TIMI-1208; No. of Pages 2
Spotlight
How Ebola has been evolving in West Africa Si-Qing Liu, Simon Rayner, and Bo Zhang Key Laboratory of Etiology and Biosafety for Emerging and Highly Infectious Diseases, Wuhan Institute of Virology, Chinese Academy of Sciences, Wuhan 430071, China
The ongoing Ebola outbreak in West Africa has generated fears of a global epidemic. Particularly, estimates of higher substitution rates have raised concerns about increased transmissibility or virulence. A recent study using a more comprehensive datasets demonstrates lower variation, highlighting the importance of representative datasets and limitations of computational modelling. The recent Ebola virus (Zaire ebolavirus, EBOV) outbreak in West Africa (www.cdc.gov/vhf/ebola/outbreaks/2014west-africa/case-counts.html) is the most serious to date with more than 25 000 cases of infection and 10 000 deaths reported in Guinea, Liberia, and Sierra Leone, exceeding totals for all previous EBOV outbreaks combined. Despite efforts to completely eradicate the virus, new cases continue to emerge. A byproduct of this public health emergency has been to raise global awareness of the disease and to stimulate development of EBOV-specific vaccines and antiviral therapy. Detailed information regarding genetic variability and evolutionary dynamics can complement these efforts, for example, by providing insight as to how the EBOV has been evolving during the West Africa epidemic. A recent study in Science by Hoenen et al. reports the sequence and analysis of four additional full-length genomes of EBOV from Mali and provides new insight into the current outbreak [1]. The average nucleotide substitution rate in RNA viruses varies from 10 5 to 10 3 per site per year [2]. In addition to changes instigated by replication enzymes errors or nucleotide modifications, substitution rates of viruses are affected by other intrinsic effects including mutation rate, generation time, effective population size, and fitness [3]. They can also show significant variation due to extrinsic factors across different viruses under different conditions and different time scales. Additionally, different regions of a viral RNA genome will typically exhibit different degrees of genetic variation according to the functional and intrinsic constraints as well as selection and extrinsic pressure [4]. In an attempt to predict the evolution and likelihood of future EBOV epidemics, several studies have examined the rate and pattern of nucleotide substitutions of isolate sequences from previous outbreaks. These studies generally revealed that the genetic variability was low, and even Corresponding author: Zhang, B. ([email protected]). Keywords: Ebola; evolution; nucleotide substitutions rate; West Africa; low variation. 0966-842X/ ß 2015 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.tim.2015.05.003
lower for single outbreaks [5,6]. It was proposed that this was due to a combination of a severe bottleneck effect [7], short coalescence time of the most recent common ancestor (TMRCA) [5], and strong functional constraints against viral genome mutability. Importantly, these investigations probably have been impeded by limited sampling or sample diversity. In the most recent outbreak, a large number of whole genome sequences of EBOV isolates from Guinea and Sierra Leone were rapidly deposited in public databases [8,9], leading to several investigations of the genetic diversity and mutations in this outbreak. An initial report based on genomic sequences from three isolates in Guinea suggested the emergence of a new EBOV strain [9], but this was discounted after it was shown to be a consequence of the selection of root species. A subsequent investigation of 81 full-length genomes of EBOV samples from the West Africa epidemic combined with those from earlier outbreaks indicated that the substitution rate during the current outbreak is approximately twice as fast as previously observed [8], raising concerns regarding the origins of this difference and the potential consequences on global health. Moreover, based on these analyses, many nonsynonymous mutations were proposed as monitored markers for molecular diagnostics, vaccine design, and therapeutic strategies, in case of the potential for generation of changed transmissibility and virulence of EBOV during the current outbreak [8]. The Science report by Hoenen et al. incorporates four additional full-length genomes of EBOV from Mali, where infections were imported from core outbreak countries [1]. They re-estimate the substitution rate based on this expanded dataset and reveal a value of 9.6 3 10 4 substitutions per site per year which is roughly consistent with rates reported for the earlier outbreaks in Central Africa (6.2 3 10 4 to 9.5 3 10 4) [5,7,8,10]. Additionally, analysis of all available EBOV genotypes showed that most of mutations were synonymous, or otherwise occurred sporadically within a single genotype. Thus, although the pairwise differences between the Mali sequences and other West African variants from EBOV outbreak are in the range of 10–20 nucleotides, no evidence supports the occurrence of functional changes within the affected proteins [1]. There are a range of software tools that can be used for estimating substitution rates which vary in complexity and available parameters. The plethora of choices is reflected in the various reports on EBOV outbreaks, which adopt a range of substitution and population models such as variation in base frequencies or transition/transversion rates, and assume or reject a strict molecular clock (i.e. the virus Trends in Microbiology xx (2015) 1–2
1
TIMI-1208; No. of Pages 2
Spotlight
Trends in Microbiology xxx xxxx, Vol. xxx, No. x
Table 1. Calculations of nucleotide substitution rate within EBOV in different studies Time frame a
Glycoprotein (GP) GP
Substitution rate (/site/year) 3.6 3 10 5 9.5 3 10 4
Genome Genome
6.2 3 10 4 7.06 3 10 4
Genome
9.3 3 10
Genome
1.9 3 10
Genome
9.6 3 10
Gene or genome
Substitution model NA b GTRc + I d
Clock model
1976–1995 1976–2005
Number of samples 9 13
1976–1995 1976–2008
2 22
NA HKY + G e
4
1976–2014
101
HKY + G
3
Mar-Jun 2014
81
HKY + G
Mar-Nov 2014
NR g
HKY
NA Relaxed uncorrelated lognormal Relaxed uncorrelated lognormal Relaxed uncorrelated lognormal Strict
4
NA Strict
Population model NA Exponential or constant NA Constant
Refs [2] [10] [7] [5]
Skygrid f
[8]
Skygrid
[8]
Constant
[1]
a
Time frame, the time interval of EBOV sampling.
b
NA, not applicable.
c
GTR, general time reversible. This model assumes that all substitutions occur at different rates.
d
I, invariant sites.
e
G, gamma distribution. This distribution simulates the rate heterogeneity across different sites.
f
Skygrid, a Bayesian nonparametric model. This model jointly integrates mutation parameters, a Gaussian Markov random field (GMRF) model, and genealogies representing the ancestries of populations at different multiple loci.
g
NR, not reported. Sequences from Mali, Sierra Leone, and Guinea that had an associated specific date of sampling were included. A total of 106 viral sequences were used for all analyses in the Hoenen et al. [1] study.
is evolving at a constant rate) (Table 1). For example, prior to the West Africa outbreak, Carroll et al. [5] adopted a HKY (different transition/transversion rates and unequal base frequencies) + gamma model, a relaxed uncorrelated lognormal clock (different lineages can be evolving at different rates over time), and a constant size coalescent prior (the effective population size of the virus is unknown but is assumed to be constant back through time). For the current outbreak, the Hoenen et al. [1] analysis estimates substitution rate using a comparable HKY substitution model and constant size coalescent prior, but assumes a strict clock, whereas Gire et al. [8] implement a Skygrid coalescent prior (Table 1) which allows the population size to change at fixed and pre-specified points in time. Choices of models and parameters can significantly influence estimates of substitution rates and assessment of evolutionary dynamics, but are generally selected based on the available data. In the current EBOV outbreak, there were a large number of full genome sequences that were initially available, but Gire et al. spanned a relatively short time interval (from 17 March to 20 June, 2014) and a geographically limited range corresponding to the introduction of EBOV into West Africa [8]. Moreover, many of the mutations that were identified from the fatal or nonfatal samples in their study could be random and undirectional, and the rate of biologically relevant mutations during human-to-human transmission appears to be overestimated in light of this new dataset. The earlier EBOV studies were valuable in that they yielded initial insight into the epidemic at the genetic level but, although they were based on large numbers of
2
sequences from the current outbreak, the absence of significant diversity within the samples or relatively short timescale limited the collective value of the data. This is highlighted by the findings of Hoenen et al. [1] study and reminds us that caution should prevail to avoid over-interpretation of results based on computational inferences. Acknowledgments We apologize that many relevant studies could not be cited here due to space constraints. This work was supported by the National Basic Research Program of China (Grants 2011CB504701, 2012CB518904).
References 1 Hoenen, T. et al. (2015) Mutation rate and genotype variation of Ebola virus from Mali case sequences. Science 348, 117–119 2 Suzuki, Y. and Gojobori, T. (1997) The origin and evolution of Ebola and Marburg viruses. Mol. Biol. Evol. 14, 800–806 3 Marz, M. et al. (2014) Challenges in RNA virus bioinformatics. Bioinformatics 30, 1793–1799 4 Liu, S.Q. et al. (2015) Identifying the pattern of molecular evolution for Zaire ebolavirus in the 2014 outbreak in West Africa. Infect. Genet. Evol. 32, 51–59 5 Carroll, S.A. et al. (2013) Molecular evolution of viruses of the family Filoviridae based on 97 whole-genome sequences. J. Virol. 87, 2608–2616 6 Rodriguez, L.L. et al. (1999) Persistence and genetic stability of Ebola virus during the outbreak in Kikwit, Democratic Republic of the Congo, 1995. J. Infect. Dis. 179, S170–S176 7 Biek, R. et al. (2006) Recent common ancestry of Ebola Zaire virus found in a bat reservoir. PLoS Pathog. 2, 885–886 8 Gire, S.K. et al. (2014) Genomic surveillance elucidates Ebola virus origin and transmission during the 2014 outbreak. Science 345, 1369–1372 9 Baize, S. et al. (2014) Emergence of Zaire Ebola virus disease in Guinea. N. Engl. J. Med. 371, 1418–1425 10 Walsh, P.D. et al. (2005) Wave-like spread of Ebola Zaire. PLoS Biol. 3, e371
Spotlight
How Ebola has been evolving in West Africa Si-Qing Liu, Simon Rayner, and Bo Zhang Key Laboratory of Etiology and Biosafety for Emerging and Highly Infectious Diseases, Wuhan Institute of Virology, Chinese Academy of Sciences, Wuhan 430071, China
The ongoing Ebola outbreak in West Africa has generated fears of a global epidemic. Particularly, estimates of higher substitution rates have raised concerns about increased transmissibility or virulence. A recent study using a more comprehensive datasets demonstrates lower variation, highlighting the importance of representative datasets and limitations of computational modelling. The recent Ebola virus (Zaire ebolavirus, EBOV) outbreak in West Africa (www.cdc.gov/vhf/ebola/outbreaks/2014west-africa/case-counts.html) is the most serious to date with more than 25 000 cases of infection and 10 000 deaths reported in Guinea, Liberia, and Sierra Leone, exceeding totals for all previous EBOV outbreaks combined. Despite efforts to completely eradicate the virus, new cases continue to emerge. A byproduct of this public health emergency has been to raise global awareness of the disease and to stimulate development of EBOV-specific vaccines and antiviral therapy. Detailed information regarding genetic variability and evolutionary dynamics can complement these efforts, for example, by providing insight as to how the EBOV has been evolving during the West Africa epidemic. A recent study in Science by Hoenen et al. reports the sequence and analysis of four additional full-length genomes of EBOV from Mali and provides new insight into the current outbreak [1]. The average nucleotide substitution rate in RNA viruses varies from 10 5 to 10 3 per site per year [2]. In addition to changes instigated by replication enzymes errors or nucleotide modifications, substitution rates of viruses are affected by other intrinsic effects including mutation rate, generation time, effective population size, and fitness [3]. They can also show significant variation due to extrinsic factors across different viruses under different conditions and different time scales. Additionally, different regions of a viral RNA genome will typically exhibit different degrees of genetic variation according to the functional and intrinsic constraints as well as selection and extrinsic pressure [4]. In an attempt to predict the evolution and likelihood of future EBOV epidemics, several studies have examined the rate and pattern of nucleotide substitutions of isolate sequences from previous outbreaks. These studies generally revealed that the genetic variability was low, and even Corresponding author: Zhang, B. ([email protected]). Keywords: Ebola; evolution; nucleotide substitutions rate; West Africa; low variation. 0966-842X/ ß 2015 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.tim.2015.05.003
lower for single outbreaks [5,6]. It was proposed that this was due to a combination of a severe bottleneck effect [7], short coalescence time of the most recent common ancestor (TMRCA) [5], and strong functional constraints against viral genome mutability. Importantly, these investigations probably have been impeded by limited sampling or sample diversity. In the most recent outbreak, a large number of whole genome sequences of EBOV isolates from Guinea and Sierra Leone were rapidly deposited in public databases [8,9], leading to several investigations of the genetic diversity and mutations in this outbreak. An initial report based on genomic sequences from three isolates in Guinea suggested the emergence of a new EBOV strain [9], but this was discounted after it was shown to be a consequence of the selection of root species. A subsequent investigation of 81 full-length genomes of EBOV samples from the West Africa epidemic combined with those from earlier outbreaks indicated that the substitution rate during the current outbreak is approximately twice as fast as previously observed [8], raising concerns regarding the origins of this difference and the potential consequences on global health. Moreover, based on these analyses, many nonsynonymous mutations were proposed as monitored markers for molecular diagnostics, vaccine design, and therapeutic strategies, in case of the potential for generation of changed transmissibility and virulence of EBOV during the current outbreak [8]. The Science report by Hoenen et al. incorporates four additional full-length genomes of EBOV from Mali, where infections were imported from core outbreak countries [1]. They re-estimate the substitution rate based on this expanded dataset and reveal a value of 9.6 3 10 4 substitutions per site per year which is roughly consistent with rates reported for the earlier outbreaks in Central Africa (6.2 3 10 4 to 9.5 3 10 4) [5,7,8,10]. Additionally, analysis of all available EBOV genotypes showed that most of mutations were synonymous, or otherwise occurred sporadically within a single genotype. Thus, although the pairwise differences between the Mali sequences and other West African variants from EBOV outbreak are in the range of 10–20 nucleotides, no evidence supports the occurrence of functional changes within the affected proteins [1]. There are a range of software tools that can be used for estimating substitution rates which vary in complexity and available parameters. The plethora of choices is reflected in the various reports on EBOV outbreaks, which adopt a range of substitution and population models such as variation in base frequencies or transition/transversion rates, and assume or reject a strict molecular clock (i.e. the virus Trends in Microbiology xx (2015) 1–2
1
TIMI-1208; No. of Pages 2
Spotlight
Trends in Microbiology xxx xxxx, Vol. xxx, No. x
Table 1. Calculations of nucleotide substitution rate within EBOV in different studies Time frame a
Glycoprotein (GP) GP
Substitution rate (/site/year) 3.6 3 10 5 9.5 3 10 4
Genome Genome
6.2 3 10 4 7.06 3 10 4
Genome
9.3 3 10
Genome
1.9 3 10
Genome
9.6 3 10
Gene or genome
Substitution model NA b GTRc + I d
Clock model
1976–1995 1976–2005
Number of samples 9 13
1976–1995 1976–2008
2 22
NA HKY + G e
4
1976–2014
101
HKY + G
3
Mar-Jun 2014
81
HKY + G
Mar-Nov 2014
NR g
HKY
NA Relaxed uncorrelated lognormal Relaxed uncorrelated lognormal Relaxed uncorrelated lognormal Strict
4
NA Strict
Population model NA Exponential or constant NA Constant
Refs [2] [10] [7] [5]
Skygrid f
[8]
Skygrid
[8]
Constant
[1]
a
Time frame, the time interval of EBOV sampling.
b
NA, not applicable.
c
GTR, general time reversible. This model assumes that all substitutions occur at different rates.
d
I, invariant sites.
e
G, gamma distribution. This distribution simulates the rate heterogeneity across different sites.
f
Skygrid, a Bayesian nonparametric model. This model jointly integrates mutation parameters, a Gaussian Markov random field (GMRF) model, and genealogies representing the ancestries of populations at different multiple loci.
g
NR, not reported. Sequences from Mali, Sierra Leone, and Guinea that had an associated specific date of sampling were included. A total of 106 viral sequences were used for all analyses in the Hoenen et al. [1] study.
is evolving at a constant rate) (Table 1). For example, prior to the West Africa outbreak, Carroll et al. [5] adopted a HKY (different transition/transversion rates and unequal base frequencies) + gamma model, a relaxed uncorrelated lognormal clock (different lineages can be evolving at different rates over time), and a constant size coalescent prior (the effective population size of the virus is unknown but is assumed to be constant back through time). For the current outbreak, the Hoenen et al. [1] analysis estimates substitution rate using a comparable HKY substitution model and constant size coalescent prior, but assumes a strict clock, whereas Gire et al. [8] implement a Skygrid coalescent prior (Table 1) which allows the population size to change at fixed and pre-specified points in time. Choices of models and parameters can significantly influence estimates of substitution rates and assessment of evolutionary dynamics, but are generally selected based on the available data. In the current EBOV outbreak, there were a large number of full genome sequences that were initially available, but Gire et al. spanned a relatively short time interval (from 17 March to 20 June, 2014) and a geographically limited range corresponding to the introduction of EBOV into West Africa [8]. Moreover, many of the mutations that were identified from the fatal or nonfatal samples in their study could be random and undirectional, and the rate of biologically relevant mutations during human-to-human transmission appears to be overestimated in light of this new dataset. The earlier EBOV studies were valuable in that they yielded initial insight into the epidemic at the genetic level but, although they were based on large numbers of
2
sequences from the current outbreak, the absence of significant diversity within the samples or relatively short timescale limited the collective value of the data. This is highlighted by the findings of Hoenen et al. [1] study and reminds us that caution should prevail to avoid over-interpretation of results based on computational inferences. Acknowledgments We apologize that many relevant studies could not be cited here due to space constraints. This work was supported by the National Basic Research Program of China (Grants 2011CB504701, 2012CB518904).
References 1 Hoenen, T. et al. (2015) Mutation rate and genotype variation of Ebola virus from Mali case sequences. Science 348, 117–119 2 Suzuki, Y. and Gojobori, T. (1997) The origin and evolution of Ebola and Marburg viruses. Mol. Biol. Evol. 14, 800–806 3 Marz, M. et al. (2014) Challenges in RNA virus bioinformatics. Bioinformatics 30, 1793–1799 4 Liu, S.Q. et al. (2015) Identifying the pattern of molecular evolution for Zaire ebolavirus in the 2014 outbreak in West Africa. Infect. Genet. Evol. 32, 51–59 5 Carroll, S.A. et al. (2013) Molecular evolution of viruses of the family Filoviridae based on 97 whole-genome sequences. J. Virol. 87, 2608–2616 6 Rodriguez, L.L. et al. (1999) Persistence and genetic stability of Ebola virus during the outbreak in Kikwit, Democratic Republic of the Congo, 1995. J. Infect. Dis. 179, S170–S176 7 Biek, R. et al. (2006) Recent common ancestry of Ebola Zaire virus found in a bat reservoir. PLoS Pathog. 2, 885–886 8 Gire, S.K. et al. (2014) Genomic surveillance elucidates Ebola virus origin and transmission during the 2014 outbreak. Science 345, 1369–1372 9 Baize, S. et al. (2014) Emergence of Zaire Ebola virus disease in Guinea. N. Engl. J. Med. 371, 1418–1425 10 Walsh, P.D. et al. (2005) Wave-like spread of Ebola Zaire. PLoS Biol. 3, e371
