Molecular characterization and antibiotic susceptibility profiles of bacteria associated with selected aquaculture farms in Sarawak

Kathleen Michelle, Mikal (2014) Molecular characterization and antibiotic susceptibility profiles of bacteria associated with selected aquaculture farms in Sarawak. Masters thesis, Universiti Malaysia Sarawak, (UNIMAS).

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Abstract

Disease threats and high demands of aquaculture products have forced aquaculturists to use antibiotics as a solution. However, misuse of antibiotics has led to the increase of antibiotic resistant bacteria population which threatens public health. This study aims to determine the extent of antibiotic resistance and to detect the resistant genes in bacteria isolated from Sarawak aquaculture. Bacteria were isolated from five aquaculture farms in Sarawak (Borneo, Malaysia) and were analysed by (GTG)5-Polymerase Chain Reaction (PCR) for de-replication of clonal isolates. After dendrogram analysis, 94 selected isolates were identified using 16S rRNA PCR and DNA sequencing. Bacillus (38.3%) dominated the bacterial population followed by 16 other genera such as Staphylococcus and Acinetobacter. Susceptibility of all isolates towards 24 antibiotic discs was assessed using disk diffusion assay. Based on standard interpretative table by Clinical and Laboratory Standards Institute (CLSI), only streptomycin (85%) and ampicillin (56.8%) resulted more than 50% resistance. Multiple Antibiotic Resistant (MAR) index in this study ranged from 0.0 to 0.63. Isolates with MARI > 0.2 were recovered from high risk sources. PCR analysis revealed that 35% and 2% isolates possess streptomycin (strA-strB genes) and ampicillin (blaTEM genes) resistant genes, respectively. Resistance in other isolates might be expressed by genes which were not detected in this study. Findings in this study reveal low level of antibiotic resistance in Sarawak aquaculture bacteria except for the commonly used antibiotics in aquaculture sectors since decades ago. It is essential to continuously monitor the antibiotic resistant patterns to predict emerging MAR bacteria as well as preventing its widespread.

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