Development of the LCPDb-MET database facilitating selection of PCR primers for the detection of metal metabolism and resistance genes in bacteria

Abstract

Bacterial metal metabolism and resistance genes are important indicators for microbial ecology, biogeochemistry, and biotechnology. Their investigation enables understanding how bacteria influence the geochemical cycles of elements and how bacteria can be employed in the bioremediation of polluted areas. For environmental screening, the polymerase chain reaction (PCR) technique has been used as the method of choice for the detection of metal metabolism and resistance genes, and subsequently, many different PCR primer pairs have been developed for such screening. The aim of this study was the development of a database of PCR primers suitable for the screening of metal metabolism and resistance genes in environmental samples. We conducted an in silico benchmark of 291 previously published PCR primer pairs designed to amplify genes involved in bacterial metabolism and resistance to arsenic, cadmium, chromium, cobalt, copper, gold, iron, lead, mercury, nickel, silver, and zinc. Our analysis showed that only 16 PCR primer pairs can be considered well designed and suitable for reliable screening in an environmental setup. Furthermore, 55 primer pairs delivered ambiguous results, while the remaining 220 pairs showed no in silico PCR product within their expected product size range. Based on the obtained results, we developed a ranked database (LCPDb-MET; http://lcpdb.ddlemb.com/met/) of primers suitable for the screening of bacterial metal metabolism and resistance genes in various environmental settings. Moreover, since in the course of the study we recognized a serious problem with the classification of metal metabolism and resistance reference sequences, we developed also the METGeneDb database (http://lcpdb.ddlemb.com/downloads). This database covers 106 genes with 7,624 unique sequences divided into 137 potential gene subclusters and it can be used as the reference database in the genomic and metagenomic studies.