In Canada, tuberculosis disproportionately affects the Inuit, a group of indigenous people inhabiting the Arctic regions. Canada is aiming to eliminate tuberculosis among the Inuit by 2030. One way to help stop transmission and prevent future outbreaks is to trace how and where the disease spreads using DNA sequencing. This information can then be used by public health organizations to identify possible interventions.

Typically, the DNA of the bacterium that causes tuberculosis – Mycobacterium tuberculosis, or Mtb for short – is sequenced 50–100 times and a consensus DNA sequence is then generated for each patient from this data. These consensus DNA sequences are then compared to help piece together who infected whom. Recently, scientists have realized that the bacteria a person is infected with may have different DNA sequences due to people being infected with more than one bacterium or the bacterium developing variations in its genome after the infection. However, current DNA sequencing practices may miss these differences, making it harder to trace how the disease spreads.

Now, Lee et al. show that sequencing the DNA of Mtb from an infected person 500–1000 times (i.e. ∼10-20 times more than usual) makes it easier to detect genetic differences and determine how tuberculosis spreads. This approach, also known as ‘deep sequencing’, was used to analyze DNA samples of Mtb collected from about 50 people during an outbreak of tuberculosis in 2011-2012, which had previously undergone standard DNA sequencing.

This deep sequencing approach identified a ‘super-spreading event’ where one person had likely transmitted tuberculosis to up to 17 others during the outbreak. Lee et al. found that most of these people had visited the same ‘gathering houses’ which are social venues in the community. Implementing targeted public health interventions at these sites may help stop future outbreaks.

To fully understand how useful this method will be for tracking the spread of tuberculosis, deep and routine sequencing will need to be compared against each other in different settings and outbreaks. Furthermore, the approach used in this study may be useful for tracking the transmission of other infectious diseases.

Tuberculosis (TB) in Canada is highest among the Inuit, an Indigenous population with a rate over 300 times that of the non-Indigenous Canadian-born population in 2016 (Inuit Tapiriit Kanatami, 2018). Canada recently set a goal of TB elimination in the Inuit by 2030, (Inuit Tapiriit Kanatami, 2018) which will not be achieved without halting ongoing transmission. Previous studies have used genomic data either alone or in conjunction with classical epidemiology to investigate TB transmission dynamics in the Canadian North, (Tyler et al., 2017; Lee et al., 2015a; Lee et al., 2015b) with the aim of identifying clusters to help guide public health interventions. Thus far, such studies have relied on identifying consensus single nucleotide polymorphisms (cSNPs), consistent with prevailing methodology in this field.

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.

This outbreak, which has been previously described, (Lee et al., 2015b; Lee et al., 2016) comprised 50 microbiologically-confirmed cases of TB who were diagnosed in a single Inuit community between 2011–2012 - a rate of 5,359/100,000 for that year. Genomic epidemiology analyses using sequencing depths of ~50x that are standard in such work, identified multiple clusters of transmission in this outbreak, (Lee et al., 2015b) however, there was insufficient genetic variation detected to infer precise person-to-person transmission events within these subgroups, given the short time frame and low mutation rate of M. tuberculosis (~0.2–0.3 SNPs/genome/year for Lineage 4; Menardo et al., 2019). In this study, we illustrate how within-host diversity can be incorporated into transmission analyses. In doing so, we find new features of the transmission networks in this community, in particular, identifying a previously unrecognized super-spreading event. We highlight a potential role for deep sequencing in public health investigations, with implications for TB control in Canada’s North as well as other high-transmission environments.

62/65 (95·4%) available TB samples from cases diagnosed between 2007–2012 were successfully sequenced and passed quality control. This included 48/49 (98·0%) of the samples with an identical Mycobacterial Interspersed Repetitive Units Variable Number Tandem Repeats (MIRU-VNTR) pattern during the outbreak year. The remaining three samples could not be re-grown. Reads that were non-MTBC were removed (Source data 1) and there was no obvious association between percent contamination and hSNP frequency. Epidemiological and clinical data on all outbreak cases are described in Lee et al. (2015b).

Average genome coverage and depth across the H37Rv reference was 98·64% [SD 0·07%] and 714·53 [SD 92·68], respectively. Our primary filtering protocol yielded 51,430 cSNPs and 4,897 hSNPs across all individual samples (Source data 2). Excluding positions that were invariant compared to the reference or where any sample was missing and/or was low-quality resulted in a core alignment of 860 cSNP positions and 136 hSNP positions (note, these are not mutually exclusive, as positions with cSNPs in some samples may have hSNPs in others).

42 positions had hSNPs that were shared across all 62 samples (Table 1, Source data 3). Depth of coverage at these positions was, on average, 39% higher than the average depth across the same sample (SD 36·7%, Source data 4). Along with manual review of alignments (Figure 1), this suggested that many of these were false positives, potentially due to alignment error (e.g., from underlying structural variation in our samples compared to the H37Rv reference).

Figure 1

Download asset Open asset

To address this, we generated a local reference genome for the outbreak, MT-0080_PB. Quality metrics for the MT-0080_PB assembly are given in Source data 5. Compared to H37Rv, mean genome coverage and depth were higher with MT-0080_PB (at 99·33% [SD 0·09%] and 717·07 [SD 93·01], respectively), fewer positions were missing/low-quality (p < 0·00005, Table 1), and overall, fewer variable positions were detected (p < 0·00005). While core cSNP distances were similar between samples regardless of the reference (Table 1), the number of hSNPs was greatly reduced using MT-0080_PB (Source data 2); while 4,897 hSNPs were identified across all individual samples using H37Rv, only 125 hSNPs were identified using MT-0080_PB. There were also no hSNPs shared across all 62 samples using MT-0080_PB. Together, these findings support our hypothesis that alignment error is responsible for many of the detected variants, and indicate a local reference is important for accurate identification of hSNPs. All further results presented are based on the MT-0080_PB alignment.

A maximum likelihood tree was generated from 90 core cSNP positions (excluding sites invariant across all samples and the reference) compared to MT-0080_PB (Figure 2). All 62 samples (historical and outbreak) were included, for comparison with our previous work. (Lee et al., 2015b) Consistent with this, (Lee et al., 2015b) hierBAPS identified two main sub-lineages (‘Mj-V’ and ‘Mj-III’ per Lee et al., 2015a), with three sub-clusters (Mj-IIIA/B/C).

Figure 2 with 1 supplement see all

Download asset Open asset

As the TB epidemic continues among the Canadian Inuit, targeted public health interventions are essential to halt ongoing transmission. In order to do so, it is important that transmission events and associated risk factors are accurately identified. Our previous work suggested that hSNP analysis could enhance resolution of TB transmission (Martin et al., 2018). To investigate how this approach could be applied for TB control, we used deep sequencing to re-examine a major TB outbreak in the Canadian Arctic.

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.

Overall, our study has a number of strengths. Firstly, we had access to a densely-sampled outbreak, which was previously investigated using ‘standard’ sequencing depth and for which detailed epidemiological data was available. This allowed us to readily compare methodological approaches, showing how and when deep sequencing might be beneficial for public health. In this study, we have also identified important methodological considerations for hSNP detection, with implications for transmission analyses, but also potentially for resistance prediction as well (Liu et al., 2015). Finally, the use of long-read data has allowed us to completely assemble a novel TB genome from Nunavik. This will serve as a valuable resource for future studies of transmission in Nunavik (given the low strain diversity in the region Lee et al., 2015a), as well as other Inuit territories.

A potential limitation of this work is that, given the historical nature of the outbreak, deep sequencing was done using DNA extracted from culture. Due to methodological challenges of sequencing directly from sputum, (Brown et al., 2015; Votintseva et al., 2017; Doyle et al., 2018) few studies have examined the effect of culture on genome diversity. A recent study by Shockey et al. (2019)., which analyzed allelic variation among reads from individual samples, suggests that some diversity may be lost during the culturing process. However, several studies looking at potential transmission (Votintseva et al., 2017; Doyle et al., 2018; Nimmo et al., 2019) found results were congruent between cSNP analyses from culture versus raw samples. In terms of hSNPs, Votintseva et al. (2017) Doyle et al. (2018); Nimmo et al. (2019) reported detecting fewer hSNPs with sequencing from culture versus from sputum, in Nimmo et al. (2019), the median hSNPs was only 4.5 versus 5 hSNPs, respectively – a difference that may not be clinically significant, regardless of statistical significance. Given the inconsistency of results and paucity of data, further study is needed to understand how hSNP diversity may be affected by the culturing process, and to assess whether this affects inferences of transmission. We note that it is likely that enhanced detection of the hSNPs present in sputum would improve the resolution over that which we present in this work.

Another potential limitation is that, while we can compare the epidemiological inferences made between our previous analysis and our deep sequencing analysis, the sequencing data and bioinformatics pipelines themselves are not directly comparable. Methods to accurately identify hSNPs and incorporate them into transmission analyses are currently an area of active research. We illustrated in our recent paper (Martin et al., 2018) that additional steps and strict thresholds must be used to minimize false positive hSNPs, and have conducted the current analysis in consideration of this. However, we note that pre-filtering, our 2015 analysis found that MT-504 had five reference alleles at position 276,685 in the H37Rv alignment (out of 75) and randomly downsampling the current data to simulate ~50 x yielded similar results (5/47 reads at position 276,544 aligned to MT-0080_PB). As most genomic studies of TB employ conservative thresholds of 75–90% allele frequency to classify cSNPs, many bioinformatics pipelines would consider this heterogeneity as potentially suspect at standard sequencing depth. This suggests that greater depth and/or different analytic approaches (e.g., Wyllie et al., 2019) are needed to ensure accurate discrimination of sequencing/analytic error from true variation; ultimately, the optimal approach used to identify variants needs to be carefully considered, and appropriate for the study question and data being analyzed.

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.

In summary, we have found evidence of mixed variants with important epidemiologic implications that would not have been detected with standard methods and common filtering criteria. To our knowledge, however, no other studies have been published comparing epidemiological inferences obtained with deep versus routine sequencing for TB outbreak resolution – thus this work represents an important methodological advance in this area. We illustrate that genomic methods, while powerful, still require careful interpretation and can still harbor considerable ambiguity when comparing very close links in a transmission chain, or, as also suggested in Xu et al. (2019), when trying to identify source cases. This finding is likely relevant beyond TB, given the increasing number of pathogens undergoing genomic investigation. Our work also highlights the importance of reproducing previous genomic epidemiology analyses; as the technology and methods used in this field continue to develop, these can lead to improved resolution of transmission and ultimately, challenge previously-held inferences – with critical implications for public health. In terms of TB control, we demonstrate that deep sequencing can aid in transmission analyses, in particular by allowing accurate identification of TB super-spreading events and associated epidemiological characteristics. We propose that deep sequencing is most useful for understanding transmission in settings with low strain diversity, and that these may benefit from routine use of this approach. We hypothesize deep sequencing may also provide additional resolution of transmission events within outbreaks occurring over short, limited timescales – irrespective of local strain diversity, as (by definition) all samples involved in recent transmission would be expected to be closely-related. However, further studies comparing deep versus routine sequencing, ideally from a diversity of clusters and epidemiologic contexts, are needed to fully quantify the added value of this approach for epidemiologic studies of TB.

Overall, this work has important implications for the Canadian North, as well as other regions with high TB transmission; as next-generation sequencing becomes a mainstay in public health surveillance, it is critical we recognize the limitations of analyses done using routine sequencing data. Accurate resolution of transmission is essential for TB control programmes, in order to better understand risk factors for such transmission and enable prioritization of public health resources. With respect to Nunavik, our findings were regarded as very valuable by the regional public health unit and local community leaders; as a direct consequence of this work, ongoing and new investigations of TB genomic epidemiology in Nunavik are using deep sequencing to inform transmission analyses. However, while costs continue to decline, we recognize deep sequencing of all samples in an outbreak may not be economically viable in every setting.

Sequencing data and the assembly for MT-0080 are available on the NCBI's Sequence Read Archive under BioProject PRJNA549270.

1. 1. Lee RS
   2. Proulx J-F
   3. McIntosh F
   4. Behr MA
   5. Hanage WP

   (2019) NCBI BioProject

   ID PRJNA549270. Deep sequencing of a major TB outbreak in the Canadian Arctic.

   https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA549270

1. 1. Bolger AM
   2. Lohse M
   3. Usadel B

   (2014) Trimmomatic: a flexible trimmer for illumina sequence data

   Bioinformatics 30:2114–2120.

   https://doi.org/10.1093/bioinformatics/btu170

   • Google Scholar
2. 1. Brettin T
   2. Davis JJ
   3. Disz T
   4. Edwards RA
   5. Gerdes S
   6. Olsen GJ
   7. Olson R
   8. Overbeek R
   9. Parrello B
   10. Pusch GD
   11. Shukla M
   12. Thomason JA
   13. Stevens R
   14. Vonstein V
   15. Wattam AR
   16. Xia F

   (2015) RASTtk: a modular and extensible implementation of the RAST algorithm for building custom annotation pipelines and annotating batches of genomes

   Scientific Reports 5:8365.

   https://doi.org/10.1038/srep08365

   • PubMed
   • Google Scholar
3. 1. Brown AC
   2. Bryant JM
   3. Einer-Jensen K
   4. Holdstock J
   5. Houniet DT
   6. Chan JZ
   7. Depledge DP
   8. Nikolayevskyy V
   9. Broda A
   10. Stone MJ
   11. Christiansen MT
   12. Williams R
   13. McAndrew MB
   14. Tutill H
   15. Brown J
   16. Melzer M
   17. Rosmarin C
   18. McHugh TD
   19. Shorten RJ
   20. Drobniewski F
   21. Speight G
   22. Breuer J

   (2015) Rapid Whole-Genome sequencing of Mycobacterium tuberculosis isolates directly from clinical samples

   Journal of Clinical Microbiology 53:2230–2237.

   https://doi.org/10.1128/JCM.00486-15

   • PubMed
   • Google Scholar
4. 1. Bryant JM
   2. Harris SR
   3. Parkhill J
   4. Dawson R
   5. Diacon AH
   6. van Helden P
   7. Pym A
   8. Mahayiddin AA
   9. Chuchottaworn C
   10. Sanne IM
   11. Louw C
   12. Boeree MJ
   13. Hoelscher M
   14. McHugh TD
   15. Bateson AL
   16. Hunt RD
   17. Mwaigwisya S
   18. Wright L
   19. Gillespie SH
   20. Bentley SD

   (2013) Whole-genome sequencing to establish relapse or re-infection with Mycobacterium tuberculosis: a retrospective observational study

   The Lancet Respiratory Medicine 1:786–792.

   https://doi.org/10.1016/S2213-2600(13)70231-5

   • PubMed
   • Google Scholar
5. 1. Cheng L
   2. Connor TR
   3. Sirén J
   4. Aanensen DM
   5. Corander J

   (2013) Hierarchical and spatially explicit clustering of DNA sequences with BAPS software

   Molecular Biology and Evolution 30:1224–1228.

   https://doi.org/10.1093/molbev/mst028

   • PubMed
   • Google Scholar
6. 1. Cingolani P
   2. Platts A
   3. Wang leL
   4. Coon M
   5. Nguyen T
   6. Wang L
   7. Land SJ
   8. Lu X
   9. Ruden DM

   (2012) A program for annotating and predicting the effects of single Nucleotide Polymorphisms, SnpEff: snps in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3

   Fly 6:80–92.

   https://doi.org/10.4161/fly.19695

   • PubMed
   • Google Scholar
7. 1. Doyle RM
   2. Burgess C
   3. Williams R
   4. Gorton R
   5. Booth H
   6. Brown J
   7. Bryant JM
   8. Chan J
   9. Creer D
   10. Holdstock J
   11. Kunst H
   12. Lozewicz S
   13. Platt G
   14. Romero EY
   15. Speight G
   16. Tiberi S
   17. Abubakar I
   18. Lipman M
   19. McHugh TD
   20. Breuer J

   (2018) Direct Whole-Genome sequencing of sputum accurately identifies Drug-Resistant Mycobacterium tuberculosis faster than MGIT culture sequencing

   Journal of Clinical Microbiology 56:e00666.

   https://doi.org/10.1128/JCM.00666-18

   • PubMed
   • Google Scholar
8. 1. Guerra-Assunção JA
   2. Houben RM
   3. Crampin AC
   4. Mzembe T
   5. Mallard K
   6. Coll F
   7. Khan P
   8. Banda L
   9. Chiwaya A
   10. Pereira RP
   11. McNerney R
   12. Harris D
   13. Parkhill J
   14. Clark TG
   15. Glynn JR

   (2015) Recurrence due to relapse or reinfection with Mycobacterium tuberculosis: a whole-genome sequencing approach in a large, population-based cohort with a high HIV infection prevalence and active follow-up

   Journal of Infectious Diseases 211:1154–1163.

   https://doi.org/10.1093/infdis/jiu574

   • PubMed
   • Google Scholar
9. 1. Gurevich A
   2. Saveliev V
   3. Vyahhi N
   4. Tesler G

   (2013) QUAST: quality assessment tool for genome assemblies

   Bioinformatics 29:1072–1075.

   https://doi.org/10.1093/bioinformatics/btt086

   • PubMed
   • Google Scholar
10. 1. Guthrie JL
    2. Strudwick L
    3. Roberts B
    4. Allen M
    5. McFadzen J
    6. Roth D
    7. Jorgensen D
    8. Rodrigues M
    9. Tang P
    10. Hanley B
    11. Johnston J
    12. Cook VJ
    13. Gardy JL

    (2019) Whole genome sequencing for improved understanding of Mycobacterium tuberculosis transmission in a remote circumpolar region

    Epidemiology and Infection 147:e188.

    https://doi.org/10.1017/S0950268819000670

    • PubMed
    • Google Scholar
11. Report

    1. Inuit Tapiriit Kanatami

    (2018)

    Inuit Tuberculosis Elimination Framework

    Inuit Tapiriit Kanatami.

    • Google Scholar
12. 1. Kline SE
    2. Hedemark LL
    3. Davies SF

    (1995) Outbreak of tuberculosis among regular patrons of a neighborhood bar

    New England Journal of Medicine 333:222–227.

    https://doi.org/10.1056/NEJM199507273330404

    • PubMed
    • Google Scholar
13. 1. Koren S
    2. Walenz BP
    3. Berlin K
    4. Miller JR
    5. Bergman NH
    6. Phillippy AM

    (2017) Canu: scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation

    Genome Research 27:722–736.

    https://doi.org/10.1101/gr.215087.116

    • PubMed
    • Google Scholar
14. 1. Lee RS
    2. Radomski N
    3. Proulx JF
    4. Levade I
    5. Shapiro BJ
    6. McIntosh F
    7. Soualhine H
    8. Menzies D
    9. Behr MA

    (2015a) Population genomics of Mycobacterium tuberculosis in the inuit

    PNAS 112:13609–13614.

    https://doi.org/10.1073/pnas.1507071112

    • PubMed
    • Google Scholar
15. 1. Lee RS
    2. Radomski N
    3. Proulx JF
    4. Manry J
    5. McIntosh F
    6. Desjardins F
    7. Soualhine H
    8. Domenech P
    9. Reed MB
    10. Menzies D
    11. Behr MA

    (2015b) Reemergence and amplification of tuberculosis in the canadian arctic

    Journal of Infectious Diseases 211:1905–1914.

    https://doi.org/10.1093/infdis/jiv011

    • PubMed
    • Google Scholar
16. 1. Lee RS
    2. Proulx JF
    3. Menzies D
    4. Behr MA

    (2016) Progression to tuberculosis disease increases with multiple exposures

    European Respiratory Journal 48:1682–1689.

    https://doi.org/10.1183/13993003.00893-2016

    • PubMed
    • Google Scholar
17. 1. Letunic I
    2. Bork P

    (2016) Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees

    Nucleic Acids Research 44:W242–W245.

    https://doi.org/10.1093/nar/gkw290

    • PubMed
    • Google Scholar
18. 1. Li H
    2. Handsaker B
    3. Wysoker A
    4. Fennell T
    5. Ruan J
    6. Homer N
    7. Marth G
    8. Abecasis G
    9. Durbin R
    10. 1000 Genome Project Data Processing Subgroup

    (2009) The sequence alignment/Map format and SAMtools

    Bioinformatics 25:2078–2079.

    https://doi.org/10.1093/bioinformatics/btp352

    • PubMed
    • Google Scholar
19. Preprint

    1. Li H

    (2013) Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM

    arXiv.

    https://arxiv.org/abs/1303.3997

    • Google Scholar
20. 1. Liu Q
    2. Via LE
    3. Luo T
    4. Liang L
    5. Liu X
    6. Wu S
    7. Shen Q
    8. Wei W
    9. Ruan X
    10. Yuan X
    11. Zhang G
    12. Barry CE
    13. Gao Q

    (2015) Within patient microevolution of Mycobacterium tuberculosis correlates with heterogeneous responses to treatment

    Scientific Reports 5:17507.

    https://doi.org/10.1038/srep17507

    • PubMed
    • Google Scholar
21. 1. Martin MA
    2. Lee RS
    3. Cowley LA
    4. Gardy JL
    5. Hanage WP

    (2018) Within-host Mycobacterium tuberculosis diversity and its utility for inferences of transmission

    Microbial Genomics 4:.

    https://doi.org/10.1099/mgen.0.000217

    • PubMed
    • Google Scholar
22. 1. McKenna A
    2. Hanna M
    3. Banks E
    4. Sivachenko A
    5. Cibulskis K
    6. Kernytsky A
    7. Garimella K
    8. Altshuler D
    9. Gabriel S
    10. Daly M
    11. DePristo MA

    (2010) The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data

    Genome Research 20:1297–1303.

    https://doi.org/10.1101/gr.107524.110

    • PubMed
    • Google Scholar
23. 1. Meehan CJ
    2. Goig GA
    3. Kohl TA
    4. Verboven L
    5. Dippenaar A
    6. Ezewudo M
    7. Farhat MR
    8. Guthrie JL
    9. Laukens K
    10. Miotto P
    11. Ofori-Anyinam B
    12. Dreyer V
    13. Supply P
    14. Suresh A
    15. Utpatel C
    16. van Soolingen D
    17. Zhou Y
    18. Ashton PM
    19. Brites D
    20. Cabibbe AM
    21. de Jong BC
    22. de Vos M
    23. Menardo F
    24. Gagneux S
    25. Gao Q
    26. Heupink TH
    27. Liu Q
    28. Loiseau C
    29. Rigouts L
    30. Rodwell TC
    31. Tagliani E
    32. Walker TM
    33. Warren RM
    34. Zhao Y
    35. Zignol M
    36. Schito M
    37. Gardy J
    38. Cirillo DM
    39. Niemann S
    40. Comas I
    41. Van Rie A

    (2019) Whole genome sequencing of Mycobacterium tuberculosis: current standards and open issues

    Nature Reviews Microbiology 17:533–545.

    https://doi.org/10.1038/s41579-019-0214-5

    • PubMed
    • Google Scholar
24. 1. Menardo F
    2. Duchêne S
    3. Brites D
    4. Gagneux S

    (2019) The molecular clock of Mycobacterium tuberculosis

    PLOS Pathogens 15:e1008067.

    https://doi.org/10.1371/journal.ppat.1008067

    • PubMed
    • Google Scholar
25. 1. Menzies D

    (1997) Issues in the management of contacts of patients with active pulmonary tuberculosis

    Canadian Journal of Public Health 88:197–201.

    https://doi.org/10.1007/BF03403887

    • PubMed
    • Google Scholar
26. 1. Milne I
    2. Stephen G
    3. Bayer M
    4. Cock PJ
    5. Pritchard L
    6. Cardle L
    7. Shaw PD
    8. Marshall D

    (2013) Using tablet for visual exploration of second-generation sequencing data

    Briefings in Bioinformatics 14:193–202.

    https://doi.org/10.1093/bib/bbs012

    • PubMed
    • Google Scholar
27. 1. Nguyen LT
    2. Schmidt HA
    3. von Haeseler A
    4. Minh BQ

    (2015) IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies

    Molecular Biology and Evolution 32:268–274.

    https://doi.org/10.1093/molbev/msu300

    • PubMed
    • Google Scholar
28. 1. Nimmo C
    2. Shaw LP
    3. Doyle R
    4. Williams R
    5. Brien K
    6. Burgess C
    7. Breuer J
    8. Balloux F
    9. Pym AS

    (2019) Whole genome sequencing Mycobacterium tuberculosis directly from sputum identifies more genetic diversity than sequencing from culture

    BMC Genomics 20:389.

    https://doi.org/10.1186/s12864-019-5782-2

    • PubMed
    • Google Scholar
29. 1. O'Toole RF
    2. Gautam SS

    (2017) Limitations of the Mycobacterium tuberculosis reference genome H37Rv in the detection of virulence-related loci

    Genomics 109:471–474.

    https://doi.org/10.1016/j.ygeno.2017.07.004

    • PubMed
    • Google Scholar
30. 1. Page AJ
    2. Taylor B
    3. Delaney AJ
    4. Soares J
    5. Seemann T
    6. Keane JA
    7. Harris SR

    (2016) SNP-sites: rapid efficient extraction of SNPs from multi-FASTA alignments

    Microbial Genomics 2:e000056.

    https://doi.org/10.1099/mgen.0.000056

    • PubMed
    • Google Scholar
31. 1. Roetzer A
    2. Diel R
    3. Kohl TA
    4. Rückert C
    5. Nübel U
    6. Blom J
    7. Wirth T
    8. Jaenicke S
    9. Schuback S
    10. Rüsch-Gerdes S
    11. Supply P
    12. Kalinowski J
    13. Niemann S

    (2013) Whole genome sequencing versus traditional genotyping for investigation of a Mycobacterium tuberculosis outbreak: a longitudinal molecular epidemiological study

    PLOS Medicine 10:e1001387.

    https://doi.org/10.1371/journal.pmed.1001387

    • PubMed
    • Google Scholar
32. 1. Séraphin MN
    2. Norman A
    3. Rasmussen EM
    4. Gerace AM
    5. Chiribau CB
    6. Rowlinson MC
    7. Lillebaek T
    8. Lauzardo M

    (2019) Direct transmission of within-host Mycobacterium tuberculosis diversity to secondary cases can lead to variable between-host heterogeneity without de novo mutation: a genomic investigation

    EBioMedicine 47:293–300.

    https://doi.org/10.1016/j.ebiom.2019.08.010

    • PubMed
    • Google Scholar
33. 1. Shockey AC
    2. Dabney J
    3. Pepperell CS

    (2019) Effects of host, sample, and in vitro Culture on Genomic Diversity of Pathogenic Mycobacteria

    Frontiers in Genetics 10:77.

    https://doi.org/10.3389/fgene.2019.00477

    • PubMed
    • Google Scholar
34. 1. Stein RA

    (2011) Super-spreaders in infectious diseases

    International Journal of Infectious Diseases 15:e510–e513.

    https://doi.org/10.1016/j.ijid.2010.06.020

    • PubMed
    • Google Scholar
35. 1. Tyler AD
    2. Randell E
    3. Baikie M
    4. Antonation K
    5. Janella D
    6. Christianson S
    7. Tyrrell GJ
    8. Graham M
    9. Van Domselaar G
    10. Sharma MK

    (2017) Application of whole genome sequence analysis to the study of Mycobacterium tuberculosis in Nunavut, Canada

    PLOS ONE 12:e0185656.

    https://doi.org/10.1371/journal.pone.0185656

    • PubMed
    • Google Scholar
36. 1. van Soolingen D
    2. Hermans PW
    3. de Haas PE
    4. Soll DR
    5. van Embden JD

    (1991) Occurrence and stability of insertion sequences in Mycobacterium tuberculosis complex strains: evaluation of an insertion sequence-dependent DNA polymorphism as a tool in the epidemiology of tuberculosis

    Journal of Clinical Microbiology 29:2578–2586.

    https://doi.org/10.1128/JCM.29.11.2578-2586.1991

    • PubMed
    • Google Scholar
37. 1. Votintseva AA
    2. Bradley P
    3. Pankhurst L
    4. Del Ojo Elias C
    5. Loose M
    6. Nilgiriwala K
    7. Chatterjee A
    8. Smith EG
    9. Sanderson N
    10. Walker TM
    11. Morgan MR
    12. Wyllie DH
    13. Walker AS
    14. Peto TEA
    15. Crook DW
    16. Iqbal Z

    (2017) Same-Day diagnostic and surveillance data for tuberculosis via Whole-Genome sequencing of direct respiratory samples

    Journal of Clinical Microbiology 55:1285–1298.

    https://doi.org/10.1128/JCM.02483-16

    • PubMed
    • Google Scholar
38. 1. Walker BJ
    2. Abeel T
    3. Shea T
    4. Priest M
    5. Abouelliel A
    6. Sakthikumar S
    7. Cuomo CA
    8. Zeng Q
    9. Wortman J
    10. Young SK
    11. Earl AM

    (2014) Pilon: an integrated tool for comprehensive microbial variant detection and genome assembly improvement

    PLOS ONE 9:e112963.

    https://doi.org/10.1371/journal.pone.0112963

    • PubMed
    • Google Scholar
39. 1. Witney AA
    2. Bateson AL
    3. Jindani A
    4. Phillips PP
    5. Coleman D
    6. Stoker NG
    7. Butcher PD
    8. McHugh TD
    9. RIFAQUIN Study Team

    (2017) Use of whole-genome sequencing to distinguish relapse from reinfection in a completed tuberculosis clinical trial

    BMC Medicine 15:71.

    https://doi.org/10.1186/s12916-017-0834-4

    • PubMed
    • Google Scholar
40. 1. Wood DE
    2. Salzberg SL

    (2014) Kraken: ultrafast metagenomic sequence classification using exact alignments

    Genome Biology 15:R46.

    https://doi.org/10.1186/gb-2014-15-3-r46

    • PubMed
    • Google Scholar
41. 1. Worby CJ
    2. Lipsitch M
    3. Hanage WP

    (2017) Shared genomic variants: identification of transmission routes using pathogen Deep-Sequence data

    American Journal of Epidemiology 186:1209–1216.

    https://doi.org/10.1093/aje/kwx182

    • PubMed
    • Google Scholar
42. Preprint

    1. Wyllie D
    2. Do T
    3. Myers R

    (2019) M. tuberculosis microvariation is common and is associated with transmission: analysis of three years prospective universal sequencing in England

    bioRxiv.

    https://doi.org/10.1101/681502

    • Google Scholar
43. 1. Xu Y
    2. Cancino-Muñoz I
    3. Torres-Puente M
    4. Villamayor LM
    5. Borrás R
    6. Borrás-Máñez M
    7. Bosque M
    8. Camarena JJ
    9. Colomer-Roig E
    10. Colomina J
    11. Escribano I
    12. Esparcia-Rodríguez O
    13. Gil-Brusola A
    14. Gimeno C
    15. Gimeno-Gascón A
    16. Gomila-Sard B
    17. González-Granda D
    18. Gonzalo-Jiménez N
    19. Guna-Serrano MR
    20. López-Hontangas JL
    21. Martín-González C
    22. Moreno-Muñoz R
    23. Navarro D
    24. Navarro M
    25. Orta N
    26. Pérez E
    27. Prat J
    28. Rodríguez JC
    29. Ruiz-García MM
    30. Vanaclocha H
    31. Colijn C
    32. Comas I

    (2019) High-resolution mapping of tuberculosis transmission: whole genome sequencing and phylogenetic modelling of a cohort from Valencia region, Spain

    PLOS Medicine 16:e1002961.

    https://doi.org/10.1371/journal.pmed.1002961

    • PubMed
    • Google Scholar

Acceptance summary:

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.

Decision letter after peer review:

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.

As is customary in eLife, the reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

Summary:

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.

Essential revisions:

1) The crux of this work is about identifying MT-504 as a super spreader. From the core SNP tree in Figure 1A this would not be evident at all based on their location in a subgroup of the sublineage and not in a position closer to the root. The use of hSNPs to uncover this is well done but the paragraph is difficult to read. Indeed only because this is my field do I think I was able to follow. Perhaps authors could construct a phylogenetic network of this subsection to show how the MT-504 resides as a central hub compared to the tree? In any case this section needs to be made easy to read as without it the more un-accustomed reader will get lost.

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.

3) The portion on super spreaders being found at diagnosis in the Discussion is interesting but not directly related to the findings here. We are a long way from identifying super spreaders based on some genomic trait (especially as this could be host related) and the WGS work here won't lead to such clinical decision making. I suggest this section be vastly shortened or removed so readers don't think this is a possibility from WGS.

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.

5) The same criticism could be levelled at the conclusion that a local reference is important. It was in this case, but data has not been presented from which to conclude that it is routine necessity.

Essential revisions:

1) The crux of this work is about identifying MT-504 as a super spreader. From the core SNP tree in Figure 1A this would not be evident at all based on their location in a subgroup of the sublineage and not in a position closer to the root. The use of hSNPs to uncover this is well done but the paragraph is difficult to read. Indeed only because this is my field do I think I was able to follow. Perhaps authors could construct a phylogenetic network of this subsection to show how the MT-504 resides as a central hub compared to the tree? In any case this section needs to be made easy to read as without it the more un-accustomed reader will get lost.

We apologize for any lack of clarity. It has been a challenge to present a comparison with previous work, wherein we need to show our results clearly but also be mindful of not replicating previously published text or figures.

The consensus SNP tree in Figure 1A (Figure 2A in the revised version, as the supplementary figure was moved to the main text) included all available samples from this village from 2007-2012 (i.e., 62/65 outbreak and historical cases, not only from IIIB, but IIIA, IIIC and VA as well). We did this to illustrate that, despite changes in sequencing platforms and analysis, we still recapitulate the original three clusters (IIIA/B/C) from this community. We have added the following clarification to the Results: “All 62 samples (historical and outbreak) were included, for comparison with our previous work.” Based on our previous work, cluster IIIA (which was first seen in the community in 2007, while IIIB and IIIC were not seen until 2009 and 2012, respectively) was ancestral to clusters Mj-IIIB and IIIC. We would therefore not expect MT-504, which is in cluster IIIB, to be at the root of this particular tree.

In response to the reviewers’ comment, we have done the following to help make this figure and corresponding section of the text more clear for eLife readers:

A) We have extensively revised the section entitled “hSNPs identify super-spreaders and more accurately resolve transmission clusters” to walk the reader through the data in more detail (hSNPs identify super-spreaders and more accurately resolve transmission clusters).

B) We have added the informative alleles to this figure (2A in the revised version)

C) We have also added a new epidemiologic curve showing the cases stratified by sub-group of transmission according to the original analysis to Figure 1B (2B in the revised version), to assist the reader in understanding the timeline.

On reviewing the raw fasta file, we also wanted to report that we realized some missing sites had inadvertently been included despite using a well-known tool (‘snp-sites’) to exclude all non-AGTC characters for this step. This led to minor differences in the maximum likelihood tree structure from the submitted version. This has now been updated (and did not alter our overall epidemiologic inferences).

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.

We thank the reviewers for this comment. It is true that the current study examines the application of deep sequencing to an outbreak in a low-diversity setting. However, given the low mutation rate of M. tuberculosis, samples from cases involved in recent transmission (e.g., within 2 years), can often have 0 consensus SNPs between them. Indeed, our collaborators at the US Center for Disease Control’s Division of TB Elimination have reported to us difficulties resolving recent transmission using consensus SNPs alone for this reason; we therefore anticipate deep sequencing could help provide additional resolution, by helping resolve person-to-person transmission events within these groups of transmission. In this case, the utility of deep sequencing in this case would be irrespective of the diversity of strains circulating in the setting. We do, however, agree that deep sequencing would likely be more useful in low-diversity settings for surveillance over longer time scales than it would be in high-diversity settings, as more fixed variation would be expected to occur over samples spread over greater time periods.

We had previously stated that we believe deep sequencing may be useful 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.” We have also added the following statement to the Discussion: “We propose that deep sequencing is most useful for understanding transmission in settings with low strain diversity, and that these may benefit from routine use of this approach. […] However, further studies comparing deep versus routine sequencing, ideally from a diversity of clusters and epidemiologic contexts, are needed to fully quantify the added value of this approach for epidemiologic studies of TB.” We also discuss representativeness of this dataset further under point #4 below.

3) The portion on super spreaders being found at diagnosis in the Discussion is interesting but not directly related to the findings here. We are a long way from identifying super spreaders based on some genomic trait (especially as this could be host related) and the WGS work here won't lead to such clinical decision making. I suggest this section be vastly shortened or removed so readers don't think this is a possibility from WGS.

Thank you for your feedback on this section. We have cut this text from the manuscript to avoid further confusion, however, to clarify, we did not mean to imply that deep sequencing could be used to identify bacterial characteristics predictive of future super-spreading events. We absolutely agree with the reviewer that such a goal is generally ‘a long way off’, and, with respect to Nunavik, our previous work (Lee et al., 2015) actually suggested that bacterial characteristics are not likely to be driving transmission in this region. Furthermore, even if there were such bacterial characteristics, delays in sequencing (due to shipping samples south, batching of samples, need for culture, etc.) generally mean these findings would not be available in real-time to assist with outbreak response. What we are instead proposing to use deep sequencing to help accurately discriminate super-spreading events, and subsequently, identify epidemiological and clinical characteristics that are associated with these using more ‘classical’ epidemiologic methods (i.e., regression analyses). Dr. Lee is currently leading such a study for this region. These communities have very limited resources in terms of clinical staff, who are typically managing numerous routine health needs in addition to TB; if we can help identify the most likely clinical and epidemiological characteristics of these historical super-spreaders, nursing staff and physicians can potentially triage cases with these characteristics in the future as they are diagnosed, and better prioritize investigation of their contacts.

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.

We appreciate the reviewers’ feedback, and we agree that the present work is not sufficient to offer guidelines for the study of all outbreaks, in settings with high and low strain diversity and TB burden. However, while this is indeed one outbreak, it is an essential and necessary step to enable such work in the future. We had in this study a unique opportunity to compare approaches for inferring transmission under essentially ideal conditions, i.e., where (due to the scale of the outbreak, and geography of Nunavik, limiting out-migration), there was all cases were detected, and detailed epidemiological data on each was available. In departing from the standard approaches to genomic epidemiology, this paper is a crucial starting point for future work; further studies are needed – with deep and regular sequencing data from all samples, along with epidemiological data – in order to assess the added value of deep sequencing across settings and contexts. In line with point #2 above, we have modified the Discussion to address this.

In addition to this, we have also added the following: “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. […] Further studies are needed to quantify the degree to which super-spreading occurs in TB, and examine how and when deep sequencing be used to detect this.” We have also removed our suggestion in the Discussion that deep sequencing may be needed for all smear-positive cases, to make the text less prescriptive.

5) The same criticism could be levelled at the conclusion that a local reference is important. It was in this case, but data has not been presented from which to conclude that it is routine necessity.

We found a significant reduction in what appear to be false positive hSNPs using a local reference genome, and do think such a reference is useful to help minimize these. We have been careful to indicate that this may not be necessary for cSNP detection for TB (though we would note, in our experience, that a local reference is often preferred for this purpose for other pathogens). Our findings are also consistent with other studies that have looked at virulence factors; the H37Rv strain has been passaged numerous times since its isolation, and gene content is not identical to clinical strains. However, in response to the reviewer’s comment, we have modified our text from “We propose these [i.e., local reference genomes] are also warranted for hSNP analysis” to “Our analysis suggests that these may also be warranted for hSNP analysis”. We have also changed our ‘recommendation’ to a ‘suggestion’ that “where it is possible to do so – it would be best to generate and use a local reference”.

Author details

1. Robyn S Lee

   1. Epidemiology Division, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
   2. Center for Communicable Disease Dynamics, Harvard TH Chan School of Public Health, Boston, United States
   3. Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, United States

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Project administration

   For correspondence

   robyn.s.c.lee@gmail.com

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7120-9053

2. Jean-François Proulx

   Nunavik Regional Board of Health and Social Services, Kuujjuaq, Canada

   Contribution

   Data curation, Investigation

   Competing interests

   No competing interests declared

3. Fiona McIntosh

   The Research Institute of McGill University Health Centre, Montréal, Canada

   Contribution

   Resources

   Competing interests

   No competing interests declared

4. Marcel A Behr

   The Research Institute of McGill University Health Centre, Montréal, Canada

   Contribution

   Resources

   Competing interests

   No competing interests declared

5. William P Hanage

   1. Center for Communicable Disease Dynamics, Harvard TH Chan School of Public Health, Boston, United States
   2. Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, United States

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Methodology

   Competing interests

   No competing interests declared

• 2,488

  Page views

• 371

  Downloads

• 20

  Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.