Assessing antigenic drift and phylogeny of influenza A (H1N1) pdm09 virus in Kenya using HA1 sub-unit of the hemagglutinin gene.

Influenza A (H1N1) pdm09 virus emerged in North America in 2009 and has been established as a seasonal strain in humans. After an antigenic stasis of about six years, new antigenically distinct variants of the virus emerged globally in 2016 necessitating a change in the vaccine formulation for the first time in 2017. Herein, we analyzed thirty-eight HA sequences of influenza A (H1N1) pdm09 strains isolated in Kenya during 2015-2018 seasons, to evaluate their antigenic and molecular properties based on the HA1 sub-unit. Our analyses revealed that the A (H1N1) pdm09 strains that circulated in Kenya during this period belonged to genetic clade 6B, subclade 6B.1 and 6B.2. The Kenyan 2015 and 2016 isolates differed from the vaccine strain A/California/07/2009 at nine and fourteen antigenic sites in the HA1 respectively. Further, those isolated in 2017 and 2018 correspondingly varied from A/Michigan/45/2015 vaccine strain at three and fifteen antigenic sites. The predicted vaccine efficacy of A/California/07/2009 against Kenyan 2015/2016 was estimated to be 32.4% while A/Michigan/45/2015 showed estimated vaccine efficacies of 39.6% - 41.8% and 32.4% - 42.1% against Kenyan 2017 and 2018 strains, respectively. Hemagglutination-inhibition (HAI) assay using ferret post-infection reference antiserum showed that the titers for the Kenyan 2015/2016 isolates were 2-8-fold lower compared to the vaccine strain. Overall, our results suggest the A (H1N1) pdm09 viruses that circulated in Kenya during 2015/2016 influenza seasons were antigenic variants of the recommended vaccine strains, denoting sub-optimal vaccine efficacy. Additionally, data generated point to a swiftly evolving influenza A (H1N1) pdm09 virus in recent post pandemic era, underscoring the need for sustained surveillance coupled with molecular and antigenic analyses, to inform appropriate and timely influenza vaccine update.

Introduction

The influenza A (H1N1) pdm09 virus emerged in North America in March 2009 and rapidly spread worldwide causing the first influenza pandemic of the 21st Century [1-4]. The virus genome comprised a rare mixture of gene segments from swine, avian and human influenza viruses [5-8]. The A (H1N1) pdm09 virus was first detected in Kenya on June 2009 [9, 10]. Presently, A (H1N1) pdm09 has become endemic in humans, co-circulating with A/H3N2 and B as seasonal influenza viruses [1, 2, 5, 11]. Influenza virus hemagglutinin (HA) protein is the prime target of host's neutralizing antibodies [12-15]. It is typically cleaved by host cells proteases into HA1 and HA2 domains [16]. Proteolytic cleavage of influenza virus HA protein is crucial for virus infectivity and spread [17]. The A/H1N1 virus HA1 sub-unit comprise five major antigenic sites (epitopes) designated Sa, Sb, Ca1, Ca2 and Cb [18, 19]. However, mapping of H3 epitopes to H1 hemagglutinin (H3 numbering) has provided an alternative approach for identifying antigenic sites in A /H1 viruses [20]. The principal strategy for prevention of influenza disease is vaccination [21, 22]. However, build-up of amino acid mutations coupled with N-linked glycosylation at epitope sites can diminish antibody recognition leading to waning vaccine efficacy and intermittent seasonal epidemics [5, 14, 20, 22, 23]. Since 2010 the World Health Organization (WHO) recommended using A/California/7/2009 as the vaccine component for A (H1N1) pdm09 virus in Northern and Southern hemispheres [2, 21]. This was later replaced in 2017 with A/Michigan/45/2015-like virus due to emergence of antigenic drift variants [2, 15, 24].

The annually formulated influenza vaccine comprises HA antigens from A (H3N2), A (H1N1) pdm09 and one lineage of B (trivalent vaccine) or both lineages (quadrivalent vaccine) as predicted from circulating strains in the preceding seasons [16, 25]. The effectiveness of influenza vaccines fluctuate yearly in part due to genetic evolution of the virus leading to antigenic distance between the vaccine strain and circulating viruses [2, 16, 20, 21]. Different methods including hemagglutination inhibition (HAI) assay and Pepitope model may be used to estimate antigenic distance amongst influenza viruses and thus evaluate vaccine efficacy [26, 27]. Nevertheless, previous works have demonstrated that data generated by the Pepitope model correlate fairly well with vaccine efficacy [20, 27]. The Pepitope model calculates genetic changes in dominant epitopes of the vaccine strain and circulating viruses, providing a measure that corresponds linearly with vaccine efficacy [2, 16, 20, 27]. After an antigenic stasis of about six years characterized by little change in the antigenic epitopes of the HA1 protein, new antigenically distinct variants of the virus emerged in 2016 globally necessitating a change in the vaccine formulation for the first time in the 2017 influenza season [2, 15, 24]. The present study sought to evaluate antigenic and phylogenetic aspects of influenza A (H1N1) pdm09 viruses circulating in Kenya during 2015/2018 when there was increased evolution of the virus, focusing on the HA1 domain of hemagglutinin protein.

Methods

Sample collection and preparation

Respiratory nasal swab specimens used in this study were obtained from patients presenting with influenza-like illness (ILI) or severe acute respiratory infection (SARI) symptoms based on the WHO case definitions [28]. They were collected from hospitals comprising human respiratory virus sentinel surveillance network in the Department of Emerging Infectious Diseases (DEID) of the United States Army Medical Research Directorate-Africa (USAMRD-A), within the Kenya Medical Research Institute (KEMRI). The hospital sites are well distributed across the country. The samples were processed at the laboratory as previously described [29].

Ethics

Two ethical review boards, the Walter Reed Army Institute of Research (WRAIR) Institutional Review Board (IRB) and the Kenya Medical Research Institute (KEMRI) Scientific and Ethics Review Unit (SERU) reviewed and approved the study protocol under approval numbers WRAIR#1267 and SSC#981, respectively. The study conforms to recognized standards of the US Federal Policy for the Protection of Human Subjects. All participants involved in this study gave consent and consenting of patients prior to sample collection was carried out as previously described [30].

RNA extraction, RT-PCR and Sanger sequencing

RNA extraction from the isolates was carried out using QIAmp Viral RNA Mini Kit (Qiagen, Inc., USA) according to the manufacturer’s instructions. RT-PCR amplification of the HA gene was performed using Superscript III One Step RT-PCR System (Invitrogen Corporation, USA) and a set of previously described M13 tagged primers [31]. A final reaction mix of 25μL containing 2 X Reaction Mix, 20pmoles/μL of both Forward and Reverse primers, Superscript RT/Platinum Taq enzyme mix, Nuclease-free water (Promega Corporation, USA) and 3μL RNA template was prepared. The reaction mix was run at 50°C for 30 min, 94°C for 2 min followed by 35 cycles of (94°C for 30 sec, 55°C for 30 sec, 68°C for 1 min) and 68°C for 7 min on a 9700 FAST ABI Thermal Cycler (Applied Biosystems, USA). The PCR amplicons (frag 1: Σ 976 bp; frag 2: Σ 890 bp) were resolved on a 1% Agarose gel (Sigma-Aldrich Co., USA) stained with ethidium bromide (0.5 mg/ml) (Sigma-Aldrich Co., USA) and visualized using the E-box gel documentation system (Vilber Lourmat, France) according to the manufacturer’s instructions. The PCR products were cleaned using Exonuclease I/Shrimp Alkaline Phosphatase (ExoSap-IT) enzyme (Affymetrix, USA) and sequenced directly on both strands using universal M13 forward and reverse primers, as previously described [29, 31]. Cycle sequencing was carried out using the Big Dye Terminator Cycle sequencing kit v3.1 (Applied Biosystems, USA) and products were resolved on an automated 3500xL Genetic Analyzer (Applied Biosystems, USA) according to the manufacturer’s instructions.

Nucleotide sequence accession numbers

The sequences of A (H1N1) pdm09 isolates reported in this work are available in GenBank (www.ncbi.nlm.nih.gov/genbank) under accession numbers: ANH22064—ARK18942; MH316121, MG815810; MH356637—MH356647 and MK692755—MK692776.

Phylogenetic analyses

Nucleotide sequence fragments were processed into contigs using DNA baser v3.2 [32] and aligned with Muscle v3.8 software [33]. A phylogenetic tree was constructed using Bayesian Markov Chain Monte Carlo (MCMC) inference method implemented in MrBayes v3.2 software [34], under the best fit HKY+G nucleotide substitution model as predicted by the jModelTest software [35]. The MCMC was run for 10 million generations, with sampling every 1000 generations and a 10% burn-in. Sequences of relevant reference strains comprising those of known clades and WHO recommended vaccine strains for the southern hemisphere [A/California/7/2009 (H1N1) pdm09-like virus (2015–2016) & A/Michigan/45/2015 (H1N1) pdm09-like virus (2017–2018)] were retrieved from GenBank/ GISAID databases (S1 Table) and included in the analyses.

Natural selection pressure

Adaptive selection pressures within the HA1 domain were inferred from the ratio of non-synonymous to synonymous changes (dN/dS) using methods available in Datamonkey web server [36]. Selection pressure across the HA1 (mean dN/dS = ω) and specific codon sites were estimated by the single likelihood ancestor counting (SLAC) and fixed effects likelihood (FEL) methods [37]. The dN/dS ratio was calculated based on neighbor-joining trees under the HKY85 substitution model [38]. Strong evidence of selection was accepted at a P-value <0.05.

Prediction of N-glycosylation sites

Prediction of potential N-glycosylation sites (amino acid series: Asparagine -X-Serine/Threonine, where X stands for any amino acid except Aspartate or Proline) in the HA1 domain of hemagglutinin protein was carried out using the online NetNGlyc 1.0 server [39]. A score cut-off value of > 0.5 was considered suggestive of glycosylation.

Determination of vaccine efficacy using Pepitope model

The vaccine efficacy of A (H1N1) pdm09 was predicted using the Pepitope model [20, 27]. Pepitope is expressed as a ratio of amino acid changes in the dominant HA epitope between the vaccine strain and circulating virus [26, 27]. It is computed by dividing the number of amino acid changes in the HA epitope by the total number of amino acids in the epitope) [16, 26, 27]. An epitope with the highest substitutions (antigenic distance) is considered dominant [20, 27]. The epitope regions of A (H1N1) pdm09 isolates analyzed in this study were predicted using H3 numbering as previously described [2, 20]. The vaccine efficacy was calculated by E = (0.47–2.47 x Pepitope) x 100 [2, 20, 27].

Hemagglutination-inhibition (HAI) assay

To assess the antigenic relatedness between A (H1N1) pdm09 strains isolated in Kenya and vaccine viruses A/California/7/2009 and A/Michigan/45/2015, hemagglutination-inhibition (HAI) assay was performed using the WHO Collaborating Centre for Reference and Research on Influenza (VIDRL, Melbourne, Australia) HAI typing assay kit, according to the manufacturer’s instructions (http://www.influenzacentre.org/flucentres_HIassay.htm).

Results

Thirty-eight (38) HA sequences of A (H1N1) pdm09 strains isolated in Kenya during 2015 (N = 5), 2016 (N = 2), 2017 (N = 2) and 2018 (N = 29) were analyzed. Homology analysis based on HA1 showed that Kenyan 2015/2016 strains shared 97.6–98.4% (nucleotide) and 93.3–97.2% (amino acid) sequence identities with the vaccine strain A/California/7/2009, whereas the 2017/2018 strains had 97.9–99.4% (nucleotide) and 97.5–99% (amino acid) identities with A/Michigan/45/2015 vaccine virus (Table 1).

Table 1

Sequence homology of HA1 domain of A (H1N1) pdm09 strains isolated in Kenya relative to WHO vaccine strains.

Year	No of strains	Vaccine strain	% identity in HA1 domain
			Nucleotide	Amino acid
2015	5	A/California/7/2009	97.6–98.4	93.3–97.2
2016	2	A/California/7/2009	98–98.2	96.6
2017	2	A/Michigan/45/2015	99.3–99.4	98.7–99
2018	29	A/Michigan/45/2015	97.9–99.4	97.5–99

Phylogenetic analysis based on HA1 domain of hemagglutinin gene showed that all A (H1N1) pdm09 viruses circulating in Kenya during 2015/2018 belonged to genetic clade 6B; subclade 6B.2 (2015/2016 isolates) and 6B.1 (2017/2018 isolates) (Fig 1). Compared to the vaccine strain A/California/7/2009, Kenyan 2015/2016 strains showed several amino acid changes in HA1 including S84N (antigenic site E), S203T, E374K, S451N, S185T (antigenic site B), D97N, K283E (antigenic site C), S162R (antigenic site B), K163Q (antigenic site D), A256T and E499K (Fig 1). Similarly, relative to A/Michigan/45/2015 vaccine virus, Kenyan 2017 strains showed S74R, S162N (antigenic site E), S164T (antigenic site D), I295V (antigenic site C) and I510T amino acid variations in HA1, whereas the 2018 strains displayed additional T120A (antigenic site A), S451A (n = 2), K36R (n = 1), P137S (n = 6) (antigenic site A), A141E (n = 6) (antigenic site A), H273Y (n = 6) (antigenic site C), T2K (n = 12), I149L (n = 3) (antigenic site A), A315V and V479I amino acid mutations (Figs 1 and 2). Noteworthy, the bulk of these amino acid changes were not unique to Kenyan isolates as they were also observed among the sequences of foreign strains included in the study (S1–S4 Figs).

Fig 1

Phylogenetic relationship of HA1 protein sequences of A (H1N1) pdm09.

Sequences of Kenyan isolates (shown in blue) were analyzed relative to reference strains of known clades (shown in green), vaccine reference strains for southern hemisphere (shown in red) and other reference strains (shown in black). The tree was re-constructed using MrBayes v3.2 with a HKY+G nucleotide substitution model. Numbers at the nodes represent percentage posterior probability values while the scale bar indicates number of amino acid substitutions per site.

Fig 2

WebLogo depicting frequency of amino acid changes at the epitope sites (A-E) within the HA1 protein of Kenyan influenza A (H1N1) pdm09 strains isolated between 2015 and 2018. Amino acid alignment positions along the x-axis in (A) indicate variable sites among Kenyan 2015–2016 strains relative to the vaccine strain A/California/2009 while those in (B) depict variable sites among the 2017–2018 isolates relative to the vaccine strain A/Michigan/45/2015. The height of the residue indicates the relative frequency of each amino acid at that particular position. These graphics were created using WebLogo (https://weblogo.berkeley.edu/).

Natural selection analysis by the SLAC method showed that the mean dN/dS (ω) value for Kenyan A (H1N1) pdm09 strains in the HA1 sub-unit was 0.65 (95% CI), suggesting negative (purifying) selection. No specific codon sites within the HA1 sub-unit were detected to be evolving under negative or positive selection by either the SLAC or FEL methods at P-value < 0.05). All 2015/2016 Kenyan strains retained four potential N-glycosylation sites (at amino acid residue positions 11, 23, 87 and 287) present in the vaccine strain A/California/7/2009. The 2017/2018 strains (with exception of a strain designated A/Kenya/066/2018, accession no: MK692755) had an extra N-glycosylation site (at amino acid residue position 162) present in vaccine component A/Michigan/45/2015. The gain of a glycosylation motif in 2017/2018 A (H1N1) pdm09 viruses was due to a S162N amino acid substitution.

The predicted vaccine efficacies of vaccine strains A/California/7/2009 and A/Michigan/45/2015 against Kenyan 2015/2016 and 2017/2018 strains are summarized in Table 2. The Pepitope between vaccine strain A/California/7/2009 and Kenyan 2015/2016 strains was 0.059 (dominant epitope E; substitutions P83S and S84N), suggesting a predicted vaccine efficacy of 32.4% of that of a perfect match with the vaccine strain. The Pepitope between the vaccine strain A/Michigan/45/2015 and Kenyan 2017 strains ranged from 0.021–0.03, suggesting a predicted vaccine efficacy of 39.6–41.8%. Antigenic drift in Kenyan 2018 strains was observed mainly on epitopes C, D and E, yielding a predicted vaccine efficacy range of 32.4–42.1%. The predicted vaccine efficacy of the 2019 influenza A H1N1 pdm09 vaccine strain component (A/Brisbane/02/2018) against Kenyan 2018 strains yielded vaccine efficacy range of 32.2–42.1% of that of a perfect match, with mutations observed mostly on epitopes C and D. Remarkably, the vaccine effectiveness between A/Michigan/45/2015 and A/Brisbane/02/2018 vaccine strains against Kenyan 2018 A/H1N1 pdm09 isolates fell within the same margin (Σ 32–42%), plausibly implying that the latter A/H1N1 pdm09 vaccine component would not have provided improved protection against these viruses.

Table 2

Estimated vaccine efficacy of recommended WHO vaccine strains against influenza A (H1N1) pdm09 strains circulating in Kenya during 2015 to 2018 seasons.

Dominant epitopes are shown in Bold.

Year (N)	Vaccine Strain	No. of strains	Dominant Epitope	No. of mutations	Residue differences	Pepitope	Efficacy (%)
2015 (N = 5)	A/California/7/2009	2	C	1	R45K	0.03	39.6
		5	C	1	K283E	0.03	39.6
		1	E	1	A48P	0.029	39.8
		5	E	2	P83S, S84N	0.059	32.4
		1	A	2	N129D	0.042	36.6
		1	A	1	T133P	0.042	36.6
		5	D	1	K163Q	0.021	41.8
		5	B	1	S185T	0.045	35.9
2016 (N = 2)	A/California/7/2009	1	E	1	L70F	0.029	39.8
		2	E	2	P83S, S84N	0.059	32.4
		1	E	1	M257L	0.029	39.8
		1	B	1	N156K	0.045	35.9
		1	B	1	S162R	0.045	35.9
		2	B	1	S185T	0.045	35.9
		2	D	1	K163Q	0.021	41.8
		2	C	1	K283E	0.03	39.6
2017 (N = 2)	A/Michigan/45/2015	2	E	1	S74R	0.029	39.8
		2	D	1	S164T	0.021	41.8
		2	C	1	I295V	0.03	39.6
2018 (N = 29)	A/Michigan/45/2015	1	C	1	K36R	0.03	39.6
		1	C	1	R45K	0.03	39.6
		6	C	1	H273Y	0.03	39.6
		29	C	1	I295V	0.03	39.6
		29	E	1	S74R	0.059	32.4
		1	E	1	A261T	0.029	39.8
		2	B	1	S183P	0.045	35.9
		21	A	1	T120A	0.042	36.6
		1	A	1	S128L	0.042	36.6
		6	A	2	P137S, A141E	0.083	26.5
		3	A	1	I149L	0.042	36.6
		29	D	1	S164T	0.02	42.1
		2	D	1	T216I	0.02	42.1
		1	D	1	E235K	0.02	42.1
		1	D	1	T245P	0.02	42.1
	A/Brisbane/02/2018	21	A	1	T120A	0.042	36.6
		6	A	2	P137S, A141E	0.083	26.5
		1	A	1	I149L	0.042	36.6
		1	A	1	S128L	0.042	36.6
		29	C	2	G45R, V298I	0.06	32.2
		6	C	1	H273Y	0.03	39.6
		1	C	1	K36R	0.03	39.6
		29	D	1	R223Q	0.02	42.1
		1	D	1	T245P	0.02	42.1
		2	D	1	T216I	0.02	42.1
		27	B	1	P183S	0.045	35.9
		1	E	1	A261T	0.059	32.4

A summary of the HAI assay titers for Kenyan A/H1N1 pdm09 isolates and vaccine viruses A/California/7/2009 (2015/2016 influenza seasons) and A/Michigan/45/2015 (2017/2018 influenza seasons) is shown in Table 3. The Kenyan 2015/2016 isolates exhibited lower HAI titers with the A/California/7/2009 reference antiserum relative to the vaccine antigen A/California/7/2009, suggesting reduced antibody recognition and binding to antigenic sites among the Kenyan viruses. Conversely, all the Kenyan isolates, including those obtained in 2017/2018 exhibited higher HAI assay titers with the A/Michigan/45/2015 reference antiserum, suggesting enhanced recognition and binding to antigenic sites among the study isolates.

Table 3

HAI assay titers of A (H1N1) pdm09 strains isolated in Kenya and vaccine antigens with WHO reference antisera for 2015/2016 and 2017/2018 influenza seasons.

The numbers indicate HAI titers.

Reference Antigens	Reference Antisera
	A/California/7/2009 (2015/2016)	A/Michigan/45/2015 (2017/2018)
A/California/7/2009 (2015/2016)	256	512
A/Michigan/45/2015 (2017/2018)	128	1024
Test isolates
A/Kenya/001/2015	64	512
A/Kenya/002/2015	128	1024
A/Kenya/004/2015	64	512
A/Kenya/005/2015	64	512
A/Kenya/006/2015	32	256
A/Kenya/013/2018	128	1024
A/Kenya/017/2016	32	256
A/Kenya/017/2018	256	1024
A/Kenya/019/2018	64	512
A/Kenya/020/2018	256	1024
A/Kenya/022/2016	64	512
A/Kenya/024/2018	128	512
A/Kenya/025/2018	128	512
A/Kenya/027/2018	64	512
A/Kenya/028/2018	32	256
A/Kenya/029/2018	64	256
A/Kenya/035/2018	128	512
A/Kenya/036/2018	64	256
A/Kenya/039/2018	64	512
A/Kenya/041/2018	128	512
A/Kenya/042/2018	64	512
A/Kenya/043/2018	256	512
A/Kenya/045/2017	64	512
A/Kenya/045/2018	64	512
A/Kenya/046/2018	64	512
A/Kenya/047/2018	64	512
A/Kenya/051/2018	128	512
A/Kenya/055/2018	64	256
A/Kenya/056/2018	64	512
A/Kenya/057/2017	128	512
A/Kenya/057/2018	32	256
A/Kenya/059/2018	128	512
A/Kenya/060/2018	64	256
A/Kenya/062/2018	64	256
A/Kenya/063/2018	64	256
A/Kenya/064/2018	64	256
A/Kenya/065/2018	64	256
A/Kenya/066/2018	64	512

Discussion

In line with global circulation trend [2, 11, 21, 24, 40–44], we have shown that influenza A (H1N1) pdm09 viruses that circulated in Kenya during 2015–2018 belonged to phylogenetic clade 6B; sub-clades 6B.1 and 6B.2. Clade 6B of the A (H1N1) pdm09 variants are defined by D97N, K163Q (antigenic site D), S185T (antigenic site B), K283E (antigenic site C) and A256T amino acid mutations in HA1 while those belonging to sub-clade 6B.1 harbor additional S74R (antigenic site E), S162N (antigenic site B), S164T (antigenic site D), I216T (antigenic site D) and I295V (antigenic site C) amino acid substitutions [11, 20, 21, 42]. Influenza A (H1N1) pdm09 clade 6B/6B.1/6B.2 variants have been associated with increased disease severity compared to non-6B/6B.1/6B.2 members, partly due to antigenic alterations the former have undergone [11].

Scrutiny of our analyzed data revealed nine to thirteen antigenic amino acid alterations in HA1 sub-unit of Kenyan 2015/2016 strains compared to the vaccine strain A/California/7/2009, parallel to 2018 strains which showed fifteen disparate antigenic variations with respect to A/Michigan/45/2015 vaccine virus. This result indicates that influenza A (H1N1) pdm09 viruses that circulated in Kenya during 2015/2016 and 2018 seasons were genetic variants of the vaccine strains and reverberates findings of similar studies reported elsewhere [11, 21, 45]. Previous works have shown that four or more amino acid modifications at different HA1 epitope sites of an influenza virus is antigenically significant as it can give rise to new drift variants with altered antigenic properties [29, 46, 47]. These observations suggest that besides being genetic variants of the vaccine strains, the Kenyan viruses were antigenically divergent from those vaccine strains, plausibly implying that the vaccines provided sub-optimal protection against the A (H1N1) pdm09 viruses.

Further analyses showed that consistent with previous findings [2, 42, 48] all potential N-linked glycosylation sites were conserved in all but one influenza A (H1N1) pdm09 strains circulating in Kenya during 2015/2018 seasons, comparative to respective vaccine strains. All analyzed Kenyan A (H1N1) pdm09 subclade 6B.1 members displayed an additional N-glycosylation at residue sites 162–164 attributed to the S162N substitution [42]. The potential gain or loss of N-linked glycosylation at HA1 epitope sites can alter influenza virus antigenic properties [13, 49].

The average dN/dS (ω) value indicated that the A (H1N1) pdm09 viruses circulating in Kenya during 2015/2018 were evolving under purifying selection in HA1 protein. Additionally, no specific codon sites in this hemagglutinin protein region were found to be positively or negatively selected. Our result is consistent to findings by Tewawong et al., (2015) [16].

The predicted vaccine efficacy of A/California/7/2009 vaccine strain against influenza A (H1N1) pdm09 strains circulating in Kenya during 2015/2016 season was 32.4%. The sub-optimal vaccine efficacy suggests antigenic drift and corresponds with our genetic analysis data. This result corroborates findings of previous works and buttresses the WHO September 2016 decision to update southern hemisphere A (H1N1) pdm09 vaccine strain [21, 22, 41, 45]. The estimated vaccine efficacy between A/Michigan/45/2015 vaccine strain and influenza A (H1N1) pdm09 viruses circulating in Kenya during 2017 season ranged from 39.6 to 41.8%. These estimates imply improved vaccine efficacy and reflect findings of related studies conducted elsewhere [50] Subsequently, the estimated vaccine efficacy of A/Michigan/45/2015 vaccine strain against Kenyan 2018 viruses ranged from 32.4% to 42.1%, indicating a decrease in vaccine effectiveness compared to the preceding year. This observation is attributed to several antigenic amino acid alterations noticed among Kenyan 2018 strains and yet again supports the WHO February 2019 decision to update influenza A (H1N1) pdm09 virus vaccine component for northern hemisphere during the 2019–2020 season A/Brisbane /02/2018 [51]. Assessment of the vaccine efficacy of A/Brisbane /02/2018 vaccine strain against Kenyan 2018 viruses indicated a predicted vaccine efficacy range of 32.2% - 42.1%, suggesting that the new vaccinating virus will not provide improved protection against the circulating viruses.

To investigate whether antigenic drift observed among the Kenyan A/H1N1 pdm09 strains was attributable to the lower vaccine efficacy predictions, HAI assay characterization of the isolates using reference post-infection ferret antiserum was conducted. Data generated revealed that the A/H1N1 pdm09 strains isolated in Kenya during the 2015/2016 influenza seasons had lower HAI titers (≥2-fold) compared to the vaccine strain, suggesting that despite reports indicating that the A/H1N1 pdm09 viruses that circulated in the world during this period were antigenically similar to A/California/7/2009 [52, 53], those circulating in Kenya had undergone considerable antigenic divergence from the vaccine strain, leading to sub-optimal vaccine efficacy. This finding is consistent with that of a similar study reported elsewhere [54], and supports the WHO September 2016 decision to update the A/H1N1 pdm09 strain component of influenza vaccine from A/California/7/2009 to A/Michigan/45/2015 [53]. Consequently, those viruses isolated during 2017/2018 exhibited high HAI titers with the reference antiserum, indicating higher antigenic relatedness with the vaccine strain A/Michigan/45/2015 and improved vaccine efficacy.

This study had a few shortcomings. First, the small number of sequences analyzed for viruses that circulated in Kenya during 2015 /2017 was somewhat not representative. Secondly, we predicted antigenic drift and vaccine efficacy using H3 numbering system [20] since there is yet to be a consensus method for Pepitope calculation in A (H1N1) pdm09 viruses [2, 16, 27]. Despite these shortcomings, we have provided evidence that influenza A (H1N1) pdm09 viruses isolated in Kenya during 2015/2018 belonged to phylogenetic clade 6B, subclades 6B.2 and 6B.1. Moreover, we have demonstrated that the A (H1N1) pdm09 vaccine strain A/ California/07/2009 exhibited sub-optimal vaccine efficacy against Kenyan 2015/2016 strains, denoting antigenic drift among these viruses. Overall, these findings indicate a swiftly evolving A (H1N1) pdm09 virus in recent post pandemic era, underscoring the need for sustained surveillance coupled with molecular and antigenic analyses, to inform appropriate and timely influenza vaccine update.

Accession numbers in GISAID and GenBank databases of HA gene sequences of A/H1N1 pdm09 reference strains included in the analysis.

(DOCX)

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Alignment of HA1 amino acid sequences of A/H1N1 pdm09 strains isolated in Kenya in 2015 with foreign strains, relative to vaccine virus A/California/7/2009.

(PDF)

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Alignment of HA1 amino acid sequences of A/H1N1 pdm09 strains isolated in Kenya in 2016 with foreign strains relative to vaccine virus A/California/7/2009.

(PDF)

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Alignment of HA1 amino acid sequences of A/H1N1 pdm09 strains isolated in Kenya in 2017 with foreign strains relative to vaccine virus A/Michigan/45/2015.

(PDF)

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Alignment of HA1 amino acid sequences of A/H1N1 pdm09 strains isolated in Kenya in 2018 with foreign strains relative to vaccine virus A/Michigan/45/2015.

(PDF)

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12 Nov 2019

PONE-D-19-29545

Assessing Antigenic Drift and Phylogeny of Influenza A (H1N1) pdm09 Virus in Kenya the Using HA1 sub-unit of the Hemagglutinin gene.

PLOS ONE

Dear Mr Opanda,

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Ronald Dijkman, PhD

Academic Editor

PLOS ONE

Journal Requirements:

1. When submitting your revision, we need you to address these additional requirements.

Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

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2. Please amend the manuscript submission data (via Edit Submission) to include authors Wallace Bulimo, George Gachara, Christopher Ekuttan and Evans Amukoye.

Additional Editor Comments:

Dear Author,

Both reviewers and myself, highly recommend you to include HAI antigenic data in the revised version to complement the current in silico data.

Thank you for your time and understanding.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Partly

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: N/A

Reviewer #2: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: In their manuscript, Opanda et. al. describe an in silico study of A/H1N1 viruses circulating in Kenya from 2015-2018. The paper is well written, clear, and the data is important in showing the natural drift in influenza A/H1N1 viruses in Kenya and issues that might arrive from decreased vaccine effectiveness. However, to increase the novelty of the manuscript, more work should be done with data available from other sources as well as in vitro analysis to confirm these in silico results. A few major comments for improvement exist.

1) It is unclear if the strains used in this analysis are new, or are what is already available on the Influenza Research database (fludb.org) and on GISAID (gisaid.org)…if these are new strains, then the 46 available H1N1 strains from Kenya on IRD with addition of non-duplicates from the 121 strains available on GISAID should be included in this study as it will greatly increase the power of the analyses performed. This will alleviate the first limitation of the study, somewhat.

2) Ideally, it would be good to show HAI antigenic data (and even microneutralization data using reference serum) to show how these in silico results correlate to wet laboratory results. Indeed, antigenic cartography of these viruses would be a good way to further show drift and possible vaccine escape. This will definitely alleviate the second shortcoming of the study as mentioned in the discussion.

3) Lines 241-244: This is extremely important information and should be highlighted more in the results, as it is not discussed previously.

4) While calculating the predicted efficacy of the vaccine is useful (under ideal conditions, of course) ,it would be very good to compare the results from this study with the vaccine effectiveness calculated in Kenya for each year so as to show that these predictive values d hold true to their intended results.

5) How does this predicted drift compare to viruses isolated in the greater geographic area? Is there some hypothesis as to why the viruses in Kenya appear to be drifting in such a manner?

Reviewer #2: Overall, the manuscript is well written the main points are clearly stated. The use of bibliography is adequate and the tables and figures are sufficiently clear to help interpret the results.

Comments:

-line 45: The first pandemic of the 21st century was SARS, please amend.

-line 102: what primers? How large is the resulting fragment? Does it cover the complete ORF?

-line 111: M13 sequencing primers: where is the description of the cloning vector and process used to sequence with the M13 primers?

-line 123: What inference method was used? Also, how many generations where run and sampled every how many times. Any % burn-ins excluded? It is also important, going back to line 102, how large the gene fragments are to asses how reliable the analysis is.

-line 125: please provide accession numbers in supplemental material

-line 142: I would indicate that you should also consider the NGlyc result in your estimation of glycosylated sites. The algorithm is very specific in that a >/= score of (++) should be used for describing an N-glycosylation site with high specificity. I would include that criteria as well and only inform those sites that also meet that criteria. A bit more conservative, since you are using only computational analyses throughout this manuscript.

-line 247: It is a good practice to state the shortcomings of a manuscript, however, you should also give a reason why. In this case it would have been very interesting to see how the HAI results correlate to the results of this manuscript. HAI is a relatively simple procedure if you have the reagents. At least give an explanation of why it was not incorporated.

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Reviewer #1: No

Reviewer #2: No

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27 Dec 2019

Below is a point to point response to the comments by the academic editor reviewers:

ACADEMIC EDITOR PLOS ONE:

1. When submitting your revision, we need you to address these additional requirements.

Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming

2. Please amend the manuscript submission data (via Edit Submission) to include authors Wallace Bulimo, George Gachara, Christopher Ekuttan and Evans Amukoye.

3. Both reviewers and myself, highly recommend you to include HAI antigenic data in the revised version to complement the current in silico data.

Thank you, all these have been addressed.

REVIEWER # 1:

In their manuscript, Opanda et. al. describe an in silico study of A/H1N1 viruses circulating in Kenya from 2015-2018. The paper is well written, clear, and the data is important in showing the natural drift in influenza A/H1N1 viruses in Kenya and issues that might arrive from decreased vaccine effectiveness. However, to increase the novelty of the manuscript, more work should be done with data available from other sources as well as in vitro analysis to confirm these in silico results. A few major comments for improvement exist.

1. It is unclear if the strains used in this analysis are new, or are what is already available on the Influenza Research database (fludb.org) and on GISAID (gisaid.org)…if these are new strains, then the 46 available H1N1 strains from Kenya on IRD with addition of non-duplicates from the 121 strains available on GISAID should be included in this study as it will greatly increase the power of the analyses performed. This will alleviate the first limitation of the study, somewhat.

Thank you for this observation. The Kenyan A/H1N1 pdm09 HA gene sequence data used in this study is not new but are the ones already available in the genetic databases under accession numbers: ANH22064 - ARK18942; MH316121, MG815810; MH356637 - MH356647 and MK692755 - MK692776 in the manuscript (lines 119 – 120). The study focused on the sequence data generated during 2015 to 2018 influenza seasons in Kenya.

2. Ideally, it would be good to show HAI antigenic data (and even microneutralization data using reference serum) to show how these in silico results correlate to wet laboratory results. Indeed, antigenic cartography of these viruses would be a good way to further show drift and possible vaccine escape. This will definitely alleviate the second shortcoming of the study as mentioned in the discussion.

Thank you for this suggestion. The HAI assay antigenic data has been incorporated in the revised manuscript to validate the in-silico analyses to alleviate the second limitation of the study (Table 3, lines 250 – 253).

3. Lines 241-244: This is extremely important information and should be highlighted more in the results, as it is not discussed previously.

We concur with the reviewer. A statement highlighting the difference in vaccine efficacies between A/Michigan/45/2015 and A/Brisbane/02/2018 vaccine strains against Kenyan 2018 A/H1N1 pdm09 isolates has been incorporated in the revised manuscript results section (lines 231 – 235). Unfortunately, we could to provide HAI assay antigenic data between the Kenyan 2018 isolates and A/Brisbane/02/2018 reference anti-serum, since our lab ceased being recognized as an NIC in 2018 and therefore we no longer receive HAI assay Kits from WHO CC reference labs.

4. While calculating the predicted efficacy of the vaccine is useful (under ideal conditions, of course), it would be very good to compare the results from this study with the vaccine effectiveness calculated in Kenya for each year so as to show that these predictive values hold true to their intended results.

Thank you for your suggestion. The calculated vaccine effectiveness across the study period (2015-2018) have been presented in the manuscript in lines 221-235 and summarized in Table 2. However, we would like to point out that no vaccine effectiveness studies involving clinical trials are ever done in Kenya due to cost considerations. Hence, we are unable to compare the calculated vaccine effectiveness against predicted/observed (via clinical trials) vaccine effectiveness for Kenya.

5. How does this predicted drift compare to viruses isolated in the greater geographic area? Is there some hypothesis as to why the viruses in Kenya appear to be drifting in such a manner?

Actually, the drifting the A/H1N1 pdm09 strains reported here is not unique to Kenyan viruses. The bulk of amino acid changes observed affecting antigenic sites of the HA1 domain among the Kenyan isolates have also been noticed in the sequences of foreign strains included in the study. To address this question, additional supporting information S1 Fig, S2 Fig, S3 Fig and S4 Fig has been added in the revised manuscript.

REVIEWER # 2:

1. - Line 45: The first pandemic of the 21st century was SARS, please amend.

Thank you for this observation. This has been amended in the revised manuscript (line 45).

2. - Line 102: what primers? How large is the resulting fragment? Does it cover the complete ORF?

These are A/H1N1 pdm09 HA segment-specific primers tagged with flanking universal M13 primers to facilitate ease of sequencing. We have included a citation in the revised manuscript to address this (line101). The HA segment was amplified using two sets of primers to yield two overlapping PCR fragments: frag 1: ⁓ 976 bp and frag 2: ⁓ 890 bp. The contig of the two fragments (1.8 kb) covers the complete open reading frame of the HA gene (lines 106 – 107).

3. -line 111: M13 sequencing primers: where is the description of the cloning vector and process used to sequence with the M13 primers?

We did not clone the amplicons into a vector. Instead the M13 primer tags were incorporated to provide anchors flanking the amplicons to facilitate sequencing of the amplicons. The statement has been edited accordingly in the revised version of the manuscript providing the source detailing the process of sequencing the PCR amplicons with the M13 primers (lines 111-112).

4. -line 123: What inference method was used? Also, how many generations were run and sampled every how many times. Any % burn-ins excluded? It is also important, going back to line 102, how large the gene fragments are to assess how reliable the analysis is.

Thank you for this comment. The phylogenetic tree was constructed using Bayesian Monte Carlo Markov Chain (MCMC) inference method. The MCMC was run for 10 million generations, with sampling every 1000 generations and a 10% burn-in. These statements have been incorporated in the revised manuscript (lines 124 – 128).

5. -line 125: please provide accession numbers in supplemental material.

Thank you for your suggestion. Accession numbers of HA sequences of all reference strains included in the analysis has been provided as supplemental material (S1 Table).

6. -line 142: I would indicate that you should also consider the NGlyc result in your estimation of glycosylated sites. The algorithm is very specific in that a >/= score of (++) should be used for describing an N-glycosylation site with high specificity. I would include that criteria as well and only inform those sites that also meet that criteria. A bit more conservative, since you are using only computational analyses throughout this manuscript.

We concur with the reviewer’s comment. A score of (++) describes an N-glycosylation with high specificity. However, as stated in line 146 of the manuscript we considered as score cut-off value of > 0.5 suggestive of an N-glycosylation. We took (+) and (++) scores as evidence of potential glycosylation.

7. -line 247: It is a good practice to state the shortcomings of a manuscript, however, you should also give a reason why. In this case it would have been very interesting to see how the HAI results correlate to the results of this manuscript. HAI is a relatively simple procedure if you have the reagents. At least give an explanation of why it was not incorporated.

Again, we appreciate the reviewer’s comment. We have not only mentioned the shortcomings but also addressed the strengths of the study. The HAI assay antigenic data has been incorporated in the revised manuscript (lines 251 – 254) and they corroborate the PEpitope findings which is reassuring about the power of PEpitope model.

Submitted filename: Response to Reviewers.doc

Click here for additional data file.

7 Jan 2020

Assessing Antigenic Drift and Phylogeny of Influenza A (H1N1) pdm09 Virus in Kenya Using HA1 sub-unit of the Hemagglutinin gene.

PONE-D-19-29545R1

Dear Dr. Opanda,

All the best wishes for 2020.

We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.

Within one week, you will receive an e-mail containing information on the amendments required prior to publication. When all required modifications have been addressed, you will receive a formal acceptance letter and your manuscript will proceed to our production department and be scheduled for publication.

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If your institution or institutions have a press office, please notify them about your upcoming paper to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, you must inform our press team as soon as possible and no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

With kind regards,

Ronald Dijkman, PhD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: (No Response)

Reviewer #2: (No Response)

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

3 Feb 2020

PONE-D-19-29545R1

Assessing Antigenic Drift and Phylogeny of Influenza A (H1N1) pdm09 Virus in Kenya Using HA1 sub-unit of the Hemagglutinin gene.

Dear Dr. Opanda:

I am pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

For any other questions or concerns, please email plosone@plos.org.

Thank you for submitting your work to PLOS ONE.

With kind regards,

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on behalf of

Dr. Ronald Dijkman

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PLOS ONE