The possible bacterial hosts for a given set of antibiotic resistance genes were occasionally predicted using network analysis lacking downstream resistance phenotype validation. This should provide important information on possible bacterial hosts, gene expression, and horizontal transferability. In sequence-based meta-genomic studies, the genetic context of the resistant genes, or the flanking regions containing promoter- and repressor-sequences that define the bacterial host origin, was not consistently reported. 24 In cases where up to 20% difference in the amino acid sequence was observed, the resistance phenotype of the bacterial strains concerned would therefore be questionable. 8, 19, 20, 23 It is well-established that a single amino acid substitution could sufficiently change the susceptibility level of a strain by altering the binding affinity of the drug target site to a corresponding antibiotic. ![]() 21, 22 The function of the detected resistance genes was only occasionally validated by expressing in competent bacterial hosts. The degree of similarity was arbitrary, ranging from 80% of amino acid sequence identity 6 to 95% nucleotide sequence identity. Where the assembled contigs displayed sequence similarity beyond a defined threshold, phenotypic resistance was assumed. ![]() In sequence-based meta-genomic studies, de novo assembled contigs were compared to the existing AR gene databases. 7, 14, 16, 20 Another issue is in comparing gene and/or protein sequence similarities and defining functional conservation. 15, 19 Across studies, disparate genes were selected to represent resistance to a given antibiotic class. 5– 7, 13, 14 Several reports employed high-throughput microfluidic PCR or customized microarrays by designing probes with reference to existing AR gene databases and targeted a selection of resistance genes, 15– 17 with the resulting number of AR genes varying from less than 50 7, 14, 18 to thousands. 12 Studies have applied this definition variably with considerable heterogeneity in candidate genes, methodology and bioinformatics pipelines, precluding direct comparison across studies. The term resistome is defined as the complete collective assemblage of antibiotic, antiseptic and heavy metal resistance genes in a microbial ecosystem. 4– 8 The ubiquity of resistance genes was exemplified by their unanticipated isolation from various environmental habitats waste water treatment plant and soil to food production chain and wild animals. ![]() In recent years, the advent of affordable high-throughput sequencing and analysis applied to online antibiotic resistance gene databases enables the avoidance of bacterial culture, facilitating massive resistome-wide studies of potential reservoirs of antibiotic resistance genes. ![]() 3 In addition to being time-consuming, this technique is limited to bacteria that can be cultivated. 2 If a candidate resistance gene could not be identified, genomic DNA is cloned into expression vectors and then transformed into a heterologous sensitive bacterial host for molecular and phenotypic characterization. 1– 3 For bacterial isolates displaying resistance phenotypes, total genomic DNA is extracted and probed for candidate AR genes using polymerase chain reaction (PCR)-based method or comparative whole-genome sequencing with reference to sensitive type strains. Traditionally, studies of antimicrobial resistance (AR) genes in bacteria started from massive screening of antibiotic resistance phenotypes using macro- or micro-broth dilution methods. Antibiotic resistance is a global public health concern and thus recognition of its reservoirs could facilitate the control of its dissemination.
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