10.1128@jcm.00298 17

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Comprehensive whole-genome sequencing and reporting of drug resistance profiles on1
clinical cases of Mycobacterium tuberculosis in New York State2
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Joseph Shea, Tanya A. Halse, Pascal Lapierre, Matthew Shudt, Donna Kohlerschmidt,5
Patrick Van Roey, Ronald Limberger, Jill Taylor, Vincent Escuyer, Kimberlee A. Musser *6
Wadsworth Center, New York State Department of Health, Albany, New York
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Running Head: Resistance profiling of M. tuberculosis with WGS9
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*
Corresponding author. Mailing address: Wadsworth Center, New York State Department of11
Health, 120 New Scotland Avenue, Albany, NY 12208. Phone: (518) 474-4177. Fax: (518) 486-12
7971. E-mail: kimberlee.musser@health.ny.gov13
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Keywords: Mycobacterium tuberculosis, WGS, drug resistance prediction, MGIT, reporting15
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JCM Accepted Manuscript Posted Online 5 April 2017
J. Clin. Microbiol. doi:10.1128/JCM.00298-17
Copyright © 2017 American Society for Microbiology. All Rights Reserved.
onApril6,2017byYORKUNIVERSITYhttp://jcm.asm.org/Downloadedfrom

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ABSTRACT22
Whole-genome sequencing (WGS) is a newer alternative for tuberculosis (TB) diagnostics,23
capable of providing rapid drug resistance profiles while performing species identification and24
capturing the data necessary for genotyping. Our laboratory developed and validated a25
comprehensive and sensitive WGS assay to characterize Mycobacterium tuberculosis and other26
M. tuberculosis complex (MTBC) strains, comprised of a novel DNA extraction, optimized27
library preparation, paired-end WGS, and an in-house developed bioinformatics pipeline. This28
new assay was assessed using 608 MTBC isolates, 146 during the validation portion of this study29
and 462 received prospectively. In February of 2016 this assay was implemented to test all30
clinical cases of MTBC in New York State including isolates and early positive BACTEC31
mycobacteria growth indicator tube (MGIT) 960 cultures from primary specimens. Since32
inception we have assessed the accuracy of identification of MTBC strains to the species level,33
concordance with culture-based drug susceptibility testing (DST), and turnaround time. Species34
identification by WGS was determined to be 99% accurate. Concordance between drug35
resistance profiles generated by WGS and culture-based DST methods was 96% for eight drugs,36
with an average resistance-predictive value of 93% and susceptible-predictive value of 96%.37
This single comprehensive WGS assay has replaced seven molecular assays and has resulted in38
resistance profiles being reported to physicians an average of 9 days sooner than culture-based39
DST for first-line drugs and 32 days sooner for second-line drugs.40
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42
43
44

3
INTRODUCTION45
The global rates of cases with diagnosed multidrug-resistant (MDR) and extensively46
drug-resistant (XDR) Mycobacterium tuberculosis threaten tuberculosis (TB) control, and47
necessitate faster methods for accurate diagnosis (1, 2). Drug-resistant TB has been classified as48
a serious threat by the Centers for Disease Control and Prevention (CDC) in the United States49
due to the complications and lower cure rates associated with the long-term treatments and the50
lack of new drugs available to combat these drug-resistant strains. In New York State (NYS), the51
rate of MDR-TB has remained at a constant number (6-16 cases per year since 2008) despite the52
overall reduction in TB cases each year53
(https://www.health.ny.gov/statistics/diseases/communicable/tuberculosis). Early detection of54
resistance is critical to limiting the spread of drug-resistant M. tuberculosis and providing55
physicians with the necessary information to implement effective treatment.56
Culture-based drug susceptibility testing (DST), considered the gold standard, can take57
up to 3 months to generate a complete drug resistance profile for both first and second-line58
drugs, during which time patients may be on suboptimal treatments therefore increasing the risk59
of emergence and spread of further resistant strains. Culture-based DST is limited by the slow60
growth rate of M. tuberculosis and the poor reliability of results for pyrazinamide and61
ethambutol, drugs to which false resistance has been well documented (3-6). Many specific62
mutations associated with drug resistance have been described (7-10) and molecular assays63
targeting these mutations have improved the turnaround time (TAT) to detect resistance (11-13).64
However, these assays are limited to interrogating small portions of the genome (14), and strains65
of M. tuberculosis acquire resistance to drugs through various mechanisms, including66
chromosomal insertions, deletions, and single-nucleotide polymorphisms (SNPs). Transmissible67

4
mobile genetic elements such as plasmids and transposons do not play a significant role in M.68
tuberculosis drug resistance (15, 16). As tuberculosis treatment usually requires a multidrug69
regimen, examination of many genes and genomic regions across the entire 4.4 Mb M.70
tuberculosis genome is necessary to generate the most comprehensive resistance profile, a feat71
that targeted molecular methods cannot achieve. Whole-genome sequencing (WGS) has recently72
been recognized as having the potential to improve diagnostics and reduce TAT to determine73
susceptibility to anti-tuberculous drugs in clinical cases (17-19).74
The utility of WGS in retrospective outbreak analyses to more accurately identify clusters75
and true transmission events between patients has been well documented (20-22). Recent76
publications have described the potential of implementing WGS in a clinical setting for rapid77
resistance profiling of M. tuberculosis. Witney et al. demonstrated the clinical utility of WGS for78
drug resistance profiling of suspected XDR-TB cases, and found that WGS could predict79
resistance weeks earlier than complete culture-based DST (23). However, as their approach used80
WGS as a second-tier tool to supplement the information provided by DST for drug resistant81
strains, the TAT was not optimal and could not provide information for susceptibility to first-line82
drugs prior to culture-based DST results. Our goal is to use WGS as a universal screening tool to83
rapidly generate a comprehensive drug resistance profile for each case of TB in NYS without84
screening for known resistance markers or waiting for culture-based DST results. Using this85
method, we aim to achieve a rapid and clinically relevant TAT for WGS-based resistance profile86
reporting.87
Here we present the validation of a comprehensive and sensitive whole-genome88
sequencing (WGS) assay implemented in a public health reference laboratory. This novel assay89
is capable of M. tuberculosis complex (MTBC) species identification and drug resistance90

5
profiling for eight TB drugs and drug classes: rifampin (RIF), isoniazid (INH), pyrazinamide91
(PZA), ethambutol (EMB), streptomycin (SM), kanamycin (KAN), fluoroquinolones (FLQ), and92
ethionamide (ETH)) from early positive mycobacteria growth indicator tubes (MGIT) cultures.93
This assay can simultaneously interrogate the entire M. tuberculosis genome (4.4 Mb) for94
genotyping and surveillance purposes, resulting in the ability to report high-resolution strain95
relatedness information being reported to epidemiologists approximately 15 days from MGIT96
culture positivity. Such an improvement in TAT can be expected to impact infection control and97
improve patient treatment.98
99
MATERIALS AND METHODS100
101
Clinical isolates. A total of 608 unique MTBC strains received as isolates or cultured in-house102
from clinical specimens by the Mycobacteriology Laboratory at the Wadsworth Center, NYS103
Department of Health (NYSDOH) between 7 May 2007 and 17 June 2016 were included in this104
study. Prior to leaving the BSL-3 laboratory, all liquid aliquots of clinical isolates were heat105
inactivated at ≥80°C for 60 min then removed from the BSL-3 and stored at -20°C until106
extraction.107
Culture-based drug susceptibility testing. Culture-based DST was performed using the liquid108
MGIT 960 system (BACTEC MGIT SIRE & PZA package inserts; Becton Dickinson) and solid109
7H10 agar proportion method according to the Clinical and Laboratory Standards Institute’s110
recommendations (Susceptibility testing of Mycobacteria, Nocardia and other aerobic111
Actinomycetes: Approved standard—second edition. CLSI document M24-A2. 2011) for first-112
line and second-line drugs, respectively. Second-line DST was not performed on all strains, only113

6
for strains exhibiting resistance to INH or RIF, or upon physician request. Isolates received in114
the laboratory were re-cultured in MGIT media prior to first-line DST being set up. First-line115
DST includes RIF (1.0 ug/mL), INH (0.1, 0.4 ug/mL), EMB (5.0 ug/mL), SM (1.0 ug/mL), and116
PZA (100 ug/mL), while second-line DST includes RIF (1.0 ug/mL), INH (0.2, 1.0 ug/mL),117
EMB (5.0, 10.0 ug/mL), SM (2.0, 10.0 ug/mL), ETH (5.0 ug/mL), KAN (5.0 ug/mL), and118
ofloxacin (OFL) (1.0, 2.0, 4.0 ug/mL). Ofloxacin is used in our laboratory as a representative of119
the FLQ drug class.120
Real-time PCR. Prior to WGS, an in-house developed real-time PCR assay (11) was utilized to121
detect MTBC in all samples received. MTBC species identification accuracy by WGS was122
determined by comparing results to another laboratory developed real-time PCR assay capable of123
identifying six members of the MTBC (24).124
Identification of resistance-associated mutations. Pyrosequencing of the rpoB (n=44) (11),125
katG (n=36), inhA-promoter (n=18), gyrA and gyrB (n=13) genes and Sanger sequencing of the126
pncA & pncA upstream region (n=22), rrs (n=11), rpsL (n=26), and embB (n=27) were127
performed during validation to confirm the presence or absence of mutations used to predict drug128
resistance.129
DNA extraction. A novel DNA extraction, termed InstaGene/FastPrep (IG/FP) method, was130
developed and optimized for the extraction of WGS-suitable DNA from MTBC cultures, with a131
particular focus on early positive MGIT cultures with little biomass. In a BSL-2 laboratory, 1 mL132
of heat-inactivated samples was pelleted by centrifugation at 15,000 rpm for 15 min. Two-133
hundred microliters of well-mixed InstaGene matrix (BioRad) was added to the pellet and134
samples were heated at 56°C for 30 min. Three sterile 3-mm diameter glass beads were added to135
each tube. After vortexing for 10 s, the samples were boiled in a heat block at 100°C for 20 min136

7
and processed using a FastPrep-24 5G Tissue Homogenizer (MP Biomedicals) ‘M. tuberculosis137
cells’ program for two 45 s cycles of 6.0 m/sec. Extracted DNA was then separated from the138
beads/ matrix by centrifugation for 15 min at 15,000 RPM. Each extraction included a 1 mL139
aliquot of early positive MGIT culture of an M. bovis-BCG strain as a positive extraction control.140
Aliquots (1 ml) of 15 early positive MGIT samples were also extracted in duplicate using the ZR141
Fungal/Bacterial DNA MiniPrep extraction (Zymo Research, Irvine, CA) according to142
manufacturer’s instructions and by our novel IG/FP method for comparison. Average DNA143
yields for both methods were measured by Qubit fluorometry and success rate for WGS was144
evaluated.145
Whole-genome sequencing (WGS). Paired end 250 bp DNA sequencing was carried out using146
the Illumina MiSeq platform following Nextera XT library prep with a 15 cycle PCR indexing147
step (44). Sequencing runs were either comprised fully of MTBC (15-17 samples) or of MTBC148
samples and other bacterial samples. A negative control was included through each library149
preparation and on each sequencing run.150
Wadsworth Center TB WGS Bioinformatics Pipeline. Raw reads were mapped on the H37Rv151
reference genome using BWA-MEM version 0.7.12 (25) and sorted using SAMtools version152
0.1.19 (26) (Figure 1). Read duplicates were marked using Picard tools version 1.129153
(http://broadinstitute.github.io/picard/). Indels were realigned with GATK IndelRealigner, and154
SNP’s and indels were called separately using GATK UnifiedGenotyper version 3.3 (27),155
allowing for a ploidy of 2 and a minimum mapping quality of Phred 20. A ploidy of 2 for SNP156
detection is required to detect emerging resistant subpopulations. LowQual positions were157
automatically rejected and assigned ‘N’ for unknown state. Each genomic position was assessed158
and filtered with a minimum depth (DP) of 10, mapping quality score (MQ) of 40, minimum159

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Quality per Depth (QD) of 2, maximum Fisher’s exact test to detect strand bias (FS) of 200 and a160
minimum ReadPosRankSum of -20. All positions failing these requirements were also161
designated as unknown (N) when creating the consensus sequence and identifying resistance-162
associated mutations. Positions showing heterogeneity were also designated as unknown (N) in163
the consensus sequence. Lumpy-SV version 0.2.9 (28) was used to screen for the presence of164
larger deletions in the sequenced genomes that could account for antibiotic resistance. Large165
deletions detected by Lumpy-SV must be confirmed by the absence of mapped reads over the166
deleted region to be valid. Reports generated by the Wadsworth Center TB WGS bioinformatics167
pipeline include species identification, spoligotype, and resistance-associated mutations for 8168
drugs and drug classes (RIF, INH, PZA, EMB, SM, ETH, KAN, FLQ). Any mutations present in169
13 resistance-associated genes or non-coding regions are identified. However, only a select list170
of 64 ‘high-confidence’ SNPs, insertions, and deletions across these 13 genes and non-coding171
regions (Table 1) were used to predict resistance. Mutations, except for those in the pncA gene172
and promoter region, were considered high-confidence if at least two of the three following173
criteria were met: support in the literature from available publications describing known174
resistance-associated mutations with available DST at defined drug concentrations (29-34); at175
least one strain identified in our laboratory harboring the candidate mutation with resistant176
culture-based DST results to the associated drug; and/or the mutation was found in a curated177
database with available DST results at defined drug concentrations. Mutations in the pncA gene178
and promoter region were not limited to those meeting these criteria, as there is a well-179
documented association with mutations throughout this gene resulting in PZA resistance (3, 7).180
The same negative control used for library preparation and sequencing was also used as a control181

9
for bioinformatic analysis. The control was considered passing if the DP of mapped reads to the182
H37Rv reference sequence was less than 5X.183
Samples exceeding a 40X genome-wide average DP, 20X DP for each SNP locus used to184
predict resistance, and reference genome coverage of at least 95% were considered acceptable185
and included in analysis. Species identifications were made using Kraken v0.10.5-beta (35) on186
the raw reads, utilizing a local database created from available fully sequenced and draft187
genomes of Mycobacterium species from NCBI. To improve runtime without affecting the188
accuracy of the SNP calling, samples exceeding 80X genome-wide average depth were189
downsampled using SAMtools to achieve an average depth of ~80X prior to the SNP calling190
step. A schematic of the Wadsworth Center TB WGS Bioinformatics Pipeline can be found in191
Figure 1.192
Electronic reporting. Following analysis, the species identification, genotype, detected high-193
confidence mutations, drug resistance profiles, sequencing metrics, and quality control194
information were imported into our clinical laboratory information management system195
(CLIMS). Results were reviewed and released to submitting laboratories the same day along with196
a description of the test and limitations of the assay.197
Specificity and reproducibility. The specificity of the WGS assay was assessed by testing DNA198
extracted from nontuberculous mycobacteria (NTM) and other organisms capable of growing in199
MGIT culture including Mycobacterium gordonae, Mycobacterium abscessus, Mycobacterium200
avium, Nocardia nova, Tsukamurella sp., and Gordonia sp. Each of these organisms was201
previously identified using real-time PCR, MALDI-TOF mass spectrometry, and/ or rpoB/16S202
rDNA sequencing. To assess the intra-assay reproducibility of this assay, 1 mL aliquots of M.203
bovis-BCG cultured in a BACTEC MGIT 960 system were extracted in triplicate on the same204

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day and sequenced in a single run. Additional aliquots of this strain were extracted on two205
different days and run on separate sequencing runs to assess inter-assay reproducibility. Studies206
were designed in accordance with guidance provided by the NYS Clinical Laboratory Evaluation207
Program (http://www.wadsworth.org/regulatory/clep).208
Retrospective Study. A total of 96 clinical MTBC isolates received or cultured between June209
2007 and June 2015 were re-grown from frozen stocks. Isolates were extracted from suspensions210
of growth from 7H10 agar in 7H9 broth (n=52) and from 1mL aliquots of MGIT positive culture211
(n=44). Retrospective isolates were selected based on previous molecular and culture-based DST212
characterization, covering a wide range of species identifications, drug resistance associated213
mutations, and resistance patterns.214
Prospective study. Between July 2015 and June 2016, a total of 512 clinical samples were215
received as isolates or cultured from primary specimens. Of these, 57 were excluded from216
analysis due to WGS failure (library preparation failure or low depth of coverage, (n=33) or217
culture contaminated with other bacteria (n=24), resulting in 455 unique MTBC sequences.218
Prospective isolates identified as MTBC by real-time PCR underwent DNA extraction. Primary219
specimens initially identified as MTBC by real-time PCR were first cultured in MGIT media220
followed by DNA extraction after they flagged positive and were confirmed MTBC by real-time221
PCR. MGIT cultures from both primary specimens and re-cultured isolates were incubated at222
37°C after culture positivity until extraction for 0-6 days.223
Assessment of turnaround time (TAT). TAT was assessed for 294 samples following224
implementation of WGS from Feb. 15, 2016 to Jun. 30, 2016. The TAT was calculated for each225
sample from the day of DNA extraction to the day of WGS resistance profile reporting (n=294),226
and the day of culture flag positive to the day of WGS resistance profile reporting for primary227

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specimens received and isolates that were re-cultured (n=62). The date of WGS resistance profile228
reporting for each sample was compared to date of reporting first-line culture-based DST (n=98),229
and second-line culture-based DST (n=8).230
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RESULTS232
233
234
Development and optimization of a novel DNA extraction method. A novel DNA extraction235
method to extract pure DNA from difficult-to-lyse mycobacteria and yield high-quality WGS236
data was developed and optimized for this study. DNA extractions of MTBC isolates using this237
IG/FP method were found to provide robust and reliable WGS results. For liquid MGIT cultures,238
this extraction yielded an average 0.8 ng/μL from representative 1 mL aliquots of MGIT cultures239
of 15 samples removed from the BACTEC MGIT 960 instrument at 0-3 days after being240
detected as positive. The IG/FP method outperformed the Zymo Research Bacterial/Fungal241
extraction, which yielded an average 0.12 ng/uL when applied to equivalent aliquots of early242
positive MGIT cultures with minimal biomass. WGS was successful from 11/15 (73%) of these243
DNA extracts using the IG/FP method compared to just 3/15 (20%) extracted using the Zymo244
Research Kit.245
Specificity and reproducibility studies. Each of the five organisms tested in the specificity246
study were correctly identified as non-MTBC by our bioinformatics pipeline, and therefore not247
analyzed further for resistance-associated mutations. All samples tested in both the intra-assay248
and inter-assay reproducibility studies passed quality control measures and provided correct and249
reproducible species identification, genotype, and identical drug resistance profiles.250
Species identification. Kraken analysis of WGS data from the validation study samples251
identified 146 (100%) to the MTBC species level, including M. tuberculosis (n=140), M. bovis252

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(n=2), M. bovis-BCG (n=2), M. africanum (n=1), and M. canettii (n=1). These identifications253
were concordant with identification by real-time PCR for 137 samples (93.8%). The remaining 9254
(6.2%) had real-time PCR results determined to be inconclusive due to deletions in the255
primer/probe regions (as elucidated by WGS), while Kraken identified these specimens to the256
species level. Prospective testing included 404 (99.7%) strains identified to the MTBC species257
level, including M. tuberculosis (n=379), M. bovis (n=9), M. bovis-BCG (n=11), and M.258
africanum West African 2 (n=1), with the remaining 1 sample that could only be identified as259
MTBC. These identifications were concordant with identifications by real-time PCR for 287/293260
(97.9%) samples tested. The discordant samples included M. tuberculosis strains harboring261
primer/probe deletions resulting in inconclusive real-time PCR (n=3), M. africanum West262
African 1 strains misidentified by Kraken as M. tuberculosis (n=2), and M. caprae, not identified263
correctly by either method (n=1).264
Identification of resistance-associated mutations. Many of the 96 strains tested during the265
retrospective portion of this study were characterized by molecular sequencing assays of drug266
resistance-associated genes, including pyrosequencing of rpoB (n=70), katG (n=71), inhA267
(n=58), gyrA and gyrB (n=33), and by Sanger sequencing of pncA (n=40), rrs (n=12), rpsL268
(n=26), and embB (n=31). Pyrosequencing assays, previously validated in our laboratory, were269
used for genes in which the majority of resistance-associated mutations are clustered together.270
Sanger sequencing was selected when no pyrosequencing assay was available, or for genes with271
resistance-associated mutations occuring throughout the gene. We found high levels of272
concordance for the detection of high- confidence single nucleotide polymorphisms (SNPs) by273
both pyrosequencing/ Sanger sequencing methods and WGS: rpoB (97%), katG (98%), inhA274
(100%), gyrA and gyrB (100%), pncA (100%), rrs (100%), rpsL (100%), embB (96%).275

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Discrepancies between methods in all cases were due to WGS detecting mutations outside of the276
region targeted by the other molecular sequencing assays. The frequencies of high-confidence277
mutations detected during the retrospective and prospective phases of this study are located in278
Figure 2.279
Drug resistance prediction. We compared M. tuberculosis WGS drug resistance profiles to280
culture-based DST results for 8 drugs. For all samples tested we found an average concordance281
of 96% across all 8 drugs and drug classes, ranging from 83% (ETH) to 100% (KAN, FLQ).282
Resistance predictive values were as follows: 85% (RIF), 98% (INH), 86% (PZA), 89% (EMB),283
100% (SM), 100% (FLQ), 100% (KAN), and 94% (ETH). Sensitivity, specificity, predictive284
values, concordance, and the number of strains evaluated for each drug can be found in Table 2.285
The incidence of MDR-TB cases in our prospective study was 1.75%.286
Turnaround time (TAT). Following implementation of this TB WGS assay on all clinical287
cases of TB in NYS we assessed TAT for reporting WGS drug resistance profiles and culture-288
based DST results on all samples received during a 136-day period. Culture-based DST, DNA289
extractions, library preparations, and WGS were batched and performed on a weekly basis. After290
analysis, we determined that WGS-generated drug resistance profiles for 8 drugs were reported291
an average of 9 days earlier than conventional culture-based first-line DST for 5 drugs (n=96)292
and an average 32 days earlier than the less frequently tested culture-based second-line DST293
(n=8). The average TAT for WGS reports was found to be 7 days from extraction and 15 days294
from date of culture positivity. DNA extraction and quantitation, library preparation, and295
sequencing each took two days, while analysis required an additional day, totaling 7 days from296
extraction to report date.297
298

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DISCUSSION299
We developed and validated a WGS assay to provide species identification, genotype,300
and comprehensive drug resistance prediction for isolates of MTBC. This assay was301
implemented into clinical testing in February of 2016 to universally test for all new cases of TB302
identified in NYS as an early comprehensive screen for drug resistance. Since implementation,303
this assay has surpassed previously utilized molecular testing in breadth of information obtained,304
improved TAT over culture-based DST, and provided high-resolution genotyping information305
useful in epidemiological investigations.306
Our novel DNA extraction method effectively lyses MTBC cells and removes inhibitors,307
yielding purified DNA suitable for WGS even from early positive MGIT cultures. We found that308
this method (IG/FP) provided the most consistent results for low volumes of early positive MGIT309
cultures when compared to the ZR Fungal/Bacterial DNA MiniPrep extraction, while also310
reducing hands-on time. CTAB extraction, a commonly used method, is labor-intensive, time311
consuming, and requires chemicals that made it undesirable for routine use. As extracts of solid312
media cultures using IG/FP were found to have comparable WGS results to CTAB extracts, the313
former was selected for our assay development process (unpublished data). This IG/FP314
extraction method provided robust WGS data when applied directly to early positive MGIT315
cultures and isolates received in the laboratory with no additional manipulation. Additionally,316
this method was found to be cost-effective, providing a savings of $2.50 per sample when317
compared to the ZR Fungal/Bacterial DNA MiniPrep extraction.318
Differentiation of MTBC species by WGS was accomplished by identifying the number319
of species-specific reads in each sample using Kraken, an approach which interrogates320
significantly more of the genome than our existing real-time PCR assay which can identify 6321

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MTBC members (24). While our WGS assay has the capability to identify specimens previously322
reported as inconclusive with this real-time PCR assay, it is important to note that Kraken is323
limited by the composition of the library being used. Indeed, we encountered 4 strains for which324
species identification was not conclusive using Kraken. These included M. africanum West325
African 1 strains which are too closely related to M. tuberculosis to be differentiated by Kraken,326
as well as 1 strain of M. caprae and 1 strain of M. orygis, for which there are currently not327
suitable reference sequences available to be included in our database. Further library additions of328
less common MTBC members such as M. pinnipedii, M. mungi, and M. microti are necessary to329
improve the capabilities of Kraken to identify a broader range of MTBC species. However, we330
now can utilize WGS data to resolve still unidentified species by combining the construction of a331
phylogenetic tree with the search for species-specific SNPs (36-38). The additional analysis of332
WGS data as described provided 100% success for identification to the species level of these333
initially discrepant samples.334
Drug resistance profiling with WGS is a more complex task, as it relies not only on the335
detection of specific mutations but also on proper interpretation to predict drug resistance or336
susceptibility. Detection of mutations by WGS is reliable, as evidenced by the comparisons to337
other sequencing assays for all targets assessed. The discrepant results we identified were due338
either to the presence of mutations outside of the target area of our pyrosequencing assay, or a339
deletion within the target gene. Therefore, these mutations were missed by our previous340
sequencing assays but were identified by WGS. This highlights another advantage of WGS; it341
can detect mutations outside the target area of existing assays while assessing other target genes342
not currently covered by our specific molecular sequencing assays. As an example, we identified343
two strains resistant to rifampin with mutations in the rpoB gene (V251F, I572F) and four strains344

16
resistant to isoniazid with mutations in the katG gene (Q525P, 589ins, gene deletions (n=2)) that345
were not detectable by pyrosequencing assays performed in the laboratory. Before WGS, the346
strains harboring these mutations could only be identified as resistant once culture-based DST347
was completed.348
Detection of rifampin resistance-associated mutations outside of the 81-base pair349
rifampin resistance determining region (RRDR) of rpoB resulted in improved sensitivity over350
other methods that only target this region, as it has been reported that 4-10% of RIF-resistant351
strains carry mutations outside of the RRDR (39-41). Commonly utilized molecular tests for352
drug resistance detection include the Xpert® MTB/RIF (Cepheid, Sunnyvale, USA) and the353
MTBDRplus (Hain Lifescience, GmbH, Nehren, Germany). Limitations of the Xpert®354
MTB/RIF and the MTBDRplus include detecting only mutations in the RRDR and yielding355
occasional false rifampin resistant results due to silent rpoB mutations (42-44). Isoniazid356
resistance can arise from a number of mutations across multiple genes, resulting in imperfect357
sensitivities for targeted molecular assays such as the MTBDRplus (45) and katG or combined358
katG/inhA pyrosequencing (39,46). WGS improves upon the sensitivities of these methods due to359
the comprehensive and detailed sequencing data obtained; however, it is important to note that360
unlike these other molecular methods our WGS assay is not currently applicable to primary361
specimens. A recent study has described successful resistance profiling from WGS of smear-362
positive respiratory samples, significantly improving the impact that WGS assays may have on363
treatments and patient outcomes (47).364
In developing this assay, we decided on a conservative approach in selecting high-365
confidence mutations for accurate and reliable resistance prediction. As patient treatment may be366
altered based on mutations identified by WGS, we decided mutations would only be categorized367

17
as high-confidence if they met a minimum level of support as described in materials and methods368
to achieve a low rate of false resistance prediction. Consequently, our assay will not currently369
predict resistance in strains harboring rare or less well-documented mutations that confer370
resistance. These candidate mutations will be closely tracked until enough evidence is obtained371
to warrant inclusion on the high-confidence list, thereby improving the predictive power of this372
assay over time. Although the conservative nature of our approach minimizes false predictions of373
resistance, strains found to have high confidence mutations without displaying culture-based374
DST resistance may still occur in a small number of cases. In these cases it is possible that the375
concentrations of antibiotic used for DST were inadequate to detect lower levels of resistance,376
especially in the case of certain “disputed” RIF-resistance associated rpoB mutations (48).377
Further DST on these specimens using lower drug concentrations, repeat testing, and/or more378
paired mutation and DST data from databases and literature reviews will help refine the379
predictive value of molecular resistance detection in these cases.380
Currently, our testing algorithm includes both resistance predictions using WGS and381
conventional culture-based DST to ensure optimal detection of resistance in M. tuberculosis.382
Collecting WGS data longitudinally allows for the discovery of previously unknown mutations383
and mutation patterns that are associated with drug resistance. Our bioinformatics pipeline can be384
refined and expanded to include new resistance-associated mutations as they are discovered,385
allowing the predictive values of this method to improve over time. Publicly released in 2008,386
the TB Drug Resistance Mutation Database (TBDReaMDB) was one of the earliest centralized387
databases compiling potential resistance associated mutations from a systematic literature388
review, providing a valuable resource for molecular M. tuberculosis diagnostics and further389
research in this field (49). Since the initial release of TBDReaMDB, efforts to build upon this390

18
data set to construct curated databases linking genotype data with phenotypic DST results have391
been undertaken, and are critical to improving the accuracy of molecular based susceptibility392
profiling (50-52).393
Web-based tools capable of analyzing WGS data of MTBC for the purposes of394
genotyping and drug resistance profiling have become available, including PhyResSe and TGS-395
TB, among others (53, 54). Two such platforms, TB-Profiler and MUBII-TB-DB, were396
compared for their success at predicting resistance to the major TB drugs (55). When we397
compared our WGS assay with these two databases, we found the sensitivity and specificity to be398
comparable for INH, RIF, PZA, and EMB, higher for FLQ and KAN, and lower for ETH and399
SM predictions. Sensitivity for predicting resistance to ETH (62%) was significantly lower than400
the 73.6% reported for TB-Profiler (55). This difference probably results from the absence of the401
ethA target gene in our high-confidence list, while the TB-Profiler database uses mutations in402
this gene to predict ETH resistance. Indeed, 12 of our 15 ETH discordant strains harbor403
mutations in the ethA gene, further supporting the evidence that mutations in ethA can confer404
resistance to ETH. Similarly, our high-confidence list does not include the gidB gene for SM405
resistance, although mutations in this gene have been associated with low-level resistance. While406
a compelling 21 of our 22 phenotypically resistant strains harbored gidB mutations, we have also407
found many SM-susceptible strains carrying mutations in gidB, emphasizing the importance of408
parsing out which mutations truly confer resistance before including this target in our high409
confidence list. Notably, our predictions for FLQ and KAN were more sensitive and specific410
than both TB-Profiler and MUBII-TB-DB (50, 55). For this drug and class of drugs, the411
mutations included in these databases and our high-confidence list are highly similar, suggesting412
that the difference in resistance predictions may be the result of differences in populations of413

19
strains tested, differences in sample size, and/or differences in threshold for detecting emerging414
resistance (30). During this study, four samples were correctly predicted to be FLQ resistant415
based on the presence of subpopulations harboring gyrA mutations. Because our culture-based416
DST testing for second-line drugs is performed reflexively when first-line drug resistance to INH417
or RIF is detected, our results may be biased due to testing fewer strains and potentially missing418
strains that are phenotypically resistant only to second-line drugs that do not harbor any419
mutations in our high-confidence list. Longitudinal WGS with parallel culture-based DST will420
aid in the curation of our mutation database to improve our resistance profiling.421
In our laboratory, comprehensive predicted drug resistance profiles generated by WGS422
are being reported to physicians an average of 9 days earlier than first-line DST results and 32423
days earlier than complete second-line DST results. This improvement in TAT is due in part to424
the success of WGS when directly applied to isolates received in the laboratory, as the425
approximately 8-14 day culture step (MGIT Procedure Manual, BD) required for DST setup is426
eliminated. Our assay concurrently identifies MTBC species, determines spoligotype, and427
provides important information for epidemiological investigations. Since this WGS assay has428
been implemented clinically in our laboratory, we have already detected resistance that would429
have been missed by previous molecular methods. We have also ruled out suspected resistance,430
leading to the implementation of the most effective treatment regimens. One noteworthy case431
received by the laboratory for confirmation of MTBC infection was predicted by WGS to be432
multidrug resistant, with further resistance to second-line drugs. This strain yielded invalid433
culture-based DST results several times due to poor growth, and the first culture-based results434
were obtained 73 days after the WGS report. Without WGS testing, this could have potentially435
been on an ineffective therapy for over three months. Furthermore, we correctly predicted INH436

20
resistance in 9 strains that would not have been detected by previous pyrosequencing methods. In437
addition, we detected 9 strains harboring mutations in the gyrA gene, predicted to be FLQ mono-438
resistant; this resistance would not have been identified by our standard DST algorithm.439
Failure rates of this WGS assay since clinical testing began have remained low, averaging440
6.7% when the concentration of extracted DNA from pure M. tuberculosis cultures is 0.2 ng/uL441
or higher. Although longer culturing time improves the DNA yield and overall likelihood of442
success, we determined that the benefit of the earliest possible TB WGS analysis for patient443
management offsets this small increase in the potential for failures by using early positive MGIT444
cultures. It is important to note that all failed samples were successful on repeat, using remaining445
culture incubated until the next extraction batch, or beginning with a new MGIT culture if the446
original sample was exhausted. During this study, several changes were made to attain the best447
possible TAT within the constraints of work schedules and cost considerations. This included448
scheduling regular shipments of MTBC cultures from submitting labs, batching extractions early449
in the week, and setting up sequencing runs to take place over the weekend. These changes450
resulted in WGS reports being released an average of 7 days from extraction and 15 days from451
culture positivity.452
Implementation of this assay provided several additional benefits for our laboratory. It453
reduced personnel hands-on time and proved cost-effective by replacing several molecular454
assays and streamlining laboratory testing while increasing the breadth of information obtained.455
Previously, our tiered molecular testing algorithm included an upfront real-time PCR for MTBC456
identification followed by a real-time PCR assay for species identification, five pyrosequencing457
assays for drug resistance mutation identification, and a bead-based Luminex assay for458
spoligotyping. Our new algorithm still includes upfront real-time PCR assay for identification of459

21
MTBC; however, all of the subsequent molecular assays have been replaced by WGS, resulting460
in a cost savings of $140 per specimen. This savings has helped to offset the cost of WGS, which461
is currently $200 per sample. In addition, the amount of technician time spent on setting up462
assays has been reduced, while review of results, data management, and quality controls have463
been consolidated.464
Finally, optimal TAT for molecular resistance profiling can be achieved by performing465
WGS of M. tuberculosis directly on primary clinical specimens. Recent reports describing466
successful WGS of MTBC from primary respiratory specimens have highlighted a significant467
reduction in TAT with this approach (46, 56). In this study, our main objective was to develop a468
rapid and cost-effective WGS assay for clinical isolates of MTBC that could provide469
reproducible results even on early positive MGIT cultures. We found that initial culture of a470
specimen followed by optimized DNA extraction and sequencing currently provides the most471
reliable and reproducible results, and is still more rapid than conventional DST. Implementation472
of this WGS assay on all cases of MTBC in NYS has aided TB control efforts and improved the473
accuracy of molecular resistance predictions being reported to physicians, resulting in more474
effective patient management.475
476
ACKNOWLEDGMENTS477
478
This publication was supported in part by Cooperative Agreement # U60OE000103 funded by479
the Centers for Disease Control and Prevention through the Association of Public Health480
Laboratories and NIH/NIAID grant AI-117312. Its contents are solely the responsibility of the481
authors and do not necessarily represent the official views of CDC or the Department of Health482

22
and Human Services, Office of Surveillance, Epidemiology and Laboratory Services (OSELS),483
National Center for HIV, Viral Hepatitis, STDs and TB Prevention (PS), National Center for484
Zoonotic, Vector-borne, and Enteric Diseases (CK), National Center for Immunization and485
Respiratory Diseases (IP), National Center for Environmental Health (NCEH), National Center486
for Birth Defects and Developmental Disabilities (NCBDD), or the Association of Public Health487
Laboratories.488
We acknowledge the Wadsworth Center Applied Genomic Technologies Core Facility489
for Next Generation DNA Sequencing, specifically Melissa Leisner, Helen Ling, Nathalie490
Boucher, and Zhen Zhang, and the Wadsworth Center Bioinformatics Core for developing the491
Wadsworth Center TB WGS bioinformatics pipeline and bioinformatics support. Tammy492
Quinlan, Justine Edwards, Susan Wolfe, and Michelle Isabelle are thanked for their technical493
contributions. We thank Victoria Derbyshire and Erasmus Schneider for regulatory guidance and494
their long-term commitment to incorporating this testing at the Wadsworth Center.495
496
497
498
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724
725
Figure 1. Schematic Representation of the Wadsworth Center TB WGS Bioinformatics Pipeline.726
Red box: input reads analyzed by the pipeline. Green boxes: genotyping and taxonomic727
identification. Blue boxes: antimicrobial resistance profiling. Yellow boxes: outbreak tracking &728
epidemiology. The SNP calling process in the yellow workflow is also used for antimicrobial729
resistance profiling. White box: final report from all analytical modules.730
731
732
733
Figure 2. Prevalence of High-confidence Mutations Detected by the Wadsworth Center TB734
WGS Bioinformatics Pipeline.735
Validation samples were sequenced between June 2014 and June 2015 (n=146), prospective736
samples were sequenced between July 2015 and June 2016 (n=405).737
Frameshift mutations and large deletions were only considered high-confidence in rpoB, katG,738
and pncA & pncA promoter genes, for RIF, INH, and PZA resistance, respectively.739

28
740

29
Fig. 2741

30
742
0
10
20
30
40
50
katG Ser315Thr
katG frameshift
katG gene deletion
katG Gln525Pro
katG Ser315Asn
inhA C-15T
mabA Leu203Leu
inhA T-8A
inhA T-8C
inhA G-17T
oxyR-ahpC C-81T
rpoB Ser531Leu
rpoB His526Tyr
rpoB Asp516Val
rpoB Asp516Tyr
rpoB Ser531Trp
rpoB 3nt insertion
rpoB Leu511Pro
rpoB Leu533Pro
rpoB Gln513Pro
rpoB His526Asn
rpoB His526Asp
rpoB Ile572Phe
rpoB Ser531Cys
rpoB Val251Phe
pncA His57Asp
pncA frameshift
pncA promoter A-11G
pncA Thr47Ala
pncA Trp68Arg
pncA promoter G-33A
pncA promoter T-12C
pncA promoter A-11C
pncA Ala146Glu
pncA Asp12Ala
pncA Asp8His
pncA Glu37Ala
pncA Glu37Val
pncA Leu116Arg
pncA Leu151Ser
pncA Leu182Ser
pncA Leu19Pro
pncA Met175Val
pncA Thr135Pro
pncA Thr76Pro
pncA Thr87Met
pncA Trp199Arg
pncA Tyr103Cys
pncA Tyr103His
pncA Val163Met
pncA Val163Ala
pncA Val21Ala
pncA Val7Gly
embB Met306Val
embB Met206Ile
embB Gln497Arg
embB Gly406Ala
embB Gly406Asp
embB Gly406Ser
embB Met306Leu
rpsL Lys43Arg
rpsL Lys88Arg
rrs A513C
rpsL Lys88Met
rrs C516T
rpsL Lys43Asn
gyrA Asp94Gly
gyrA Ala90Val
gyrA Ser91Pro
gyrA Asp94Asn
gyrA Asp94His
gyrA Asp94Tyr
rrs A1400G
eis promoter G-37T
eis promoter G-10A
validationset
prospectiveset
NumberofOccurrences
High-ConfidenceMutationsbyDrug

31
743
744
745
746
747
748
749
750

32
Table 1. List of High-Confidence Mutations Used to Predict Resistance by Drug/ Drug751
Class.752
753
Antimicrobial
(abbreviation)
Locus Codon/NT position Function
Rifampin (RIF) rpoBa
251, 511, 513, 516, 522, 526, 531, 533,
572b
Coding
Isoniazid (INH) katGa
279,315,525 Coding
Isoniazid (INH) oxyR-ahpC -81 Non-coding
Isoniazid (INH) mabA-inhA
promoter
region
-17, -15, -8, Non-coding
Isoniazid (INH) mabA 203 Coding
Pyrazinamide
(PZA)
pncAa
Any nonsynonymous mutation Coding
Pyrazinamide
(PZA)
pncA
promotera
Any nonsynonymous mutation Non-
Coding
Ethambutol (EMB) embB 306, 406, 497 Coding
Streptomycin (SM) rrs 512, 513, 516, 906c
Non-coding
Streptomycin (SM) rpsL 43,88 Coding
Fluoroquinolones
(FLQ)
gyrA 74, 90, 91, 94 Coding
Fluoroquinolones
(FLQ)
gyrB 510 Coding
Ethionamide (ETH) mabA-inhA
promoter
region
-17, -15, -8, Non-coding
Ethionamide (ETH) mabA 203 Coding
Kanamycin (KAN) rrs 1400d
Non-coding
Kanamycin (KAN) eis
promoter
-37, -10 Non-
Coding
754
a
Frameshift deletion/insertions in rpoB, pncA, and katG are considered high-confidence755
mutations756
b
E. coli numbering system is utilized, which is commonly found in literature757
c
Due to the presence of different numbering systems for the rrs gene, these may be reported as758
513, 514, 517, 907 in other publications759
d
Due to of the presence of different numbering systems for the rrs gene, this may be reported as760
1401 in other publications761
762
763
764
765
766
767
768

33
769
Table 2. Concordance of WGS Resistance Predictions and Culture-based DST Results.770
771
772
773
774
a
Predictive value for resistance b
Predictive value for susceptibility775
(EMB: Ethambutol, FLQ: Fluoroquinolones, INH: Isoniazid, PZA: Pyrazinamide, RIF:776
Rifampin, SM: Streptomycin, KAN: Kanamycin, ETH: Ethionamide)777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
EMB FLQ INH PZA RIF SM KAN ETH ALL
Sensitivity 0.79 1.00 0.87 0.86 1.00 0.73 1.00 0.62 0.83
Specificity 0.99 1.00 0.99 0.98 0.97 1.00 1.00 0.97 0.99
PPVa
(Res) 0.89 1.00 0.98 0.86 0.85 1.00 1.00 0.94 0.93
NPVb
(Susc) 0.97 1.00 0.95 0.98 1.00 0.92 1.00 0.80 0.96
Concordance 0.96 1.00 0.96 0.96 0.98 0.94 1.00 0.83 0.96
# Tested 348 128 357 352 349 350 119 119

34
802

Raw NGS reads
(MiSeq 2 x 250bp)
40x min. depth
Look for spacer
matches in
sequence reads
Derive Spoligotypes
from CDC and NYS
octal codes
Taxonomic
classification with
Kraken
Map reads over
Reference H37Rv
with minimum Q20
SNP calling with
indels only, GATK
Diploid mode, 10x
min. depth
SNP calling without
indels, GATK Diploid
mode, 10x min. depth
Generate high
quality consensus
sequence
ML SNP tree using
FastTree, GTR model,
4 categories
Large genomic
deletion detection
with LumpyUSV
Annotate SNPs/Indels
at loci known to
cause resistance
Generate final
taxonomic reports,
resistance profiles and
phylogenetic tree

10.1128@jcm.00298 17

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