A rapid, sensitive, low-cost assay for detecting hydrogenotrophic methanogens in anaerobic digesters using loop-mediated isothermal amplification

Abstract

Understanding how the presence, absence, and abundance of different microbial genera supply specific metabolic functions for anaerobic digestion (AD) and how these impact on gas production is critical for a long-term understanding and optimization of the AD process. The strictly anaerobic methanogenic archaea are essential for methane production within AD microbial communities. Methanogens are a phylogenetically diverse group that can be classified into three metabolically distinct lineages based on the substrates they use to produce methane. While process optimization based on physicochemical parameters is well established in AD, measurements that could allow manipulation of the underlying microbial community are seldom used as they tend to be non-specific, expensive, or time-consuming, or a combination of all three. Loop-mediated isothermal amplification (LAMP) assays combine a simple, rapid, low-cost detection technique with high sensitivity and specificity. Here, we describe the optimization of LAMP assays for the detection of four different genera of hydrogenotrophic methanogens: Methanoculleus, Methanothermobacter, Methanococcus, and Methanobrevibacter spp. By targeting archaeal elongation factor 2 (aEF2), these LAMP assays provide a rapid, low-cost, presence/absence indication of hydrogenotrophic methanogens that could be used as a real-time measure of process conditions. The assays were shown to be sensitive to 1 pg of DNA from most tested methanogen species, providing a route to a quantitative measure through simple serial dilution of samples. The LAMP assays described here offer a simple, fast, and affordable method for the specific detection of four different genera of hydrogenotrophic methanogens. Our results indicate that this approach could be developed into a quantitative measure that could provide rapid, low-cost insight into the functioning and optimization of AD and related systems.