Seafloor Incubation Experiment with Deep-Sea Hydrothermal Vent Fluid Reveals Effect of Pressure and Lag Time on Autotrophic Microbial Communities.

Depressurization and sample processing delays may impact the outcome of shipboard microbial incubations of samples collected from the deep sea. To address this knowledge gap, we developed a remotely operated vehicle (ROV)-powered incubator instrument to carry out and compare results from in situ and shipboard RNA stable isotope probing (RNA-SIP) experiments to identify the key chemolithoautotrophic microbes and metabolisms in diffuse, low-temperature venting fluids from Axial Seamount. All the incubations showed microbial uptake of labeled bicarbonate primarily by thermophilic autotrophic Epsilonbacteraeota that oxidized hydrogen coupled with nitrate reduction. However, the in situ seafloor incubations showed higher abundances of transcripts annotated for aerobic processes, suggesting that oxygen was lost from the hydrothermal fluid samples prior to shipboard analysis. Furthermore, transcripts for thermal stress proteins such as heat shock chaperones and proteases were significantly more abundant in the shipboard incubations, suggesting that depressurization induced thermal stress in the metabolically active microbes in these incubations. Together, the results indicate that while the autotrophic microbial communities in the shipboard and seafloor experiments behaved similarly, there were distinct differences that provide new insight into the activities of natural microbial assemblages under nearly native conditions in the ocean.IMPORTANCE Diverse microbial communities drive biogeochemical cycles in Earth's ocean, yet studying these organisms and processes is often limited by technological capabilities, especially in the deep ocean. In this study, we used a novel marine microbial incubator instrument capable of in situ experimentation to investigate microbial primary producers at deep-sea hydrothermal vents. We carried out identical stable isotope probing experiments coupled to RNA sequencing both on the seafloor and on the ship to examine thermophilic, microbial autotrophs in venting fluids from an active submarine volcano. Our results indicate that microbial communities were significantly impacted by the effects of depressurization and sample processing delays, with shipboard microbial communities being more stressed than seafloor incubations. Differences in metabolism were also apparent and are likely linked to the chemistry of the fluid at the beginning of the experiment. Microbial experimentation in the natural habitat provides new insights into understanding microbial activities in the ocean.

Phytoplankton Temporal Strategies Increase Entropy Production in a Marine Food Web Model.

We develop a trait-based model founded on the hypothesis that biological systems evolve and organize to maximize entropy production by dissipating chemical and electromagnetic free energy over longer time scales than abiotic processes by implementing temporal strategies. A marine food web consisting of phytoplankton, bacteria, and consumer functional groups is used to explore how temporal strategies, or the lack thereof, change entropy production in a shallow pond that receives a continuous flow of reduced organic carbon plus inorganic nitrogen and illumination from solar radiation with diel and seasonal dynamics. Results show that a temporal strategy that employs an explicit circadian clock produces more entropy than a passive strategy that uses internal carbon storage or a balanced growth strategy that requires phytoplankton to grow with fixed stoichiometry. When the community is forced to operate at high specific growth rates near 2 d-1, the optimization-guided model selects for phytoplankton ecotypes that exhibit complementary for winter versus summer environmental conditions to increase entropy production. We also present a new type of trait-based modeling where trait values are determined by maximizing entropy production rather than by random selection.

Fluid geochemistry, local hydrology, and metabolic activity define methanogen community size and composition in deep-sea hydrothermal vents.

The size and biogeochemical impact of the subseafloor biosphere in oceanic crust remain largely unknown due to sampling limitations. We used reactive transport modeling to estimate the size of the subseafloor methanogen population, volume of crust occupied, fluid residence time, and nature of the subsurface mixing zone for two low-temperature hydrothermal vents at Axial Seamount. Monod CH4 production kinetics based on chemostat H2 availability and batch-culture Arrhenius growth kinetics for the hyperthermophile Methanocaldococcus jannaschii and thermophile Methanothermococcus thermolithotrophicus were used to develop and parameterize a reactive transport model, which was constrained by field measurements of H2, CH4, and metagenome methanogen concentration estimates in 20-40 °C hydrothermal fluids. Model results showed that hyperthermophilic methanogens dominate in systems where a narrow flow path geometry is maintained, while thermophilic methanogens dominate in systems where the flow geometry expands. At Axial Seamount, the residence time of fluid below the surface was 29-33 h. Only 1011 methanogenic cells occupying 1.8-18 m3 of ocean crust per m2 of vent seafloor area were needed to produce the observed CH4 anomalies. We show that variations in local geology at diffuse vents can create fluid flow paths that are stable over space and time, harboring persistent and distinct microbial communities.

Microbial Communities Are Well Adapted to Disturbances in Energy Input.

Although microbial systems are well suited for studying concepts in ecological theory, little is known about how microbial communities respond to long-term periodic perturbations beyond diel oscillations. Taking advantage of an ongoing microcosm experiment, we studied how methanotrophic microbial communities adapted to disturbances in energy input over a 20-day cycle period. Sequencing of bacterial 16S rRNA genes together with quantification of microbial abundance and ecosystem function were used to explore the long-term dynamics (510 days) of methanotrophic communities under continuous versus cyclic chemical energy supply. We observed that microbial communities appeared inherently well adapted to disturbances in energy input and that changes in community structure in both treatments were more dependent on internal dynamics than on external forcing. The results also showed that the rare biosphere was critical to seeding the internal community dynamics, perhaps due to cross-feeding or other strategies. We conclude that in our experimental system, internal feedbacks were more important than external drivers in shaping the community dynamics over time, suggesting that ecosystems can maintain their function despite inherently unstable community dynamics. IMPORTANCE Within the broader ecological context, biological communities are often viewed as stable and as only experiencing succession or replacement when subject to external perturbations, such as changes in food availability or the introduction of exotic species. Our findings indicate that microbial communities can exhibit strong internal dynamics that may be more important in shaping community succession than external drivers. Dynamic "unstable" communities may be important for ecosystem functional stability, with rare organisms playing an important role in community restructuring. Understanding the mechanisms responsible for internal community dynamics will certainly be required for understanding and manipulating microbiomes in both host-associated and natural ecosystems.

The Thermodynamics of Marine Biogeochemical Cycles: Lotka Revisited.

Nearly 100 years ago, Alfred Lotka published two short but insightful papers describing how ecosystems may organize. Principally, Lotka argued that ecosystems will grow in size and that their cycles will spin faster via predation and nutrient recycling so as to capture all available energy, and that evolution and natural selection are the mechanisms by which this occurs and progresses. Lotka's ideas have often been associated with the maximum power principle, but they are more consistent with recent developments in nonequilibrium thermodynamics, which assert that complex systems will organize toward maximum entropy production (MEP). In this review, we explore Lotka's hypothesis within the context of the MEP principle, as well as how this principle can be used to improve marine biogeochemistry models. We need to develop the equivalent of a climate model, as opposed to a weather model, to understand marine biogeochemistry on longer timescales, and adoption of the MEP principle can help create such models.

Relationships between soil organic matter, nutrients, bacterial community structure, and the performance of microbial fuel cells.

Microbial fuel cells (MFCs) offer the potential for generating electricity, mitigating greenhouse gas emissions, and bioremediating pollutants through utilization of a plentiful renewable resource: soil organic carbon. We analyzed bacterial community structure, MFC performance, and soil characteristics in different microhabitats within MFCs constructed from agricultural or forest soils in order to determine how soil type and bacterial dynamics influence MFC performance. Our results indicated that MFCs constructed from agricultural soil had power output about 17 times that of forest soil-based MFCs and respiration rates about 10 times higher than forest soil MFCs. Agricultural soil MFCs had lower C:N ratios, polyphenol content, and acetate concentrations than forest soil MFCs. Bacterial community profile data indicate that the bacterial communities at the anode of the high power MFCs were less diverse than in low power MFCs and were dominated by Deltaproteobacteria, Geobacter, and to a lesser extent, Clostridia, while low-power MFC anode communities were dominated by Clostridia. These results suggest that the presence of organic carbon substrate (acetate) was not the major limiting factor in selecting for highly electrogenic bacterial communities, while the quality of available organic matter may have played a significant role in supporting high performing bacterial communities.

Extended local similarity analysis (eLSA) of microbial community and other time series data with replicates.

BACKGROUND: The increasing availability of time series microbial community data from metagenomics and other molecular biological studies has enabled the analysis of large-scale microbial co-occurrence and association networks. Among the many analytical techniques available, the Local Similarity Analysis (LSA) method is unique in that it captures local and potentially time-delayed co-occurrence and association patterns in time series data that cannot otherwise be identified by ordinary correlation analysis. However LSA, as originally developed, does not consider time series data with replicates, which hinders the full exploitation of available information. With replicates, it is possible to understand the variability of local similarity (LS) score and to obtain its confidence interval. RESULTS: We extended our LSA technique to time series data with replicates and termed it extended LSA, or eLSA. Simulations showed the capability of eLSA to capture subinterval and time-delayed associations. We implemented the eLSA technique into an easy-to-use analytic software package. The software pipeline integrates data normalization, statistical correlation calculation, statistical significance evaluation, and association network construction steps. We applied the eLSA technique to microbial community and gene expression datasets, where unique time-dependent associations were identified. CONCLUSIONS: The extended LSA analysis technique was demonstrated to reveal statistically significant local and potentially time-delayed association patterns in replicated time series data beyond that of ordinary correlation analysis. These statistically significant associations can provide insights to the real dynamics of biological systems. The newly designed eLSA software efficiently streamlines the analysis and is freely available from the eLSA homepage, which can be accessed at http://meta.usc.edu/softs/lsa.

Ecosystem biogeochemistry considered as a distributed metabolic network ordered by maximum entropy production.

We examine the application of the maximum entropy production principle for describing ecosystem biogeochemistry. Since ecosystems can be functionally stable despite changes in species composition, we use a distributed metabolic network for describing biogeochemistry, which synthesizes generic biological structures that catalyse reaction pathways, but is otherwise organism independent. Allocation of biological structure and regulation of biogeochemical reactions is determined via solution of an optimal control problem in which entropy production is maximized. However, because synthesis of biological structures cannot occur if entropy production is maximized instantaneously, we propose that information stored within the metagenome allows biological systems to maximize entropy production when averaged over time. This differs from abiotic systems that maximize entropy production at a point in space-time, which we refer to as the steepest descent pathway. It is the spatio-temporal averaging that allows biological systems to outperform abiotic processes in entropy production, at least in many situations. A simulation of a methanotrophic system is used to demonstrate the approach. We conclude with a brief discussion on the implications of viewing ecosystems as self-organizing molecular machines that function to maximize entropy production at the ecosystem level of organization.

Efficient export of carbon to the deep ocean through dissolved organic matter.

Oceanic dissolved organic carbon (DOC) constitutes one of the largest pools of reduced carbon in the biosphere. Estimated DOC export from the surface ocean represents 20% of total organic carbon flux to the deep ocean, which constitutes a primary control on atmospheric carbon dioxide levels. DOC is the carbon component of dissolved organic matter (DOM) and an accurate quantification of DOM pools, fluxes and their controls is therefore critical to understanding oceanic carbon cycling. DOC export is directly coupled with dissolved organic nitrogen and phosphorus export. However, the C:N:P stoichiometry (by atoms) of DOM dynamics is poorly understood. Here we study the stoichiometry of the DOM pool and of DOM decomposition in continental shelf, continental slope and central ocean gyre environments. We find that DOM is remineralized and produced with a C:N:P stoichiometry of 199:20:1 that is substantially lower than for bulk pools (typically >775:54:1), but greater than for particulate organic matter (106:16:1--the Redfield ratio). Thus for a given mass of new N and P introduced into surface water, more DOC can be exported than would occur at the Redfield ratio. This may contribute to the excess respiration estimated to occur in the interior ocean. Our results place an explicit constraint on global carbon export and elemental balance via advective pathways.

Modeling microbial consortiums as distributed metabolic networks.

Biogeochemistry is the study of how living systems in combination with abiotic reactions process and cycle mass and energy on local, regional, and global scales (Schlesinger, 1997). Understanding how these biogeochemical cycles function and respond to perturbations has become increasingly important, as anthropogenic impacts have significantly altered many of these cycles (Galloway and Cowling, 2002; Houghton et al., 2002). Biogeochemistry is strongly governed by microbial processes, and it appears to closely follow thermodynamic constraints in that electron acceptor (O(2), NO(3)(-), SO(4)(2-), etc.) utilization closely follows a priori expectations based on energetics (Vallino et al., 1996; Hoehler et al., 1998; Jakobsen and Postma, 1999; Amend and Shock, 2001). Consortiums of microorganisms seem to have evolved to exploit chemical potentials wherever they exist in the environment, as manifested by the recent discovery of anaerobic methane oxidation by sulfate (Boetius et al., 2000) or sulfide oxidation by nitrate (Schulz et al., 1999). Three and a half billion years of natural selection have produced living systems capable of degrading most chemical potentials. We may therefore ask: If all ecosystem niche space is filled, is the biogeochemistry we observe in the environment dependent on the organisms that occupy that environment, or is the biogeochemistry determined by fundamental forces, with the evolution of living systems being the implementation of those forces? Recent developments in nonequilibrium thermodynamics (NET) are beginning to support the latter alternative, and advances in genomics are allowing us to explore microbial consortiums in detail. Taking advantage of ideas being suggested by NET, we have developed a modeling framework that views microbial consortiums as an inter-species distributed metabolic network. When combined with experimental observations, the model should help us test hypotheses that govern how living systems function.