and contributes significantly to childhood pneumonia. Current knowledge and understanding on its patterns of spread, prevalence and persistence in communities is limited.

Methods

We present findings of a molecular-epidemiological analysis of nasal samples from children < 5 years of age admitted with syndromic pneumonia between 2007 and 2016 to Kilifi County Hospital, coastal Kenya. HMPV infection was detected using real-time RT-PCR and positives sequenced in the fusion (F) and attachment (G) genes followed by phylogenetic analysis. The association between disease severity and HMPV genotype was assessed using Chi-square test for independence.

Results

Over 10 years, 274/6756 (4.10%) samples screened were HMPV positive. Annual prevalence fluctuated between years ranging 1.2% to 8.7% and lowest in the recent years (2014-2016). HMPV detections were most frequent between October of one year to April of the following year. Genotyping was successful for 205/274 (74.8%) positives revealing A2b (41.0%), A2c (10.7%), B1 (23.4%) and B2 (24.9%). The dominance patterns were: genotype A2b between 2007-11, B1 between 2012-14, and A2c in more recent epidemics. Genotype B2 viruses were present in all the years. These patterns mirrored the global picture. Temporal phylogenetic clustering within the genotypes for both local and global sequence data was seen. Genotypes occurring in each epidemic season were comprised of multiple variants. Pneumonia severity did not vary by genotype (p=0.264). In both the F and G gene, the sequenced regions were found to be predominantly under purifying selection.

Conclusion

Genotype and strain patterns from this rural African setting temporally map with global strain distribution, suggesting a well-mixed global virus transmission pool of HMPV. Persistence in the local community is characterized by repeated introductions of HMPV variants from the global pool. The factors underlying the declining prevalence of HMPV in this population should be investigated.

Internal Medicine Specialties

Human metapneumovirus

prevalence

genotype

epidemic

temporal

Human metapneumovirus (HMPV) is single-stranded, negative-sense RNA virus with a genome of about 13kb (1, 2). The virus belongs to the Pneumoviridae family and genus Metapneumovirus. HMPV infections occur across all ages with severe disease predominantly occurring in children below 2 years of age and the elderly (3-11). Since first description in 2001 (12), HMPV has been detected in all continents and its disease prevalence varies widely (13-18). Nearly every child by the age of 5 years has been infected by HMPV (12, 19). Clinical presentation of HMPV infection ranges from mild upper respiratory tract illness to severe lower respiratory tract disease (1, 17, 19) and overlaps with that of other common respiratory viruses especially respiratory syncytial virus (RSV) (20-23).

HMPV clinical isolates are classified into two major subgroups, subtype A and B, based on nucleotide differences in the fusion (F) and attachment (G) glycoprotein encoding genes (1, 24-26). The two groups can be distinguished serologically using group-specific antibodies (24, 27, 28). Phylogenetic analyses of F and G sequences have divided the two subtypes into genotypes i.e. A1 and A2 (HMPV A), and B1 and B2 (HMPV B) genotypes. A2 is further classified into A2a and A2b sub-lineages (26, 29, 30). A few studies have revealed increased heterogeneity of A2 due to the identification of a unique sub-lineage, provisionally assigned A2c, first described in 2007 (31). Geographically, HMPV genotypes have been reported to circulate widely and cluster temporally (15), with the exception of genotype A1 that has been identified in few countries (15, 31, 32). There is frequent co-circulation of genotypes with replacement of the predominant genotype after a period of one or two seasons, although the drivers of this phenomena are unclear (33-35). Virus prevalence also varies from year-to-year within the same location (16, 36-38).

Few studies have reported long-term genotype circulation patterns of HMPV, necessary for improved epidemiological understanding and, in due course, of potential value to the design and implementation of control measures. In addition, there are very few studies on HMPV in Africa, which bears a high burden of pneumonia morbidity and mortality (39, 40). In this study, we analysed the surface F and G gene nucleotide sequences collected through paediatric pneumonia surveillance at the Kilifi County Hospital in rural coastal Kenya, from 2007 to 2016, to describe the molecular epidemiology and gain insights into the spread and the evolutionary dynamics of HMPV. The two genes (F and G) code for immunogenic surface proteins that are the targets for vaccine development. G gene is highly variable and is used to assess the diversity of HMPV. Similarly, F gene which is quite conserved, has been used to characterise HMPV and is the gene of target in many molecular diagnostic assays. This report extends our previously published work from the same location for the period 2007-11 (41) and the analysis includes sequences from this previous work.

Study population. The study was conducted at the Kilifi County Hospital (KCH) as part of long-term surveillance aimed at understanding the epidemiology and disease burden of RSV-associated pneumonia cases (42). KCH, located in coastal Kenya, is a referral hospital serving the population of around 260,000 (circa 2012) within the Kilifi Health and Demographic Surveillance System (43), and beyond in the wider Kilifi County. The population is mainly rural-agrarian. Children (59 months of age) admitted to the paediatric ward between January 1st 2007 and December 31st 2016 were recruited for this study and participants were eligible if they presented with modified WHO defined syndromic severe or very severe pneumonia as previously described (42). During the period between 2007 and 2009, only admissions arising from residents of Kilifi Health and Demographic Surveillance System (KHDSS) were eligible, whereas in later years, non-KHDSS residents were included. A nasal wash, nasopharyngeal (NP) swab or nasopharyngeal/oropharyngeal (NP/OP) swab was obtained for those who provided a written informed consent for study participation and transferred into viral transport medium for laboratory screening. Ethical approval for the study was obtained from the Kenya Medical Research Institute Scientific and Ethics Review Unit.

RNA extraction and rRT-PCR. For samples collected from 2007 to 2011 the methods for extraction and detection were as previously described (44, 45). In brief, for samples collected in 2007 RNA was extracted using Magnapure LC32 total nucleic acid extractor (Roche, Manheim, Germany) and virus screening conducted using the LightCycler Fast Start DNA MasterPLUS Hyb-Probe kit (Roche, Mannheim, Germany) following manufacturer’s instructions. From 2008 to 2011, sample extraction was by MagNaPure LC32 Kit (Roche, Manheim, Germany) or QIAmp Viral RNA Minikit (Qiagen, Germany) and virus detection carried out using a one-step in-house multiplex real time reverse transcription-PCR (rRT-PCR) using a TaqMan probe-based system targeting the fusion (F) gene (41, 45). Samples with a rRT-PCR cycle threshold (Ct) value <35 were considered HMPV positive. From 2012-2016 total RNA extraction was performed using Qiacube HT automated total nucleic acid extractor (Qiagen, Germany) and virus detection carried out using the same one-step in-house multiplex real time reverse transcription-PCR (rRT-PCR) used for samples from 2008-11 (41).

F and G gene sequencing. HMPV positive samples identified between 2007 and 2011 (n=160) were partially sequenced in the F and G genes, 345 and 606 bp, respectively (41). For HMPV positive samples identified between 2012 and 2016 (n=114), we amplified full F and G encoding genes, approximately 1620bp and 700bp, respectively, using one-step RT-PCR kit (Qiagen, Germany) according to manufacturer’s instructions. RT-PCR and sequencing primers are shown in additional table S1. Thermocycling conditions for both genes were set at: 50°C for 30 minutes, 95°C for 15 minutes, 38 cycles of 94°C for 1 minute, 53°C for 1 minute, 72°C for 1 minute, and a final extension of 10 minutes at 72°C. HMPV A and B subtype specific primers were used to increase the sensitivity and the performance of G gene amplification. HMPV positive samples were sequenced in both forward and reverse directions using Big dye terminator v1.3 chemistry run on ABI 3130xl instrument (46).

Sequence analysis and diversity. Sequence reads for the successfully amplified HMPV positives identified between 2012 to 2016 were edited and assembled using Sequencher v5.0.1 (Gene Codes Corporation). The newly assembled F and G genes sequences were deposited in GenBank under accession numbers MH482553 to MH482743. The accession numbers (MH482553 to MH482743) include additional 31 sequences that were detected by different rRT-PCR assay and therefore they were excluded in this analysis. The accession numbers for the excluded sequences (15 for F gene and 16 for G gene) are given in additional figure S1. For subsequent analyses, previously sequenced data for HMPV positives identified between 2007 and 2011 i.e. F (n=123) and G (n=56) genes (41) (accession numbers KT191299 to KT191484) was combined with the newly sequenced data, making a total of 210 sequences for F gene and 118 sequences for G gene used in subsequent analyses. Sequences were aligned with MAFFT v7.220 (47). Nucleotide and amino acid mean genetic distances within and between genotypes were determined using Kimura-2 parameter model in MEGA v7. 0.2.6 (48). Further, to assess sequence diversity within epidemics, collated G gene sequences were aligned by epidemic sampling to show changes in the G gene within epidemics.

Phylogenetic analysis. Best fitting nucleotide substitution and site heterogeneity models were estimated in MEGA v7. 0.2.6 and phylogenetic trees inferred using Maximum Likelihood (ML) with 1000 bootstrap replicates. Genotypes were assigned based on F and G gene ML trees using previously described F and G gene references from GenBank (FJ168778, AY525843, AF371337, AB503857, AY530095 and GQ153651) (49). Genotypes were confirmed if sequences clustered with the reference sequences within a major branch with >70 % bootstrap support on the ML tree. Substitution rates and time to the most recent common ancestor (tMRCA) were determined under uncorrelated lognormal relaxed molecular clock using Bayesian Markov Chain Monte Carlo-based approach implemented in BEAST v1.8.4. Demographic histories of HMPV genotypes were inferred using GMRF Bayesian Skyride model. BEAST analysis was set at 50 million states with sampling every 2500 steps.

To assess the phylogenetic relatedness of the Kilifi strains with globally circulating strains, contemporaneous sequences (2007 to 2016) were obtained from GenBank for both F and G genes. Maximum Likelihood trees were prepared for the combined Kilifi and global data and branch reliability analysed by bootstrap support for 1000 replicates. Mean nucleotide genetic distances were also determined to assess sequence similarity between Kilifi and the global data set.

Selection pressure analysis. Selection pressure within F and G genes was analysed by estimating the ratios of non-synonymous to synonymous substitutions (dN/dS = ω), using the codon-based phylogenetic method (CODEML) in PAML v 4.8a. Different site models were employed i.e M0 (one ratio), M1a (Nearly Neutral), M2a (Positive Selection) and M3 (discrete) model. M0 model calculates a rough average value (ω) distributed across all sites (homogeneous ratios). M1a assumes two categories of sites: conserved sites with ω = 0 and neutral sites with ω = 1. M2a allows for three classes i.e. neutral ω = 1, purifying (0 , ω 1), and positive selection ω > 1. M3 estimates ω ratios as M2a but using an unconstrained discrete distribution (50, 51). The models M0 and M1a were used to test for selection over the entire sequenced regions while M2a and M3 tested for positively selected sites. Only sites with posterior probabilities >95% were considered as positively selected based on Bayes Empirical Bayes method.

HMPV genotype changes and disease severity. Different Kilifi HMPV genotypes identified between 2007-2016 were determined. To assess the association between HMPV genotype and disease severity, we performed a chi-square test for independence. Individuals were defined syndromically as either severe or very severe based on WHO definitions (52). The clinical definitions have been described previously (41, 42, 44). A p-value of 0.05 was considered to be significant. Statistical analysis was conducted using STATA v13.1 (College Station Texas, USA).

HMPV prevalence in paediatric hospital admissions. Between January 2007 and December 2016, 9079 individuals below 60 months of age were admitted to the paediatric wards at KCH with severe or very severe pneumonia. Samples were collected from 6756 (74%) individuals and 274 (4.1%) were determined HMPV positive (Table 1). Decreased HMPV prevalence was recorded in more recent years i.e. from 2014 to 2016 (Table 1). Among the 274 HMPV positive cases, 43.1 % (n=118) were in children <6 months of age and 71.5% (n=196) in children <2 year of age. Children 3 years accounted for only 3.6% of the cases (n=11) (Table 2).

HMPV genotype assignment and temporal occurrence of HMPV in Kilifi. Phylogenetic analysis of Kilifi and reference F and G gene sequences showed clustering of Kilifi HMPV viruses into the two known HMPV subtypes, A (106/205, 51.7%) and B (99/205, 48.3%), Figure 1. All the subtype A sequences clustered within genotype A2 and further into sub-lineage A2b (84/106, 79.2%) and the unique sub-lineage A2c (22/106, 20.8%). No A1 or A2a sequences were observed. Within subtype B, 48.5% (48/99) of the sequences clustered within genotype B1 and 51.5% (51/99) of the sequences clustered within genotype B2. During the 10-year period of surveillance described here, HMPV infections occurred in a seasonal pattern, mainly between the month of October of one year and April of the following year, Figure 2, which was used to define an epidemic year, i.e. starting September of one year through August of the following year. Thus, subsequent analyses were done by epidemic year. Multiple genotypes co-circulated within a single epidemic year from 2007-2016, Figure 3b. There was a shift in dominance from A2b (2007 to 2011) to B1 (2012 to 2014) with subsequent rise of the sub-lineage A2c in recent years. Notably, B2 genotype remained persistent but non-dominant. A2b sub- lineage circulated only until February 2012.

HMPV genotype prevalence and dominance in Kilifi was compared to other countries around the world. This was based on analysis of 714 F gene sequences from 18 different countries and 500 G gene sequences from 15 countries (additional figure S1). Similar patterns were observed for Kilifi and global data sets, with predominance of A2b and B2 in 2007-2010 and subsequent rise of B1 and A2c in years that followed, Figure 3. Notably, in both Kilifi and other settings, B2 viruses persisted over the 9 years. A1 and A2a were the least commonly detected genotypes globally and not observed in Kilifi.

Phylogenetic analysis of Kilifi HMPV viruses. Based on maximum likelihood phylogenies of F and G genes, sequences from consecutive epidemics largely clustered together. However, across multiple epidemics sequences from the earlier years clustered together away from those in recent years, Figure 4. Further analysis of G gene sequences aligned temporally, revealed circulation of multiple variants of a single genotype in a single epidemic year, Figure 5. Each variant differed by at least five nucleotides (Figure5) and clustered into separate clades, Figure 4b. In comparison to contemporaneous viruses (714 F gene sequences and 500 G gene sequences) (additional figure S1), viruses from Kilifi and those from other locations were highly similar, with an average nucleotide identity of 97.1% and 95.6% (in F gene), and 90.4% and 85.9% (in G gene) for subgroups A and B, respectively. In phylogenies of F and G, combining contemporaneous and Kilifi sequence data, the Kilifi sequences did not cluster separately from others but interspersed with sequences from Canada, Spain, Malaysia, Japan, USA, India and Croatia (Additional figure S2), and similarly, those viruses collected from earlier years in Kilifi clustered away from those in recent years.

F gene sequence analysis. Due to partial gene sequencing (345bp) of the previously reported F gene Kilifi sequences retrieved from GenBank (41), combined Kilifi sequences were trimmed to 345bp. The F gene sequences were highly conserved both at the nucleotide (nt) and amino acid (aa) level with overall mean sequence identity of 89% (nt) and 97% (aa). Within the individual genotypes, the nucleotide and amino acid sequences were more conserved: there was 100% aa sequence similarity within sub-lineages A2b, A2c and genotype B1, and 99.9% within B2 genotype, and the mean nucleotide identities were 98.2% within B1, 99.2% within sub-lineage A2c, 98.3% within both sub-lineage A2b and B2 genotype. The evolutionary rates for F gene were estimated at 1.220× 10-3 (95% highest posterior density (HPD) = 1.317× 10-3 to 5.518× 10-3 ), 1.857× 10-3 (95% HPD = 1.111× 10-3 to 2.289× 10-3 ), 1.242× 10-3 (95%HPD = 8.303× 10-4 to 1.689× 10-3) and 1.050× 10-3 (95%HPD = 5.937× 10-4 to 1.600× 10-3), nucleotide substitutions/site/year for A2c, A2b, B1 and B2, respectively. Selection pressure analysis of the F gene showed overall mean dN/dS ratio of ω= 0.10, ω= 0.15, ω= 0.01 and ω= 0.12 for A2c, A2b, B1 and B2, respectively (Additional figure S3). Using M2a and M3 models, no codon sites were identified as positively selected among the four types (sub-lineage A2b, sub-lineage A2c, B1 and B2 genotypes). (Additional figure S3).

G gene sequence analysis (Kilifi). G gene sequences were less conserved compared to F gene sequences with overall mean sequence identity of 73% (nt) and 56% (aa). However, within genotypes the sequences were highly conserved: the mean identities were estimated at 97% (nt) and 96% (aa) for sub-lineage A2c, 93% (nt) and 89% (aa) for sub-lineage A2b, 94% (nt) and 90% (aa) for B1, 92%(nt) and 87% for B2 genotypes. The deduced G gene amino acid sequences differed in lengths among genotypes: sub-lineage A2c (220aa), sub-lineage A2b (218 and 229aa), B2 (238aa) and B1 (232 and 242aa) long. The varying protein lengths for sub-lineage A2b and B1 genotype were due to changes in stop codon positions. The evolutionary rates for the G gene were estimated at 3.420× 10-3 (95% HPD interval = 1.317× 10-3 to 5.518× 10-3), 8.022× 10-3 (95% HPD = 5.893× 10-3 to 1.060× 10-2) and 9.570× 10-3 (95% HPD = 5.911× 10-3 to 1.33× 10-2) nucleotide substitutions/site/year for A2c, A2b and B1, respectively. Evolutionary rate estimates for B2 genotypes were not determined due to inadequate temporal signal. Among all the genotypes, sub-lineage A2c exhibited significantly lower substitution rates within the G gene (95% HPD = 1.317× 10-3 to 5.518× 10-3). The estimated times of the most common ancestor of the individual genotypes dated to recent origin in both F and G genes, i.e., 2009 (95% HPD = 2005 -2011) for sub-lineage A2c sequences, 2008 (95% HPD = 2006 -2009) for sub-lineage A2b, 2005 (95% HPD = 2002 -2007) for B1 and 2001(95% HPD = 1997-2005) for B2 sequences. The estimated mean dN/dS ratio (ω) was <1 for A2b (ω= 0.57), A2c (ω= 0.38) and B1(ω= 0.78). For the genotype B2, a higher average dN/dS ratio (ω= 1.21) was determined for G gene, indicating a signal of positive selection (Additional figure S3). Using M2a and M3 models, 3 positively selected sites were identified within A2c (17, 24 and 28) and A2b (87, 123 and 135), 1 site (96) in B1 and 2 sites (12,127) within B2 sequences (Additional figure S3).

Genotype changes and Disease severity. We did not observe a statistically significant association between HMPV genotype and pneumonia severity (p-value=0.264) (Table 3).

There remains limited information on long-term circulation patterns and prevalence of HMPV in Africa. This study reports HMPV subtype incidence and molecular evolution in coastal Kenya over a 10-year period of surveillance (2007 to 2016). HMPV incidence in children under 5 years of age varied annually ranging from 1.2% to 8.7% and 71.5% of HMPV infections were in children aged <2 years. Wide variation in annual prevalence has been reported elsewhere from long-term studies of acute respiratory infection (ARI) aetiology in children: in Germany, HMPV prevalence over 10 years of surveillance varied between 1.4% and 32.8% (15), and in Italy, the prevalence fluctuated from 37% to 7% and then to 43% over a 3 year period (36). To date the cause of the varied prevalence and whether there is difference in disease severity among HMPV genotype remains unclear. Different studies report contrasting results on the association of disease severity among the HMPV genotypes (21, 53). In this study, no significant association was found between genotype and disease severity or clinical presentation. Further studies are necessary to evaluate whether changes in genotypes are associated with changes in disease severity and prevalence. Climatic factors such as temperature, rainfall and relative humidity have also been shown to influence HMPV activity in the tropics (31). In this study, seasonal increase in cases tended to coincide with low rainfall, lower relative humidity and higher temperatures. Further investigation is required to determine whether changes in such environmental factors influence HMPV activity.

HMPV A2b genotype dominated between 2007 and 2011, while B1 dominated between 2012 and 2014 and B2 persisted throughout. Occurrence of the sub-lineage A2c (31) has increased in Kilifi in recent years, as similarly reported in Bangladesh and Croatia (54, 55), suggestive of global spread of HMPV variants. In contrast to continued circulation of sub-lineage A2b globally (56), in Kilifi, A2b circulated from 2007 to February 2012 and no longer. The replacement patterns of HMPV genotypes could be attributed to genotype-specific herd immunity offering protection against circulating homologous strains but not against heterologous strains. This observation is not unique to Kilifi, as similar patterns were reported from long-term surveillance studies in Germany and France (15, 37). Consistent with earlier studies, G gene exhibited higher rates of evolution compared to F gene (25). F gene is more conserved and our findings concur with previous studies on F protein diversity (11). The rates of G gene evolution were in the range of 3.42× 10-3 to 9.57× 10-3 , similar to findings from previous studies (57, 58). Among the genotypes, the rates of evolution for A2b and B1 were at least 2 fold higher than other genotypes, this concurs with the findings from a similar study (57). Compared to other Pneumoviruses, such as human respiratory syncytial virus (HRSV), HMPV G gene has been shown to have higher rates of evolution (58). Our estimates show comparable results, the rates for HMPV A2b and B1 were 3 to 4 times higher than those reported for HRSV (3.58× 10-3 , 95% HPD = 3.04× 10-3 to 4.16× 10-3 ) (59).

Temporal analysis revealed circulation of multiple genotypes within a single epidemic. Further analysis of the G glycoprotein encoding gene revealed circulation of multiple variants for a single genotype within an epidemic. Multiple variants observed might be attributed to high rate of G gene evolution (substitutions/site/year) resulting in diversification in situ during a season. However, the level of variation observed within a genotype within epidemics was greater that would be expected from in situ diversification, implying multiple variant introductions of a genotype during single epidemic years.

As is the norm for HMPV detection in respiratory samples, we used molecular PCR-based diagnostics (16). Recent studies have shown that mutations at primer and probe binding sites can lead to false negative diagnostic results and hence underestimation of disease burden (60, 61). Evaluation of the in-house diagnostic rRT-PCR assay would be important to determine whether there are any missed variants/genotypes and whether this can be associated with the apparent gradual decline in HMPV prevalence observed in the current dataset. There is some evidence that reduction in bacterial pneumonia (eg S. pneumoniae), as has been seen in this coastal location over this study period (62), results in a reduction in viral pneumonia (63, 64). Future investigations will be necessary to give more insight.

The F and G protein encoding genes were generally determined to be under purifying selection pressure, which drives RNA virus evolution by purifying the deleterious mutations due to RNA replication errors (65). However, for the B2 genotype, a higher dN/dS ratio was observed in G gene sequences suggestive of diversifying selection within B2 viruses. The distinct diversifying selection and persistence of the B2 viruses observed requires further investigation. Overall, our F gene sequence analysis was based on 345 bp, and therefore, although our analysis was based on 205 sequences, the genetic distance estimates, evolutionary and selection pressure analysis were limited to the partial length which may reduce the potential to discriminate genetic clusters. In addition, the newly designed G gene subtype specific primers allowed sequencing of all HMPV subtypes for the newly generated data and significantly improved G PCR recovery by two-fold compared to previously reported assay. This improved the study power to characterise the different circulating HMPV variants.

Our analyses on HMPV epidemiology in Kilifi were limited to two viral genes only. Whole genome sequencing might give more insights into transmission, HMPV genotype characterisation and molecular evolution. Our estimation and inference on the association between HMPV genotype and disease severity was biased to in-patient surveillance data, and therefore future studies should include outpatient surveillance data. In addition, inpatient surveillance and sampling of HMPV infections might not be representative of the full variant population circulating in this community and not correctly reflect incidence and prevalence, as HMPV infections have also been reported in outpatient settings (66). Future studies across different locations in Kenya and in Africa will be important for tracing the introduction and transmission patterns of the virus.

Conclusions

In conclusion, this report shows HMPV activity characterised by high annual variation in prevalence, genotype prevalence patterns reflecting those in other locations globally, gradual replacement of genotypes over time, and multiple circulating variants each epidemic varying year to year. This suggests, widescale and rapid circulation of HMPV genotypes, seasonal epidemics resulting from multiple introductions rather than single source, and evidence of herd immunity replacement at the genotype and within-genotype cluster level.

ARI: Acute respiratory infections

HMPV: Human metapneumovirus

HPD: Highest posterior density

KCH: Kilifi County Hospital

KHDSS: Kilifi Health and Demographic Surveillance System

rRT-PCR: Real time Reverse Transcription Polymerase Chain Reaction

RNA: Ribonucleic acid

tMRCA: Time to the most recent common ancestor

Ethics approval and consent to participate

The Kenya Medical Research Institute Scientific and Ethics Review Unit (SERU) approved the study. A written informed consent was obtained from participant’s parent or guardian.

Consent for publication

Not applicable.

Availability of data and material

All data generated or analysed during this study has been deposited to the Virus Epidemiology and Control (VEC), KEMRI-Wellcome Trust Research Programme, data server under the DOI: https://doi.org/10.7910.

Competing interests

The authors declare that they have no competing interest.

Funding

This study was supported by the Wellcome Trust, United Kingdom (grants 102975, 100542, 084633, and 077092). The funder had no role in other aspects of the study including its design, data collection, data analysis, data interpretation, or writing of this manuscript.

Authors’ contributions

DJN and CNA: conceived and designed the study, supervised the work and gave technical guidance. JOW, JRO, EK: participated in laboratory, sequence and Phylogenetic analyses, GPO: carried clinical data analysis and data curation. JOW, DJN and CAN: wrote the manuscript. All authors read and approved the final manuscript.

Acknowledgement

We thank the study participants for providing the study samples. We thank all members of the Virus Epidemiology and Control (VEC) Research Group in Kilifi who were involved at various stages of this study that has allowed this current report to be written. We thank Dr Matt Cotten for sharing his scripts for generating the hiliter plots. We also thank the parents and guardians of the children for accepting to participate in this study. This work is published with permission of director KEMRI.

Corresponding author

John Oketch

Address: P.O BOX 230-80108, KEMRI Wellcome Trust.

Telephone: +254 715 496 856

Email: [email protected] / [email protected]

1. Schildgen V, van den Hoogen B, Fouchier R, Tripp RA, Alvarez R, Manoha C, et al. Human Metapneumovirus: lessons learned over the first decade. 2011;24(4):734-54.

2. Piyaratna R, Tollefson SJ, Williams JVJVr. Genomic analysis of four human metapneumovirus prototypes. 2011;160(1-2):200-5.

3. Abiko C, Mizuta K, Itagaki T, Katsushima N, Ito S, Matsuzaki Y, et al. Outbreak of human metapneumovirus detected by use of the Vero E6 cell line in isolates collected in Yamagata, Japan, in 2004 and 2005. 2007;45(6):1912-9.

4. Debiaggi M, Canducci F, Terulla C, Sampaolo M, Marinozzi MC, Alessandrino PE, et al. Long-term study on symptomless human metapneumovirus infection in hematopoietic stem cell transplant recipients. 2007;30(3):255.

5. Ebihara T, Endo R, Kikuta H, Ishiguro N, Ishiko H, Hara M, et al. Human metapneumovirus infection in Japanese children. 2004;42(1):126-32.

6. Edwards KM, Zhu Y, Griffin MR, Weinberg GA, Hall CB, Szilagyi PG, et al. Burden of human metapneumovirus infection in young children. 2013;368(7):633-43.

7. Gregianini TS, Seadi CF, Menegolla I, Martins LG, Ikuta N, Wolf JM, et al. Human metapneumovirus in Southern Brazil. Revista da Sociedade Brasileira de Medicina Tropical. 2018;51(1):30-8.

8. Lambert SB, Allen KM, Druce JD, Birch CJ, Mackay IM, Carlin JB, et al. Community epidemiology of human metapneumovirus, human coronavirus NL63, and other respiratory viruses in healthy preschool-aged children using parent-collected specimens. 2007;120(4):e929-e37.

9. Ren L, Gonzalez R, Wang Z, Xiang Z, Wang Y, Zhou H, et al. Prevalence of human respiratory viruses in adults with acute respiratory tract infections in Beijing, 2005–2007. 2009;15(12):1146-53.

10. Vinomarlini G, Samuel L, Thayan R, Zainah S, Bhassu S. Incidence of Human Metapneumovirus in Hospitalized Patients Admitted for Respiratory Illness in Malaysia. 2014.

11. Yang C-F, Wang CK, Tollefson SJ, Piyaratna R, Lintao LD, Chu M, et al. Genetic diversity and evolution of human metapneumovirus fusion protein over twenty years. 2009;6(1):138.

12. Van den Hoogen BG, de Jong JC, Groen J, Kuiken T, de Groot R, Fouchier RA, et al. A newly discovered human pneumovirus isolated from young children with respiratory tract disease. 2001;7(6):719.

13. Arnott A, Vong S, Sek M, Naughtin M, Beauté J, Rith S, et al. Genetic variability of human metapneumovirus amongst an all ages population in Cambodia between 2007 and 2009. 2013;15:43-52.

14. Ludewick HP, Abed Y, Van Niekerk N, Boivin G, Klugman KP, Madhi SAJEid. Human metapneumovirus genetic variability, South Africa. 2005;11(7):1074.

15. Reiche J, Jacobsen S, Neubauer K, Hafemann S, Nitsche A, Milde J, et al. Human metapneumovirus: insights from a ten-year molecular and epidemiological analysis in Germany. 2014;9(2):e88342.

16. Panda S, Mohakud NK, Pena L, Kumar SJIjoid. Human metapneumovirus: review of an important respiratory pathogen. 2014;25:45-52.

17. Bastien N, Ward D, Van Caeseele P, Brandt K, Lee SH, McNabb G, et al. Human metapneumovirus infection in the Canadian population. 2003;41(10):4642-6.

18. Loo LH, Tan BH, Ng LM, Tee NW, Lin RT, Sugrue RJJEid. Human metapneumovirus in children, Singapore. 2007;13(9):1396.

19. Shafagati N, Williams JJF. Human metapneumovirus-what we know now. 2018;7.

20. Moe N, Krokstad S, Stenseng IH, Christensen A, Skanke LH, Risnes KR, et al. Comparing human metapneumovirus and respiratory syncytial virus: viral co-detections, genotypes and risk factors for severe disease. 2017;12(1):e0170200.

21. Papenburg J, Hamelin M-È, Ouhoummane N, Carbonneau J, Ouakki M, Raymond F, et al. Comparison of risk factors for human metapneumovirus and respiratory syncytial virus disease severity in young children. 2012;206(2):178-89.

22. Pelletier G, Déry P, Abed Y, Boivin GJEid. Respiratory tract reinfections by the new human metapneumovirus in an immunocompromised child. 2002;8(9):976.

23. van den Hoogen B, van Doornum GJJID. F ockens JC, Cornelissen JJ, Beyer WE, de Groot R, Osterhaus AD, Fouchier RA. Prevalence and clinical symptoms of human metapneumovirus infection in hospitalized patients. 2003.

24. Biacchesi S, Skiadopoulos MH, Boivin G, Hanson CT, Murphy BR, Collins PL, et al. Genetic diversity between human metapneumovirus subgroups. 2003;315(1):1-9.

25. Kim JI, Park S, Lee I, Park KS, Kwak EJ, Moon KM, et al. Genome-wide analysis of human metapneumovirus evolution. 2016;11(4):e0152962.

26. Papenburg J, Carbonneau J, Isabel S, Bergeron MG, Williams JV, De Serres G, et al. Genetic diversity and molecular evolution of the major human metapneumovirus surface glycoproteins over a decade. 2013;58(3):541-7.

27. Endo R, Ebihara T, Ishiguro N, Teramoto S, Ariga T, Sakata C, et al. Detection of four genetic subgroup-specific antibodies to human metapneumovirus attachment (G) protein in human serum. 2008;89(8):1970-7.

28. Van den Hoogen BG, Herfst S, Sprong L, Cane PA, Forleo-Neto E, De Swart RL, et al. Antigenic and genetic variability of human metapneumoviruses. 2004;10(4):658.

29. Feuillet F, Lina B, Rosa-Calatrava M, Boivin GJJoCV. Ten years of human metapneumovirus research. 2012;53(2):97-105.

30. Huck B, Scharf G, Neumann-Haefelin D, Puppe W, Weigl J, Falcone VJEid. Novel human metapneumovirus sublineage. 2006;12(1):147.

31. Chow WZ, Chan YF, Oong XY, Ng LJ, Nor’E SS, Ng KT, et al. Genetic diversity, seasonality and transmission network of human metapneumovirus: identification of a unique sub-lineage of the fusion and attachment genes. 2016;6:27730.

32. Lo Presti A, Cammarota R, Apostoli P, Cella E, Fiorentini S, Babakir-Mina M, et al. Genetic variability and circulation pattern of human metapneumovirus isolated in Italy over five epidemic seasons. 2011;34(4):337.

33. Aberle JH, Aberle SW, Redlberger-Fritz M, Sandhofer MJ, Popow-Kraupp TJTPidj. Human metapneumovirus subgroup changes and seasonality during epidemics. 2010;29(11):1016-8.

34. Peret T, Hall CB, Schnabel KC, Golub JA, Anderson LJJJoGV. Circulation patterns of genetically distinct group A and B strains of human respiratory syncytial virus in a community. 1998;79(9):2221-9.

35. Peret TC, Hall CB, Hammond GW, Piedra PA, Storch GA, Sullender WM, et al. Circulation patterns of group A and B human respiratory syncytial virus genotypes in 5 communities in North America. 2000;181(6):1891-6.

36. Maggi F, Pifferi M, Vatteroni M, Fornai C, Tempestini E, Anzilotti S, et al. Human metapneumovirus associated with respiratory tract infections in a 3-year study of nasal swabs from infants in Italy. 2003;41(7):2987-91.

37. Pitoiset C, Darniot M, Huet F, Aho SL, Pothier P, Manoha CJJomv. Human metapneumovirus genotypes and severity of disease in young children (n= 100) during a 7‐year study in Dijon hospital, France. 2010;82(10):1782-9.

38. Rafiefard F, Yun Z, Örvell CJJomv. Epidemiologic characteristics and seasonal distribution of human metapneumovirus infections in five epidemic seasons in Stockholm, Sweden, 2002–2006. 2008;80(9):1631-8.

39. Green RJ, Kolberg JMJP. Neonatal pneumonia in sub-Saharan Africa. 2016;8(1):3.

40. Liu L, Johnson HL, Cousens S, Perin J, Scott S, Lawn JE, et al. Global, regional, and national causes of child mortality: an updated systematic analysis for 2010 with time trends since 2000. 2012;379(9832):2151-61.

41. Owor BE, Masankwa GN, Mwango LC, Njeru RW, Agoti CN, Nokes DJJBid. Human metapneumovirus epidemiological and evolutionary patterns in Coastal Kenya, 2007-11. 2016;16(1):301.

42. Nokes DJ, Ngama M, Bett A, Abwao J, Munywoki P, English M, et al. Incidence and severity of respiratory syncytial virus pneumonia in rural Kenyan children identified through hospital surveillance. 2009;49(9):1341-9.

43. Scott JAG, Bauni E, Moisi JC, Ojal J, Gatakaa H, Nyundo C, et al. Profile: the Kilifi health and demographic surveillance system (KHDSS). 2012;41(3):650-7.

44. Berkley JA, Munywoki P, Ngama M, Kazungu S, Abwao J, Bett A, et al. Viral etiology of severe pneumonia among Kenyan infants and children. 2010;303(20):2051-7.

45. Hammitt LL, Kazungu S, Welch S, Bett A, Onyango CO, Gunson RN, et al. Added value of an oropharyngeal swab in detection of viruses in children hospitalized with lower respiratory tract infection. 2011;49(6):2318-20.

46. Rosenblum B, Lee L, Spurgeon S, Khan S, Menchen S, Heiner C, et al. New dye-labeled terminators for improved DNA sequencing patterns. 1997;25(22):4500-4.

47. Katoh K, Standley DMJMb, evolution. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. 2013;30(4):772-80.

48. Tamura K, Dudley J, Nei M, Kumar SJMb, evolution. MEGA4: molecular evolutionary genetics analysis (MEGA) software version 4.0. 2007;24(8):1596-9.

49. de Graaf M, Osterhaus AD, Fouchier RA, Holmes ECJJogv. Evolutionary dynamics of human and avian metapneumoviruses. 2008;89(12):2933-42.

50. Yang Z, Swanson WJJMb, evolution. Codon-substitution models to detect adaptive evolution that account for heterogeneous selective pressures among site classes. 2002;19(1):49-57.

51. Yang ZJMb, evolution. PAML 4: phylogenetic analysis by maximum likelihood. 2007;24(8):1586-91.

52. Organization WH. Acute respiratory infections in children: case management in small hospitals in developing countries, a manual for doctors and other senior health workers. Geneva: World Health Organization; 1990.

53. Vicente D, Montes M, Cilla G, Perez-Yarza EG, Perez-Trallero EJCid. Differences in clinical severity between genotype A and genotype B human metapneumovirus infection in children. 2006;42(12):e111-e3.

54. Rahman MZ, Sumiya M, Sahabuddin M, Pell LG, Gubbay JB, Rahman R, et al. Genetic characterization of human metapneumovirus identified through community and facility‐based surveillance of infants in Dhaka, Bangladesh. 2018.

55. Jagušić M, Slović A, Ljubin‐Sternak S, Mlinarić‐Galinović G, Forčić DJJomv. Genetic diversity of human metapneumovirus in hospitalized children with acute respiratory infections in Croatia. 2017;89(11):1885-93.

56. Saikusa M, Kawakami C, Nao N, Takeda M, Usuku S, Sasao T, et al. 180-nucleotide duplication in the G gene of human metapneumovirus A2b subgroup strains circulating in Yokohama City, Japan, since 2014. 2017;8:402.

57. Li J, Ren L, Guo L, Xiang Z, Paranhos-Baccala G, Vernet G, et al. Evolutionary dynamics analysis of human metapneumovirus subtype A2: genetic evidence for its dominant epidemic. 2012;7(3):e34544.

58. Padhi A, Verghese BJVr. Positive natural selection in the evolution of human metapneumovirus attachment glycoprotein. 2008;131(2):121-31.

59. Otieno JR, Agoti CN, Gitahi CW, Bett A, Ngama M, Medley GF, et al. Molecular evolutionary dynamics of respiratory syncytial virus group A in recurrent epidemics in coastal Kenya. 2016;90(10):4990-5002.

60. Gunson RN, Collins TC, Carman WF. Practical experience of high throughput real time PCR in the routine diagnostic virology setting. J Clin Virol. 2006;35(4):355-67.

61. Kamau E, Agoti CN, Lewa CS, Oketch J, Owor BE, Otieno GP, et al. Recent sequence variation in probe binding site affected detection of respiratory syncytial virus group B by real-time RT-PCR. J Clin Virol. 2017;88:21-5.

62. Silaba M, Ooko M, Bottomley C, Sande J, Benamore R, Park K, et al. Effect of 10-valent pneumococcal conjugate vaccine on the incidence of radiologically-confirmed pneumonia and clinically-defined pneumonia in Kenyan children: an interrupted time-series analysis. The Lancet Global health. 2019;7(3):e337-e46.

63. Fathima P, Blyth CC, Lehmann D, Lim FJ, Abdalla T, de Klerk N, et al. The Impact of Pneumococcal Vaccination on Bacterial and Viral Pneumonia in Western Australian Children: Record Linkage Cohort Study of 469589 Births, 1996-2012. Clin Infect Dis. 2018;66(7):1075-85.

64. Madhi SA, Klugman KP. A role for Streptococcus pneumoniae in virus-associated pneumonia. Nat Med. 2004;10(8):811-3.

65. Hughes AL, Hughes MA. More effective purifying selection on RNA viruses than in DNA viruses. Gene. 2007;404(1-2):117-25.

66. Nyiro JU, Munywoki P, Kamau E, Agoti C, Gichuki A, Etyang T, et al. Surveillance of respiratory viruses in the outpatient setting in rural coastal Kenya: baseline epidemiological observations. Wellcome open research. 2018;3:89.

Table 1: Number of samples tested for HMPV and cases positives per year from 2007-16 amongst pneumonia paediatric admissions to Kilifi County Hospital, Kenya.

Year	Admissionsa	Eligibleb	Testedc	HMPV Positive	HMPV Prevalence (95% CI)

2007	3506	1174	726	21	2.90 (1.80-4.39)
2008	3204	1009	435	34	7.80 (5.47-10.75)
2009	3633	1105	526	46	8.70 (6.47-11.49)
2010	3091	1090	903	27	3.00 (1.98-4.32)
2011	3005	906	730	32	4.40 (3.02-6.13)
2012	2715	863	943	50	5.30 (3.96-6.93)
2013	2074	566	540	28	5.20 (3.47-7.41)
2014	2947	797	695	18	2.60 (1.54-4.06)
2015	3029	793	658	8	1.20 (0.53-2.38)
2016	2803	776	600	10	1.70 (0.80-3.04)
Overall	30007	9079	6756	274	4.10

Abbreviations: CI, confidence intervals; HMPV, human metapneumovirus.

a. Admissions under 5 years of age; b. Cases of severe or very severe pneumonia amongst the admissions (42) ; c. Samples eligible for testing from the pneumonia admissions included only residents from within the Kilifi Health and Demographic Surveillance System (KHDSS) for years 2007-9, and residents or non-residents for other years.

Table 2: HMPV positives stratified by age of patients admitted to Kilifi County Hospital, Kenya 2007-2016

Age Categories	Frequency	Percent

0-2M	51	18.61
3-5M	67	24.45
6-8M	43	15.69
9-11M	35	12.77
12-17M	20	7.30
18-23	20	7.30
25-35M	27	9.85
36+M	11	4.01
Total	274	100

Table 3: Association between different HMPV genotypes and disease severity.

Subtype		WHO Pneumonia Status		Total
		Severe	Very severe

A2b	n	71	13	84
	%	84.52	15.48	100

A2c	n	20	2	22
	%	90.91	9.09	100

B1	n	35	13	48
	%	72.92	27.08	100

B2	n	41	10	51
	%	80.39	19.61	100
	n
Total	%	167	38	205
		81.46	18.54	100

				x2=4.18, p-value =0.264

Additional table S1: Newly designed F and G gene PCR and sequencing primers.

File format- .docx

Additional figure S1: Panel A and B, GenBank accession numbers for global contemporaneous F (714) and G (500) gene sequences retrieved from GenBank. Panel C, GenBank accession numbers for Kilifi sequences detected by alternative rt-PCR assay.

File format- .pdf file

Additional figure S2: ML phylogeny of F and G gene sequences colored by geographical region, including those obtained from Kilifi between 2007 and 2016. Panel A, HMPV A, F and G gene phylogenies. Panel B, HMPV B, F and G gene phylogenies. The numbers next to branches indicate the bootstrap values.

File format- .pdf file

Additional figure S3: Parameter estimates, dN/dS ratios, positively selected sites and log likelihood scores for F and G gene nucleotide sequences for the HMPV genotypes identified between 2007 and 2016.

File format- .pdf file

Download PDF

Journal Publication

published 30 Aug, 2019

Read the published version in BMC Infectious Diseases →

Editorial decision: Major revision
23 May, 2019
Review #2 received at journal
12 May, 2019
Review #1 received at journal
04 May, 2019
Reviewer #2 agreed at journal
29 Apr, 2019
Submission checks completed at journal
11 Apr, 2019
Editor invited by journal
11 Apr, 2019
Editor assigned by journal
11 Apr, 2019
Reviewers invited by journal
11 Apr, 2019
Reviewer #1 agreed at journal
11 Apr, 2019
First submitted to journal
29 Mar, 2019

You are reading this older preprint version

Read the latest preprint version →

Human metapneumovirus prevalence and patterns of genotype persistence identified through surveillance of pediatric pneumonia hospital admissions in coastal Kenya, 2007-2016

Status:

Journal Publication

Version 1

Abstract

Figures

Background

Methods

Results

Discussion

Abbreviations

Declarations

References

Tables

Supplemental File Figure Legends

Supplementary Files

Status:

Journal Publication

Version 1