Abstract
Biological control is emerging as a feasible alternative to chemical pesticides in agriculture. Measuring the microbial biocontrol agent (mBCA) populations in the environment is essential for an accurate environmental and health risk assessment and for optimizing the usage of an mBCA-based plant protection product. We hereby show a workflow to obtain a large number of qPCR markers suitable for robust strain-specific quantification. The workflow starts from whole genome sequencing data and consists of four stages: (i) identifying the strain-specific sequences, (ii) designing specific primer/probe sets for qPCR, and (iii) empirically verifying the performance of the assays. The first two stages involve exclusively computer work, but they are intended for researchers with little or no bioinformatic background: Only a knowledge of the BLAST suite tools and work with spreadsheets are required; a familiarity with the Galaxy environment and next-generation sequencing concepts are strongly advised. All bioinformatic work can be implemented using publicly available resources and a regular desktop computer (no matter the operating system) connected to the Internet. The workflow was tested with five bacterial strains from four different genera under development as mBCAs and yielded thousands of candidate markers and a triplex qPCR assay for each candidate mBCA. The qPCR assays were successfully tested in soils of different natures, water from different sources, and with samples from different plant tissues. The mBCA detection limits and population dynamics in the different matrices are similar to those in qPCR assays designed by other means. In summary, a new accessible, cost-effective, and robust workflow to obtain a large number of strain-specific qPCR markers is presented.
Highlights
Identification and quantification of bacteria at strain level are cornerstones in several fields such as medicine, microbiology, food science and technology, aquaculture, and plant biology (Marx, 2016)
The high number of potential markers obtained with the proposed workflow contrasts with the few – in the order of tens – markers obtained usually identified in RAPD or AFLP experiments (Hermosa et al, 2001; Dauch et al, 2003; Felici et al, 2008; Xiang et al, 2010; Perez et al, 2014)
The use of genomic DNA sequences instead of ORFs may yield a larger number of potential markers; in the present manuscript, ORFs were used in order to restrain the amount of strain-specific sequences and flag possible functional information about the markers
Summary
Identification and quantification of bacteria at strain level are cornerstones in several fields such as medicine, microbiology, food science and technology, aquaculture, and plant biology (Marx, 2016). The most common techniques to monitor bacteria at strain level are dilution-plate counting and PCR-derived methods. PCR is highly specific so long as the primers anneal only to DNA sequences that are specific of the intended target strain. These DNA sequences are most often identified by DNA fingerprinting techniques such as RAPD (Random Amplified Polymorphic DNA) or AFLP (Amplification Fragment Length Polymorphism) followed by sequence characterization (Hermosa et al, 2001; Dauch et al, 2003; Felici et al, 2008; Xiang et al, 2010; Perez et al, 2014). Combining DNA fingerprinting and real-time quantitative PCR (qPCR) provides a powerful tool to identify and quantify bacteria at strain level, and this has made it a widespread method to monitor bacterial strains when dilution-plate counting is not feasible. PCR targets DNA sequences that may be present in viable cells, VBNC, or cell debris, but further refinement using viability PCR (vPCR) is gaining importance to distinguish viable from other states in the target bacterial populations (Nogva et al, 2003; Elizaquivel et al, 2014)
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