Previously undetected super-spreading of Mycobacterium tuberculosis revealed by deep sequencing
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 .
All 50 samples from the 2011–2012 outbreak ( Lee et al . , 2015b ) were eligible for inclusion , as well as samples from all cases ( n = 15 ) diagnosed in same village in the preceding five years ( 2007 onwards ) , 13/15 of which were caused by the same strain of M . tuberculosis ( the ‘Major [Mj]-III’ sublineage; Lee et al . , 2015a ) . There were two episodes of recurrent TB ( i . e . , where an individual had microbiologically-confirmed TB once , was cured , but developed TB again during the study period ) ; otherwise , all samples are from unique individuals . All cases had pulmonary TB that was Lineage 4 ( Euro-American; Lee et al . , 2015b ) . Cross-contamination was ruled out as described in Lee et al . ( 2015b ) . Samples were cultured once on Middlebrook 7H10 agar and plate sweeps were collected for DNA extraction using the van Soolingen method ( van Soolingen et al . , 1991 ) . Genomic DNA was quantified using the Quant-iT PicoGreen dsDNA Assay ( ThermoFisher Scientific , Massachusetts , USA ) . Library preparation and sequencing were done at the McGill University/Genome Québec Innovation Centre . The Illumina HiSeq 4000 was used to produce paired-end 100 bp reads . To obtain the depth of coverage needed for this study ( ~500–1000x for deep sequencing , compared to ~50–100x as routinely done by public health ) , pooled libraries were run on four independent lanes . FastQC ( v . 0 . 11 . 5 , https://www . bioinformatics . babraham . ac . uk/projects/fastqc/ ) was used to assess sequencing data quality and reads were trimmed to remove low-quality bases using Trimmomatic ( v . 0 . 36 Bolger et al . , 2014 ) . Kraken ( v . 1 . 1 Wood and Salzberg , 2014 ) was then used to identify potential contamination with the miniKraken database ( minikraken_20171019 ) . Reads classified as ‘Mycobacterium tuberculosis complex’ ( MTBC ) were extracted using Seqtk ( v . 1 . 2 , Li H , available at: https://github . com/lh3/seqtk ) . Reads were then aligned using the Burrows Wheeler Aligner MEM algorithm ( v . 0 . 7 . 15 Li , 2013 ) to the H37Rv reference ( NC_000962 . 3 in the National Center for Biotechnology Information [NCBI] RefSeq database ) and sorted using Samtools ( v . 1 . 5 Li et al . , 2009 ) . Analyses were later repeated using a local reference genome ( described below ) . Reads were with ambiguous mappings were excluded , as were reads with excessive soft-clipping ( i . e . , more than 20% of read length ) based on our previous work ( Martin et al . , 2018 ) . Duplicate reads were marked using Picard MarkDuplicates ( v . 2 . 9 . 0 , https://broadinstitute . github . io/picard/ ) and reads were locally re-aligned around indels using Genome Analysis ToolKit ( GATK , v . 3 . 8 McKenna et al . , 2010 ) . All sites were called using GATK’s Unified Genotyper algorithm , with the -d 1500 to avoid downsampling to 250 ( done by default with this tool during variant calling ) . Variants ( cSNPs and heterogenous SNPs [hSNPs] ) versus H37Rv were annotated using snpEff ( v . 4 . 3t Cingolani et al . , 2012 ) . Variants were filtered for quality using custom Python scripts ( v . 3 . 6 ) with the following thresholds: Phred < 50 , Root Mean Squared Mapping Quality ( RMS-MQ ) ≤ 30 , depth ( DP ) < 20 , Fisher Strand Bias ( FS ) ≥ 60 and read position strand bias ( ReadPos ) < −8 ( Martin et al . , 2018 ) . cSNPs were classified as positions where ≥ 95% of reads were the alternative allele ( ALT ) , hSNPs were classified as positions where > 5% and < 95% of reads were ALT , and positions with the ALT present in ≤5% of reads were classified as ‘reference’ . We also compared inferences of transmission from this analysis to i ) when these thresholds were increased to the minimum values among cSNPs in the initial H37Rv analysis , and ii ) when cSNPs were classified using a threshold of ≥ 99% , and hSNPs were classified when 1% < ALT < 99% , in order to assess the robustness of inferences to different filtering protocols . Low-quality variants , variants in proline-proline-glutamic acid ( PE ) and proline-glutamic-acid/polymorphic-guanine-cytosine-rich sequence ( PE_PGRS ) genes , transposons , phage and integrase , and positions with missing data , were excluded . All samples were drug-susceptible , except for MT-6429 , which was rendered resistant to isoniazid by a frameshift deletion at position 1284 in the catalase-peroxidase gene katG . As such , positions associated with drug resistance were not masked in this analysis . Alignments with informative hSNPs were reviewed using Tablet ( v . 1 . 17 . 08 . 17 , Milne et al . , 2013 ) . Concatenated core cSNP alignments were made using snp-sites -c ( v . 2 . 4 . 0 Page et al . , 2016 ) , with positions with hSNPs excluded . Pairwise cSNP distances between samples were computed using snp-dists ( v . 0 . 6 , available at https://github . com/tseemann/snp-dists ) . The frequency of hSNPs at each position in the genome was tabulated and hSNPs were reviewed to identify variants shared between samples . Core cSNP alignments were used to generate maximum likelihood trees using IQ-Tree ( v . 1 . 6 . 8 Nguyen et al . , 2015 ) . Model selection was based on the lowest Bayesian Information Criterion . Hierarchical Bayesian Analysis of Population Structure ( Cheng et al . , 2013 ) was run in R ( v . 3 . 5 . 2 ) to identify clusters . Phylogenetic trees were visualized using Interactive Tree of Life ( Letunic and Bork , 2016 ) . To examine the influence of potential alignment errors in identification of hSNPs , we used SMRT sequencing with the PacBio RSII platform to create a local reference genome for the outbreak . Sample MT-0080 was chosen for sequencing because this was previously identified as the probable source for as many as 19 of the 50 cases diagnosed in 2011–2012 ( Lee et al . , 2015b ) . Prior to sequencing , the culture was grown on a Middlebrook 7H10 agar plate . A single colony was then selected and grown further in 3 mL of Middlebrook 7H9 Broth to provide sufficient DNA for SMRT sequencing and Illumina MiSeq ( for polishing of the long-read assembly ) . DNA for SMRT sequencing was extracted using the MagAttract High Molecular Weight DNA Kit from Qiagen ( Maryland , USA ) . High molecular weight fragments were verified using gel electrophoresis . Library preparation and sequencing were then done at the McGill University/Genome Québec Innovation Centre . Prior to sequencing , fragment size was evaluated using a BioAnalyzer and the BluePippin system ( Sage Science , Massachusetts , USA ) was used for size selection . DNA for Illumina MiSeq was extracted using the van Soolingen method , as previous ( van Soolingen et al . , 1991 ) . Long-reads were assembled and corrected using Canu ( v . 1 . 7 . 1 Koren et al . , 2017 ) . Pilon ( v . 1 . 23 Walker et al . , 2014 ) was then used to polish the assembly using the Illumina MiSeq reads from the same colony . This was re-run until no further corrections were possible . Quast ( v . 5 . 0 . 2 , Gurevich et al . , 2013 ) was used to evaluate assembly quality . RASTtk ( v . 2 . 0 Brettin et al . , 2015 ) was used for annotation , to identify regions for masking as previous . Epidemiological and clinical data were collected on all cases and contacts using standardized questionnaires as part of the routine public health response , described previously in Lee et al . ( 2015b ) ; Lee et al . ( 2016 ) . A two-sample test of proportions was used to compare overall proportions across references , and the Wilcoxon Signed Rank test was used to compare paired SNP distances . Analyses were done in Stata ( v . 15 , StataCorp , College Station , TX , USA ) .
The core cSNPs and hSNPs between samples are shown in Source data 6 , with the sub-groups identified in the original analysis indicated . Overlaying hSNPs with the cSNP-based analysis revealed a novel super-spreader ( MT-504 ) in Cluster Mj-IIIB , undetected by genomic epidemiology analyses relying on lower sequencing depth . ( Lee et al . , 2015b ) . In brief , our previous analysis using routine sequencing depth had suggested that Mj-IIIB was comprised of two distinct transmission networks ( which we refer to as ‘subgroups’ for consistency with our earlier work ) ; the first subgroup consisted of five cases diagnosed between December 2011 and October 2012 , while the second subgroup consisted of 13 cases diagnosed between March and November 2012 . Epidemiologic curves for these subgroups are given in Figure 2—figure supplement 1A . These two subgroups were distinguished from one another based on the presence or absence of a shared cSNP ( at position 276 , 685 according to H37Rv/276 , 544 in the MT-0080_PB alignment , Source datas 6 , 7 , 8 ) ; all samples in the subgroup of five cases shared an alternative ‘C’ allele at this position , while all samples in the subgroup of 13 cases shared the reference ‘A’ allele . Given the short time period , low mutation rate of TB , and overall low diversity of strains circulating in the village , we would expect 0 SNPs to accrue in recent transmission , refuting transmission between these subgroups . In the original analysis , MT-504 was identified as the probable source for the subgroup of five cases . This individual was diagnosed in late 2011 with smear-positive , cavitary disease , and had attended the same local community ‘gathering houses’ ( social venues specifically identified by public health during the outbreak ) as all four others in this subgroup . For the second subgroup of 13 cases , MT-2474 was identified as the probable source , as this was the first smear-positive case in this subgroup ( diagnosed in May 2012 , Figure 2—figure supplement 1A ) . In contrast to the analysis based on routine sequencing data , our in-depth investigation of within-host diversity using deep sequencing data revealed that MT-504 harboured both the alternative allele ( 563 reads [80·9%] ) shared by all members of the subgroup of five as well as the reference allele ( 133 reads [19·1%] ) , shared by all members of the subgroup of 13 ( Figure 2—figure supplement 1B ) . Given that MT-504 was the first contagious case diagnosed in Mj-IIIB ( Figure 2—figure supplement 1A ) , and all 13 cases in the second subgroup had attended or resided in a gathering house ( with 9/13 [69·2%] reporting attendance at the same houses as MT-504 ) , this strongly suggests that MT-504 is in fact the most probable source for both subgroups . Sample 68995 and MT-5543 were from 2007 , and were the only strains from the Mj-VA sub-lineage in this village . Previous analysis indicated Mj-VA strains from other villages were distantly related , ( Lee et al . , 2015b ) while these two samples were separated from one another by zero core cSNPs . This suggests direct transmission between these historical cases , a hypothesis strongly supported by hSNP analysis , as the samples share hSNPs that are not found in any other sample in the dataset . These hSNPs were present even when highly conservative filtering thresholds were used ( Source data 7 ) , but were not included when using H37Rv as the reference - potentially due to differences in annotation and subsequent filtering . Six individuals had TB recurrence in 2011–2012 . Paired samples were available for two of these ( Patient 1: samples MT-5195 [Mj-IIIA] in 2007 and MT-1838 [Mj-IIIC] in 2012; Patient 2: samples MT-5543 [Mj-VA] in 2007 and MT-1206 [Mj-IIIB] in 2012 , Figure 2 ) . Clinically , both patients had new lesions detected at their second episode , compared to their previous chest x-rays . cSNP-based analyses suggested their second episodes of TB were due to re-infection with a new strain , rather than relapse with the strain causing their original disease . Investigation of within-host diversity strongly supported this conclusion; using deep sequencing , we verified that there was a single , different strain present at both baseline and their second episodes of TB . There was no evidence for mixed infection at either baseline or second episode with these strains , more definitively ruling out relapse in this low diversity setting ( Source datas 6 , 7 , 8 ) . To ensure we were not missing lower frequency variants using the prior cSNP/hSNP thresholds , we re-ran our analysis such that hSNPs were classified when 1% < ALT < 99% . Quality scores for individual cSNPs and hSNPs are given in Source data 9 and the core cSNP/hSNP alignment is shown in Source data 8 . While our primary analysis using a threshold of ≥95% for cSNPs identified a single cSNP ( A to G ) shared across all samples compared to MT-0080_PB , close examination of the MT-0080 deep sequencing data ( obtained using DNA from a sweep of the plate ) showed that this sample had both alleles at this position , with only the minority ‘A’ allele ( 33 reads/1189 [2·8%] ) isolated for SMRT sequencing . Based on this , we recommend sequencing samples both using a clean sweep ( with an alternative sequencing platform ) and a single colony pick when generating a reference genome for TB , as using the latter alone may introduce error and affect epidemiological inferences . With this exception , no other informative hSNPs were detected using these thresholds .
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 .
Tuberculosis disproportionately affects the Canadian Inuit . To address this , it is imperative we understand transmission dynamics in this population . We investigate whether ‘deep’ sequencing can provide additional resolution compared to standard sequencing , using a well-characterized outbreak from the Arctic ( 2011–2012 , 50 cases ) . Samples were sequenced to ~500–1000x and reads were aligned to a novel local reference genome generated with PacBio SMRT sequencing . Consensus and heterogeneous variants were identified and compared across genomes . In contrast with previous genomic analyses using ~50x depth , deep sequencing allowed us to identify a novel super-spreader who likely transmitted to up to 17 other cases during the outbreak ( 35% of the remaining cases that year ) . It is increasingly evident that within-host diversity should be incorporated into transmission analyses; deep sequencing may facilitate more accurate detection of super-spreaders and transmission clusters . This has implications not only for TB , but all genomic studies of transmission - regardless of pathogen .
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 .