A Genome-Scale Model of Shewanella piezotolerans Simulates Mechanisms of Metabolic Diversity and Energy Conservation

High hydrostatic pressure stimulates microbial nitrate reduction in hadal trench sediments under oxic conditions

Hadal trenches are extreme environments situated over 6000 m below sea surface, where enormous hydrostatic pressure affects the biochemical cycling of elements. Recent studies have indicated that hadal trenches may represent a previously overlooked source of fixed nitrogen loss; however, the mechanisms and role of hydrostatic pressure in this process are still being debated. To this end, we investigate the effects of hydrostatic pressure (0.1 to 115 MPa) on the chemical profile, microbial community structure and functions of surface sediments from the Mariana Trench using a Deep Ocean Experimental Simulator supplied with nitrate and oxygen. We observe enhanced denitrification activity at high hydrostatic pressure under oxic conditions, while the anaerobic ammonium oxidation - a previously recognized dominant nitrogen loss pathway - is not detected. Additionally, we further confirm the simultaneous occurrence of nitrate reduction and aerobic respiration using a metatranscriptomic dataset from in situ RNA-fixed sediments in the Mariana Trench. Taken together, our findings demonstrate that hydrostatic pressure can influence microbial contributions to nitrogen cycling and that the hadal trenches are a potential nitrogen loss hotspot. Knowledge of the influence of hydrostatic pressure on anaerobic processes in oxygenated surface sediments can greatly broaden our understanding of element cycling in hadal trenches.

Prophage enhances the ability of deep-sea bacterium Shewanella psychrophila WP2 to utilize D-amino acid

Prophages are prevalent in the marine bacterial genomes and reshape the physiology and metabolism of their hosts. However, whether and how prophages influence the microbial degradation of D-amino acids (D-AAs), which is one of the widely distributed recalcitrant dissolved organic matters (RDOMs) in the ocean, remain to be explored. In this study, we addressed this issue in a representative marine bacterium, Shewanella psychrophila WP2 (WP2), and its integrated prophage SP1. Notably, compared to the WP2 wild-type strain, the SP1 deletion mutant of WP2 (WP2ΔSP1) exhibited a significantly lower D-glutamate (D-Glu) consumption rate and longer lag phase when D-Glu was used as the sole nitrogen source. The subsequent transcriptome analysis identified 1,523 differentially expressed genes involved in diverse cellular processes, especially that multiple genes related to inorganic nitrogen metabolism were highly upregulated. In addition, the dynamic profiles of ammonium, nitrate, and nitrite were distinct between the culture media of WP2 and WP2ΔSP1. Finally, we provide evidence that SP1 conferred a competitive advantage to WP2 when D-Glu was used as the sole nitrogen source and SP1-like phages may be widely distributed in the global ocean. Taken together, these findings offer novel insight into the influences of prophages on host metabolism and RDOM cycling in marine environments.IMPORTANCEThis work represents the first exploration of the impact of prophages on the D-amino acid (D-AA) metabolism of deep-sea bacteria. By using S. psychrophila WP2 and its integrated prophage SP1 as a representative system, we found that SP1 can significantly increase the catabolism rate of WP2 to D-glutamate and produce higher concentrations of ammonium, resulting in faster growth and competitive advantages. Our findings not only deepen our understanding of the interaction between deep-sea prophages and hosts but also provide new insights into the ecological role of prophages in refractory dissolved organic matter and the nitrogen cycle in deep oceans.

Genome-scale metabolic modelling of extremophiles and its applications in astrobiological environments

Metabolic modelling approaches have become the powerful tools in modern biology. These mathematical models are widely used to predict metabolic phenotypes of the organisms or communities of interest, and to identify metabolic targets in metabolic engineering. Apart from a broad range of industrial applications, the possibility of using metabolic modelling in the contexts of astrobiology are poorly explored. In this mini-review, we consolidated the concepts and related applications of applying metabolic modelling in studying organisms in space-related environments, specifically the extremophilic microbes. We recapitulated the current state of the art in metabolic modelling approaches and their advantages in the astrobiological context. Our review encompassed the applications of metabolic modelling in the theoretical investigation of the origin of life within prebiotic environments, as well as the compilation of existing uses of genome-scale metabolic models of extremophiles. Furthermore, we emphasize the current challenges associated with applying this technique in extreme environments, and conclude this review by discussing the potential implementation of metabolic models to explore theoretically optimal metabolic networks under various space conditions. Through this mini-review, our aim is to highlight the potential of metabolic modelling in advancing the study of astrobiology.

Piezophilic Phenotype Is Growth Condition Dependent and Correlated with the Regulation of Two Sets of ATPase in Deep-Sea Piezophilic Bacterium Photobacterium profundum SS9

Alteration of respiratory components as a function of pressure is a common strategy developed in deep-sea microorganisms, presumably to adapt to high hydrostatic pressure (HHP). While the electron transport chain and terminal reductases have been extensively studied in deep-sea bacteria, little is known about their adaptations for ATP generation. In this study, we showed that the deep-sea bacterium Photobacterium profundum SS9 exhibits a more pronounced piezophilic phenotype when grown in minimal medium supplemented with glucose (MG) than in the routinely used MB2216 complex medium. The intracellular ATP level varied with pressure, but with opposite trends in the two culture media. Between the two ATPase systems encoded in SS9, ATPase-I played a dominant role when cultivated in MB2216, whereas ATPase-II was more abundant in the MG medium, especially at elevated pressure when cells had the lowest ATP level among all conditions tested. Further analyses of the ΔatpI, ΔatpE1 and ΔatpE2 mutants showed that disrupting ATPase-I induced expression of ATPase-II and that the two systems are functionally redundant in MB2216. Collectively, we provide the first examination of the differences and relationships between two ATPase systems in a piezophilic bacterium, and expanded our understanding of the involvement of energy metabolism in pressure adaptation.

Radionuclide Reduction by Combinatorial Optimization of Microbial Extracellular Electron Transfer with a Physiologically Adapted Regulatory Platform

Microbial extracellular electron transfer (EET) is the basis for many microbial processes involved in element geochemical recycling, bioenergy harvesting, and bioremediation, including the technique for remediating U(VI)-contaminated environments. However, the low EET rate hinders its full potential from being fulfilled. The main challenge for engineering microbial EET is the difficulty in optimizing cell resource allocation for EET investment and basic metabolism and the optimal coordination of the different EET pathways. Here, we report a novel combinatorial optimization strategy with a physiologically adapted regulatory platform. Through exploring the physiologically adapted regulatory elements, a 271.97-fold strength range, autonomous, and dynamic regulatory platform was established for Shewanella oneidensis, a prominent electrochemically active bacterium. Both direct and mediated EET pathways are modularly reconfigured and tuned at various intensities with the regulatory platform, which were further assembled combinatorically. The optimal combinations exhibit up to 16.12-, 4.51-, and 8.40-fold improvements over the control in the maximum current density (1009.2 mA/m²) of microbial electrolysis cells and the voltage output (413.8 mV) and power density (229.1 mW/m²) of microbial fuel cells. In addition, the optimal strains exhibited up to 6.53-fold improvement in the radionuclide U(VI) removal efficiency. This work provides an effective and feasible approach to boost microbial EET performance for environmental applications.

Reconstruction and Analysis of Thermodynamically Constrained Models Reveal Metabolic Responses of a Deep-Sea Bacterium to Temperature Perturbations

Microbial acclimation to different temperature conditions can involve broad changes in cell composition and metabolic efficiency. A systems-level view of these metabolic responses in nonmesophilic organisms, however, is currently missing. In this study, thermodynamically constrained genome-scale models were applied to simulate the metabolic responses of a deep-sea psychrophilic bacterium, Shewanella psychrophila WP2, under suboptimal (4°C), optimal (15°C), and supraoptimal (20°C) growth temperatures. The models were calibrated with experimentally determined growth rates of WP2. Gibbs free energy change of reactions (Δ_rG'), metabolic fluxes, and metabolite concentrations were predicted using random simulations to characterize temperature-dependent changes in the metabolism. The modeling revealed the highest metabolic efficiency at the optimal temperature, and it suggested distinct patterns of ATP production and consumption that could lead to lower metabolic efficiency under suboptimal or supraoptimal temperatures. The modeling also predicted rearrangement of fluxes through multiple metabolic pathways, including the glycolysis pathway, Entner-Doudoroff pathway, tricarboxylic acid (TCA) cycle, and electron transport system, and these predictions were corroborated through comparisons to WP2 transcriptomes. Furthermore, predictions of metabolite concentrations revealed the potential conservation of reducing equivalents and ATP in the suboptimal temperature, consistent with experimental observations from other psychrophiles. Taken together, the WP2 models provided mechanistic insights into the metabolism of a psychrophile in response to different temperatures. IMPORTANCE Metabolic flexibility is a central component of any organism's ability to survive and adapt to changes in environmental conditions. This study represents the first application of thermodynamically constrained genome-scale models in simulating the metabolic responses of a deep-sea psychrophilic bacterium to various temperatures. The models predicted differences in metabolic efficiency that were attributed to changes in metabolic pathway utilization and metabolite concentration during growth under optimal and nonoptimal temperatures. Experimental growth measurements were used for model calibration, and temperature-dependent transcriptomic changes corroborated the model-predicted rearrangement of metabolic fluxes. Overall, this study highlights the utility of modeling approaches in studying the temperature-driven metabolic responses of an extremophilic organism.

From the raw bar to the bench: Bivalves as models for human health

Bivalves, from raw oysters to steamed clams, are popular choices among seafood lovers and once limited to the coastal areas. The rapid growth of the aquaculture industry and improvement in the preservation and transport of seafood have enabled them to be readily available anywhere in the world. Over the years, oysters, mussels, scallops, and clams have been the focus of research for improving the production, managing resources, and investigating basic biological and ecological questions. During this decade, an impressive amount of information using high-throughput genomic, transcriptomic and proteomic technologies has been produced in various classes of the Mollusca group, and it is anticipated that basic and applied research will significantly benefit from this resource. One aspect that is also taking momentum is the use of bivalves as a model system for human health. In this review, we highlight some of the aspects of the biology of bivalves that have direct implications in human health including the shell formation, stem cells and cell differentiation, the ability to fight opportunistic and specific pathogens in the absence of adaptive immunity, as source of alternative drugs, mucosal immunity and, microbiome turnover, toxicology, and cancer research. There is still a long way to go; however, the next time you order a dozen oysters at your favorite raw bar, think about a tasty model organism that will not only please your palate but also help unlock multiple aspects of molluscan biology and improve human health.

A Comparative Study of Network Motifs in the Integrated Transcriptional Regulation and Protein Interaction Networks of Shewanella

The Shewanella species shows a remarkable respiratory versatility with a great variety of extracellular electron acceptors (termed Extracellular Electron Transfer, EET). To explore relevant mechanisms from the network motif view, we constructed the integrated networks that combined transcriptional regulation interactions (TRIs) and protein-protein interactions (PPIs) for 13 Shewanella species, identified and compared the network motifs in these integrated networks. We found that the network motifs were evolutionary conserved in these integrated networks. The functional significance of the highly conserved motifs was discussed, especially the important ones that were potentially involved in the Shewanella EET processes. More importantly, we found that: 1) the motif co-regulated PPI took a role in the "standby mode" of protein utilization, which will be helpful for cells to rapidly response to environmental changes; and 2) the type II cofactors, which involved in the motif TRI interacting with a third protein, mainly carried out a signalling role in Shewanella oneidensis MR-1.

FindPrimaryPairs: An efficient algorithm for predicting element-transferring reactant/product pairs in metabolic networks

The metabolism of individual organisms and biological communities can be viewed as a network of metabolites connected to each other through chemical reactions. In metabolic networks, chemical reactions transform reactants into products, thereby transferring elements between these metabolites. Knowledge of how elements are transferred through reactant/product pairs allows for the identification of primary compound connections through a metabolic network. However, such information is not readily available and is often challenging to obtain for large reaction databases or genome-scale metabolic models. In this study, a new algorithm was developed for automatically predicting the element-transferring reactant/product pairs using the limited information available in the standard representation of metabolic networks. The algorithm demonstrated high efficiency in analyzing large datasets and provided accurate predictions when benchmarked with manually curated data. Applying the algorithm to the visualization of metabolic networks highlighted pathways of primary reactant/product connections and provided an organized view of element-transferring biochemical transformations. The algorithm was implemented as a new function in the open source software package PSAMM in the release v0.30 (https://zhanglab.github.io/psamm/).