Author response: Host-associated microbe PCR (hamPCR) enables convenient measurement of both microbial load and community composition
2021-06-02

Article Figures and data Abstract Introduction Results Discussion Materials and methods Appendix 1 Data availability References Decision letter Author response Article and author information Metrics Abstract The ratio of microbial population size relative to the amount of host tissue, or ‘microbial load’, is a fundamental metric of colonization and infection, but it cannot be directly deduced from microbial amplicon data such as 16S rRNA gene counts. Because existing methods to determine load, such as serial dilution plating, quantitative PCR, and whole metagenome sequencing add substantial cost and/or experimental burden, they are only rarely paired with amplicon sequencing. We introduce host-associated microbe PCR (hamPCR), a robust strategy to both quantify microbial load and describe interkingdom microbial community composition in a single amplicon library. We demonstrate its accuracy across multiple study systems, including nematodes and major crops, and further present a cost-saving technique to reduce host overrepresentation in the library prior to sequencing. Because hamPCR provides an accessible experimental solution to the well-known limitations and statistical challenges of compositional data, it has far-reaching potential in culture-independent microbiology. Introduction Knowing the relative abundance of individual taxa reveals important information about any ecological community, including microbial communities. An expedient means of learning their composition in a sample is to sequence and count a defined number of 16S or 18S rRNA genes (hereafter rDNA), the internal transcribed spacer (ITS) of rRNA arrays, or other amplicons that distinguish microbial species in a sample. However, these common amplicon counting-by-sequencing methods do not provide information on the density or load of the microbes. Critically, such microbial sequence counts lack a denominator accounting for the amount of the habitat sampled, and thus, sparsely-colonized and densely-colonized samples become indistinguishable, despite most study systems being open systems in which the total number of microbial cells can vary over many orders of magnitude. Another limitation of such compositional data is that because the sum of all microbes is constrained, an increase in the abundance of one microbe reduces the relative abundance of all other microbes, creating misleading interpretations in the absence of appropriate statistical methods (Barlow et al., 2020; Gloor et al., 2017; Morton et al., 2019; Tsilimigras and Fodor, 2016). Experimental determination of the microbial load, for example by relating microbial abundance to sample volume, mass, or surface area, has led to important insights in microbiome research that otherwise would have been missed with relative abundance data (Humphrey and Whiteman, 2020; Niu et al., 2017; Props et al., 2017; Regalado et al., 2020; Smets et al., 2016; Stämmler et al., 2016; Tkacz et al., 2018; Vandeputte et al., 2017). For many host-associated microbiome samples, in particular those from plants (Regalado et al., 2020), nematodes (Ogier et al., 2020), insects (Ellegaard et al., 2020; Gendrin et al., 2015; Parker et al., 2020), and other organisms in which it is difficult or impossible to physically separate microbes from host tissues, a thorough DNA extraction yields both host and microbial DNA. For such samples, the amount of DNA from host and microbe is directly proportional to the number of cells sampled (Davies, 1977; Massonnet et al., 2011), and therefore the ratio of microbial DNA to host DNA is an intrinsic measure of the microbial load of the sample (Humphrey and Whiteman, 2020; Karasov et al., 2019; Karasov et al., 2018; Lebeis et al., 2015; Regalado et al., 2020). Researchers have attempted to exploit this property and use the host rDNA amplified as a byproduct of microbial rDNA to calculate microbial load (Humphrey and Whiteman, 2020; Lebeis et al., 2015), but because host nuclear ribosomal arrays may have hundreds or thousands of copies (Rabanal et al., 2017), and organellar DNA is also overabundant, these methods are inefficient and require noisy interventions to increase the microbial signal. Sufficiently deep whole metagenome sequencing (WMS) also can in principle describe the microbial community composition and measure the microbial load, but is rarely practical because of a similar overrepresentation of host DNA (Karasov et al., 2019; Regalado et al., 2020). For example, WMS of a leaf extract from wild Arabidopsis thaliana typically yields >95% plant DNA and <5% microbial DNA. Furthermore, many WMS reads remain unclassifiable and thus unquantifiable in complex samples (Karasov et al., 2019; Regalado et al., 2020). Most commonly, researchers combine amplicon sequencing with an additional orthogonal method. These include supplementary shallow WMS (Regalado et al., 2020), quantitative PCR (qPCR) or digital PCR of host and/or microbial genes (Anderson and McDowell, 2015; Barlow et al., 2020; Ellegaard et al., 2020; Guo et al., 2020; Jian et al., 2020; Karasov et al., 2019; Nadkarni et al., 2002), adding sequenceable ‘spike-ins’ calibrated based on sample volume (Lin et al., 2019), mass (Stämmler et al., 2016), or qPCR-determined host DNA content (Guo et al., 2020), counting colony-forming units (CFU)(Chen et al., 2020; Niu et al., 2017), and flow cytometry (Props et al., 2017; Vandeputte et al., 2017). The multitude of methods and publications hints at the enduring nature of this problem. While combining amplicon sequencing with any of these other approaches improves data, it requires more work, consumes more sample material, and introduces technical caveats, such as a reliance on accurately pipetting small quantities. Here, we introduce host-associated microbe PCR or ‘hamPCR’, a robust and accurate single-reaction method to co-amplify a low-copy host gene and one or more microbial regions, such as 16S rDNA. We accomplish this with a two-step PCR protocol (Carlson et al., 2013; Gohl et al., 2016; Lundberg et al., 2013; Symeonidi et al., 2020; Wen and Zhang, 2012). In hamPCR, gene-specific primer pairs bind to the ‘raw’ templates in a first short step, which is run for only two cycles to limit propagating amplification biases related to primer annealing and primer availability. In the second exponential step, a single set of primers with complementarity to the universal overhangs add barcodes and sequencing adapters. Such co-amplification of diverse fragments is used in many RNA-seq and WMS protocols (Kukurba and Montgomery, 2015; Quince et al., 2017). Notably, Carlson et al., 2013 similarly used a two-step PCR including a multiplexed first step of five to seven cycles to sequence and quantify both variable and joining segments at human T and B cell receptor loci, providing strong proof-of-concept for our method applied to the microbiome. We designed our host and microbe amplicons to have slightly different lengths, such that they can be resolved by electrophoresis for quality control. We further show that after pooling finished sequencing libraries, the amplicons can be separately purified and re-mixed at any favorable ratio prior for sequencing (for example, with host DNA representing an affordable 5–10%), and sequence counts can be accurately scaled back to original levels in-silico. Thus, in stark contrast to WMS, samples with initially unfavorable host-to-microbe ratios can be easily adjusted prior to sequencing without loss of information. Because of the practical simplicity and flexibility of hamPCR, it has the potential to supplant traditional microbial amplicon sequencing in host-associated microbiomes. Results hamPCR generates quantitative sequencing-based microbial load The first two-cycle host-and-microbe template tagging step (‘HM-tagging’) of hamPCR multiplexes two or more primer pairs in the same reaction, at least one of which targets a single- or low-copy host gene (Appendix 1—figure 1). The HM-tagging primers are then cleaned with Solid Phase Reversible Immobilization (SPRI) magnetic beads (Rohland and Reich, 2012; Appendix 1—figure 2). Next, an exponential PCR of 20–30 cycles is performed using universal barcoded primers (Figure 1a, Appendix 1—figure 1). As a host amplicon in A. thaliana samples, we targeted a fragment of the GIGANTEA (GI) gene, which is well conserved and present as a single copy in A. thaliana and many other plant species (Duarte et al., 2010). As microbial amplicons, we initially targeted widely used regions of 16S rDNA. Further considerations for primer design are discussed in Appendix 1. Figure 1 with 1 supplement see all Download asset Open asset Synthetic samples demonstrate technical reproducibility. (a) Schematic showing the two steps of hamPCR. The HM-tagging reaction (left) shows two primer pairs: one for the host (E-a and F-b) and one for microbes (E-c and F-d). Each primer pair adds the same universal overhangs E and F. The PCR reaction (right) shows a single primer pair (P7-E and P5-i-F) that can amplify all tagged products. (b) Representative 2% agarose gel of hamPCR products from the synthetic titration panel, showing a V4 16S rDNA amplicon at ~420 bp and an A. thaliana GI amplicon at 502 bp. The barplot underneath shows the predicted number of original GI and 16S rDNA template copies. Numbers boxed below the barplot indicate the percent bacterial genomic DNA of total DNA. (c) Relative abundance of the host and microbial ASVs in the synthetic titration panel, as determined by amplicon counting. Pure E. coli, pure A. thaliana without PNAs, and blanks were excluded. (d) Data in (c) converted to microbial load by dividing by host abundance, with a fourth-root transformed y-axis to better visualize lower abundances. To assess the technical reproducibility of the protocol, we made a titration panel of artificial samples combining varying amounts of pure A. thaliana plant DNA with pure bacterial DNA that reflects a simple synthetic community (Materials and methods). These represented a realistic range of bacterial concentrations as previously observed from WMS of wild leaves, ranging from about 0.25% to 24% bacterial DNA (Regalado et al., 2020). All DNA preps employed heavy bead beating to ensure thorough lysis of both host and microbes, as an incomplete DNA extraction can lead to underrepresentation of hard-to-lyse cells (Albertsen et al., 2015; Yuan et al., 2012). We applied hamPCR to the panel, pairing each of three commonly-used 16S rDNA amplicons for the V4, V3V4, and V5V6V7 variable regions with either a 502 bp or 466 bp GI amplicon (Materials and methods, Supplementary file 1), such that the host and microbial amplicons differed by approximately 80 bp in length and were resolvable by gel electrophoresis. In all pairings, the GI band intensity increased as the 16S rDNA band intensity decreased (Figure 1b, Figure 1—figure supplement 1). Focusing on the V4 16S rDNA primer set, 515F - 799R, paired with the 502 bp GI amplicon, we amplified the entire titration panel in four independently-mixed technical replicates. Although plant organelle sequences can be removed bioinformatically, we attempted to block their amplification as much as possible. In addition to use of the chloroplast-avoiding 799R primer (Chelius and Triplett, 2001), plant organelle-blocking peptide nucleic acids (PNAs) (Lundberg et al., 2013) further prevented unwanted 16S rDNA signal from organelles in the pure plant sample (Figure 1b). We note that although these PNAs are widely used and extremely effective, they do not work for all plant hosts (Fitzpatrick et al., 2018) and they can interfere with analysis of certain bacteria present in some environments (Jackrel et al., 2017). We pooled the replicates and sequenced them as part of a paired-end HiSeq 3000 lane. Because the 150 bp forward and reverse reads were not long enough to assemble into full amplicons, we analyzed only the forward reads (Materias and methods), processing the sequences into Amplicon Sequence Variants (ASVs) and making a count table of individual ASVs using Usearch (Edgar, 2010). After identifying the ASVs corresponding to host GI and the bacteria in the synthetic community, we plotted the relative abundance of A. thaliana GI, the three Sphingomonas ASVs, and the single E. coli ASV across the samples of the titration panel (Figure 1c). There was high consistency between the four replicates, more than what was visually apparent in the gel (Figure 1c, Figure 1—figure supplement 1). We next divided ASV abundances in each sample by the abundance of the host ASV in that sample to give the quantity of microbes per unit of host, a measure of the microbial load. Plotting the data with a fourth-root transformed Y axis for better visualization of low bacterial loads, we observed consistent and accurate quantification of absolute microbial abundance from 0 up to about 16% total bacterial DNA (Figure 1d). Through this range, the actual sequence counts for total bacteria matched theoretical expectations based on the volumes pipetted to make the titration (solid black line, Figure 1d). At 16% bacterial DNA, bacteria contributed more than 96% of sequences, and the microbe-to-host template ratio was near 25. At higher microbial loads the trend was still apparent, and the decrease in precision was likely exacerbated by the effects of small numbers; when the host ASV abundance is used as a denominator and the abundance approaches 0, load approaches infinity and sampling error has a greater and greater influence on the quotient. Eventually, this creates unacceptable uncertainty. We defined a ‘noise factor’ N as the full range in microbial load quotients that would result from adding a single host count and subtracting a microbial count from a sample ([microbe counts + 1] / [host counts - 1]) or vice versa ([microbe counts - 1] / [host counts + 1]). N increases as microbial load increases, but this is overcome with increased total sequencing depth. We determined conservatively that samples for which N > 0.22 should only be classified as ‘highly colonized’, and should not be used for quantitative measurements (Appendix 1—figure 3). In our case, only a minority of highly infected hosts reached bacterial abundances above the highly quantitative range. hamPCR does not distort the detected composition of the microbial community We amplified products from a wild A. thaliana phyllosphere template DNA preparation (Regalado et al., 2020), either with four technical replicates using V4 16S rDNA primers alone, or alternatively with four technical replicates using hamPCR. After sequencing and deriving ASVs, we first compared ASV abundances within identically-prepared replicates of the pure 16S rDNA protocol to demonstrate best-case technical reproducibility of this established technique. As expected, this resulted in a nearly perfect correlation, with a coefficient of determination R2 of 0.99 and abundance distributions that were indistinguishable by a Kolmogorov–Smirnov test (Figure 2b). Next, we removed the ASV corresponding to A. thaliana GI from the hamPCR data and rescaled the remaining microbial ASVs to 100% to give relative abundance data. We then compared microbial ASVs from the four pure 16S rDNA replicates to those from the four rescaled hamPCR replicates. In this comparison as well, R2 was 0.99 and the distributions were essentially identical (Figure 2c). Thus, the inclusion of a host amplicon in the reaction did not introduce taxonomic biases. Figure 2 with 2 supplements see all Download asset Open asset hamPCR is robust and does not distort a complex microbial community. (a) DNA extracted from wild A. thaliana phyllospheres was used as a template for both V4 16S rDNA PCR (left, 515F and 799R) and hamPCR (right, V4 16S rDNA and GI 502 bp primers). Four replicates were produced with two cycles of the HM-tagging reaction and 30 cycles of PCR, and additional replicates with 3 to 10 HM-tagging cycles paired with 29 to 22 PCR cycles (for a constant total of 32 cycles). The stacked columns show the relative abundances of major ASVs. Boxed upper case letters demarcate groups of samples compared below. (b) Correlation of fourth-root transformed ASV abundances for the 16S rDNA samples above panel (a) box [A] to the 16S rDNA samples above box [B]. Only ASVs with a minimum relative abundance of 0.05% were compared. R2, coefficient of determination. p-value from Kolmogorov-Smirnov test. (c) Same as (b), but for the four 16S rDNA samples above box [A] and [B] compared to the four hamPCR samples above box [D]. For hamPCR, the A. thaliana GI ASV was removed and the bacterial ASVs were rescaled to 100% prior to the comparison. (d) Same as (b) and (c), but for the four 16S rDNA samples above box [A] and [B] compared to the 16S rDNA samples above box [C]. (e) Same as (b), (c), and (d), but for the four hamPCR samples above box [D] compared to the four hamPCR samples above box [E]. Sensitivity to number of HM-tagging cycles and template concentration Two HM-tagging cycles minimize amplification biases that might otherwise have compounding effects due to differential primer efficiencies for the host and microbial templates. However, for templates at borderline low concentrations, inefficiencies due to SPRI cleanup could represent a bottleneck in amplification. Additionally, some techniques that prevent off-target organelle amplification (Agler et al., 2016b; Song and Xie, 2020) may benefit from additional HM-tagging cycles. To investigate the sensitivity of the results to additional HM-tagging cycles, we applied hamPCR for 2 through 10 HM-tagging cycles, both on the wild A. thaliana phyllosphere DNA described above and on a synthetic plasmid-borne template that contains bacterial rDNA and a A. thaliana GI gene template in in a ratio (Appendix 1 - Discussion 2). for the primers used was apparent influence of additional HM-tagging cycles, as HM-tagging cycles the same of host and 16S rDNA ASV abundances as two cycles > was for hamPCR and for 16S rDNA primers (Figure and result may not be for all primer pairs and should be but it is consistent with data that either or HM-tagging cycles results for the human receptor (Carlson et al., and with the that designed can be used in that is with many cycles et al., We that of hamPCR to the synthetic template an of host GI and with HM-tagging number (Figure supplement 1 and Figure supplement 2). and consistent in of GI may be a result of in HM-tagging primer or primer and should be by primer concentration (Carlson et al., 2013; Appendix 1—figure As a further of the of the protocol, we applied hamPCR to a range of total A. thaliana leaf template concentrations of between and total DNA per reaction, a template range of Through the range, was in microbe or host ASV abundances. At or the host amplicon to be slightly because the 16S rDNA primers to become at these concentrations (Appendix 1—figure of host-to-microbe ratio host DNA be present that microbial load can be but sequencing much host DNA would add We that the size between host and microbe in hamPCR not only visualization of both amplicons on a single but also and of the host and microbial in the pooled library prior to in to cost We a strategy by which the hamPCR amplicons are pooled and one of the is to a sample that the original microbe The of the is run on a gel and the host and microbial are separately and to reduce host and more microbial The which was not and the original can be sequenced separately or into the library prior to sequencing. the sample provides the to the host and microbe simple of the entire library back to original levels (Figure Figure supplement 1). Figure 3 with 2 supplements see all Download asset Open asset After hamPCR amplicons for original abundances can be (a) of of individual are pooled at ratios into a single An of DNA from the in is with cycles of PCR to all barcodes in the with a creating a sample. An of the from is physically into host and microbial agarose gel electrophoresis. The host and microbial and the sample are pooled in the ratio for sequencing. All sequences are quality and classified using the same and microbial amplicon counts are from the samples the pooled library and and from the sample and and are used to calculate the constant for the All host sequence counts are by to the original microbe-to-host original abundances. (b) Relative abundance of actual sequence counts from our original HiSeq 3000 (c) Relative abundance of actual sequence counts from our adjusted library showing host and four (d) The data from (c) after original host abundance using the (e) The total of host other ASVs in the original host and Relative abundances in the original and library for all ASVs with a 0.05% minimum abundance, on fourth-root transformed R2, coefficient of determination. from Kolmogorov–Smirnov test. To demonstrate this we four samples for our HiSeq 3000 which much of the data from Figure 1 and Figure and then separately purified the host and microbial of the library (Figure supplements 1 and 2). on amplicon of the host and microbial we for a but amount of host DNA, the samples, and sequenced the as part of a HiSeq 3000 lane. of relative abundances for all samples on the original run the host A. thaliana GI ASV highly in some samples, on for about of total sequences in the run (Figure and The host library nearly total host GI slightly lower than our of (Figure and The samples of host GI to the host in the original library. After using the samples to the original host abundance in the we the of the stacked from the original library Figure the fourth-root abundances for ASVs above a 0.05% were compared between the original and libraries, the R2 coefficient of determination was with between the distributions > Thus, hamPCR have been and the host amplicon is overabundant, the host in the pooled can be easily and accurately using agarose gel to more sequencing. hamPCR with different 16S rDNA regions compared to whole metagenome sequencing We next applied hamPCR to leaf DNA from wild A. thaliana plants that we previously analyzed by WMS, and from which we therefore an accurate of the microbial load as the number of microbial reads divided by the number of plant reads (Regalado et al., 2020). We applied hamPCR with primer the host GI gene and either the V3V4, V4, or V5V6V7 variable regions of the 16S rDNA. We produced three replicates for each primer set, which we for analysis (Figure Figure supplement 1). WMS and the three hamPCR amplicon the relative abundance of bacterial was consistent (Figure with likely due to the different taxonomic used for the metagenome reads (Regalado et al., 2020), as well as biases from amplification or of different 16S rDNA variable regions et al., 2018; et al., 2017). After both WMS and hamPCR bacterial reads to load by dividing by the plant count in each we a similar despite the quantification with lower total loads from plant to plant and individual bacterial loads showing similar (Figure Relative in load when the different hamPCR amplicons are likely in part due to different of the 16S rDNA primer pairs for their targets in different bacterial and rDNA copy number the microbial et al., 2012). To quantify the consistency of hamPCR load with load we plotted the loads each other and strong with hamPCR using V4 rDNA the to (Figure Figure with 1 supplement see all Download asset Open asset hamPCR with three common 16S rDNA amplicons consistent results that with whole metagenome sequencing (a) showing the relative abundance of bacterial in wild A. thaliana leaf samples, as determined by The corresponding to the first 10 from to are in reverse on the showing the bacterial load of the same bacterial ratio microbe-to-host The bacterial load ratios for the 10 major bacterial on a fourth-root transformed across the samples are as a to visualize (b) to but with abundances from hamPCR a 502 bp A. thaliana GI amplicon and a bp 16S rDNA (c) to (b), but with the 16S rDNA primers a ~420 bp V4 16S rDNA (d) to (b), but with a 466 bp A. thaliana GI amplicon and a bp V5V6V7 16S rDNA (e) transformed abundance of each bacterial determined by hamPCR of 16S rDNA plotted the fourth-root transformed bacterial load from R2 of determination. Same as but for hamPCR of V4 16S rDNA. Same as but for V5V6V7 16S rDNA. is important to note that relative load ratios between samples were consistent across hamPCR primer the total microbe-to-host ratio with the V5V6V7 16S rDNA total load at than three host, and the V4 16S rDNA total load near is likely due to in GI and 16S rDNA primer To make a about the ratio of plant cells to bacterial cells using hamPCR, it would be important to include samples with bacterial load and to each bacterial by its rDNA copy hamPCR for determination of and bacterial load of 16S rDNA primers is that they amplify sequences from bacterial but their targets are from other microbes such as and other primer therefore be used to these other We attempted to overcome this limitation by up hamPCR not only with 16S rDNA and host GI sequences as described but also adding a primer pair which include important of A. We first universal primers internal transcribed spacer rDNA (Materials and in with A. thaliana GI primers and 16S rDNA primers the bacterial V4, V3V4, or V5V6V7 regions, using as template our synthetic that templates for the three primer in (Materials and methods, Appendix 1 - Discussion 2). of all three amplicons to work for the V4 (Figure supplement 1), and with this we set up a simple As we of the and and the bacterial and