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In this experiment, the soil microbiota was estimated to contain: 7. A similar value of 1. Hence, the spiking approach allowed for repeated estimation of the soil microbiota abundance with and without addition of a substantial amount of Rhizobium cells. DNA from cultured Rhizobium gave an average estimate of 1.

This may introduce a significant bias from estimation of the DNA quantity. Moreover, while the copy number of 16S rRNA is known for a defined species such as Rhizobium , it can only be approximated for unknown OTUs in the total microbiota. For comparative purposes the Rhizobium values were used, with a genome 7. Based on these results, the most accurate results are obtained when spikes are added to the raw material prior to DNA isolation.

As addition of spikes allows accurate estimation of the abundance of the soil microbiota, total prokaryotic, eukaryotic and fungal abundances were measured in two contrasting soils. As expected, the higher the amount of synthetic spike added to soil samples, the higher their recovery in the sequencing output, although this plateaus at the highest levels of F synthetic spike Fig. In order to determine the optimum spike level, a simple model was constructed showing the expected number of synthetic sequences per total sequences.

First, the microbial gene abundance was averaged Fig. Model of synthetic spike addition. Experimental results are shown by solid symbols while model data is presented with lines of corresponding colours. The model shows the expected spike contribution in the sequencing output for each spike level using the averaged gene abundance for a specific soil as in Table 2. Dotted green lines indicate the region with — synthetic reads per reads.

Spike levels on the x -axis correspond to the synthetic spike levels in Table 1. NS is the number of P, E or F spikes added per gram of soil. The experimental data Fig. Furthermore, adding synthetic spikes levels 1—8, Table 1 did not cause a significant change in the measured composition of the prokaryotic, eukaryotic and fungal communities Fig.

These two soil samples were probably contaminated with a piece of earthworm. MDS plots of microbial community structure. The microbial community of Bawburgh warm colours and Wytham cool colours soils. For each soil, eight different colours are used, each representing a different level of synthetic spike 1—8, Table 1. Estimated abundance of in situ microbial genes in Bawburgh B and Wytham W soils using synthetic spikes.

Gene copy number per gram soil on the y -axis is plotted against synthetic spike level 1—8, Table 1 on the x -axis. Omitting samples with less than and more than spike reads per total reads dotted green lines on Fig. Using only samples fulfilling these criteria Table 2 , the estimation of abundance of prokaryotic 16S rRNA was reduced from 1. Eukaryotic 18S rRNA gene abundance remained almost identical at 1. Fungal spike levels 3—8 were saturated with synthetic spikes and under-represented in situ microbial ITS abundance Table 2.

The requirement to tailor synthetic spike levels depending on the amplification target i. These results correlate with the fact that Wytham soil is richer in organic matter and macronutrients than that from Bawburgh. We observe a gradient in microbiota abundance estimation based on the P and F spike level addition. Generally, the more spikes were added the lower microbiota abundance was recorded.

It suggests that the low levels of spikes may be retained in soil and hence soil type may cause a significant bias in the microbiota estimation, while very high levels oversaturate the DNA pool. There is greater abundance of prokaryotes in Wytham soil approx. The relative Fig. Proteobacteria, Actinobacteria and Acidobacteria are dominant phyla in Bawburgh and Wytham soils Fig. This prokaryotic profile is commonly found in medium pH soils [ 30 ].

Using a relative approach Fig. However, when the quantitative correction was applied Fig. This is consistent with Wytham soil being richer than Bawburgh with a greater abundance of most groups. Prokaryotic and eukaryotic community structure in Bawburgh and Wytham soils. Fungal community structure in Bawburgh and Wytham soils.

Fungi, especially Ascomycota, Basidiomycota and Mucoromycota, dominate the eukaryotic community in both Bawburgh and Wytham soils. Quantitation indicates that the dominant phyla of Ascomycota and Chytridiomycota are more abundant in Wytham soil, while Mucoromycota is prevalent in that from Bawburgh Fig. As might be expected, a substantial piece of animal tissue increased the abundance of 18S rRNA copies approx.

However, ITS-based analysis indicates that fungal abundance is much lower than the results suggested from the 18S rRNA analysis with values of 8. It is known that 18S rRNA primers are not able to unravel detail in fungal taxonomy; however, results from both primer sets agree on soil to soil comparison where Ceratocystis , Pleosporales and Aspergillus are more abundant in Wytham soil, while Thelebolus is more abundant in Bawburgh soil.

Amplification reactions using different primer pairs may not target a full range of potential microbial taxa. However, assuming that these primers capture most of the community, the ratio of prokaryotic to eukaryotic ribosomal content can be determined Fig. Prokaryotes make up The limitation of our approach is that we cannot easily compare the results with microscope-based counts as prokaryotic cells normally have only a few ribosomal operons [ 31 , 32 , 33 ], while eukaryotes may have hundreds of copies [ 34 , 35 , 36 ].

This potentially represents a biological mechanism controlling inter-domain relationships in fallow soils. Comparison of absolute and relative microbiota abundance in soil. By comparing the relative amplification efficiency of the 16S and 18S rRNA genes targets, we have calculated that eukaryotes contribute to 7.

Our values can also be compared with results published from a meta-transcriptomic study, which showed that prokaryotic 16S rRNA and eukaryotic 18S rRNA contribute towards A study comparing soils of contrasting fungi to bacteria ratio found that fungi contributed 0.

The ratio of fungi to bacteria in our study is higher, 8. An elevated ratio of fungi to bacteria has been associated with high soil fertility, high levels of organic carbon and lack of tillage [ 38 ]. The fallow soils used in this study may indeed have a high fungal abundance, as their organic content is high and they have not been tilled for decades. Absolute quantitation of the microbiota is essential for all aspects of microbial ecology, and our approach accurately estimated bacterial culture and soil microbiota numbers.

The number of synthetic spikes added to soil over several orders of magnitude was directly proportional to the number of sequence reads obtained. While variation between replicates was usually low, some samples had larger errors, confirming that with very complex samples, such as soil, high numbers of replicates are advisable. For example, in previous work investigating the plant-soil microbiota, we used twenty-four biological replicates for each condition [ 24 ].

In this work, due to the need to test a large gradient of synthetic spike levels, we reduced the number to three replicates for each of the eight synthetic spike levels. However, ideally for critical analysis of the soil microbiota, replicate numbers should be greater than this. Prokaryotic 16S rRNA was detected at around 10 9 and eukaryotic 18S rRNA at around 10 8 copies per gram of soil; however, there may be up to an order of magnitude difference in contrasting soils [ 38 ].

Using the van Bammelen factor conversion 0. Our values of 1. Wytham soil, which is rich in organic matter, showed higher microbial ribosome abundance than poorer Bawburgh soil. This is true both for prokaryotic and eukaryotic microbiotas Fig.

Quantitation allows for statistical correction of soil microbial abundance. Many microbial taxa are actually more abundant in Wytham soil even though their relative presence is higher in Bawburgh Figs. We believe this to be fundamentally important for all aspects of microbiota research and ecology, and it applies to microbiotas from all environments, from soil to the mammalian gut.

Without absolute quantitation of groups, the underlying physiology and ecology of the role of specific microbial taxa may be masked by their relative abundance. For example, changes in the absolute abundance of keystone symbionts or pathogens may be masked by unaltered relative abundance or the relative abundance may go in the opposite direction to absolute abundance.

This is likely to be because the relative abundance of any specific group is highly dependent on the absolute abundance of the most numerous organisms. Since the experimental setup allows detection of the majority of microbial taxa, i. These results agree with previous RNA-based estimates of the soil microbiota [ 27 ]. Adding synthetic spikes allowed accurate detection of microbial 16S rRNA presence in control samples of a Rhizobium culture, with it being more effective to add spikes directly to environmental samples rather than to isolated DNA Fig.

Propidium monoazide dye reacts with DNA not protected by a cellular membrane and subsequently blocks its PCR amplification [ 39 ]. Synthetic spikes could be added to aliquots of the initial sample and that with relic DNA removed. This approach would reveal which taxa are alive and their absolute presence. It is strongly recommended that an initial calibration curve is performed to determine the optimal synthetic spike level for a given environmental condition.

Spike addition in too high or too low, an amount compared to the targeted microbiota, may skew the quantitative results although the level of synthetic spike used did not alter the measured structure of the microbial community Figs. However, once an initial calibration is performed, the level of spike could be varied over three orders of magnitude with high reproducibility. Care needs to be taken to ensure efficient isolation of DNA and its stabilisation from different environments as this can bias any method of quantification.

However, given this caveat, our approach is simple, requiring only addition of known amounts of synthetic spike DNA and a single bioinformatic step post-sequencing, in order to quantify the absolute abundance of prokaryotes, eukaryotes and fungi in microbiota studies. Quantification of the active microbiota will contribute to a better understanding of functional groups in environmental microbiology and can help in producing better microbiota interactions models [ 40 ]. The gut microbiome shapes intestinal immune responses during health and disease.

Nat Rev Immun. Abundance of broad bacterial taxa in the sargasso sea explained by environmental conditions but not water mass. Appl Environ Microbiol. Evrensel A, Ceylan ME. The gut-brain axis: the missing link in depression. Clin Psychopharmacol and Neurosci. Selection on soil microbiomes reveals reproducible impacts on plant function.

ISME J. Host genotype and age shape the leaf and root microbiomes of a wild perennial plant. Nat Commun. Nitrososphaera and Bradyrhizobium are inversely correlated and related to agricultural practices in long-term field experiments. Front Microbiol. Editorial: Bifidobacteria and their role in the human gut microbiota. Optimizing a PCR protocol for cpnbased microbiome profiling of samples variously contaminated with host genomic DNA. BMC Res Notes.

Predicting relatedness of bacterial genomes using the chaperonin universal target cpn60 UT : application to Thermoanaerobacter species. Syst Appl Microbiol. PLoS One. Marker genes that are less conserved in their sequences are useful for predicting genome-wide similarity levels between closely related prokaryotic strains. Soil bacterial quantification approaches coupling with relative abundances reflecting the changes of taxa. Sci Rep. Quantitative microbiome profiling links gut community variation to microbial load.

Adjusting microbiome profiles for differences in microbial load by spike-in bacteria. Synthetic spike-in standards for RNA-seq experiments. Genome Res. Normalization of RNA-seq data using factor analysis of control genes or samples. Nat Biotech. Microbiota and host nutrition across plant and animal kingdoms. Cell Host Microbe. Revealing genetic diversity of eukaryotic microorganims in aquatic environments by denaturating gradient gel electrophoresis.

J Phycol. Comparison of different primer sets for use in automated ribosomal intergenic spacer analysis of complex bacterial communities. New Phytol. Conversion of grassland to arable decreases microbial diversity and alters community composition. Appl Soil Ecol.

Article Google Scholar. Stability and succession of the rhizosphere microbiota depends upon plant type and soil composition. Beringer JE. R factor transfer in Rhizobium leguminosarum. Myo-inositol catabolism and catabolite regulation in Rhizobium leguminosarum bv. Comparative metatranscriptomics reveals kingdom level changes in the rhizosphere microbiome of plants. Systematic improvement of amplicon marker gene methods for increased accuracy in microbiome studies.

Edgar RC. The bacterial biogeography of British soils. Environ Microbiol. Article PubMed Google Scholar. Divergence and redundancy of 16S rRNA sequences in genomes with multiple rrn operons. J Bacteriol. Vetrovsky T, Baldrian P. The variability of the 16S rRNA gene in bacterial genomes and its consequences for bacterial community analyses. J Clin Microbiol. Expansion and contraction of ribosomal DNA repeats in Saccharomyces cerevisiae : requirement of replication fork blocking Fob1 protein and the role of RNA polymerase I.

Genes Dev. The correlation between rDNA copy number and genome size in eukaryotes. Soil fungal:bacterial ratios are linked to altered carbon cycling. Communication in the phytobiome. Relic DNA is abundant in soil and obscures estimates of soil microbial diversity. Nat Microbiol. Microbiome networks: a systems framework for identifying candidate microbial assemblages for disease management. Microbiome-wide association studies link dynamic microbial consortia to disease.

Metagenome-wide association study and machine learning prediction of bulk soil microbiome and crop productivity. Download references. Sequencing data for this project are available through the EBI short read archive primary accession nos. Spikes are deposited at addgene as plasmids , and Bash and Python codes used for sequencing output analysis are provided as Additional file 8.

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