Controlled motility in the cyanobacterium Trichodesmium regulates aggregate architecture.

The ocean's nitrogen is largely fixed by cyanobacteria, including Trichodesmium, which forms aggregates comprising hundreds of filaments arranged in organized architectures. Aggregates often form upon exposure to stress and have ecological and biophysical characteristics that differ from those of single filaments. Here, we report that Trichodesmium aggregates can rapidly modulate their shape, responding within minutes to changes in environmental conditions. Combining video microscopy and mathematical modeling, we discovered that this reorganization is mediated by "smart reversals" wherein gliding filaments reverse when their overlap with other filaments diminishes. By regulating smart reversals, filaments control aggregate architecture without central coordination. We propose that the modulation of gliding motility at the single-filament level is a determinant of Trichodesmium's aggregation behavior and ultimately of its biogeochemical role in the ocean.

Multiscale Multiobjective Systems Analysis (MiMoSA): an advanced metabolic modeling framework for complex systems.

In natural environments, cells live in complex communities and experience a high degree of heterogeneity internally and in the environment. Even in 'ideal' laboratory environments, cells can experience a high degree of heterogeneity in their environments. Unfortunately, most of the metabolic modeling approaches that are currently used assume ideal conditions and that each cell is identical, limiting their application to pure cultures in well-mixed vessels. Here we describe our development of Multiscale Multiobjective Systems Analysis (MiMoSA), a metabolic modeling approach that can track individual cells in both space and time, track the diffusion of nutrients and light and the interaction of cells with each other and the environment. As a proof-of concept study, we used MiMoSA to model the growth of Trichodesmium erythraeum, a filamentous diazotrophic cyanobacterium which has cells with two distinct metabolic modes. The use of MiMoSA significantly improves our ability to predictively model metabolic changes and phenotype in more complex cell cultures.

Metacaspase involvement in programmed cell death of the marine cyanobacterium Trichodesmium.

Metacaspases are cysteine specific proteases implicated in cell-signalling, stress acclimation and programmed cell death (PCD) pathways in plants, fungi, protozoa, bacteria and algae. We investigated metacaspase-like gene expression and biochemical activity in the bloom-forming, N2 -fixing, marine cyanobacterium Trichodesmium, which undergoes PCD under low iron and high-light stress. We examined these patterns with respect to in-silico analyses of protein domain architectures that revealed a diverse array of regulatory domains within Trichodesmium metacaspases-like (TeMC) proteins. Experimental manipulations of laboratory cultures and oceanic surface blooms of Trichodesmium from the South Pacific Ocean triggered PCD under Fe-limitation and high light along with enhanced TeMC activity and upregulated expression of diverse TeMC representatives containing different regulatory domains. Furthermore, TeMC activity was significantly and positively correlated with caspase-like activity, which has been routinely observed to increase with PCD induction in Trichodesmium. Although both TeMC and caspase-like activities were stimulated upon PCD induction, inhibitor treatments of these proteolytic activities provided further evidence of largely distinct substrate specificities, even though some inhibitory crossover was observed. Our findings are the first results linking metacaspase expression and activity in PCD induced mortality in Trichodesmium. Yet, the role/s and specific activities of these different proteins remain to be elucidated.

The physiological cost of diazotrophy for Trichodesmium erythraeum IMS101.

Trichodesmium plays a significant role in the oligotrophic oceans, fixing nitrogen in an area corresponding to half of the Earth's surface, representing up to 50% of new production in some oligotrophic tropical and subtropical oceans. Whilst Trichodesmium blooms at the surface exhibit a strong dependence on diazotrophy, colonies at depth or at the surface after a mixing event could be utilising additional N-sources. We conducted experiments to establish how acclimation to varying N-sources affects the growth, elemental composition, light absorption coefficient, N2 fixation, PSII electron transport rate and the relationship between net and gross photosynthetic O2 exchange in T. erythraeum IMS101. To do this, cultures were acclimated to growth medium containing NH4+ and NO3- (replete concentrations) or N2 only (diazotrophic control). The light dependencies of O2 evolution and O2 uptake were measured using membrane inlet mass spectrometry (MIMS), while PSII electron transport rates were measured from fluorescence light curves (FLCs). We found that at a saturating light intensity, Trichodesmium growth was ~ 10% and 13% lower when grown on N2 than with NH4+ and NO3-, respectively. Oxygen uptake increased linearly with net photosynthesis across all light intensities ranging from darkness to 1100 µmol photons m-2 s-1. The maximum rates and initial slopes of light response curves for C-specific gross and net photosynthesis and the slope of the relationship between gross and net photosynthesis increased significantly under non-diazotrophic conditions. We attribute these observations to a reduced expenditure of reductant and ATP for nitrogenase activity under non-diazotrophic conditions which allows NADPH and ATP to be re-directed to CO2 fixation and/or biosynthesis. The energy and reductant conserved through utilising additional N-sources could enhance Trichodesmium's productivity and growth and have major implications for its role in ocean C and N cycles.

The use of genome-scale metabolic network reconstruction to predict fluxes and equilibrium composition of N-fixing versus C-fixing cells in a diazotrophic cyanobacterium, Trichodesmium erythraeum.

BACKGROUND: Computational, genome based predictions of organism phenotypes has enhanced the ability to investigate the biological phenomena that help organisms survive and respond to their environments. In this study, we have created the first genome-scale metabolic network reconstruction of the nitrogen fixing cyanobacterium T. erythraeum and used genome-scale modeling approaches to investigate carbon and nitrogen fluxes as well as growth and equilibrium population composition. RESULTS: We created a genome-scale reconstruction of T. erythraeum with 971 reactions, 986 metabolites, and 647 unique genes. We then used data from previous studies as well as our own laboratory data to establish a biomass equation and two distinct submodels that correspond to the two cell types formed by T. erythraeum. We then use flux balance analysis and flux variability analysis to generate predictions for how metabolism is distributed to account for the unique productivity of T. erythraeum. Finally, we used in situ data to constrain the model, infer time dependent population compositions and metabolite production using dynamic Flux Balance Analysis. We find that our model predicts equilibrium compositions similar to laboratory measurements, approximately 15.5% diazotrophs for our model versus 10-20% diazotrophs reported in literature. We also found that equilibrium was the most efficient mode of growth and that equilibrium was stoichiometrically mediated. Moreover, the model predicts that nitrogen leakage is an essential condition of optimality for T. erythraeum; cells leak approximately 29.4% total fixed nitrogen when growing at the optimal growth rate, which agrees with values observed in situ. CONCLUSION: The genome-metabolic network reconstruction allows us to use constraints based modeling approaches to predict growth and optimal cellular composition in T. erythraeum colonies. Our predictions match both in situ and laboratory data, indicating that stoichiometry of metabolic reactions plays a large role in the differentiation and composition of different cell types. In order to realize the full potential of the model, advance modeling techniques which account for interactions between colonies, the environment and surrounding species need to be developed.