21
have high potential impact in understanding antimicrobial resistance (Winokur
et al. 2001).
The usage of big data and its correlational mathematical analysis with the confounding factors is not yet fully realized and would need comprehensive efforts and
action to achieve reliable predictions for emergence and spread of antimicrobial
resistance (Bailar and Travers 2002; Marshall and Levy 2011). The culture based
and PCR based low-throughput methods only provides limited information and are
time-consuming and need prior information about the antibiotic resistance genes.
Contrary to these, the multi-omics approaches utilize the samples directly from the
environment/niche and provide high-throughput information into the type of antibiotic resistance genes, mobile genetic elements, expression pattern and levels of
genes and gene clusters and the actual metabolites present in the niche (Spicknall
et al. 2013). These chunks of data are amenable for usage in advanced statistical
softwares and computer programming languages, to provide meaningful information.
The meaningful information includes (1) the relative abundance and movement
of antibiotic resistance genes, (2) probability for spread of antibiotic resistance
genes between niches, (3) predictions for development of resistance due to mutations in known antibiotic resistance genes, (4) prevalence of mobile genetic elements like integrons, plasmids, etc. and (5) interrelation between the relative
proportion of MGEs and the antibiotic resistance genes in driving the antimicrobial
resistance. All these studies can be well complemented with functional metagenomics in which novel antibiotic resistance genes can be identified from different niches.
However, these techniques are limited due to high costs and need for complex data
analysis. Future advancements in next generation sequence analysis and more interest in environmental samples could make these technologies even cheaper for future
use (Munk et al. 2017; Hendriksen et al. 2019).
1.4 Antimicrobial Resistance and One Health Approach
In context of antimicrobial resistance, there are multiple benefits for adopting and
working in the framework of ‘One Health’ concept. The prime concern for humans
is the emergence of resistance in human pathogens and gain of resistance in human
pathogens form their distant relatives, present in the environment (Forsberg et al.
2012). Even though, there is clear evidence for animal-human overlaps, but strictly
confining to humans, ‘One Health’ could be beneficial in strengthening the surveillance network to improve the understanding of the changing dynamics of antimicrobial resistance in human pathogens. This includes documenting the resistant
microbial phenotypes isolated from human samples during microbiological examination of patients. This reporting (every year) of the prevailing antibiotic resistance
in the area would also help in identifying the pattern endemic to the region (Critchley
and Karlowsky 2004). The physicians would follow evidence-based approach for
requirement of antibiotics by the patient and would have clear standard guidelines
to aid in prescription of antibiotics. Physicians can directly influence the patient
1 Antimicrobial Resistance Paradigm and One-Health Approach
have high potential impact in understanding antimicrobial resistance (Winokur
et al. 2001).
The usage of big data and its correlational mathematical analysis with the confounding factors is not yet fully realized and would need comprehensive efforts and
action to achieve reliable predictions for emergence and spread of antimicrobial
resistance (Bailar and Travers 2002; Marshall and Levy 2011). The culture based
and PCR based low-throughput methods only provides limited information and are
time-consuming and need prior information about the antibiotic resistance genes.
Contrary to these, the multi-omics approaches utilize the samples directly from the
environment/niche and provide high-throughput information into the type of antibiotic resistance genes, mobile genetic elements, expression pattern and levels of
genes and gene clusters and the actual metabolites present in the niche (Spicknall
et al. 2013). These chunks of data are amenable for usage in advanced statistical
softwares and computer programming languages, to provide meaningful information.
The meaningful information includes (1) the relative abundance and movement
of antibiotic resistance genes, (2) probability for spread of antibiotic resistance
genes between niches, (3) predictions for development of resistance due to mutations in known antibiotic resistance genes, (4) prevalence of mobile genetic elements like integrons, plasmids, etc. and (5) interrelation between the relative
proportion of MGEs and the antibiotic resistance genes in driving the antimicrobial
resistance. All these studies can be well complemented with functional metagenomics in which novel antibiotic resistance genes can be identified from different niches.
However, these techniques are limited due to high costs and need for complex data
analysis. Future advancements in next generation sequence analysis and more interest in environmental samples could make these technologies even cheaper for future
use (Munk et al. 2017; Hendriksen et al. 2019).
1.4 Antimicrobial Resistance and One Health Approach
In context of antimicrobial resistance, there are multiple benefits for adopting and
working in the framework of ‘One Health’ concept. The prime concern for humans
is the emergence of resistance in human pathogens and gain of resistance in human
pathogens form their distant relatives, present in the environment (Forsberg et al.
2012). Even though, there is clear evidence for animal-human overlaps, but strictly
confining to humans, ‘One Health’ could be beneficial in strengthening the surveillance network to improve the understanding of the changing dynamics of antimicrobial resistance in human pathogens. This includes documenting the resistant
microbial phenotypes isolated from human samples during microbiological examination of patients. This reporting (every year) of the prevailing antibiotic resistance
in the area would also help in identifying the pattern endemic to the region (Critchley
and Karlowsky 2004). The physicians would follow evidence-based approach for
requirement of antibiotics by the patient and would have clear standard guidelines
to aid in prescription of antibiotics. Physicians can directly influence the patient
1 Antimicrobial Resistance Paradigm and One-Health Approach