Identifying Genomic and Metabolic Underpinnings of Algal-Bacterial Interactions via Metatranscriptomics, NanoSIMS Isotope Tracing, and Genome-Scale Metabolic Modeling
Megan Morris1, Vanessa Brisson1, Hyu Kim2, Jeffrey Kimbrel1, Austin Wessel1, Ali Navid1, Cullen Buie2, Peter K. Weber1, Rhona Stuart1, and Xavier Mayali1*
1Lawrence Livermore National Laboratory, and 2Massachusetts Institute of Technology
Algal and plant systems have the unrivaled advantage of converting solar energy and CO2 into useful organic molecules. Their growth and efficiency are largely shaped by the microbial communities in and around them. The μBiospheres science focus area (SFA) seeks to understand phototroph-heterotroph interactions that shape productivity, robustness, the balance of resource fluxes, and the functionality of the surrounding microbiome. The team hypothesizes that different microbial associates not only have differential effects on host productivity but can change an entire system’s resource economy. The approach encompasses single cell analyses, quantitative isotope tracing of elemental exchanges, omics measurements, and multiscale modeling to characterize microscale impacts on system-scale processes. Team members aim to uncover crosscutting principles that regulate these interactions and their resource allocation consequences to develop a general predictive framework for system-level impacts of microbial partnerships.
The team’s previous work has shown that heterotrophic bacteria influence algal productivity through complex metabolic interactions and resource utilization. However, those interactions, varying from mutualistic to antagonistic, may be context-dependent based on resource availability. Identifying these dependencies requires a better fundamental characterization of the interactions’ genomic and metabolic underpinnings. To accomplish this, researchers present a study system comprised of a previously uncharacterized and uncultivated antagonistic parasitic bacterium that attaches to and crashes bioenergy-relevant algae in days. Team members hypothesized that the acute phenotypic changes would coincide with bacterial uptake of algal-derived substrates, and that bacterial metabolism during infection would indicate key resources required by the bacterium. To address these hypotheses, the team used a combination of amplicon sequencing and genome-resolved metagenomics, fluorescent in situ hybridization (FISH), stable isotope probing, metatranscriptomics, and metabolic modeling.
To set up the system, team members first identified a bacterial enrichment community that killed the alga Phaeodactylum tricornutum. Using 16S amplicon sequencing in tandem with genome-resolved metagenomics, researchers initially annotated the presumed parasitic bacterium as a previously uncharacterized Rickettsiales sp. Then, using custom FISH probes, positively identified the Rickettsiales sp. directly attached to algal cells. Researchers next quantified the ability of the parasitic Rickettsiales sp. to incorporate algal-derived substrates using stable isotope probing with 13C bicarbonate and 15N nitrate and measured single-cell uptake with nanoscale secondary ion mass spectrometry (NanoSIMS). Researchers found that algal cells with attached parasitic bacteria had lower single-cell carbon fixation, and when attached to the algal host, bacterial cells were enriched in both 13C and 15N, with some bacteria more highly enriched in 15N compared to the host. This suggests that Rickettsiales sp. incorporated algal-derived substrates and may have the capacity to siphon newly metabolized N resources from its host.
In order to provide insight into the identity of these host resources, researchers used metatranscriptomics to quantify gene-level expression changes in P. tricornutum and Rickettsiales during infection. They found that prior to Rickettsiales attachment, it overexpressed genes for iron and trace metal scavenging, amino acid starvation, ribosomal hibernation, oxidative stress, and flagellar and pilin assembly, among others. Once attached, Rickettsiales upregulated genes for chemotaxis and signal transduction, antibiotic resistance, production of proteases and peptidoglycanases, type IV secretion system, gene transfer agents implicated in virulence, membrane transporters, and amino acid metabolism. Taken together, this suggests that free-living parasites are likely starved for nutrients, particularly amino acids and trace metals, and once attached can produce enzymes that degrade algal cell wall material, transfer virulence factors to the host, and potentially import and metabolize algal-derived N-rich amino acids and proteins. To better predict specific substrate utilization by bacteria, the team has curated a genome-scale model from the near-complete metagenome-assembled genome (MAG) of the Rickettsiales sp. bacterium and integrated the gene expression data into a metabolic flux balance analysis (GX-FBA). Optimization of the model is ongoing; however, researchers anticipate that it will putatively identify specific algal-derived substrates metabolized by the parasitic bacterium during the interaction, which can then be tested in subsequent experiments. To this end, the team has ongoing research to identify taxon-level resource partitioning of P. tricornutum exudates by its microbiome, using metabolomics, stable isotope probing, and the newly designed porous microplate incubation system. Thus far, researchers have conducted exometabolomics studies to characterize metabolite uptake profiles and potential resource partitioning among a suite of algal associated bacterial isolates, and further investigated those interactions with pairwise sequential growth experiments. Following this sequential interaction study, the team has confirmed the bacterial competition by measuring each growth response to algal exudates in situ using a co-culture porous microplate. Moving forward, the team will expand upon these simplified approaches to quantify how intimate algal-bacterial interactions change the flow of carbon system-wide using more complex microbial communities.
This work was performed under the auspices of the U.S. Department of Energy at Lawrence Livermore National Laboratory under Contract DE-AC52- 07NA27344 and supported by the Genome Sciences Program of the Office of Biological and Environmental Research under the LLNL Biofuels SFA, FWP SCW1039 LLNL-ABS-845199.