Input data

Dataset input is handled by the Phyloseq R package, a feature-rich package for general microbiome data analysis that can be used to import many common microbiome data formats [9]. HTSSIP includes convenience functions to easily and flexibly designate the experimental design of the SIP experiment for downstream HTS-SIP analyses (Fig 1).

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Fig 1. A diagram depicting the possible HTS-SIP analyses available in the HTSSIP R package.

The R functions to conduct each workflow step are italicized, and the figure references refer to example data produced by these workflow steps.

https://doi.org/10.1371/journal.pone.0189616.g001

HTS-SIP dataset exploratory analyses

A common first step in analyzing nucleic acid SIP data is to quantify the total nucleic acid concentration or gene copy number (estimated by qPCR) across density gradients in order to determine the buoyant density (BD) “shift” of nucleic acids in isotopically labeled treatments versus unlabeled controls [10,11]. The general expectation is that a “shift” of nucleic acid BD from “light” towards “heavy” densities is indicative of isotope incorporation. However, in a well designed SIP experiment, the ratio of added to endogenous substrate should be small, and this can produce an imperceptible BD shift [4]. In addition, an extensive shift may indicate excessive cross-feeding [12]. HTS-SIP methods can detect taxa that have incorporated low levels of isotope, or occur at frequencies that are so low that they do not cause a shift in the overall BD of community nucleic acids [5]. As a result, analysis of the BD distribution of total nucleic acids within density gradients is of little utility in assessing the results of nucleic acid SIP experiments performed on complex communities.

As a simpler alternative, which leverages the power of high-throughput sequencing techniques, BD “shifts” can be inferred solely from sequence data [4,5]. Given that incorporators will be more abundant in “heavy” gradient fractions of the labeled treatment versus the unlabeled control, a BD shift can be inferred by assessing the beta-diversity between treatment and control gradient fractions. This approach is more sensitive for detecting community-level isotope incorporation than the approach of quantifying total nucleic acid concentration across the density gradient [7]. The HTSSIP R package implements two methods for using beta-diversity to assess isotope incorporation at the community-level: an ordination approach and an approach that expresses beta-diversity between corresponding treatment and control fractions as a function of their BD (Fig 1).

The ordination approach simply involves pairwise calculations of a beta-diversity metric between all gradient fractions from isotopically labeled treatments and corresponding unlabeled controls, followed by visualizing the distance matrix with either principal coordinates analysis (PCoA) or non-metric multidimensional scaling (NMDS). An increase in beta-diversity between corresponding gradient fractions of labeled samples and controls is expected if isotope incorporation causes a change in the BD of OTUs (Fig 2A & 2B).

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Fig 2. Examples of analyses available in the HTSSIP R package for visualizing results from HTS-SIP experiments.

These figures were generated using data from an HTS-SIP experiment that consisted of 3 treatments where only the isotopic label was varied: an unlabled control (12C-Con), ¹³C-xylose (13C-Xyl), and ¹³C-cellulose (13C-Cel) [7]. DNA was extraced from each treatment 3 and 14 days after substrate addition, subjected to CsCl fractionation, and 16S rRNA sequencing was performed on ~24 fractions per CsCl gradient. Analyzing beta-diversity between gradient fractions can reveal changes in the buoyant density (BD) distribution of DNA in ¹³C-labeled treatments versus the unlabeled control. Panel A is a non-metric multidimensional scaling (NMDS) ordination comparing beta-diversity (weighted Unifrac) between gradient fractions from two unlabeled replicates, which indicates that sequence composition varies greatly with BD but not between experimental replicates. Panel B is an NMDS ordination similar to Panel A, but each facet contains a ¹³C treatment and the corresponding unlabeled control. Large relative distance between “heavy” control and labeled gradient fractions indicates a change in community composition caused by shift in DNA BD that resulted from ¹³C isotope incorporation. The NMDS stress values ranged from 0.06 to 0.07. Panels C and D depict the same data as in Panels A and B, respectively, but only depict beta-diversity among gradient fractions that correspond in BD between labeled treatment and unlabeled control fractions. In this visualization, increases in beta-diversity indicate a change in community composition caused by a shift in DNA BD resulting from ¹³C isotope incorporation. The line ranges represent 95% confidence intervals (CI) calculated by permuting OTU abundances and recalculating beta-diversity (100 bootstrap replicates), and actual beta-diversity values are represented by colored circles. "BD shift windows" (blue circles) indicate regions defined by ≥3 consecutive fractions with beta-diversities greater than expected by chance (i.e. exceeding the CI).

https://doi.org/10.1371/journal.pone.0189616.g002

While the ordination approach provides a useful overview of community-wide isotope incorporation, the extent of incorporation is difficult to compare among multiple treatments (e.g. ¹³C-cellulose versus ¹³C-xylose). The second approach implemented in the HTSSIP package visualizes DNA BD shifts by calculating pairwise beta-diversity of corresponding gradient fractions between treatment and control gradients (or control versus control to assess technical variation among replicates). To deal with partially overlapping gradient fractions between gradients, the weighted mean beta-diversity is calculated from all treatment gradient fractions that overlap each control gradient fraction, with weights defined as the percent overlap in the BD range of each fraction (Fig 2C & 2D). A permutation test is used to identify BD ranges of high beta-diversity resulting from BD shifts (“BD shift windows”). The permutation test involves constructing bootstrap confidence intervals (CI) of beta-diversity by permuting OTU abundances among control fractions to generate a null model “treatment” that is compared against the actual control fractions. This null model represents the baseline beta-diversity expected if the treatment was just a randomly shuffled version of the control. A note in interpreting these data is that isotope incorporation will cause DNA to shift out of “light” gradient fractions and into “heavy” gradient fractions. Hence, in the presence of isotope incorporation, high beta-diversity can be observed in both “heavy” and “light” gradient fractions. Alternatively, in the absence of isotope incorporation, beta-diversity will remain low across all gradient fractions.

Identifying incorporators

HR-SIP, MW-HR-SIP, and qSIP can all be used to identify incorporators. To illustrate the application of HR-SIP, MW-HR-SIP, and qSIP in the HTSSIP R package, we simulated a simplified HTS-SIP dataset consisting of 10 OTUs (Fig 3A). Our purpose here is merely to illustrate functions of the HTSSIP R package; comprehensive assessment of the accuracy of these techniques (and historically used DNA- and RNA-SIP analysis methods) is available elsewhere [7]. Briefly, HR-SIP identifies incorporators by utilizing DESeq2 to identify OTUs that have high differential relative abundance in “heavy” fractions of labeled treatment versus unlabeled control [8]. MW-HR-SIP takes the same relative abundance based approach as HR-SIP but uses multiple overlapping “heavy” BD windows (while correcting for multiple hypotheses). In contrast, qSIP uses qPCR data to transform OTU relative abundance distributions into pseudo-absolute abundance distributions (Fig 3A), and then BD shifts are determined from these transformed distributions by calculating the difference in center of mass for each OTU in treatment versus control gradients. Atom fraction excess can thus be calculated for specific isotopes (e.g. ¹³C or ¹⁵N) based on the calculations described in the work of Hungate and colleagues [3]. In order to identify incorporators, a permutation test is used to construct bootstrap confidence intervals of atom fraction excess. The HTSSIP package also provides functions for conducting historically used DNA- and RNA-SIP analysis methods (e.g. using t-tests to compare OTU abundances between “heavy” control versus treatment fractions). We refer to these methods collectively as “heavy-SIP”, which have been extensively compared to HR-SIP methods in other work [7]. Sensitivity in identifying incorporators can depend on the methods used (Fig 3B), and although we only provide a simple comparison of methods here, the senstivity and specifity of these results reflect those of Youngblut and Buckley [7]. SIP experiments can be simulated using the SIPSim toolset [7], and these data analyzed using the HTS-SIP R package. Such in silico evaluation is valuable for predicting possible experimental outcomes and the expected analytical accuracy of SIP experiments based on details of experimental design prior to conducting experiments.

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Fig 3. Examples of using the HTSSIP R package for data processing, data exploration, incorporator identification, and quantification of BD shifts.

The SIPSim toolset was used to simulate a simple HTSSIP dataset consisting of two treatments: a ¹³C-treatment (“13C-Treat”) and a ¹²C-control (“12C-Con”). Each treatment has 3 replicates, with each consisting of 24 gradient fractions [7]. Half of the ten OTUs (OTU 1, 3, 4, 7, and 8) were randomly assigned an atom excess ¹³C between 30 and 100%. [3]. Panel A depicts the raw abundances (“Counts”), fractional relative abundance (“Rel. Abund.”), and relative abundances transformed by simulated qPCR data (“Rel. Abund. qPCR-trans.”). For clarity, only 1 of the 3 experimental replicates is shown. Panel B shows those OTUs identified as “incorporators” (incorporated ¹³C into genomic DNA) by HR-SIP, MW-HR-SIP, qSIP, or by using a “heavy-SIP” approach (the Mann Whitney U test). Panel C shows the BD shift of each OTU as determined by ΔBD or qSIP. The dashed line signifies a BD shift (Z) of 0.0 g ml^-1, and the red bars show the true theoretical BD shift resulting from ¹³C isotope incorporation.

https://doi.org/10.1371/journal.pone.0189616.g003

The HTSSIP package implements HR-SIP based on the code provided in the work of Pepe-Ranney and colleagues [5]. MW-HR-SIP is implemented in HTSSIP based on the R code provided in the SIPSim HTS-SIP dataset simulation toolset [7]. The HTSSIP implementation of qSIP is based on the method’s description in the work of Hungate and colleagues [3]. Implementations of each method include the option for parallel processing of each algorithm. Parallelization is implemented through the plyr R package [13], which allows for various parallel backends to be used such as doSNOW and doParallel.

Quantifying isotopic enrichment

Unlike HR-SIP and MW-HR-SIP, the main goal of qSIP and ΔBD is to quantify isotopic enrichment. To illustrate the use of the HTSSIP package for conducting qSIP and ΔBD, we applied both analyses to the simplified HTS-SIP dataset described above (Fig 3C). ΔBD is implemented in HTSSIP as described in the work of Pepe-Ranney and colleagues [5]. As shown in Youngblut and Buckley [7], qSIP and ΔBD can produce substantially different estimates of isotope incorporation.

Simulating datasets

The HTSSIP package provides functions to simulate simple HTS-SIP datasets for use in software testing, analysis pipeline development, and gaining familiarity with software and data formats. However, the SIPSim toolset is recommended for evaluating possible SIP experimental designs and for testing the accuracy of HTS-SIP analyses, because the simulation framework for SIPSim is based on the physics of isopycnic centrifugation, unlike the simulations possible with HTSSIP [7]. HTSSIP utilizes coenocliner, an R package designed for simulating taxon abundance across environmental gradients, to simulate taxon abundance distributions across buoyant density gradient fractions [13].