High-Throughput Isolation of Nucleic Acids from Soil

High-throughput DNA extraction and cost-effective miniaturized metagenome and amplicon library preparation of soil samples for DNA sequencing

Reductions in sequencing costs have enabled widespread use of shotgun metagenomics and amplicon sequencing, which have drastically improved our understanding of the microbial world. However, large sequencing projects are now hampered by the cost of library preparation and low sample throughput, comparatively to the actual sequencing costs. Here, we benchmarked three high-throughput DNA extraction methods: ZymoBIOMICS™ 96 MagBead DNA Kit, MP BiomedicalsTM FastDNATM-96 Soil Microbe DNA Kit, and DNeasy® 96 PowerSoil® Pro QIAcube® HT Kit. The DNA extractions were evaluated based on length, quality, quantity, and the observed microbial community across five diverse soil types. DNA extraction of all soil types was successful for all kits, however DNeasy® 96 PowerSoil® Pro QIAcube® HT Kit excelled across all performance parameters. We further used the nanoliter dispensing system I.DOT One to miniaturize Illumina amplicon and metagenomic library preparation volumes by a factor of 5 and 10, respectively, with no significant impact on the observed microbial communities. With these protocols, DNA extraction, metagenomic, or amplicon library preparation for one 96-well plate are approx. 3, 5, and 6 hours, respectively. Furthermore, the miniaturization of amplicon and metagenome library preparation reduces the chemical and plastic costs from 5.0 to 3.6 and 59 to 7.3 USD pr. sample. This enhanced efficiency and cost-effectiveness will enable researchers to undertake studies with greater sample sizes and diversity, thereby providing a richer, more detailed view of microbial communities and their dynamics.

Bacterial endophytes as indicators of susceptibility to Cercospora Leaf Spot (CLS) disease in Beta vulgaris L

The fungus Cercospora beticola causes Cercospora Leaf Spot (CLS) of sugar beet (Beta vulgaris L.). Despite the global importance of this disease, durable resistance to CLS has still not been obtained. Therefore, the breeding of tolerant hybrids is a major goal for the sugar beet sector. Although recent studies have suggested that the leaf microbiome composition can offer useful predictors to assist plant breeders, this is an untapped resource in sugar beet breeding efforts. Using Ion GeneStudio S5 technology to sequence amplicons from seven 16S rRNA hypervariable regions, the most recurring endophytes discriminating CLS-symptomatic and symptomless sea beets (Beta vulgaris L.ssp. maritima) were identified. This allowed the design of taxon-specific primer pairs to quantify the abundance of the most representative endophytic species in large naturally occurring populations of sea beet and subsequently in sugar beet breeding genotypes under either CLS symptomless or infection stages using qPCR. Among the screened bacterial genera, Methylobacterium and Mucilaginibacter were found to be significantly (p < 0.05) more abundant in symptomatic sea beets with respect to symptomless. In cultivated sugar beet material under CLS infection, the comparison between resistant and susceptible genotypes confirmed that the susceptible genotypes hosted higher contents of the above-mentioned bacterial genera. These results suggest that the abundance of these species can be correlated with increased sensitivity to CLS disease. This evidence can further prompt novel protocols to assist plant breeding of sugar beet in the pursuit of improved pathogen resistance.

Quantification of rhizomania virus by automated RNA isolation and PCR based methods in sugar beet

Rhizomania is a grave disease affecting sugar beet (Beta vulgaris L.). It is caused by the Beet Necrotic Yellow Vein Virus (BNYVV), an RNA virus transmitted by the plasmodiophorid vector Polymyxa betae. Genetic resistance to the virus has been accomplished mostly using phenotype-genotype association studies. As yet, the most convenient method to ascertain plant resistance has been the quantification of viral titer in roots through the ELISA test. This method is particularly time-consuming and clashes with the necessities of modern plant breeding. Here, we propose an alternative and successful phenotyping method based on the automatic extraction of the viral RNA from sugar beet roots and its relative and absolute quantification by quantitative real-time PCR (qRT-PCR) and digital PCR (dPCR), respectively. Such a method enables an improved standardization of the study, as well as an accurate quantification of the virus also in those samples presenting low virus titer, with respect to the ELISA test.

Looking for Hidden Enemies of Metabarcoding: Species Composition, Habitat and Management Can Strongly Influence DNA Extraction while Examining Grassland Communities

Despite the raising preoccupation, the critical question of how the plant community is composed belowground still remains unresolved, particularly for the conservation priority types of vegetation. The usefulness of metabarcoding analysis of the belowground parts of the plant community is subjected to a considerable bias, that often impedes detection of all species in a sample due to insufficient DNA quality or quantity. In the presented study we have attempted to find environmental factors that determine the amount and quality of DNA extracted from total plant tissue from above- and belowground samples (1000 and 10,000 cm2). We analyzed the influence of land use intensity, soil properties, species composition, and season on DNA extraction. The most important factors for DNA quality were vegetation type, soil conductometry (EC), and soil pH for the belowground samples. The species that significantly decreased the DNA quality were Calamagrostis epigejos, Coronilla varia, and Holcus lanatus. For the aboveground part of the vegetation, the season, management intensity, and certain species-with the most prominent being Centaurea rhenana and Cirsium canum-have the highest influence. Additionally, we found that sample size, soil granulation, MgO, organic C, K2O, and total soil N content are important for DNA extraction effectiveness. Both low EC and pH reduce significantly the yield and quality of DNA. Identifying the potential inhibitors of DNA isolation and predicting difficulties of sampling the vegetation plots for metabarcoding analysis will help to optimize the universal, low-cost multi-stage DNA extraction procedure in molecular ecology studies.