A method for simultaneous measurement of soil bacterial abundances and community composition via 16S rRNA gene sequencing

Highlights

• High-throughput sequencing is routinely used to assess soil bacterial diversity.

• These approaches only yield information on the relative abundances of bacteria.

• We describe an approach to simultaneously estimate total bacterial 16S rRNA gene abundances and diversity.

Abstract

Many recent studies rely on 16S rRNA-based sequencing approaches to analyze bacterial or archaeal communities found in soil and other environmental samples. While this approach is valuable for determining the relative abundances of different microbial taxa found in a given sample, it does not provide information on how the abundances of targeted microbes differ across samples. Here we demonstrate how the simple addition of an internal standard at the DNA extraction step allows for the quantitative comparison of how the total abundance of bacterial 16S rRNA genes varies across samples. The reliability of this method was assessed in two ways. First, we spiked a dilution series of two different soils with internal standards to ascertain whether we could accurately quantify differences in cell abundances. We tested two different internal standards, adding DNA from Aliivibrio fischeri or Thermus thermophilus, bacterial taxa unlikely to be found in soil. The total abundances of 16S rRNA genes in soil were calculated from the number of 16S rRNA genes of the internal standard recovered in the sequence data. Both standards allowed us to accurately quantify total gene abundances in soil as there was a strong positive correlation between total 16S rRNA gene estimations and the different starting amounts of soil extracted. We then tested whether we could use this approach to quantify differences in microbial abundances across a wide range of soil types; comparing estimated 16S rRNA gene abundances measured using this approach to microbial biomass determined with more standard methods: phospholipid fatty acid (PLFA) analysis and substrate induced respiration (SIR) analysis. The gene abundances estimated with the internal standard sequencing approach were significantly correlated with the independent biomass measurements, and were in fact better correlated to SIR and PLFA estimates than either of these two biomass measurements were correlated with one another. Together, these results demonstrate that adding a DNA internal standard to soil or other environmental samples prior to DNA extraction is an effective method for comparing bacterial 16S rRNA gene abundances across samples. Given the ease of adding DNA internal standards to soil samples prior to high-throughput marker gene sequencing, 16S rRNA gene abundances and bacterial community composition can now be determined simultaneously and routinely.

Introduction

High-throughput sequencing has revolutionized the field of soil microbial ecology. In particular, 16S rRNA gene sequencing has provided unprecedented insight into the diversity of soil microbial communities, and it is now commonly used to characterize bacterial and archaeal communities in soil or related environments. However, one important limitation of this approach is that it only provides estimates of the proportional abundances of taxa – it provides no information on how the total amounts of microbial DNA may vary across samples. This can cause problems in the interpretation of results. For example, consider two samples that both have 20% of 16S rRNA sequences assigned to the phylum Acidobacteria. Such information can be used to assess how the abundance of Acidobacteria compares to other taxa, but this does not tell us anything about the total numbers of bacteria in these two samples and one sample could have far more Acidobacteria than another if it had a higher total number of bacterial cells. Likewise, two samples could appear to have very different proportional abundances of 16S rRNA reads assigned to a bacterial taxon of interest (e.g., a bacterial pathogen), but these two samples could actually have the same number of cells belonging to that taxon. Changes in bacterial community composition can also be difficult to interpret when relying on proportional abundances since it is often difficult to differentiate between one taxon increasing in abundance and another decreasing in abundance. Thus, the proportional nature of marker gene datasets makes it impossible to compare how the total amounts of microbes (or their marker genes) vary across environmental samples that could have very different total cell numbers.

While there are a myriad of non-nucleic acid techniques for estimating total microbial biomass in soil or other environments (including phospholipid fatty acid analysis, substrate induced respiration, chloroform fumigation, and direct counting) – these methods are difficult to relate directly to the relative abundances as determined by 16S rRNA gene sequencing. This is due to the fact that these methods do not provide direct information on the amounts of 16S rRNA gene copies in a given sample, but instead represent other metrics of cell abundances (e.g., amounts of phospholipid fatty acids, amounts of chloroform-extractable microbial biomass carbon, or numbers of visible cells). Perhaps more importantly, these approaches for estimating soil microbial biomass can require significant added effort, they may require analyzing fresh (unfrozen) samples, and they often do not discriminate between prokaryotes and other organisms that can be abundant in soil (like fungi, protists, plants, or soil fauna). Likewise, total DNA yield is not typically considered a useful estimate of bacterial biomass as much of the DNA in soil comes from soil organisms that are not bacteria (Leckie et al., 2004) and DNA extraction efficiencies can vary dramatically across soil types (Frostegård et al., 1999, Cruaud et al., 2014). While quantitative PCR is frequently used to determine the number of 16S rRNA genes in a given sample (e.g. Fierer et al., 2005), this requires an additional step in the analyses, and the quantitative PCR analyses are also subject to biases due to differences in DNA extraction efficiencies and varying DNA amplification efficiencies across samples (Martin-Laurent et al., 2001, Fierer et al., 2005).

Clearly it is useful to have a method in place to directly relate 16S rRNA gene sequence data to estimates of the total amount of 16S rRNA genes found in a given sample to improve our understanding of the changes in taxon abundances underlying spatial or temporal differences in community composition. Here, we describe a method that couples sequencing-based analyses of the 16S rRNA gene for the assessment of microbial diversity with the determination of the variability in 16S rRNA gene abundances across samples, thus allowing the comparison of the actual abundances of different microbial taxa across samples, not just their proportional abundances. The approach involves adding DNA from a bacterium unlikely to be found in soil to the soil sample at the DNA extraction step. We then use the proportional representation of 16S rRNA reads from this organism to calculate how the total abundances of 16S rRNA genes vary across samples (Fig. 1). By adding this simple step to pipelines for 16S rRNA gene analyses, one can simultaneously compare how the diversity and abundances of soil bacteria vary across samples.