Emergence of a novel chikungunya virus strain bearing the E1:V80A substitution, out of the Mombasa, Kenya 2017-2018 outbreak.

Between late 2017 and mid-2018, a chikungunya fever outbreak occurred in Mombasa, Kenya that followed an earlier outbreak in mid-2016 in Mandera County on the border with Somalia. Using targeted Next Generation Sequencing, we obtained genomes from clinical samples collected during the 2017/2018 Mombasa outbreak. We compared data from the 2016 Mandera outbreak with the 2017/2018 Mombasa outbreak, and found that both had the Aedes aegypti adapting mutations, E1:K211E and E2:V264A. Further to the above two mutations, 11 of 15 CHIKV genomes from the Mombasa outbreak showed a novel triple mutation signature of E1:V80A, E1:T82I and E1:V84D. These novel mutations are estimated to have arisen in Mombasa by mid-2017 (2017.58, 95% HPD: 2017.23, 2017.84). The MRCA for the Mombasa outbreak genomes is estimated to have been present in early 2017 (2017.22, 95% HPD: 2016.68, 2017.63). Interestingly some of the earliest genomes from the Mombasa outbreak lacked the E1:V80A, E1:T82I and E1:V84D substitutions. Previous laboratory experiments have indicated that a substitution at position E1:80 in the CHIKV genome may lead to increased CHIKV transmissibility by Ae. albopictus. Genbank investigation of all available CHIKV genomes revealed that E1:V80A was not present; therefore, our data constitutes the first report of the E1:V80A mutation occurring in nature. To date, chikungunya outbreaks in the Northern and Western Hemispheres have occurred in Ae. aegypti inhabited tropical regions. Notwithstanding, it has been suggested that an Ae. albopictus adaptable ECSA or IOL strain could easily be introduced in these regions leading to a new wave of outbreaks. Our data on the recent Mombasa CHIKV outbreak has shown that a potential Ae. albopictus adapting mutation may be evolving within the East African region. It is even more worrisome that there exists potential for emergence of a CHIKV strain more adapted to efficient transmission by both Ae. albopictus and Ae.aegypti simultaneously. In view of the present data and history of chikungunya outbreaks, pandemic potential for such a strain is now a likely possibility in the future. Thus, continued surveillance of chikungunya backed by molecular epidemiologic capacity should be sustained to understand the evolving public health threat and inform prevention and control measures including the ongoing vaccine development efforts.

Introduction

Chikungunya virus is a mosquito transmitted alphavirus that was first isolated during an outbreak of febrile illness in Tanzania in 1952 [1]. Since then, CHIKV has caused many outbreaks, widely distributed around the globe [2]. From mid-December 2017 to mid-May 2018, an outbreak of chikungunya fever occurred in the coastal county of Mombasa [3]. The Mombasa outbreak followed an earlier outbreak of chikungunya in Mandera county on the Kenyan border with Somalia in 2016 [4]. The Mandera outbreak occurred 12 years after the coastal Kenya outbreak which began in Mombasa city and Lamu Island concurrently [5]. That outbreak later spread to the Indian Ocean basin, South East Asia and Europe [6, 7]. During the Indian and Indian Ocean phases of the outbreak, convergent genome microevolution led to the E1-A226V amino acid substitution in the CHIKV glycoprotein. This resulted in a strain that was highly adapted for transmission by Aedes albopictus as seen in La Reunion and elsewhere [8, 9].

As the outbreak progressed more Ae. albopictus adapting mutations developed, however these mutations were observed to have no effect on transmissibility of the virus by Ae. aegypti [10-12]. In 2006 a mutation within the E1 protein, E1:K211E was detected for the first time in Kerala and Puducherry, India [13]. Subsequently in 2009 a second mutation within the E2 protein, E2:V264A, was also detected in several regions of India [13-15]. In the background of a wild type E2:226A, the two mutations increase chikungunya fitness for Ae. aegypti while having no effect on virus fitness for Ae. albopictus, [16]. The severity of these outbreaks were linked to the mutations in the envelope proteins E1 and E2 within the IOL strain [12, 17]. Mutations within the glycoprotein have been shown to increase CHIKV fitness by up to 100 fold for Ae. Albopitus transmission and up to 62 fold for Ae. aegypti transmission [16, 17].

The alphavirus glycoproteins E1, E2 and E3 are involved in virus interaction with host cells and therefore determine efficiency of disease transmission and the host immune response mechanism. Glycoprotein E1 mediates cell fusion [18], glycoprotein E2 is important for interaction with the Host receptors and glycoprotein E3 facilitates E1-p62 heterodimerization and prevents the exposure of E1 fusion loops prematurely [19, 20]. Thus, sequential adaptive mutations within the CHIKV genome and more so in the envelope protein may influence efficient virus circulation and persistence in endemic areas, and could further increase the risk of more severe, bigger, and expanded CHIKV epidemics [12]. Viral variant evolution may continue to generate strains with complicated pathogenicity as shown during the Indian and Indian Ocean outbreaks [8]. Additionally, there has been a gradual expansion in the range of areas inhabited by Ae. albopictus [21]. It follows then that any emerging Ae albopictus adapted chikungunya virus strains may be transmitted in these new areas [22-24].

In the present study, we characterized novel mutations in the E1 protein of viruses from the Mombasa 2017/2018 CHIKV outbreak. The objective was to compare genomes generated from this outbreak with those from the Mandera outbreak of 2016 and other IOL strains to determine the genome structure, molecular features and signatures unique to the Mombasa 2017/2018 outbreak genomes.

Methods

Ethics statement

The study was carried out on a protocol approved by the Walter Reed Army Institute of Research’s Institutional Review Board (#2189.0003) and Kenya Medical Research Institute’s Scientific and Ethics Review Unit (#3035) as an overarching protocol guiding investigation and reporting of arbovirus/hemorrhagic fever outbreaks in Kenya.

Patient samples and sequencing

Following widespread incidence of febrile illness cases in Mombasa County in mid-December 2017, samples were collected through the Kenya Ministry of Health (KMoH). During the course of the outbreak spanning 5 months, samples were obtained from patients as per standard KMoH procedures. Briefly, a patient presenting with sudden onset of fever >38.5° C, headache, severe joint pains and/or muscle pains while residing in Mombasa County within the preceding 3–5 days was considered a case. RNA was extracted from 17 human serum samples using TRIzol reagent according to manufacturer’s instructions (Invitrogen, Carlsbad, CA). All the samples were subjected to cDNA synthesis using superscript III (Invitrogen, Carlsbad, CA) followed by targeted amplification using a set of 8 overlapping PCR primer pairs (Table 1). Prior to library preparation, PCR amplicons for each of the primer pair were combined in equal volumes for each of the samples, followed by cleaning using Agencourt Ampure XP beads (Beckman Coulter, Beverly, MA, USA). The cleaned amplicons were quantified using the Qubit 3.0 dsDNA HS Assay Kit (ThermoFisher Scientific Inc., Wilmington, DE, USA). Library preparation was performed using Nextera XT DNA Sample Prep Kit (Illumina, San Diego, CA, USA) according to the manufacturer’s instructions. Briefly, 1ng of the PCR amplicons was used as starting material. Nextera XT Index Kit (Illumina, San Diego, CA, USA) was used to uniquely barcode the samples. Libraries were normalized before pooling using standard library normalization process (Illumina, San Diego, CA, USA). Sequencing was performed on an Illumina Miseq platform using Miseq reagent kit V3 (Illumina, San Diego, CA, USA), generating 2 x 300 base paired-end reads. Raw sequence reads obtained from sequencing were initially inspected for quality using FASTQC [25]. Initial quality control was performed with prinseqLite [26]. Sequence reads from this study were submitted to the Sequence Read Archive (SRA) under reference PRJNA655685. Filtered reads were used as input in performing both de novo and reference-guided sequence assembly using SPAdes v3.10 [27] and NGS_Mapper v1.5 [28]. Polishing of the final consensus sequences and inspection for possible primer induced mutations were performed manually using BAM files generated by NGS_Mapper and visualized in the Integrative Genomics Viewer [29]. The 17 Sequences obtained from this study have been submitted to Genbank under accession numbers MT380146-MT380162.

Table 1

Primers used for PCR.

Five combinations of the primers were used to generate amplicons for NGS sequencing.

Sequence Name (Forward)	Sequence	Sequence Name (Reverse)	Sequence
1CDSF_Kenya	5'—ATGGATCCTGTGTACGTGGAC—3'	3504R_Kenya	5'—TAATAGGCCTGGAGGGAARATG—3'
3332F_Kenya	5'—TAATAGGCCTGGAGGGAARATG—3'	7495R_Kenya	5'—AGGACCGCCGTACAAAGTTAT—3'
5065F_Kenya	5'—TGCACAGGARGCGAGTACAATC—3'	7495R_Kenya	5'—AGGACCGCCGTACAAAGTTAT—3'
7200F_Kenya	5'—ACGATACTGTGACAGGAACAGCTTG—3'	8403R_Kenya	5'—GGAGCAGGGGAACGTGGTGTTCG—3'
7200F_Kenya	5'—ACGATACTGTGACAGGAACAGCTTG—3'	10627R_Kenya	5'—GTCTTCYCTCTCAGGCGTGCGACTTT—3'

Dataset generation and alignment

The generated sequences were combined with publicly available sequences from Virus Pathogen Resource database, including CHIKV sequences obtained during the outbreak in Mandera, northeastern Kenya in 2016. All the complete genome sequences (n = 991) were downloaded and combined with the sequences from this study. Each of these sequences was linked to the country of origin as well as the date of isolation. A manual literature search was done for sequences without relevant information; those that could not be assigned were excluded from further analysis. All the remaining sequences (n = 751) were then sorted according to the country of origin. Down-sampling of the sequences was performed on each of the countries from outside Africa using Cd-hit [30]. We used a similarity threshold of between 99.5 to 99.9 percent so as to end up with a relatively equal or near equal number of sequences while at the same time preserving the diversity of sequences from each of the individual countries. All the down-sampled sequences from the different countries were combined (n = 263).

Multiple sequence alignment was performed with MAFFT v7 [31, 32]. The aligned sequences were trimmed leaving the two CHIKV open reading frames only. Thus, the 5’ and 3’ untranslated regions as well as the non-coding intergenic region between the non-structural and structural proteins were trimmed off. Furthermore, the region corresponding to the 7-aa deletion within the NSP3 region that is found among the Western Hemisphere Chikungunya viruses was also removed.—The alignment was further checked for identical sequences using Cd-hit with -C equivalent to 1. Only one sequence was picked from each cluster of similar sequences.

The final sequence alignment (n = 254) was scanned for recombination using RDP4 software suite [33]. Recombinant sequences were determined if positive recombination signal, P = 0.05, was detected by all the three primary methods used (RDP, Genecov and Maxchi) and at least one of the secondary methods (Recscan and Siscan).

Phylogenetic analysis

Phylogenetic analysis was performed using both the Maximum Likelihood, IQTREE [34], and the Bayesian (MrBayes) [35] Methods. We first tested the best nucleotide substitution model for our alignment using jModeltest2 [36]. Consequently, the best-fit substitution model was determined as General Time Reversible with Gamma distribution, GTR+G. This model was applied to both the ML and Bayesian phylogenetic analysis. Maximum Likelihood analysis was performed for 2000 bootstrap replications. Metropolis Coupled Markov Chain Monte Carlo method (MCMCMC) was applied for MrBayes, with 5 million generations performed in duplicate with 25% as burnin. The convergence of runs was determined by the average standard deviation in split frequencies of less than 0.01 as suggested by the software developers [35].

Molecular clock analysis and phylogeography

Molecular clock analysis and phylogeography was performed using only the sequences that fell under the ECSA lineage from our initial phylogenetic analysis. To determine the appropriateness of our dataset in the estimation of temporal parameters, we first assessed the root-to-tip regression of our sequence data using TempEst [37]. Subsequently, analysis was performed using Bayesian Evolutionary tools by Sampling Trees (BEAST v1.10.4) [38]. Sequences were assigned to either of the following geographical regions; Africa, Americas, East Asia, Europe, Indian Ocean Is., SE Asia/Oceania and South Asia. The Kenyan sequences were given Kenya as their geographical trait location. The analysis was performed using a relaxed molecular clock model (uncorrelated log-normal) with GTR+G model of substitution as determined by jModeltest2 [36]. A flexible non-parametric Bayesian Skyline model was used to estimate past population dynamics, and an asymmetric trait model was used to estimate ancestral states. Two independent runs of 500 million generations were performed and the output from the runs was combined after removal of 10% of the trees as burnin. To ensure adequate effective sample size (>200) was achieved, the logs from the analysis were assessed using Tracer [39]. The Maximum Clade Credibility Tree was visualized in Figtree.

Selection pressure estimation

Selection pressure estimation was performed using both the ECSA lineage dataset used for BEAST analysis and the dataset used for ML analysis. Non-synonymous/synonymous (dN/dS) ratio substitution was estimated separately on the alignment corresponding to the non-structural and the structural region of the virus using the BEAST data as well as the Mombasa outbreak data. Site-specific selection was performed using FEL (p < 0.01) and FUBAR (prob. > 0.99), both applied within the Hyphy v2.5 package [40]. Episodic selection analysis was performed in the Datamonkey webserver [41] using Mixed Effect Method of Evolution (MEME; p < 0.01). Selection on a given site was considered present when all the three methods detected positive selection at that given site.

Protein structure modelling

Mutations with potential functional significance that were unique to the Mombasa outbreak strain were mapped onto the CHIKV E1-E2 heterodimer (Protein Data Bank ID: 3N42) [42] using the Pymol Molecular Graphics System (The PyMOL Molecular Graphics System; DeLano Scientific, Palo Alto, CA).

Results

Origins of the chikungunya virus strain causing the Mombasa 2017/2018 outbreak

Seventeen (17) samples from the Mombasa 2017/2018 outbreak were sequenced. The sequenced samples covered the period between mid-December 2017 and mid-May 2018. Fifteen (15) of the samples produced near full CHIKV genomes, with two producing partial genomes with over 50% coverage. No recombination was detected in any of these genomes. Maximum likelihood and Bayesian phylogenies had a similar topology and showed that the Mombasa outbreak strain belonged to the CHIKV Indian Ocean sub-Lineage (IOL), within the ECSA lineage (Fig 1). All the Mombasa genomes clustered in a monophyletic clade with minimal diversity amongst them, as would be expected of samples from the same outbreak (Figs 1 and 2). The Mombasa outbreak cluster is closely related to the China strain of 2017 (Fig 2). The most recent common ancestor (MRCA) of the Mombasa 2017/2018 strain is estimated to have existed in early 2017 (2017.22, 95% HPD: 2016.68–2017.63). The Mombasa 2017/18 outbreak strain is estimated to have diverged from the 2017 China strain and the closely related Hong Kong 2016 strain in May 2016 (2016.43, 95% HPD: 2015.27–2017.11). The MRCA for the Mombasa, 2017–2018 strain and the Mandera 2016 strain is estimated to have existed late 2008 (2008.86, 95% HPD: 2007.34, 2010.90), (Fig 2). Whereas the Mandera outbreak originated from the India 2016 strain, the Mombasa outbreak originated from the India 2015 strain, whose MRCA is estimated to have existed in early 2011 (2011.27, 95% HPD: 2009.03, 2013.35) (Fig 2).

Fig 1

Maximum likelihood phylogenetic tree based on the complete-coding region of the four CHIKV lineages.

The Mombasa 2017 outbreak genomes are highlighted in red and the Mandera outbreak strain is highlighted in blue. The Lamu/Mombasa 2004 strains are shown in green. The ECSA lineage with tip-labels is expanded.

Fig 2

Maximum clade credibility (MCC) tree generated using BEAST analysis.

Generated using 130 CHIKV sequences of the ECSA lineage. Mombasa genomes with E1:V80A, E1:T82I and E1:V84D mutations are shown in red, while those lacking these mutations are shown in pink.

Novel mutations associated with increased fitness to Ae. albopictus emerged in Mombasa in mid-2017

Investigation of amino acids in Mombasa genomes revealed the presence of novel potential Ae. albopictus adapting glycoprotein mutations [43] in 11 of 15 CHIKV genomes. The E1:V80A, E1:T82I and E1:V84D mutations (Fig 2; in red) are herein reported for the first time in nature. This triple mutation signature seems to have emerged in Mombasa in mid- 2017 (2017.63, 95% HPD = 2017.33–2017.87). Four genomes from the outbreak did not have any of the three mutations (Fig 2; in purple). These Four are estimated to have existed within Mombasa early 2017 (2017.22, 95% HPD = 2016.68–2017.63). The other Mombasa unique substitution is E1:L136F. Another substitution observed in the present study is NSP4: R85G, which was present in the East Asia strain of 2015–2017 and maintained in the Mombasa 2017/2018 genomes. The R85G Change is lacking in the Mandera 2016 strain. Other substitutions observed in the Mombasa/Hong Kong/China 2017 clade but absent in the Mandera 2016 strain include E2:M74I, E2: A76T and Capsid: N79S. These substitutions were also observed in genomes within the South East Asia 2012–2017 clade.

Positive selective pressure was acting on NSP1-171

The present study investigated the role of positive pressure on the appearance of the observed mutations in relation to virus adaptation. Whereas none of the new mutations observed were under selective pressure, significant positive selection was detected at position NSP1:R171 by all the three methods used, namely, FUBAR (probability> 0.95), FEL (P < 0.05), and MEME (P < 0.01) in both datasets. Additionally, for the present dataset positions E1: 210, E1: 211 and E2:264 did not show any evidence of positive selection (Table 2).

Table 2

Site-specific selection pressure estimates based on two different datasets (BEAST = 247 genomes and ML = 130 genomes).

Datasets		FEL (p < 0.01)	FUBAR (p < 0.01)	MEME prob. > 0.99
ML dataset	Nonstructural protein	NSP1: 171	NSP1: 171	NSP1: 171, NSP2: 122, NSP2: 457, NSP3: 516, NSP4: 81, NSP4: 466, NSP4: 467, NSP4: 496
	Structural protein			E2: 57, E2: 221, E1: 146
BEAST dataset	Nonstructural protein	NSP1: 171	NSP1: 171, NSP4: 467	NSP1: 171, NSP2: 457, NSP3: 516, NSP4: 81, NSP4: 466, NSP4: 467
	Structural protein			E1: 146

The results of the three different methods; FEL (p value < 0.01), FUBAR (prob. > 0.99) and MEME (p value < 0.01) are given.

The E1:V80A, E1:T82I and E1:V84D signature induced structural change within CHIKV E1-E2 molecular structure

The Mombasa unique molecular signature, the triple mutation of E1:V80A, E1:T82I and E1:V84D, caused a conformational change when compared to the wild-type (Fig 3B). Among the three mutations, it is only the E1:V84D substitution that introduced a negative charge.

Fig 3

Protein figures showing; A) ribbon representation of CHIKV E1-E2 heterodimer (ID: 3N42) with the E1 monomer represented in blue and the E2 monomer represented in orange. Amino acid residues with potential functional significance are represented in spheres within the figure. B) Surface representation of the same E1-E2 heterodimer showing, i) wild type amino acids and ii) the reported mutations.

Discussion

Chikungunya glycoproteins have been shown to play a critical role in the emergence, transmission and spread of the virus. The E1:A226V substitution, which adapts CHIKV to Ae. albopictus, is credited with the IOL global outbreaks that occurred after the initial 2004 coastal Kenya outbreak. Beyond this initial Ae. albopictus adapting mutation, CHIKV has continued to undergo genome evolution, bringing about incremental fitness for both Ae. albopictus and Ae. aegypti [12, 14]. Coincidentally, the presence of the E1:A98T mutation in the Asian genotype that has recently circulated in the Americas is believed to constrain Chikungunya from infecting Ae. albopictus [11, 44]. Notwithstanding, it has been suggested that an Ae. albopictus adaptable ECSA or IOL strain could easily be introduced in these regions leading to a new wave of outbreaks [44, 45]. Even though no Ae albopictus populations have been detected in Kenya, this invasive species of mosquito has recently been detected in countries in West Africa and Central Africa [46, 47]. Phylogenetic analyses of the IOL have reported continued evolution of CHIKV since 2004 [48]. These analyses continue to track genome changes in relation to vector transmission. Our phylogenetic analysis adds to this growing body of data on the IOL (Figs 1 and 2), with the time-calibrated phylogeny showing a short period between the appearance of the Mandera and Mombasa strains (Fig 2).

The present study reports a novel CHIKV strain with a potential Ae. albopictus adapting mutation; V80A, evolving within the Kenyan Coast. It is instructive that the V80A substitution and its ability to adapt CHIKV to efficient transmission by Ae. albopictus was hitherto demonstrated within laboratory settings [10, 43, 44]. We suggest that two other mutations in the Mombasa strain, E1:T82I and E1:V84D, that are adjacent to V80A, might modulate the action of the V80A substitution. Especially since, they also lie within the CHIKV fusion loop (Fig 3A). In fact, previous mutagenesis studies showed that a substitution at position E1:82 arises as second step to that of position 80 [43]. Thus, this may lend credence to the suggestion that the E1:T82I substitution in the Mombasa 2017 CHIKV strain potentially supports the E1:V80A action. Interestingly, a substitution at position 84 has not hitherto been demonstrated elsewhere, in-vitro, or otherwise. However, the protein models generated for genomes in this study reveal that together, the three mutations introduce conformational changes on the surface of the E1 protein (Fig 3B). The E1 glycoprotein residue at position 80 plays a central role in chikungunya infectivity and dissemination by modulating viral fusion and cholesterol dependence [43]. As such, whereas the E1-80 position is traditionally involved in CHIKV cholesterol-dependent entry, the E1-V80A variant abrogates the need for cholesterol use (34). Therefore, the Mombasa 2017 CHIKV strain may potentially be amenable to Ae. Albopictus transmission as observed in laboratory based studies elsewhere [10, 44]. If this should be the case, then the Mombasa 2017 CHIKV strain would be a potential public health concern, considering that the success of the IOL outbreaks during 2005–2007 were largely premised on the E1-V226A substitution, which caused the virus to have decreased need for cholesterol dependence for CHIKV fusion [49]. Furthermore, E1-V80A, was previously shown to be the most stable variant among all position 80 mutants [43]. Thus based on this published evidence, we suggest that, the observed micro-evolutionary genome changes in respect to position E1: 80, in the Mombasa 2017 strain might be stable and sustained in the long run if it were to spread to Ae. albopictus infested areas. Interestingly, the early samples from the Mombasa 2017 outbreak lacked the E1-V80A, E1-T82I, E1-V84D signature (Fig 1). Consistent with this pattern, the acquisition of the A226V substitution was observed later in the 2005 outbreak on Re-union Islands with all earlier samples showing the wild type 226A [8, 16]. Thus, we speculate that as with the acquisition of A226V on the reunion Islands, the observed triple mutation signature of E1-V80A, E1-T82I, E1-V84D may have solely been acquired in Mombasa. Therefore, continued surveillance will be necessary to decipher the extent of public health consequences of the E1-V80A, E1-T82I, E1-V84D signature, if any, beyond the Kenyan coast.

In addition to the novel amino acid substitutions associated with Ae. albopictus in this study, the E1: K211E and E2:V264A double mutation associated with Ae. aegypti adaptation [16], was observed. The E:1 K211E and E2:V264A double mutation was also observed in the 2016 Kenyan outbreak strain [4]. Whereas previous studies have shown strong positive selection for residues in the CHIKV E protein [4, 16], none seems to have been selected for the present study. On the contrary, the present study shows significant positive selection of position 171 in the NSP1 protein involved in virus replication (Table 2) pointing to potential evolution towards efficiency in viral replication. It will therefore be interesting to assess selection pressure on this strain in an Ae. albopictus environment. We suggest that more studies on the transmissibility of this strain be carried out in order to assess in-vivo capacity for transmission under field conditions.

The emergence of a CHIKV with ability for simultaneous efficient transmission by both Ae. aegypti and Ae. albopictus presents a worrisome public health scenario, especially for areas infested with Ae.albopictus. Notwithstanding, certain mutations may be confined to regions within which they were first reported. This was particularly observed for the L210Q substitution, which is thought to have been selected in Kerala India because of specific ecological conditions, since it has not been observed in any other strain [10, 50, 51]. Whether this might end up the same for the substitutions in the Mombasa IOL sub-lineage, can only be demonstrated through continued surveillance.

Finally, our phylogenetic analyses indicate that the Mombasa 2017/2018 and the Mandera 2016 outbreaks were not directly connected to each other. The trees show that the viruses from the two outbreaks shared a common ancestor in late 2008 and were introduced into Kenya on two separate occasions. Both Mombasa and Mandera strains were most closely related to viruses circulating in Asia, suggesting standing routes of virus dissemination between Asia and Africa, and possibility of targeted prevention strategies once these routes are identified. Given that both strains also carried the E1:K211E and E2:V26A mutations further supports the notion of fixation and continuous spread of the E1: K211E and E2:V264A strains, resulting in additional outbreaks. Whereas there seemed to be no new defining genome evolutionary events with the Mandera strain, the Mombasa strain resulted in additional mutations with potential public health implications. With historical hindsight as far back as the 2004 CHIKV outbreak, continued surveillance of CHIKV in East Africa is advised.

Conclusion

The current study has unveiled a novel CHIKV strain within the Mombasa 2017 outbreak. While providing an update on chikungunya evolution, the study points to the fact that the chikungunya virus may continue to present public health challenges on the global stage. The importance of this data should be seen in the light of providing health authorities with information to help generate policies that could mitigate against the potential spread of this potential emergent sub-lineage of the IOL, to new areas. This, more so when looked at in historical context whereby the subsequent spread of a novel lineage of the chikungunya virus that emerged in Coastal Kenya in 2004, caused debilitating disease in approximately 10 Million people with deaths in the 1000s being reported [6, 7, 52–54]. Furthermore, It has been suggested elsewhere [55] that East and Central Africa may have a large diversity of chikungunya strains that are co-circulating and are yet to be sampled. Continued surveillance and search for new strains will prepare health authorities with requisite information for control and vaccine development efforts.

16 Jul 2020

PONE-D-20-18596

Emergence of a novel chikungunya virus strain out of the Mombasa, Kenya 2017-2018 outbreak.

PLOS ONE

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specifically, The 3 reviewers acknowledged the interest and quality of the results but asked for some improvement of the presentation and discussion. More importantly, more details about the NGS data have to be provided in order to give a stronger background as requested by the 1rst reviewer. As noted by the 2 rd reviewer, to adress the role of mutation in increasing CHIKV vector transmissibility, experimental proof have to be provided thus this part of the discussion should be balanced and is mainly (in the context of this article) suggestion for future work.

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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: While l agree with the authors that the emergence of CHIK in Ae. aegypti would potentially be a major issue for global public health, I have some fundamental issues with the strength of the argument they present. There are also some potentially important technical issues that need to be addressed.

Technical issues

1. the authors describe a targeted amplicon sequencing approach for CHIK, but while they present the primer sequences, they only indicate “5 combinations of these primers” but - not the specific combinations or predicted amplicons. this should be corrected.

2. Furthermore, they do not provide any data on the quality of sequencing, number of reads per library, chemistry used other than “NexteraXT” which isn’t detailed enough (2x150, 2x250? single end reads?). ALthough the genomes are deposited to GenBank, the raw sequencing reads are not available from SRA, thus preventing anyone from attempting to reproduce their results. This must be addressed.

3. the sequencing library approach they used is seriously flawed. PCR amplicons from separate single-plex reactions, which are then pooled in “equal volumes” and then cDNA purified and a library prep’d is a poorly thought out approach. There’s no ability to control for amplification efficiency across reactions, and the “equal volumes” of amplicons could have orders of magnitude differences in reads from each reaction.

4. the bioinformatics Pipeline is poorly described. De novo assembly of deep amplicon sequencing data is not a good approach unless you do substantial preprocessing of the raw reads to downsample your data. SPADES assembler inparticular can produce assembly graphs that make no sense simply by overloading the algorithm with large amounts of identicle reads, while maintaining a relatively low error rate. This is because sheer numbers of reads with errors will create incorrectly assembled contigs. Second - the “NGS_Mapper” tool they reference has... no reference... what is this? I’ve never heard of it, and there’s no citation for it. Was this a place holder the PI put in that the postdoc was supposed to “please fill in the mapper name” ? this is hopefully the error made here. In any case, even if they names the mapper - (read mapping the appropriate approach to take with known small RNA virus genomes... no assembly) - the pipeline must account for how the primers are trimmed and removed from the reads. Since they did not present any details on the amplicons, but indicated NexteraXT as the library prep chemistry, I’m left to assume that tagmentation was performed, thereby shredding the amplicons into semi random fragments (as we would expect). the issue though is that once this is done you must remove the primer sequences _after mapping_ your reference genomes - otherwise you will have significant and unwanted bias in your consensus sequences.

5. The comments made about protein structural changes need to be toned done. These are predictions ONLY, and are at best “potential” changes that “might” result in structural changes. Without structural / biochemistry studies - they can’t be sure.

6. The phylogenetic analysis of the paper is the strongest aspect of their study. They did a very thorough job on this and (correctly) used a GTR+g model for the ML tree. thumbs up on this.

Fundamental Issue

a/ lastly - the authors have discovered three new mutations from an outbreak in Mombasa. They use “potential(ly)” at least three times in the abstract - but claim this -potential- mutations are somehow connected with Ae. aegypti transmissibility. But... no lab work was done to show these mutations have anything to do with host fitness, virus life cycle, or anything to do with increased pathogenicity. At best - these are forensic markers for the outbreak they investigated - and while interesting that three mutations were unique to this outbreak in Mombasa - that’s as far as the authors should speculate.

I’m happy to discuss this review with the authors if they disagree.

Reviewer #2: The authors obtained 17 human serum samples positive to chikungunya virus (CHIKV) collected from the 2017-2018 Mombasa outbreak. They were able to sequence 14 full genomes and 3 partial genomes from these samples. The full genomes were placed in a phylogenetic context within the Indian Ocean sublineage (IOL), including the 2016 Manadera outbreak and the 2004 initial coastal Kenya outbreak. They didn’t find the Aedes albopictus adapting mutation E1:A226V but observed Aedes aegypti adapting mutations E1:K211E and E2:V264A previously detected in India. They also found mutations specific to the 2017-2018 Mombasa outbreak, E1:S210R, E1:V80A, E1:T82I and E1:V84D that could bring some increase of transmission in A. albopictus.

The analyses in the manuscript sound appropriate, figures are clear and the results support the conclusions. I have one major comment and few minors.

Major comment:

- The authors choose to mainly focus on describing mutations that could increase CHIKV vector transmissibility. I found that the manuscript lacks of results and discussion about the 2017-2018 Mombasa outbreak itself. The manuscript would benefit if the authors describe a bit more the phylogenetic patterns of the 2017-2018 Mombasa outbreak and discuss about its origin and spread.

Minor comments:

- A sequence assembly statistics table should be included in the manuscript to allow the readers to evaluate sequence and mutation robustness.

- The three partial genomes that are not included in the phylogenetic analyses could be added if they have at least 50% of sequence coverage

- A careful proofread of the manuscript is necessary to remove typos that are disseminated all along it

Reviewer #3: This study analyzed and compared 14 CHIKV complete genome sequences obtained from patients and identified a triple mutation signature (E1-V80A, E1-T82I, E1-V84D) and a double mutation (E1: K211E, E2:V264A) both associated with Aedes albopictus and Aedes aegypti adaptations in these CHIKV circulating in Mombasa, Kenyan coast. Conclusions state that a large diversity of CHIKV strains circulate in east Africa, which may emerge as a worldwide public health problem in the future, emphasizing necessity for constant surveillance and urgent vaccine development to protect people.

Title should reflect the mutations involved in mosquito adaptations (both species).

Please complete authors’ addresses

Please correct the word Alphavirus (genus) or alphavirus (in general when referring to any species from this genus). Alpha-virus is not correct (see ICTV website for virus taxonomy).

Backgroud lacks a little of “introduction” to the virus itself.

Please check for CHKIV incorrect abbreviation (correct for CHIKV).

Results: “a period spanning 5 months from mid-December 2017 to mid-May 2018.”

Two tables are assigned as “Table 1” (primer sets and site-specific selection pressure.

Phylogenetic trees are in low resolution and is not possible to read anything.

Please separate background and discussion paragraphs. Discussion does not consider phylogenetic trees, molecular clock, only the mutations (please discuss as well).

**********

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Reviewer #1: Yes: Jonathan L. Jacobs

Reviewer #2: No

Reviewer #3: No

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17 Sep 2020

Reviewer 1.

Technical issues:

1. The authors describe a targeted amplicon sequencing approach for CHIK, but while they present the primer sequences, they only indicate “5 combinations of these primers” but - not the specific combinations or predicted amplicons. This should be corrected.

We concur with the reviewer, we have added specific combinations of primers and made a correction in the table of primers.

2. Furthermore, they do not provide any data on the quality of sequencing, number of reads per library, chemistry used other than “NexteraXT” which isn’t detailed enough (2x150, 2x250? single end reads?). Although the genomes are deposited to GenBank, the raw sequencing reads are not available from SRA, thus preventing anyone from attempting to reproduce their results. This must be addressed.

We agree with the reviewer, we have corrected this section. We have also now deposited raw reads to SRA and updated the results section. The SRA reference is PRJNA655685.

3. The sequencing library approach they used is seriously flawed. PCR amplicons from separate single-plex reactions, which are then pooled in “equal volumes” and then cDNA purified and a library prep’d is a poorly thought out approach. There’s no ability to control for amplification efficiency across reactions, and the “equal volumes” of amplicons could have orders of magnitude differences in reads from each reaction.

We have used this approach before and seemed to have worked fine with publications at virus evolution and AJTMH. It seemed not to affect the eventual outcome of the sequencing run. Additionally, our study was not looking into minor variants such that we needed to equalize read coverage across the genome, we were only doing a consensus analysis. We appreciate the observation of the reviewer though and hope to consider it on our next project.

4. The bioinformatics Pipeline is poorly described. De novo assembly of deep amplicon sequencing data is not a good approach unless you do substantial preprocessing of the raw reads to down sample your data. SPADES assembler inparticular can produce assembly graphs that make no sense simply by overloading the algorithm with large amounts of identicle reads, while maintaining a relatively low error rate. This is because sheer numbers of reads with errors will create incorrectly assembled contigs. Second - the “NGS_Mapper” tool they reference has... no reference... what is this? I’ve never heard of it, and there’s no citation for it. Was this a place holder the PI put in that the postdoc was supposed to “please fill in the mapper name”? This is hopefully the error made here. In any case, even if they names the mapper - (read mapping the appropriate approach to take with known small RNA virus genomes... no assembly) - the pipeline must account for how the primers are trimmed and removed from the reads. Since they did not present any details on the amplicons, but indicated NexteraXT as the library prep chemistry, I’m left to assume that tagmentation was performed, thereby shredding the amplicons into semi random fragments (as we would expect). The issue though is that once this is done you must remove the primer sequences _after mapping_ your reference genomes - otherwise you will have significant and unwanted bias in your consensus sequences.

We agree that de novo assembly can introduce some errors, however we used both de novo and reference mapping to confirm the genomes. We agree and appreciate the reviewer on the primer issue and we have done manual curation, adding primer position to ensure that any primer induced mutations are removed, we have generated corrected consensus sequences and removed 1 mutation that was falling at position 210 of E1 and edited the manuscript accordingly. The corrected sequences have been resubmitted. No other mutation fell under the primer binding positions. We have also now referenced the NGS mapper which has been used in a number of projects before. We are grateful to the reviewer for pointing this out.

5. The comments made about protein structural changes need to be toned done. These are predictions ONLY, and are at best “potential” changes that “might” result in structural changes. Without structural / biochemistry studies - they can’t be sure.

We concur with the reviewer and we have rewritten the section on the protein structural changes.

6. The phylogenetic analysis of the paper is the strongest aspect of their study. They did a very thorough job on this and (correctly) used a GTR+g model for the ML tree. Thumbs up on this.

We appreciate the comments by the reviewer.

7. Fundamental Issue

lastly - the authors have discovered three new mutations from an outbreak in Mombasa. They use “potential(ly)” at least three times in the abstract - but claim this -potential- mutations are somehow connected with Ae. aegypti transmissibility. But... no lab work was done to show these mutations have anything to do with host fitness, virus life cycle, or anything to do with increased pathogenicity. At best - these are forensic markers for the outbreak they investigated - and while interesting that three mutations were unique to this outbreak in Mombasa - that’s as far as the authors should speculate.

The mutations referred to as being connected with Ae. aegypti transmissibility are E1:K211E and E2:V264A, these two have previously been observed in many other studies and competence studies done elsewhere (referenced in the Manuscript) to show host fitness. These two mutations were observed in the Kenyan outbreak of 2016 (referenced in the Manuscript) and the present study. The New mutations observed are discussed in connection to their action on Ae. albopictus, one of the mutation E1:V80A has previously been characterized in a laboratory setting, our claim is that we have observed three new mutations for the first time in nature and that previous laboratory based experiments have demonstrated the ability of one of the mutations, namely E1:V80A to increase transmissibility of CHIKV by Ae. albopictus. These studies as referenced increased transmissibility by Ae. albopictus due to abrogation of the need to use cholesterol. Having clarified this, we concur with the reviewer and have toned down on the language around our claim.

I’m happy to discuss this review with the authors if they disagree

Reviewer #2:

The authors obtained 17 human serum samples positive to chikungunya virus (CHIKV) collected from the 2017-2018 Mombasa outbreak. They were able to sequence 14 full genomes and 3 partial genomes from these samples. The full genomes were placed in a phylogenetic context within the Indian Ocean sublineage (IOL), including the 2016 Manadera outbreak and the 2004 initial coastal Kenya outbreak. They didn’t find the Aedes albopictus adapting mutation E1:A226V but observed Aedes aegypti adapting mutations E1:K211E and E2:V264A previously detected in India. They also found mutations specific to the 2017-2018 Mombasa outbreak, E1:S210R, E1:V80A, E1:T82I and E1:V84D that could bring some increase of transmission in A. albopictus.The analyses in the manuscript sound appropriate, figures are clear and the results support the conclusions. I have one major comment and few minors.

Major comment:

The authors choose to mainly focus on describing mutations that could increase CHIKV vector transmissibility. I found that the manuscript lacks of results and discussion about the 2017-2018 Mombasa outbreak itself. The manuscript would benefit if the authors describe a bit more the phylogenetic patterns of the 2017-2018 Mombasa outbreak and discuss about its origin and spread.

We have added information on the origin and spread into Mombasa of this outbreak strain of CHIKV. However, since there is no much differences among the Mombasa genomes and the outbreak took only 5-6 months, there is not much phylogenetic diversity among the genomes save for the a rising of the three Mombasa unique mutations. We have presented the arising of the unique mutations under the results section. We also report four genomes that seem not to have these mutations and indicate that these four are estimated to have existed within Mombasa in early 2017. Thus we show that the mutations potentially evolved within Mombasa. We have also indicated this in the discussion section. We have added the information on the origin of the Mombasa and Mandera strains as shown in the both ML and Bayesian phylogenies. As for the effect of the mutations, we have suggested further studies under field conditions.

Minor comments:

- A sequence assembly statistics table should be included in the manuscript to allow the readers to evaluate sequence and mutation robustness.

We have deposited the reads at RSA as suggested by reviewer 1 and believe the provided information together with the genomes would provide the information suggested by reviewer 2.

- The three partial genomes that are not included in the phylogenetic analyses could be added if they have at least 50% of sequence coverage

We concur with the reviewer, we have added the three genomes to the ML phylogeny as they had more 50% sequence coverage. We have also added one of the sequences to the Bayesian analysis as it is near complete.

- A careful proofread of the manuscript is necessary to remove typos that are disseminated all along it

We concur with the reviewer, and have read through the Manuscript to correct typological errors.

Reviewer #3:

This study analyzed and compared 14 CHIKV complete genome sequences obtained from patients and identified a triple mutation signature (E1-V80A, E1-T82I, E1-V84D) and a double mutation (E1: K211E, E2:V264A) both associated with Aedes albopictus and Aedes aegypti adaptations in these CHIKV circulating in Mombasa, Kenyan coast. Conclusions state that a large diversity of CHIKV strains circulate in east Africa, which may emerge as a worldwide public health problem in the future, emphasizing necessity for constant surveillance and urgent vaccine development to protect people.

Title should reflect the mutations involved in mosquito adaptations (both species).

Please complete authors’ addresses

We concur with the reviewer and have incorporated the Novel V80A mutation in the title found in the present study.

Please correct the word Alphavirus (genus) or alphavirus (in general when referring to any species from this genus). Alpha-virus is not correct (see ICTV website for virus taxonomy).

We concur with the reviewer and have corrected to alphavirus as correctly pointed out

Backgroud lacks a little of “introduction” to the virus itself.

We concur with the reviewer and have added introductory information on the virus

Please check for CHKIV incorrect abbreviation (correct for CHIKV).

We concur with the reviewer and have corrected to CHIKV as correctly pointed out by the reviewer

Results: “a period spanning 5 months from mid-December 2017 to mid-May 2018.”

The section has been changed to read “The sequenced samples covered the period between mid-December 2017 and mid-May 2018.”

Two tables are assigned as “Table 1” (primer sets and site-specific selection pressure.

The tables have now been reassigned to Table 1 and Table 2 respectively.

Phylogenetic trees are in low resolution and is not possible to read anything.

The Phylogenetic trees are provided as .tif images, if you click at the blue icon at the top right of the PDF image, you get clearer images

Please separate background and discussion paragraphs.

Background and discussion paragraphs have been separated

Discussion does not consider phylogenetic trees, molecular clock, only the mutations (please discuss as well).

We concur with the reviewer and have added a section in the discussion on IOL Phylogeny including the Mandera and Mombasa outbreak strains

Submitted filename: Responses to reviewers.docx

Click here for additional data file.

21 Oct 2020

Emergence of a novel chikungunya virus strain bearing the  E1:V80A substitution, out of the Mombasa, Kenya 2017-2018 outbreak.

PONE-D-20-18596R1

Dear Dr. Eyase,

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Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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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

Reviewer #3: Yes

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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.

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Reviewer #2: Yes

Reviewer #3: Yes

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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: I want to thank the authors for addressing the concerns of the reviewers on this important manuscript.

Reviewer #2: (No Response)

Reviewer #3: This second version is a lot improved on writing than the latest one. I do understand that these mutations leading to vector adaptations, as already demonstrated in literature, are quite concerning since it was already reported for Zika virus, previous to the emergence of the virus in the Americas. For this reason, this article should be published shortly, representing a very important contribution for arbovirus surveillance. Few comments are described above in order to contribute with author´s review.

Abstract: could be reformulated in a more organized and direct approach, still need work to achieve the same quality of writing of the rest of the manuscript. Please indicate in which CHIKV genotype these triple mutations were identified in the abstract, try to make the text more complete of results. It is not clear in the abstract, however in the manuscript/tree is possible to understand that they refer to IOL strains. Abstract should reflect more the results/discussion.

**********

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.

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Reviewer #1: Yes: Jonathan L Jacobs

Reviewer #2: No

Reviewer #3: No

26 Oct 2020

PONE-D-20-18596R1

Emergence of a novel Chikungunya virus strain bearing the E1:V80A substitution, out of the Mombasa, Kenya 2017-2018 outbreak.

Dear Dr. Eyase:

I'm 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 let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, 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.

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