A ubiquitous gammaproteobacterial clade dominates expression of sulfur oxidation genes across the mesopelagic ocean.

The deep ocean (>200 m depth) is the largest habitat on Earth. Recent evidence suggests sulfur oxidation could be a major energy source for deep ocean microbes. However, the global relevance and the identity of the major players in sulfur oxidation in the oxygenated deep-water column remain elusive. Here we combined single-cell genomics, community metagenomics, metatranscriptomics and single-cell activity measurements on samples collected beneath the Ross Ice Shelf in Antarctica to characterize a ubiquitous mixotrophic bacterial group (UBA868) that dominates expression of RuBisCO genes and of key sulfur oxidation genes. Further analyses of the gene libraries from the 'Tara Oceans' and 'Malaspina' expeditions confirmed the ubiquitous distribution and global relevance of this enigmatic group in the expression of sulfur oxidation and dissolved inorganic carbon fixation genes across the global mesopelagic ocean. Our study also underscores the unrecognized importance of mixotrophic microbes in the biogeochemical cycles of the deep ocean.

Transcriptional Mechanisms of Thermal Acclimation in Prochlorococcus.

Low temperature limits the growth and the distribution of the key oceanic primary producer Prochlorococcus, which does not proliferate above a latitude of ca. 40°. Yet, the molecular basis of thermal acclimation in this cyanobacterium remains unexplored. We analyzed the transcriptional response of the Prochlorococcus marinus strain MIT9301 in long-term acclimations and in natural Prochlorococcus populations along a temperature range enabling its growth (17 to 30°C). MIT9301 upregulated mechanisms of the global stress response at the temperature minimum (17°C) but maintained the expression levels of genes involved in essential metabolic pathways (e.g., ATP synthesis and carbon fixation) along the whole thermal niche. Notably, the declining growth of MIT9301 from the optimum to the minimum temperature was coincident with a transcriptional suppression of the photosynthetic apparatus and a dampening of its circadian expression patterns, indicating a loss in their regulatory capacity under cold conditions. Under warm conditions, the cellular transcript inventory of MIT9301 was strongly streamlined, which may also induce regulatory imbalances due to stochasticity in gene expression. The daytime transcriptional suppression of photosynthetic genes at low temperature was also observed in metatranscriptomic reads mapping to MIT9301 across the global ocean, implying that this molecular mechanism may be associated with the restricted distribution of Prochlorococcus to temperate zones. IMPORTANCE Prochlorococcus is a major marine primary producer with a global impact on atmospheric CO2 fixation. This cyanobacterium is widely distributed across the temperate ocean, but virtually absent at latitudes above 40° for yet unknown reasons. Temperature has been suggested as a major limiting factor, but the exact mechanisms behind Prochlorococcus thermal growth restriction remain unexplored. This study brings us closer to understanding how Prochlorococcus functions under challenging temperature conditions, by focusing on its transcriptional response after long-term acclimation from its optimum to its thermal thresholds. Our results show that the drop in Prochlorococcus growth rate under cold conditions was paralleled by a transcriptional suppression of the photosynthetic machinery during daytime and a loss in the organism's regulatory capacity to maintain circadian expression patterns. Notably, warm temperature induced a marked shrinkage of the organism's cellular transcript inventory, which may also induce regulatory imbalances in the future functioning of this cyanobacterium.

Flux-based hierarchical organization of Escherichia coli's metabolic network.

Biological networks across scales exhibit hierarchical organization that may constrain network function. Yet, understanding how these hierarchies arise due to the operational constraint of the networks and whether they impose limits to molecular phenotypes remains elusive. Here we show that metabolic networks include a hierarchy of reactions based on a natural flux ordering that holds for every steady state. We find that the hierarchy of reactions is reflected in experimental measurements of transcript, protein and flux levels of Escherichia coli under various growth conditions as well as in the catalytic rate constants of the corresponding enzymes. Our findings point at resource partitioning and a fine-tuning of enzyme levels in E. coli to respect the constraints imposed by the network structure at steady state. Since reactions in upper layers of the hierarchy impose an upper bound on the flux of the reactions downstream, the hierarchical organization of metabolism due to the flux ordering has direct applications in metabolic engineering.

Resolving the central metabolism of Arabidopsis guard cells.

Photosynthesis and water use efficiency, key factors affecting plant growth, are directly controlled by microscopic and adjustable pores in the leaf-the stomata. The size of the pores is modulated by the guard cells, which rely on molecular mechanisms to sense and respond to environmental changes. It has been shown that the physiology of mesophyll and guard cells differs substantially. However, the implications of these differences to metabolism at a genome-scale level remain unclear. Here, we used constraint-based modeling to predict the differences in metabolic fluxes between the mesophyll and guard cells of Arabidopsis thaliana by exploring the space of fluxes that are most concordant to cell-type-specific transcript profiles. An independent 13C-labeling experiment using isolated mesophyll and guard cells was conducted and provided support for our predictions about the role of the Calvin-Benson cycle in sucrose synthesis in guard cells. The combination of in silico with in vivo analyses indicated that guard cells have higher anaplerotic CO2 fixation via phosphoenolpyruvate carboxylase, which was demonstrated to be an important source of malate. Beyond highlighting the metabolic differences between mesophyll and guard cells, our findings can be used in future integrated modeling of multi-cellular plant systems and their engineering towards improved growth.

On the effects of alternative optima in context-specific metabolic model predictions.

The integration of experimental data into genome-scale metabolic models can greatly improve flux predictions. This is achieved by restricting predictions to a more realistic context-specific domain, like a particular cell or tissue type. Several computational approaches to integrate data have been proposed-generally obtaining context-specific (sub)models or flux distributions. However, these approaches may lead to a multitude of equally valid but potentially different models or flux distributions, due to possible alternative optima in the underlying optimization problems. Although this issue introduces ambiguity in context-specific predictions, it has not been generally recognized, especially in the case of model reconstructions. In this study, we analyze the impact of alternative optima in four state-of-the-art context-specific data integration approaches, providing both flux distributions and/or metabolic models. To this end, we present three computational methods and apply them to two particular case studies: leaf-specific predictions from the integration of gene expression data in a metabolic model of Arabidopsis thaliana, and liver-specific reconstructions derived from a human model with various experimental data sources. The application of these methods allows us to obtain the following results: (i) we sample the space of alternative flux distributions in the leaf- and the liver-specific case and quantify the ambiguity of the predictions. In addition, we show how the inclusion of ℓ1-regularization during data integration reduces the ambiguity in both cases. (ii) We generate sets of alternative leaf- and liver-specific models that are optimal to each one of the evaluated model reconstruction approaches. We demonstrate that alternative models of the same context contain a marked fraction of disparate reactions. Further, we show that a careful balance between model sparsity and metabolic functionality helps in reducing the discrepancies between alternative models. Finally, our findings indicate that alternative optima must be taken into account for rendering the context-specific metabolic model predictions less ambiguous.

Metabolic Network Constrains Gene Regulation of C4 Photosynthesis: The Case of Maize.

Engineering C3 plants to increase their efficiency of carbon fixation as well as of nitrogen and water use simultaneously may be facilitated by understanding the mechanisms that underpin the C4 syndrome. Existing experimental studies have indicated that the emergence of the C4 syndrome requires co-ordination between several levels of cellular organization, from gene regulation to metabolism, across two co-operating cell systems-mesophyll and bundle sheath cells. Yet, determining the extent to which the structure of the C4 plant metabolic network may constrain gene expression remains unclear, although it will provide an important consideration in engineering C4 photosynthesis in C3 plants. Here, we utilize flux coupling analysis with the second-generation maize metabolic models to investigate the correspondence between metabolic network structure and transcriptomic phenotypes along the maize leaf gradient. The examined scenarios with publically available data from independent experiments indicate that the transcriptomic programs of the two cell types are co-ordinated, quantitatively and qualitatively, due to the presence of coupled metabolic reactions in specific metabolic pathways. Taken together, our study demonstrates that precise quantitative coupling will have to be achieved in order to ensure a successfully engineered transition from C3 to C4 crops.

Context-Specific Metabolic Model Extraction Based on Regularized Least Squares Optimization.

Genome-scale metabolic models have proven highly valuable in investigating cell physiology. Recent advances include the development of methods to extract context-specific models capable of describing metabolism under more specific scenarios (e.g., cell types). Yet, none of the existing computational approaches allows for a fully automated model extraction and determination of a flux distribution independent of user-defined parameters. Here we present RegrEx, a fully automated approach that relies solely on context-specific data and ℓ1-norm regularization to extract a context-specific model and to provide a flux distribution that maximizes its correlation to data. Moreover, the publically available implementation of RegrEx was used to extract 11 context-specific human models using publicly available RNAseq expression profiles, Recon1 and also Recon2, the most recent human metabolic model. The comparison of the performance of RegrEx and its contending alternatives demonstrates that the proposed method extracts models for which both the structure, i.e., reactions included, and the flux distributions are in concordance with the employed data. These findings are supported by validation and comparison of method performance on additional data not used in context-specific model extraction. Therefore, our study sets the ground for applications of other regularization techniques in large-scale metabolic modeling.

Generalized framework for context-specific metabolic model extraction methods.

Genome-scale metabolic models (GEMs) are increasingly applied to investigate the physiology not only of simple prokaryotes, but also eukaryotes, such as plants, characterized with compartmentalized cells of multiple types. While genome-scale models aim at including the entirety of known metabolic reactions, mounting evidence has indicated that only a subset of these reactions is active in a given context, including: developmental stage, cell type, or environment. As a result, several methods have been proposed to reconstruct context-specific models from existing genome-scale models by integrating various types of high-throughput data. Here we present a mathematical framework that puts all existing methods under one umbrella and provides the means to better understand their functioning, highlight similarities and differences, and to help users in selecting a most suitable method for an application.