Development of a digital droplet PCR approach for the quantification of soil micro-organisms involved in atmospheric CO 2 fixation.
Marie Le GeayKyle MayersMartin KüttimBéatrice LaugaVincent E J JasseyPublished in: Environmental microbiology (2024)
Carbon-fixing micro-organisms (CFMs) play a pivotal role in soil carbon cycling, contributing to carbon uptake and sequestration through various metabolic pathways. Despite their importance, accurately quantifying the absolute abundance of these micro-organisms in soils has been challenging. This study used a digital droplet polymerase chain reaction (ddPCR) approach to measure the abundance of key and emerging CFMs pathways in fen and bog soils at different depths, ranging from 0 to 15 cm. We targeted total prokaryotes, oxygenic phototrophs, aerobic anoxygenic phototrophic bacteria and chemoautotrophs, optimizing the conditions to achieve absolute quantification of these genes. Our results revealed that oxygenic phototrophs were the most abundant CFMs, making up 15% of the total prokaryotic abundance. They were followed by chemoautotrophs at 10% and aerobic anoxygenic phototrophic bacteria at 9%. We observed higher gene concentrations in fen than in bog. There were also variations in depth, which differed between fen and bog for all genes. Our findings underscore the abundance of oxygenic phototrophs and chemoautotrophs in peatlands, challenging previous estimates that relied solely on oxygenic phototrophs for microbial carbon dioxide fixation assessments. Incorporating absolute gene quantification is essential for a comprehensive understanding of microbial contributions to soil processes. This approach sheds light on the complex mechanisms of soil functioning in peatlands.
Keyphrases
- carbon dioxide
- antibiotic resistance genes
- genome wide
- genome wide identification
- microbial community
- single cell
- heavy metals
- high intensity
- gram negative
- minimally invasive
- plant growth
- copy number
- genome wide analysis
- human health
- risk assessment
- transcription factor
- cancer therapy
- bioinformatics analysis
- air pollution