50
relationships, and act as catalogue of traits relevant for epidemiology based investigations. This has created huge impact on outbreak investigations along with diagnosis and treatment of infectious diseases. It has also impacted the practice of
microbiology and epidemiology (Allard et al. 2019). Theoretically, any biological
feature can be deduced from DNA sequences which might include ability to detect
antimicrobial resistance as clinical application. It can further provide information
regarding evolution and spread of resistant bacteria in hospital or among community. Antimicrobial resistance is a matter of global concern resulting in mortality in
huge numbers (O’Neill 2016). Antimicrobial resistance has been considered as a
measurement parameter to study growth inhibitory effects of a chemotherapeutic
agent on cultured bacterial population. Despite some additional improvements,
clinical laboratories consider diffusion and dilution methods as primary tool to
guide clinical therapy and monitor antimicrobial resistance over time. Assembled
data has shown accurate prediction of antimicrobial resistance using genomic
sequence. Sequence based approach of antimicrobial resistance detection requires
robust bioinformatics tools for visualization and analysis of microbial “resistome”
comprising antimicrobial resistance genes and their precursors (Cartwright
et al. 2013).
2.10.1 Whole Genome Sequencing in Surveillance
Whole genome sequencing is a comprehensive method for analyzing entire genome
of an organism. Whole genome sequencing is a powerful tool for genomics research,
which makes it useful for sequencing any species, such as agriculturally important
livestock, plants, or disease related microbes. Whole genome sequencing enriches
our understanding of antimicrobial resistance spread and evolution and contributes
practical information for local, national and global infection control and clinical
guidance. The United States is expanding its dimensions for monitoring antimicrobial resistance through whole genome sequencing. It comprises of coordination
among state public health laboratories and universities. Centre for Disease Control
and Prevention (CDC) coordinate with antibiotic resistance laboratory network for
rapid detection of emerging resistance threats. This comprehensive network performs whole genome sequencing in addition to other activities for numerous pathogens including all isolates of Mycobacterium tuberculosis. Whole genome
sequencing is also routinely used for characterization of Neisseria gonorrhoeae and
other pathogens involved in outbreaks. The National Antimicrobial Resistance
Monitoring System (NARMS) focuses on bacterial transmission through food
(Karp et al. 2017). NARMS began with systematic whole genome sequencing of
Salmonella in 2013 and later on included Campylobacter in 2014. Resistance trends
can be examined at genetic level by users using query filters provided by online
tools. Graphical visualizations of genotypes with change in resistance patterns over
time is provided by these tools.
S. Kumar et al.
relationships, and act as catalogue of traits relevant for epidemiology based investigations. This has created huge impact on outbreak investigations along with diagnosis and treatment of infectious diseases. It has also impacted the practice of
microbiology and epidemiology (Allard et al. 2019). Theoretically, any biological
feature can be deduced from DNA sequences which might include ability to detect
antimicrobial resistance as clinical application. It can further provide information
regarding evolution and spread of resistant bacteria in hospital or among community. Antimicrobial resistance is a matter of global concern resulting in mortality in
huge numbers (O’Neill 2016). Antimicrobial resistance has been considered as a
measurement parameter to study growth inhibitory effects of a chemotherapeutic
agent on cultured bacterial population. Despite some additional improvements,
clinical laboratories consider diffusion and dilution methods as primary tool to
guide clinical therapy and monitor antimicrobial resistance over time. Assembled
data has shown accurate prediction of antimicrobial resistance using genomic
sequence. Sequence based approach of antimicrobial resistance detection requires
robust bioinformatics tools for visualization and analysis of microbial “resistome”
comprising antimicrobial resistance genes and their precursors (Cartwright
et al. 2013).
2.10.1 Whole Genome Sequencing in Surveillance
Whole genome sequencing is a comprehensive method for analyzing entire genome
of an organism. Whole genome sequencing is a powerful tool for genomics research,
which makes it useful for sequencing any species, such as agriculturally important
livestock, plants, or disease related microbes. Whole genome sequencing enriches
our understanding of antimicrobial resistance spread and evolution and contributes
practical information for local, national and global infection control and clinical
guidance. The United States is expanding its dimensions for monitoring antimicrobial resistance through whole genome sequencing. It comprises of coordination
among state public health laboratories and universities. Centre for Disease Control
and Prevention (CDC) coordinate with antibiotic resistance laboratory network for
rapid detection of emerging resistance threats. This comprehensive network performs whole genome sequencing in addition to other activities for numerous pathogens including all isolates of Mycobacterium tuberculosis. Whole genome
sequencing is also routinely used for characterization of Neisseria gonorrhoeae and
other pathogens involved in outbreaks. The National Antimicrobial Resistance
Monitoring System (NARMS) focuses on bacterial transmission through food
(Karp et al. 2017). NARMS began with systematic whole genome sequencing of
Salmonella in 2013 and later on included Campylobacter in 2014. Resistance trends
can be examined at genetic level by users using query filters provided by online
tools. Graphical visualizations of genotypes with change in resistance patterns over
time is provided by these tools.
S. Kumar et al.