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Title: When are pathogen genome sequences informative of transmission events?}
Article:
Abstract:
Recent years have seen the development of numerous methodologies for reconstructing transmission trees in infectious disease outbreaks from densely sampled whole genome sequence data. However, a fundamental and as of yet poorly addressed limitation of such approaches is the requirement for genetic diversity to arise on epidemiological timescales. Specifically, the position of infected individuals in a transmission tree can only be resolved by genetic data if mutations have accumulated between the sampled pathogen genomes. To quantify and compare the useful genetic diversity expected from genetic data in different pathogen outbreaks, we introduce here the concept of ‘transmission divergence’, defined as the number of mutations separating whole genome sequences sampled from transmission pairs. Using parameter values obtained by literature review, we simulate outbreak scenarios alongside sequence evolution using two models described in the literature to describe transmission divergence of ten major outbreak-causing pathogens. We find that while mean values vary significantly between the pathogens considered, their transmission divergence is generally very low, with many outbreaks characterised by large numbers of genetically identical transmission pairs. We describe the impact of transmission divergence on our ability to reconstruct outbreaks using two outbreak reconstruction tools, the R packages outbreaker and phybreak, and demonstrate that, in agreement with previous observations, genetic sequence data of rapidly evolving pathogens such as RNA viruses can provide valuable information on individual transmission events. Conversely, sequence data of pathogens with lower mean transmission divergence, including Streptococcus pneumoniae, Shigella sonnei and Clostridium difficile, provide little to no information about individual transmission events. Our results highlight the informational limitations of genetic sequence data in certain outbreak scenarios, and demonstrate the need to expand the toolkit of outbreak reconstruction tools to integrate other types of epidemiological data.
Introduction:
Understanding transmission dynamics in the early stages of an infectious disease outbreak is essential for informing effective control policy. Valuable insights can be gained by the reconstruction of the transmission tree, which describes the history of infectious events at the resolution of individual cases[1–4]. Recent years have seen significant progress in the development of statistical and computational tools for inferring such trees[5–15], with a major emphasis placed on the analysis of whole genome sequence( WGS) data, now routinely collected in many outbreak scenarios[16]. Two approaches to the inference of transmission trees from WGS have emerged. One begins with an underlying transmission model, attaching to this a model of sequence evolution that relates observed genetic relationships between pathogens to unobserved epidemiological relationships( i. e. transmission pairs) between infected individuals. A simple implementation involves ruling out direct transmission events between individuals separated by more than a fixed threshold of substitutions[17–19]. More sophisticated methods have specified models of sequence evolution as components in a joint likelihood, formalising expected genetic relationships in a probabilistic manner[5–9, 20]. The other approach considers outbreak reconstruction from a phylogenetic perspective, inferring unobserved historical relationships between pathogen samples to capture more complex evolutionary dynamics. WGS data is used to reconstruct phylogenetic trees which are either treated as data upon which transmission histories are overlaid[10–12], or jointly inferred alongside the transmission tree itself[13–15]. Given the unprecedented level of detail of WGS data and the epidemiological insights it has provided in real-life scenarios[21–23], genetic analysis is clearly an indispensable tool for outbreak reconstruction. However, a fundamental and so far largely unaddressed limitation of WGS data in informing outbreak reconstruction is the requirement for genetic diversity to accumulate on epidemiological timescales. The scope of outbreak scenarios for which such requirements are met has, to our knowledge, never been described. Specifically, at least one mutation must accumulate in the time between sampling of two individuals in a given transmission pair( i. e., an infector and a secondary case) in order for their position within the transmission tree to be distinguishable by genetic means. This represents a limit in the resolution of the data itself, independent of the methodology considered. Though groups of genetically identical pathogens may be identified as a cluster of infections, finer reconstruction of the transmission events would be impossible based on genetic data alone. Such limitations may be even more problematic in methods relying on accurately estimated phylogenetic trees for inferring transmission events[11, 14]. The impact of limited genetic diversity on the reconstruction of disease outbreaks remains to be investigated. While this impact undoubtedly varies across different methods, the intrinsic informativeness of genetic data with respect to the underlying transmission tree can be evaluated. The genetic diversity accumulating along transmission chains depends on various genomic and epidemiological factors. To quantify this diversity, we introduce the concept of ‘transmission divergence’, which we define as the number of mutations accumulating between pathogen WGS sampled from transmission pairs. Transmission divergence can be estimated empirically from a transmission tree by determining the number of mutations separating pathogen samples of known transmission pairs. However, accurately reconstructed transmission trees with corresponding genetic sequence data are generally not available for most pathogens. We present here a simulation based approach for estimating the transmission divergence of different pathogens using parameters available in the literature, namely the length of the pathogen genome( L), its overall mutation rate( M), its generation time distribution( W)( i. e. the distribution of delays between primary and secondary infections[24]) and its basic reproduction number R0( i. e. the average number of secondary infections caused by an index case in a fully susceptible population[25]). Specifically, we model transmission trees alongside sequence evolution and extract the number of mutations separating individual transmission pairs. Intuitively, greater transmission divergence should enable better reconstruction of these transmission trees, although the nature of this relationship remains to be described. To explore this issue, we compare the transmission divergence of ten major outbreak-causing pathogens, namely Zaire ebolavirus( EBOV), SARS coronavirus( SARS-CoV), MERS coronavirus( MERS-CoV), pandemic influenza A( H1N1), Methicillin-Resistant Staphylococcus aureus( MRSA), Klebsiella pneumoniae( K. pneumoniae), Streptococcus pneumoniae( S. pneumoniae), Shigella sonnei( S. sonnei), Mycobacterium tuberculosis( M. tuberculosis), and Clostridium difficile( C. difficile). We first conduct a literature review to obtain estimates of W, M, L and R0 for each pathogen and then estimate transmission divergence using simulations. To compare estimates of transmission divergence under different models, we use two approaches described in the literature, namely the outbreaker model by Jombart et al.[5] and the phybreak model by Klinkenberg et al.[26] These differ significantly in their model of sequence evolution, with the prior considering a single dominant pathogen strain within each host and the latter modelling the additional complexities of multiples lineages coexisting and coalescing within host. Finally, we illustrate the impact of transmission divergence on our ability to infer transmission trees, using the outbreaker and phybreak inference algorithms for the R software[27].
Discussion:
This paper has introduced the concept of ‘transmission divergence’ as a measure of the informativeness of WGS data for reconstructing transmission chains during an infectious disease outbreak. We estimated transmission divergence for ten major outbreak causing pathogens with a simulated based approach, using two distinct models of sequence evolution for comparison. We then demonstrated how the mean transmission divergence across an outbreak affects our ability to infer transmission histories. Though average transmission divergence varied significantly amongst the diseases studied, it was generally very low, with a modal value of zero for most pathogens under both evolutionary models. Our results suggest that a large fraction or even a majority of cases will be genetically indistinguishable in many epidemic scenarios, including outbreaks of rapidly evolving RNA viruses such as EBOV and MERS. These results were generally well supported by empirical observations of genetic diversity. Our simulations accurately predicted C. difficile, Influenza A and M. tuberculosis cases to be genetically identical a majority of the time, EBOV and MERS-CoV cases to display greater diversity yet still be frequently identical, and SARS-CoV cases to be largely genetically distinct. Though the proportion of unique sequences observed across 333 C. difficile cases was unexpectedly high, this metric assumes that each case is related to another case by a direct transmission event. This appears unrealistic when considering that 120 patients( 36%) had no recorded epidemiological contact with their genetically related case[17]. A number of these pairs were probably separated by unobserved transmission events, for example by asymptomatic carriers, increasing the observed genetic diversity between supposed transmission pairs. The true proportion of unique sequences likely agrees better with our model predictions. Furthermore, though our original estimates for K. pneumoniae disagreed with empirical observations, this discrepancy was largely due to an unusually short serial interval compared to previously reported values in the literature. Once accounted for, the reported diversity agreed with our predictions. Therefore, even though predicting the specific number of unique sequences observed in an outbreak is challenging due to confounding factors such as unobserved cases and stochastic variations in epidemiological context, our predictions using two fairly simple models of sequence evolution largely agreed with the data. This suggests that our results represent broadly useful predictors of the extent to which cases in a transmission cluster are genetically resolvable. The wider conclusion that a significant proportion of cases are expected to be genetically identical for a number of different pathogens is certainly well supported. It is tempting to suggest that the data from M. tuberculosis and SARS-CoV outbreaks lend greater support to the phybreak model, thereby indicating significant levels of within-host genetic diversity among these pathogens. While this explanation is feasible, the significant variation in unique sequences across four EBOV outbreaks demonstrates the sensitivity of such individual observations to stochastic effects. In the absence of greater amounts of empirical data, any such conclusions are only weakly supported. The limited genetic diversity as predicted by our simulations had a considerable impact on our ability to reconstruct outbreaks, and clearly identified transmission divergence as a limiting factor in the utility of WGS data for many pathogens in an outbreak setting. These informational limitations were further compounded by within-host genetic diversity, which significantly reduced our ability to reconstruct outbreaks even when mean transmission divergence was high, in agreement with previous studies[11, 107]. Combined, these results demonstrate that WGS data will often be insufficient to fully resolve transmission chains, and reveal the need to incorporate other sources of information into transmission inference frameworks. Promising avenues include an analysis of deep sequencing data as an alternative to WGS, which may reveal additional within-host variation informative of likely transmission events, as well as a methodological approach to inferring transmission routes from contact data. Our results do not imply that WGS is of no use for inferring transmission routes as a whole. For example, Didelot et al. used C. difficile WGS to identify distinct transmission chains caused by separate introductions to the same ward, vastly reducing the number of plausible transmission links given only epidemiological data[23]. However, samples within the transmission chains were genetically identical and remained unresolved in the absence of additional data. Low transmission divergence therefore represents a hard limit to the resolution of various reconstruction methods using WGS as primary source of information, regardless of the underlying genetic model. This may especially impact approaches relying on previously constructed phylogenetic trees[12, 14], which are known to skew infection time estimates in the presence of multiple genetically identical sequences, as described by Hall et al.[15] We also showed that greater transmission divergence generally improved the inference of transmission histories, however only to an extent. Beyond the first discriminatory mutation, diversity between transmission pairs seemed to provide limited additional information, as demonstrated by the fact that the proportion of genetically unique sequences, rather than the average transmission divergence, best predicted the informativeness of WGS data. Though this relationship was weaker in the phybreak model, as within-host diversity increases the number of plausible transmission trees even when all cases are genetically distinct, this linear relationship held across both models. It is important to note that other epidemiological factors beyond genetic diversity will impact the accuracy of outbreak reconstruction, such as R0, heterogeneities in infectiousness, the generation time distribution and the sampling time distribution. To account for these effects, our study focused on the improvement in the accuracy of reconstructed transmission chains, compared to a baseline without WGS data while keeping these other factors constant. Importantly, in spite of considerable variation in R0 and generation time distributions( Table 1), the robustness of the correlations presented in Figs 2 and 3 suggests that our measures of genetic diversity have captured a central determinant of the utility of WGS data in reconstructing outbreaks. This study made several assumptions which might be relaxed in further work. Firstly, we assumed that the sampling time distribution is equivalent to the generation time distribution. While this assumption was largely driven by the lack of available data on sampling delay distributions, this could result in biases. For instance, our approach would underestimate transmission divergence in the presence of systematic and substantial lags between transmission times and sampling( S3 Fig). It is worth noting, however, that additional mutations accumulating in a lineage after onward transmission has ceased would increase the overall genetic distance from this lineage to all other isolates equally, without providing additional information about the underlying epidemiological relationships between hosts. Therefore, it is unclear how this additional diversity would translate in terms of improving outbreak reconstruction, and we believe the approach used in this study should capture the diversity informative of the transmission network. Secondly, outbreaks were simulated and reconstructed under idealised scenarios, in that all cases were observed, WGS were available for all cases, and the same parameter values used for simulation and inference. Most importantly, we assumed error-free sequencing. In reality, when considering that transmission divergence is generally on the order of single mutations, individual sequencing errors can heavily bias the topology of the inferred transmission tree. Our estimates of the informativeness of WGS in inferring individual transmission links are therefore likely to be optimistic. Finally, both the outbreaker and the phybreak model assumed a complete bottleneck at transmission, with a single strain being transmitted. Allowing for an incomplete bottleneck greatly increase the complexity of the problem, as two strains in a given host may have diverged several infectious generations ago and passed through multiple bottleneck together. This issue also opens up a number of questions about optimal sampling and sequencing strategies, the exact magnitude of the genetic diversity bottleneck at transmission, and the more fundamental mechanisms permitting the coexistence of multiple strains within a host. Further work should be dedicated to investigating the impact of these issues on the use of WGS for outbreak reconstruction[107, 108]. The advent of WGS data has initiated a revolution in modern infectious disease epidemiology, shedding new light into disease dynamics and evolution at a variety of scales[109, 110]. At a local scale, these data have opened up exciting perspectives for improving our understanding of the person-to-person transmission process[5, 6, 13, 20]. This work suggests that, while useful, the analysis of WGS alone will struggle to reconstruct transmission trees accurately for a large number of pathogens, in particular bacterial ones. Integrating other types of outbreak data, such as locations of patients, community structure, or contact tracing data, therefore represents a promising alternative strategy for outbreak reconstruction.
The increasing availability of genetic sequence data has sparked an interest in using pathogen whole genome sequences to reconstruct the history of individual transmission events in an infectious disease outbreak . However , such methodologies rely on pathogen genomes mutating rapidly enough to discriminate between infected individuals , an assumption that remains to be investigated . To determine pathogen outbreaks for which genetic data is expected to be informative of transmission events , we introduce here the concept of ‘transmission divergence’ , defined as the number of mutations separating pathogen genome sequences sampled from transmission pairs . We characterise transmission divergence of ten major outbreak causing pathogens using simulations and find significant variation between diseases , with viral outbreaks generally exhibiting higher transmission divergence than bacterial ones . We reconstruct these outbreaks using the R-packages outbreaker and phybreak and find that genetic sequence data , though useful for rapidly evolving pathogens , provides little to no information about outbreaks with low transmission divergence , such as Streptococcus pneumoniae and Shigella sonnei . Our results demonstrate the need to incorporate other sources of outbreak data , such as contact tracing data and spatial location data , into outbreak reconstruction tools .