The LLNL Biofuels Scientific Focus Area (SFA) is focused on the community systems biology of microbial consortia that are closely associated with bioenergy-relevant plants and algae, with the ultimate goal of developing predictive models to understand how these ecosystems will respond to external stimuli (pollution, climate). Photosynthetic 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 and assisted by their surrounding “microbiome”—the groups of microorganisms that dwell in and around plants and algae and live off photosynthate. The biogeochemical outcomes of these interactions—how specific taxonomic combinations affect energy and nutrient cycling pathways and are shaped by various environmental stressors—are fundamental concerns in the fields of microbial ecology and bioenergy production.
We seek to understand and predict ecological, biophysical, and biochemical dynamics of multi-taxa communities, as well as the metabolite fluxes that regulate trophic interactions. In our research, we focus on microscale interactions between bacteria and algae in the phycosphere (the surface of algal cells) and between soil bacteria, fungi, and plant roots in the rhizosphere and endosphere as model systems. We seek cross-cutting principles and mechanisms that drive interactions, and the effect of interactions, in order to develop a general predictive framework for ecosystem impacts of microbial partnerships in different environments. Our approach emphasizes microbial ecology, organismal interactions, quantitative isotope tracing of elemental exchanges, and consequences of environmental regulation, using techniques that exploit LLNL capabilities to measure the microscale impacts of single cells on system scale processes.
Sustainable algal biofuels and bioenergy agriculture: Our current study systems include microalgae and perennial grasses.
Algal-bacterial interactions, ranging from mutualistic to commensal to parasitic, have ecological consequences at all scales and profoundly influence community function and biogeochemical cycling. The variety of algal-bacterial interactions identified in natural systems undoubtedly also occurs in algal biofuel systems, especially open ponds, and the potential to manipulate them for economic benefit is acknowledged. However, the dynamics of pond microbial communities are not well understood, and the consequences of these dynamics on algal productivity are virtually unknown. Our SFA research is designed to produce a mechanistic understanding of specific interactions between individual algal and bacterial strains under controlled laboratory conditions, outdoor mesocosms, and large-volume ponds, significantly improving our understanding of the functional roles of bacterial communities in algal production systems.
The phycosphere is the zone on and around individual algal cells, where algal exudates that feed surrounding heterotrophs are at their highest concentration, and in and through which algae derive their nutrients. Our research has been geared toward an understanding of the role of the phycosphere in these complex symbioses.
Examples of our research in this area include:
- Quantification of C and N exchange between algae and attached bacteria using isotope probing and single cell isotopic analyses (NanoSIMS) in both field and co-cultivation (Arandia et al., 2017, De-Bashan et al., 2016)
- Temporal dynamics of outdoor algal pond microbial communities (Geng et al., 2016)
- Macromolecule incorporation by bacterial communities attached to dead algal cells (Mayali et al., 2015)
Plants are naturally colonized by microbial communities, and these interactions are essential to plant health, soil fertility, and ecosystem function. In bioenergy systems, microbial symbionts can have direct effects on plant productivity, resilience, or stress tolerance of the feedstock crop. However, plant microbial interactions span from mutualistic to parasitic, and the nature of these symbiotic relationships can change depending on current environmental conditions. Our SFA research seeks to understand the fundamental mechanisms underpinning symbioses associated with perennial bioenergy grasses in order to enable sustainable and predictable bioenergy production. We take a systems biology approach that examines multiple scales of interaction, from quantifying microscale nutrient and metabolic exchanges that occur at the interface between plants and microbes, to field scale experimentation that will provide a predictive framework for how symbioses mediate bioenergy sustainability in real-world conditions.
In plants, multiple interaction zones influence plant health and productivity. Our work focuses on three key components of the plant microbiome: the soil surrounding roots, or rhizosphere, is a critical nexus of microbial interactions nutrient exchange, C flow, and trophic connections; the interior of plant tissues, or endosphere, which mediates nutrient exchange, plant chemistry, and physiology; and soil surrounding fungal symbionts associated with roots, or hyphosphere, which extends beyond the rhizosphere and accesses nutrients in soil otherwise inaccessible to plants.
We characterize our SFA approach as an emergentist perspective within the field of microbiomics, using what we refer to as “community systems biology.” While reductionism is important to define and characterize individual isolates that play keystone roles or have unique properties and interactions, the properties of most systems are strongly dependent on the interactions among many taxa, which need to be understood from a system perspective. Our work necessarily spans from individual organisms in culture to constructed consortia and complex natural communities. We use newly developed methods to observe in situ dynamics and fluxes; we also use multi-scale modeling approaches to integrate molecular scale dynamics with systems-scale ecological processes.
Our flagship techniques use isotopic tracers to probe complex processes and link microbial identity to function in complex communities:
- High-resolution imaging mass spectrometer (Cameca NanoSIMS 50) to perform SIP at the single cell and sub-micron scale, via “nanoSIP.” NanoSIP allows in situ imaging of cell-cell exchange, preserving physical arrangement in a range of complex samples. (Pett-Ridge et al., 2012).
- Isotope probing using “Chip-SIP,” a custom microarray platform combined with NanoSIMS analysis that can identify taxon-specific isotope incorporation into rRNA. (Mayali et al., 2012).
- “Density gradient” SIP allows quantitative assessment of substrate uptake and sharing (after incubation with a compound such as 13C-bicarbonate) and can also identify taxon-specific activity (tracing 15NH4+ and/or H218O) by measuring isotope incorporation into DNA or RNA.
- Biological accelerator mass spectrometry (BioAMS), which uses a custom moving wire sample introduction system coupled to liquid chromatography separation and AMS, enabling high sample throughput for high sensitivity metabolomics.
Our approach to systems biology also relies on LLNL’s exceptional computing facilities and our in-house informatics and modeling expertise:
- To accurately predict metabolic phenomena under variable environmental conditions, we developed the GX-FBA modeling methodology, which uses gene-expression and proteomic data to constrain FBA models (Navid et al., 2012).
- We are also working to advance the frontiers of modeling multi-cellular systems; to examine the tradeoffs among the competing objectives of the microbial community members, we have developed genome-scale multi-objective flux analysis (MOFA) models.
- We simulate the behavior of multi-cellular systems using dynamic FBA (DFBA) and are developing new DFBA-based tools that further the capabilities of DFBA modeling.
- While constraint-based methods can be used to simulate metabolism within individual organisms or interactions between multiple cells, they do not scale to natural complexity. Therefore, we are using trait-based models (TBM) as an intermediate approach that preserves key mechanistic properties that determine fitness in dynamic systems. We primarily represent growth and metabolism of organisms in the form of “Quota” models or a trait based dynamic energy budget (TB-DEB) model.
Highlights of our approach
Isotopic tracing approaches are most powerful when paired with integrated ‘omics techniques, (such as next-generation sequencing, proteomics and metabolomics) to link dynamics and fluxes to gene and organism function. Some examples of combined methods that illustrate our approach are listed below:
- Identifying hydrogen producers and consumers in microbial mat communities using time-resolved metatranscriptome analysis and 13C isotope probing with NanoSIP and FISH-SIMS to determine key microbial interactions involved in nighttime flux of fermentation byproducts (Burow et al., 2013, Lee et al., 2014)
- Identifying dominant N-fixers in photosynthetic microbial mat communities using functional gene libraries (nifH) and 16S rRNA diversity, 15N2 isotope probing with NanoSIP and FISH-SIMS (Woebken et al., 2012, Woebken et al., 2015)
- Determining fate and function of cyanobacterial exopolymers in photosynthetic mats and biofilms combining NanoSIP and exoproteomics (Stuart et al., 2015, Stuart et al., 2016)
- Characterizing gut compartmentalization of the wood-feeding beetle Odontotaenius disjunctus (Ceja-Navarro et al., 2014)
- Examining C and N exchange between cellulolytic protists and bacterial ectosymbionts in the lower termite gut, combining 15N2 isotope probing and NanoSIP with single cell bacterial genome analyses (Tai et al., 2016)
- Visualization of dynamic trace metal homeostasis in Chlamydomonas reinhardtii comparing wild type and mutant strains (Hong-Hermesdorf et al., 2014)
Our current work involves applying our demonstrated community systems biology approach to phycosphere and rhizosphere systems in order to determine how specific taxonomic combinations and interactions affect energy and nutrient cycling pathways and are shaped by various environmental stressors.