A standardized quantitative analysis strategy for stable isotope probing metagenomics

Abstract

Stable isotope probing (SIP) facilitates culture-independent identification of active microbial populations within complex ecosystems through isotopic enrichment of nucleic acids. Many DNA-SIP studies rely on 16S rRNA gene sequences to identify active taxa, but connecting these sequences to specific bacterial genomes is often challenging. Here, we describe a standardized laboratory and analysis framework to quantify isotopic enrichment on a per-genome basis using shotgun metagenomics instead of 16S rRNA gene sequencing. To develop this framework, we explored various sample processing and analysis approaches using a designed microbiome where the identity of labeled genomes and their level of isotopic enrichment were experimentally controlled. With this ground truth dataset, we empirically assessed the accuracy of different analytical models for identifying active taxa and examined how sequencing depth impacts the detection of isotopically labeled genomes. We also demonstrate that using synthetic DNA internal standards to measure absolute genome abundances in SIP density fractions improves estimates of isotopic enrichment. In addition, our study illustrates the utility of internal standards to reveal anomalies in sample handling that could negatively impact SIP metagenomic analyses if left undetected. Finally, we present SIPmg, an R package to facilitate the estimation of absolute abundances and perform statistical analyses for identifying labeled genomes within SIP metagenomic data. This experimentally validated analysis framework strengthens the foundation of DNA-SIP metagenomics as a tool for accurately measuring the in situ activity of environmental microbial populations and assessing their genomic potential. IMPORTANCE Answering the questions, "who is eating what?" and "who is active?" within complex microbial communities is paramount for our ability to model, predict, and modulate microbiomes for improved human and planetary health. These questions can be pursued using stable isotope probing to track the incorporation of labeled compounds into cellular DNA during microbial growth. However, with traditional stable isotope methods, it is challenging to establish links between an active microorganism's taxonomic identity and genome composition while providing quantitative estimates of the microorganism's isotope incorporation rate. Here, we report an experimental and analytical workflow that lays the foundation for improved detection of metabolically active microorganisms and better quantitative estimates of genome-resolved isotope incorporation, which can be used to further refine ecosystem-scale models for carbon and nutrient fluxes within microbiomes.

Keywords: DNA-SIP; co-assembly; internal standards; metagenomics; spike-ins; stable isotope probing.

FIG 1

Experimental design and overview of laboratory steps in the SIP metagenomics workflow. To create a defined SIP experimental sample, DNA extracted from an unlabeled freshwater microbial community was amended with either labeled (¹³C) or unlabeled (¹²C) E. coli DNA. Pre-centrifugation spike-ins were added to each sample prior to ultracentrifugation in a CsCl gradient, and post-fractionation spike-ins (sequins) were added to each fraction after density gradient fractionation and collection. These two sets of synthetic DNA oligos served as internal standards to monitor the quality of density separations and normalize genome coverage levels.

FIG 2

The workflow scheme for SIP metagenomic data analysis includes (A) quality filtering of the raw reads and (B) generation of a unique set of medium- and high-quality MAGs used for (C) quantification of absolute taxa abundances and identification of isotope incorporators. The addition of sequins provides the means for calculating absolute bacterial abundances (C, Data Normalization), and pre-centrifugation spike-ins aid in the detection of anomalous samples (C, Outlier Handling).

FIG 3

Comparison of metagenome assembly approaches for the SIP metagenome dataset generated from spiking E. coli into unlabeled DNA from a freshwater microbiome. (A) Average number of medium- and high-quality MAGs recovered from different assembly approaches. (B) Venn diagram showing the number of unique and shared MAG clusters. (C) Compositional differences at the class level recovered from different types of assemblies (I—intact metagenome assembly with MetaSPAdes, F—separate fractions assembled with metaSPAdes [n = 371 assemblies], S—all fractions within each replicate co-assembled with metaSPAdes [co-assembly of all fractions sequenced for a single SIP replicate sample, n = 24 assemblies], M—combined assembly of all fractions using MetaHipMer; for F and S the average number of MAGs was calculated, whiskers represent standard deviation across assembly type).

FIG 4

Comparison of predicted AFE versus the expected AFE of E. coli using different approaches for measuring genome abundance across the density gradient. The qSIP method was used to estimate AFE in all cases. Genome abundance in each density fraction was determined by (A) normalization to sequin internal standards, (B) multiplying relative abundance with DNA concentration following Greenlon et al. (25), (C) multiplying relative coverage with DNA concentration following Starr et al. (24), and (D) relative coverage without additional normalization. For all comparisons, please refer to Table S3 (https://doi.org/10.6084/m9.figshare.22280632). Error bars represent the standard deviation of AFE calculated using the qSIP method’s bootstrapping approach. The expected AFE for each condition is in parentheses, and additional details about conditions, including replicate numbers, are provided in Table S1 (https://doi.org/10.6084/m9.figshare.22280632). p_cor and p_reg correspond to the P-values for the Spearman correlation and the linear regression F-statistic, respectively. The intercepts determined by linear regression were not significantly different from zero (P-value > 0.05) in any method for estimating abundance.

FIG 5

Comparison of AFE estimates produced by the (A) qSIP and (B) ΔBD methods using the amended metagenome where levels of E. coli isotopic enrichment were known a priori. Both of these methods used sequin-based normalization for estimating genome abundance. Error bars represent the standard deviation of AFE calculated using the qSIP method’s bootstrapping approach. The expected AFE of E. coli within each treatment condition is given in parentheses. p_reg and p_cor correspond to the P-values for the linear regression and Spearman correlation, respectively. The intercepts determined by linear regression for qSIP and ΔBD models were not significantly different from zero (P-value > 0.05).