ABSTRACT Background Linking the identity of wild microbes with their ecophysiological traits and environmental functions is a key ambition for microbial ecologists. Of many techniques that strive to meet this goal, Stable Isotope Probing—SIP—remains the most comprehensive for studying whole microbial communities in situ . In DNA-SIP, active microorganisms that take up an isotopically heavy substrate build heavier DNA, which can be partitioned by density into multiple fractions and sequenced. However, SIP is relatively low throughput and requires significant hands-on labor. We designed and tested a semi-automated DNA-SIP pipeline to support well-replicated, temporally-resolved amplicon or metagenomics experiments that enable studies of dynamic microbial communities over space and time. To test this pipeline, we assembled SIP-metagenome assembled genomes (MAGs) from the hyphosphere zone surrounding arbuscular mycorrhizal fungi (AMF), in combination with a 13 CO 2 plant labelling study. Results Our semi-automated pipeline for DNA fractionation, cleanup, and nucleic acid quantification of SIP density gradients requires six times less hands-on labor compared to manual SIP and allows 16 samples to be processed simultaneously. Automated density fractionation increased the reproducibility of SIP gradients and reduced variation compared to manual fractionation, and we show adding a non-ionic detergent to the gradient buffer improved SIP DNA recovery. We then tested this pipeline on samples from a highly-constrained soil microhabitat with significant ecological importance, the AMF fungal hyphosphere. Processing via our quantitative SIP pipeline confirmed the AMF Rhizophagus intraradices and its associated microbiome were highly 13 C enriched, even though the soils’ overall enrichment was only 1.8 atom% 13 C. We assembled 212 13 C-enriched hyphosphere MAGs, and the hyphosphere taxa that assimilated the most AMF-derived 13 C (range 10-33 atom%) were from the phlya Myxococcota, Fibrobacterota, Verrucomicrobiota, and the ammonia oxidizing archaeon genus Nitrososphaeara . Conclusions Our semi-automated SIP approach decreases operator time and errors and improves reproducibility by targeting the most labor-intensive steps of SIP—fraction collection and cleanup. Here, we illustrate this approach in a unique and understudied soil microhabitat—generating MAGs of active microbes living in the AMF hyphosphere (without plant roots). Their phylogenetic composition and gene content suggest predation, decomposition, and ammonia oxidation may be key processes in hyphosphere nutrient cycling.
Additional file 3: Supplemental Table S2. Assembly metrics using single fraction assembly (metaspades) verses co-assembly of all fractions (MetaHipMer2).
Linking the identity of wild microbes with their ecophysiological traits and environmental functions is a key ambition for microbial ecologists. Of many techniques that strive for this goal, Stable-isotope probing-SIP-remains among the most comprehensive for studying whole microbial communities in situ. In DNA-SIP, actively growing microorganisms that take up an isotopically heavy substrate build heavier DNA, which can be partitioned by density into multiple fractions and sequenced. However, SIP is relatively low throughput and requires significant hands-on labor. We designed and tested a semi-automated, high-throughput SIP (HT-SIP) pipeline to support well-replicated, temporally resolved amplicon and metagenomics experiments. We applied this pipeline to a soil microhabitat with significant ecological importance-the hyphosphere zone surrounding arbuscular mycorrhizal fungal (AMF) hyphae. AMF form symbiotic relationships with most plant species and play key roles in terrestrial nutrient and carbon cycling.
ABSTRACT Stable isotope probing (SIP) facilitates culture-independent identification of active microbial populations within complex ecosystems through isotopic enrichment of nucleic acids. Many SIP studies rely on 16S rRNA 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 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 analytic 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 question of ‘ who is eating what?’ within complex microbial communities is paramount for our ability to model, predict, and modulate microbiomes for improved human and planetary health. This question is often 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.
Additional file 4: Supplemental Table S3. Assembly statistics, taxonomic lineage, and atom percent excess (APE) of the metagenome amplified genomes (MAGs) created in this study.
Additional file 8: Supplemental Table S7. Number of CAZy gene homologs that can potentially target a particular substrate. Substrate assignments based off of the Glycoside Hydrolase categories from Berlemonte and Martiny 2015.
Additional file 2: Supplemental Table S1. Impact of adding different concentrations of non-ionic detergents to the SIP gradient medium on percent DNA recovery (n=3).
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.
