[159]. This means that whole-genome sequencing of
M. tuberculosis is rapidly becoming a viable way to determine resistance to multiple drugs at once [166, 167]. Many national health
programs, such as those in the UK and Netherlands, now use WGS
for determining resistance to first-line drugs in clinical settings. In
order to do this, WGS data such as in FASTQ read format are
mapped to a reference genome (usually M. tuberculosis H37Rv)
and then a database map of genome position to resistance conferring mutations is used to determine the drug resistance profile of
the sample [158, 166]. For example, the Ser431Thr (Ser512Thr in
the E. coli numbering system) RIF-related mutation in rpoB is
caused by a cytosine at position 761,098 of the M. tuberculosis
H37Rv genome instead of the wild-type guanine [157]. Thus, by
mapping a sample’s reads against the M. tuberculosis H37Rv
genome, detection of known resistance mutations in associated
databases can be achieved.
While this drug resistance prediction can be done with any
genome assembly pipeline, several tools exist for this purpose in
both online and local formats [168–171], with Mykrobe [172],
TBprofiler [173], and PhyResSE [174] being the most widely used.
Each currently uses its own specific database of mutations to determine if resistance-causing mutations are present in the sample.
These databases are updated periodically but do not always overlap,
occasionally (though rarely) resulting in slightly different predictions [171, 175].
The use of WGS data to determine resistance in nontuberculous mycobacteria (NTMs) is still in its infancy. It has been used to
determine some drug resistance patterns in M. abscessus [176],
M. ulcerans [177], and M. leprae [178, 179] but has not been as
widely implemented in systematic ways in other NTMs. Until more
is known about the underlying mechanisms of drug resistance
(either gene mutation or efflux pumps) and there is accurate phenotypic testing to use as a gold standard, the use of WGS for NTM
resistance will remain limited for some species.
5.3.2 Relational
Sequencing
for Tuberculosis (ReSeqTB)
The ReSeqTB knowledgebase platform (https://platform.reseqtb.
org/) was developed in response to a worldwide collaboration
between Bill & Melinda Gates Foundation (BMGF), Critical Path
Institute (C-Path), Foundation for Innovative New Diagnostics
(FIND), World Health Organization (WHO), US Centers for
Disease Control and Prevention (CDC), the StopTB Partnership
New Diagnostics Working Group (NDWG), and the National
Institute of Allergy and Infectious Diseases (NIAID). ReSeqTB is
designed to facilitate the analysis of WGS data for antimicrobial
susceptibility for diagnostic developers, health care professionals,
and clinical laboratories. The tool is curated and validated using
culture-based
drug
susceptibility
testing
following
442
Marco A. Riojas et al.
M. tuberculosis is rapidly becoming a viable way to determine resistance to multiple drugs at once [166, 167]. Many national health
programs, such as those in the UK and Netherlands, now use WGS
for determining resistance to first-line drugs in clinical settings. In
order to do this, WGS data such as in FASTQ read format are
mapped to a reference genome (usually M. tuberculosis H37Rv)
and then a database map of genome position to resistance conferring mutations is used to determine the drug resistance profile of
the sample [158, 166]. For example, the Ser431Thr (Ser512Thr in
the E. coli numbering system) RIF-related mutation in rpoB is
caused by a cytosine at position 761,098 of the M. tuberculosis
H37Rv genome instead of the wild-type guanine [157]. Thus, by
mapping a sample’s reads against the M. tuberculosis H37Rv
genome, detection of known resistance mutations in associated
databases can be achieved.
While this drug resistance prediction can be done with any
genome assembly pipeline, several tools exist for this purpose in
both online and local formats [168–171], with Mykrobe [172],
TBprofiler [173], and PhyResSE [174] being the most widely used.
Each currently uses its own specific database of mutations to determine if resistance-causing mutations are present in the sample.
These databases are updated periodically but do not always overlap,
occasionally (though rarely) resulting in slightly different predictions [171, 175].
The use of WGS data to determine resistance in nontuberculous mycobacteria (NTMs) is still in its infancy. It has been used to
determine some drug resistance patterns in M. abscessus [176],
M. ulcerans [177], and M. leprae [178, 179] but has not been as
widely implemented in systematic ways in other NTMs. Until more
is known about the underlying mechanisms of drug resistance
(either gene mutation or efflux pumps) and there is accurate phenotypic testing to use as a gold standard, the use of WGS for NTM
resistance will remain limited for some species.
5.3.2 Relational
Sequencing
for Tuberculosis (ReSeqTB)
The ReSeqTB knowledgebase platform (https://platform.reseqtb.
org/) was developed in response to a worldwide collaboration
between Bill & Melinda Gates Foundation (BMGF), Critical Path
Institute (C-Path), Foundation for Innovative New Diagnostics
(FIND), World Health Organization (WHO), US Centers for
Disease Control and Prevention (CDC), the StopTB Partnership
New Diagnostics Working Group (NDWG), and the National
Institute of Allergy and Infectious Diseases (NIAID). ReSeqTB is
designed to facilitate the analysis of WGS data for antimicrobial
susceptibility for diagnostic developers, health care professionals,
and clinical laboratories. The tool is curated and validated using
culture-based
drug
susceptibility
testing
following
442
Marco A. Riojas et al.