Detection of transcriptional triggers in the dynamics of microbial growth: application to the respiratorily versatile bacterium Shewanella oneidensis.

The capacity of microorganisms to respond to variable external conditions requires a coordination of environment-sensing mechanisms and decision-making regulatory circuits. Here, we seek to understand the interplay between these two processes by combining high-throughput measurement of time-dependent mRNA profiles with a novel computational approach that searches for key genetic triggers of transcriptional changes. Our approach helped us understand the regulatory strategies of a respiratorily versatile bacterium with promising bioenergy and bioremediation applications, Shewanella oneidensis, in minimal and rich media. By comparing expression profiles across these two conditions, we unveiled components of the transcriptional program that depend mainly on the growth phase. Conversely, by integrating our time-dependent data with a previously available large compendium of static perturbation responses, we identified transcriptional changes that cannot be explained solely by internal network dynamics, but are rather triggered by specific genes acting as key mediators of an environment-dependent response. These transcriptional triggers include known and novel regulators that respond to carbon, nitrogen and oxygen limitation. Our analysis suggests a sequence of physiological responses, including a coupling between nitrogen depletion and glycogen storage, partially recapitulated through dynamic flux balance analysis, and experimentally confirmed by metabolite measurements. Our approach is broadly applicable to other systems.

Temporal expression-based analysis of metabolism.

Metabolic flux is frequently rerouted through cellular metabolism in response to dynamic changes in the intra- and extra-cellular environment. Capturing the mechanisms underlying these metabolic transitions in quantitative and predictive models is a prominent challenge in systems biology. Progress in this regard has been made by integrating high-throughput gene expression data into genome-scale stoichiometric models of metabolism. Here, we extend previous approaches to perform a Temporal Expression-based Analysis of Metabolism (TEAM). We apply TEAM to understanding the complex metabolic dynamics of the respiratorily versatile bacterium Shewanella oneidensis grown under aerobic, lactate-limited conditions. TEAM predicts temporal metabolic flux distributions using time-series gene expression data. Increased predictive power is achieved by supplementing these data with a large reference compendium of gene expression, which allows us to take into account the unique character of the distribution of expression of each individual gene. We further propose a straightforward method for studying the sensitivity of TEAM to changes in its fundamental free threshold parameter θ, and reveal that discrete zones of distinct metabolic behavior arise as this parameter is changed. By comparing the qualitative characteristics of these zones to additional experimental data, we are able to constrain the range of θ to a small, well-defined interval. In parallel, the sensitivity analysis reveals the inherently difficult nature of dynamic metabolic flux modeling: small errors early in the simulation propagate to relatively large changes later in the simulation. We expect that handling such "history-dependent" sensitivities will be a major challenge in the future development of dynamic metabolic-modeling techniques.

Single cell genomes of Prochlorococcus, Synechococcus, and sympatric microbes from diverse marine environments.

Prochlorococcus and Synechococcus are the dominant primary producers in marine ecosystems and perform a significant fraction of ocean carbon fixation. These cyanobacteria interact with a diverse microbial community that coexists with them. Comparative genomics of cultivated isolates has helped address questions regarding patterns of evolution and diversity among microbes, but the fraction that can be cultivated is miniscule compared to the diversity in the wild. To further probe the diversity of these groups and extend the utility of reference sequence databases, we report a data set of single cell genomes for 489 Prochlorococcus, 50 Synechococcus, 9 extracellular virus particles, and 190 additional microorganisms from a diverse range of bacterial, archaeal, and viral groups. Many of these uncultivated single cell genomes are derived from samples obtained on GEOTRACES cruises and at well-studied oceanographic stations, each with extensive suites of physical, chemical, and biological measurements. The genomic data reported here greatly increases the number of available Prochlorococcus genomes and will facilitate studies on evolutionary biology, microbial ecology, and biological oceanography.