Go Deep By Fiona Davenport
Recent studies suggest that incorporation of within-host diversity into genomic analyses may provide greater resolution of transmission than cSNP-based approaches alone (Worby et al., 2017; Martin et al., 2018; Meehan et al., 2019; Séraphin et al., 2019). This may be particularly important for investigation of outbreaks occurring over short time scales and/or in settings such as the Canadian North, where the genetic diversity of circulating strains is especially low. In both of these circumstances, it is common to find many samples separated by zero cSNPs, hindering accurate source ascertainment. To investigate this hypothesis, we used deep sequencing (i.e., to 10-20 fold more than standard, or 500-1000x) to re-evaluate transmission in a densely-sampled outbreak in Nunavik, Québec.
Go Deep by Fiona Davenport
Several recent studies, including work by our group (Martin et al., 2018), have shown that M. tuberculosis within-host diversity can be transmitted between individuals (Séraphin et al., 2019; Guthrie et al., 2019). Using deep sequencing data allowed us to better identify this diversity in a Nunavik outbreak compared to previous analyses with standard sequencing depth, (Lee et al., 2015a; Lee et al., 2015b) and facilitated detection of a novel super-spreading event, where one source case may have transmitted to 1/3 of the other cases diagnosed between 2011 to 2012. This was in addition to a previously identified super-spreader linked to 19 secondary cases - suggesting up to 75% of the outbreak (36/48, excluding the putative super-spreaders) may be attributable to these events. Super-spreading has been described in a number of pathogens, (Stein, 2011) including TB (Kline et al., 1995). Our findings suggest this can play an important role in driving TB outbreaks, and that accurate detection of super-spreading events is important for informing appropriate public health interventions. In the case of MT-504, as nearly all of the secondary cases had attended the same local community gathering houses as the putative source, this strongly suggests these venues play an important role in facilitating transmission in this setting.
Several studies have used genomics to investigate TB recurrence, (Witney et al., 2017; Bryant et al., 2013; Guerra-Assunção et al., 2015) however, the methods used to assess for mixed infection at either time point have been inconsistent and may not be sufficient to discriminate recurrence in settings with low strain diversity. In this analysis, we provide proof-of-principle that deep sequencing can potentially help rule out relapse. The distinction between relapse and re-infection is important at individual and population levels; high rates of relapse in a community would indicate a problem with treatment or adherence, potentially warranting changes to clinical management, while re-infection would indicate the need for public health interventions such as active case finding. Also, individuals in Nunavik who have had prior treatment for active TB disease in the past are also not routinely offered prophylaxis on re-exposure, based on historical data suggesting 80% protection is afforded by prior infection (Menzies, 1997). The degree to which re-infection drives recurrence in Nunavik is currently unknown, but if re-infection is the primary cause, this clinical practice may need to be re-evaluated. A population-level genomic epidemiology study is currently underway to evaluate this.
To use deep sequencing to investigate within-host diversity, it is critical we minimize false positive hSNPs. We have shown that using a local strain as a reference can not only reduce error, but improves detection of epidemiologically-informative variants. Genomic differences between outbreak strains and H37Rv have been previously illustrated by Roetzer et al. (2013); O'Toole and Gautam (2017), with O'Toole and Gautam (2017) warning that clinical TB strains may be needed to fully detect virulence genes in reference-based analyses. Our analysis suggests these may also be warranted for hSNP analysis; where possible, we suggest using long-read sequencing to generate complete and local reference genomes.
Finally, while deep sequencing allowed us to detect a novel superspreading event in this context, this approach may not always be necessary; indeed, our previous analysis had identified another super-spreader in the same outbreak using routine sequencing. We acknowledge that this Northern outbreak may not be representative of outbreaks from other settings and/or involving other M. tuberculosis lineages. Further studies are needed to quantify the degree to which super-spreading occurs in TB, and examine how and when deep sequencing should be used to detect this.
Tuberculosis infection represents one of the major global health challenges and a better understanding of tuberculosis transmission is critical for designing effective control strategies. This work uses deep sequencing to further investigate a previously studied tuberculosis transmission cluster in Canada. It uses deep sequencing to identify co-infection of a single individual with two closely related strains, and suggests that both strains were subsequently transmitted. This increases the number of individuals thought to have been infected from a single source individual, and thus increases our estimates of the potential frequency and magnitude of 'super-spreading' events. Although this represents analysis of a single infection cluster, such detailed studies also provide a model for future outbreak investigation.
Thank you for submitting your article "Previously undetected super-spreading of Mycobacterium tuberculosis revealed by deep sequencing" for consideration by eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by Miles Davenport as Reviewing Editor, and Eduardo Franco as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Conor J Meehan.
Lee et al. report a re-analysis of a tuberculosis outbreak combining a local reference derived from long-reads and ultra-deep sequencing to reveal minor variants. They use the novel information to identify a previously undetected super-spreader. This is very carefully done piece of work into which the authors have clearly put a lot of thought. Although mixed bacterial populations have been used to identify index cases and so-called super-spreaders from WGS TB data in the past, the authors have given this issue greater focus here and emphasised the point that such mixed populations might not be detected without sequencing to a greater depth. This work is an excellent example of the benefits of this deep sequencing and outlines both the need for local outbreak reference sequences and associated hSNP analysis in low diversity settings.
2) This work is most important for low diversity settings. This should be better highlighted throughout, perhaps with a comment on how this could be applicable to high diversity settings, if at all. Many Mtb researchers think all new WGS work is applicable in all settings and it would be of help if the authors demonstrated where and when people need to make the extra effort and indeed massively extra cost of deep sequencing.
4) A major criticism is that although this is a carefully crafted analysis, it remains essentially anecdotal. To draw conclusions that deep sequencing is now ideally required for all outbreaks is probably a little overenthusiastic. A systematic analysis of far larger number of clusters is required to assess the importance of mixed populations that go undetected at routine sequencing depth before conclusions can be drawn about what should and shouldn't be routine practice. It could easily be that most mixed populations that are helpful to inferences around directionality are detectable at routine sequencing depth in the vast majority of outbreaks. That needs to be established. Thus, although the discussion assumes that superspreaders can be discovered following the approach described, it is not totally clear this is always the case. This manuscript only offers one example, so this should be mentioned as a limitation but also as a call for future work to find out how common is the phenomenon and whether it can be revealed by ultra-deep sequencing.
4) A major criticism is that this is a carefully crafted analysis, it remains essentially anecdotal. To draw conclusions that deep sequencing is now ideally required for all outbreaks is probably a little overenthusiastic. A systematic analysis of far larger number of clusters is required to assess the importance of mixed populations that go undetected at routine sequencing depth before conclusions can be drawn about what should and shouldn't be routine practice. It could easily be that most mixed populations that are helpful to inferences around directionality are detectable at routine sequencing depth in the vast majority of outbreaks. That needs to be established. Thus, although the discussion assumes that superspreaders can be discovered following the approach described, it is not totally clear this is always the case. This manuscript only offers one example, so this should be mentioned as a limitation but also as a call for future work to find out how common is the phenomenon and whether it can be revealed by ultra-deep sequencing.
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