Introduction
Tuberculosis is an acute condition that requires immediate treatment; however, some individuals develop resistance to antibiotics, which can cause severe health outcomes. Currently, several methods are used to identify and predict mutations in genes but most of them are expensive and unavailable for many countries. This paper provides a summary of the article that addresses the issue. The study discusses the effectiveness of an alternative method to detect drug resistance. The paper concludes that it is possible to substitute existing testing methods with a more cost-effective approach, which is as efficient as the existing ones.
Summary
The topic of choice for this paper is antibiotic-resistant tuberculosis because this disease can cause significant threats to individuals’ health. More than 10 million new cases are registered while the resistance to antibiotics is growing (Klotoe et al. 2018). The selected article by Klotoe et al. (2018) addresses this problem and answers the following question: Is there a more advanced way/method/technology to detect drug resistance in tuberculosis? This topic is closely related to coevolution because humans can develop resistance to antibiotics as the result of intragenomic coevolution caused by horizontal gene transfer. Coevolution between Escherichia coli and plasmid RK2 is one of the causes of this condition (Bottery et al. 2017).
The article that was selected for the review introduces the problem of antibiotics resistance and the causes of it in humans. The authors’ goal is to develop an alternative method of detecting mutations that result in resistance to drugs (Klotoe et al. 2018). They note that there are two known types of drug resistance, including multidrug one associated with Rifampin and Isoniazid, as well as an extensive one, related to these medications along with fluoroquinolone and some second-line drugs. Klotoe et al. (2018) report that the standard method of detecting mutations in DNA sequencing, but this process is expensive and requires bioinformatics skills. Thus, this method can be considered unsuitable for low-income countries.
To develop an alternative method of detecting, the authors used 61 samples of DNA, more than 50% of which were resistant to one of the second-line medications (Klotoe et al. 2018). With the help of EFI (Ethambutol, Fluoroquinolones, Injectables) testing, researchers were able to identify all targeted types of mutations, as well as diversity among them.
The method the introduced utilizes the flow-cytometry microbead-based assay. Klotoe et al. (2018) note that their approach involves a less expensive technology compared to those currently utilized in the field. Moreover, the study proves that EFI testing is validated on clinical samples and provides detailed data on each of them, including information about phenotype and genotype. Authors report that their method allows for accurate and rapid identification of drug resistance and prediction of genotypes and shows the same effectiveness as the methods currently used.
Conclusion
The review of the article reveals that EFI testing is an alternative method that can be effective for the detection of drug resistance in tuberculosis. It is less expensive and cost-effective than those used currently, which means that it can be utilized in low-income countries where the technology is unavailable. Moreover, this method allows for the rapid identification of phenotype and genotype; its efficiency is relatively high. It means that in the future, this technology can substitute the existing ones and improve the health outcomes of individuals having resistance to antibiotics. The study also suggests that research in the field of coevolution is vital for enhancing global well-being.
References
Bottery MJ, Wood AJ, Brockhurst MA. 2017. Adaptive modulation of antibiotic resistance through intragenomic coevolution. Nat Ecol Evol. 1(9): 1364-1369. Web.
Klotoe BJ, Molina-Moya B, Gomes HM, Gomgnimbou MK, Suzarte LO, Saad MHF, Ali S, Dominguez J, Pimkina E, Zholdybayeva E, et al. 2018.TB-EFI, a novel 18-Plex microbead-based method for prediction of second-line drugs and ethambutol resistance in Mycobacterium tuberculosis complex. J Microbiol Meth. 152: 10-17. Web.