20
non-human use, thereby discouraging the mass usage of low-value antibiotics in
agriculture. This move is easy to implement and would bring the production costs
using antibiotics a bit higher (close to hygienic rearing) and corresponding to lower
risks of infection and improvement in rearing conditions of animals (Hollis and
Ahmed 2013).
Although, efforts are being done to facilitate the exchange of information
between these systems and integrate them in a centralized system for monitoring of
antimicrobial resistance. Some examples are Pan American Health Organization
(PAHO) and European Resistance Surveillance Network (EARS-Net), etc., but
global networking and cooperation in this regard is required. Another problem is
defining the common detection methods, safe limits and critical breakpoints across
the world, for the levels of antibiotics and antibiotic resistant bacteria. An international surveillance system is warranted for this and also for integrating information
about antibiotic resistance from soil, water and non-pathogenic environmental
microbes, all of which could be critical as carriers of antibiotic resistance (Berendonk
et al. 2015). Defining a single standard is difficult, because issue of antimicrobial
resistance is complex and influenced by various factors. This calls for careful decision making and consideration of ecological factors and the niche conditions which
might under- or over-represent antimicrobial resistance from the samples.
An important aim for these international collaborations is to identify the factors
causing the emergence, persistence and spread of antimicrobial resistance. The
emergence of antibiotic resistant genes is not only due to mutations or co-selection
under antibiotics, heavy metals or other antimicrobial agents. But, the role of mobile
genetic elements such as plasmids, integrons and transposons, makes it even more
complex and difficult to predict. The role of biofilm formation, water bodies and
phyllosphere have also been implicated in persistence of resistance (Calero-Cáceres
et al. 2014). It demands high-throughput techniques such as metagenomics to get
holistic picture of the issue of antimicrobial resistance and understand the rate of
acquisition and spread of antibiotic resistance genes. This has been structured
through a classification system called as resistance readiness condition (Rescon),
which takes into account the severity of antimicrobial resistance due to the antibiotic resistance genes and their propensity for rapid spread (Vorholt 2012).
Understanding the transfer pathways is challenging because of multiple overlaps
between the humans, animals, agriculture and the environment. At the same time,
reliable data on antimicrobial resistance and driving genes and mobile genetic elements in these individual niches is also not available. Antibiotic resistance genes
alone might be getting transferred between these niches and are also prone to
undergo alterations in their new host. All these factors make it quite difficult to
attempt any quantitative prediction for identifying the source of antibiotic resistance
genes responsible for antimicrobial resistance (Martínez et al. 2015). Thus, the current strategies to assess the issue of antimicrobial resistance is trivial and is limited
to culturable antibiotic susceptibility tests and few molecular estimations for presence of antibiotic resistance genes. The studies involving big data/omics approach
(through metagenomics, meta-transcriptomics, meta-metabolomics, etc.) could
K. S. Singh et al.
non-human use, thereby discouraging the mass usage of low-value antibiotics in
agriculture. This move is easy to implement and would bring the production costs
using antibiotics a bit higher (close to hygienic rearing) and corresponding to lower
risks of infection and improvement in rearing conditions of animals (Hollis and
Ahmed 2013).
Although, efforts are being done to facilitate the exchange of information
between these systems and integrate them in a centralized system for monitoring of
antimicrobial resistance. Some examples are Pan American Health Organization
(PAHO) and European Resistance Surveillance Network (EARS-Net), etc., but
global networking and cooperation in this regard is required. Another problem is
defining the common detection methods, safe limits and critical breakpoints across
the world, for the levels of antibiotics and antibiotic resistant bacteria. An international surveillance system is warranted for this and also for integrating information
about antibiotic resistance from soil, water and non-pathogenic environmental
microbes, all of which could be critical as carriers of antibiotic resistance (Berendonk
et al. 2015). Defining a single standard is difficult, because issue of antimicrobial
resistance is complex and influenced by various factors. This calls for careful decision making and consideration of ecological factors and the niche conditions which
might under- or over-represent antimicrobial resistance from the samples.
An important aim for these international collaborations is to identify the factors
causing the emergence, persistence and spread of antimicrobial resistance. The
emergence of antibiotic resistant genes is not only due to mutations or co-selection
under antibiotics, heavy metals or other antimicrobial agents. But, the role of mobile
genetic elements such as plasmids, integrons and transposons, makes it even more
complex and difficult to predict. The role of biofilm formation, water bodies and
phyllosphere have also been implicated in persistence of resistance (Calero-Cáceres
et al. 2014). It demands high-throughput techniques such as metagenomics to get
holistic picture of the issue of antimicrobial resistance and understand the rate of
acquisition and spread of antibiotic resistance genes. This has been structured
through a classification system called as resistance readiness condition (Rescon),
which takes into account the severity of antimicrobial resistance due to the antibiotic resistance genes and their propensity for rapid spread (Vorholt 2012).
Understanding the transfer pathways is challenging because of multiple overlaps
between the humans, animals, agriculture and the environment. At the same time,
reliable data on antimicrobial resistance and driving genes and mobile genetic elements in these individual niches is also not available. Antibiotic resistance genes
alone might be getting transferred between these niches and are also prone to
undergo alterations in their new host. All these factors make it quite difficult to
attempt any quantitative prediction for identifying the source of antibiotic resistance
genes responsible for antimicrobial resistance (Martínez et al. 2015). Thus, the current strategies to assess the issue of antimicrobial resistance is trivial and is limited
to culturable antibiotic susceptibility tests and few molecular estimations for presence of antibiotic resistance genes. The studies involving big data/omics approach
(through metagenomics, meta-transcriptomics, meta-metabolomics, etc.) could
K. S. Singh et al.