Hi-C metagenome sequencing reveals soil phage-host interactions.

Bacteriophages are abundant in soils. However, the majority are uncharacterized, and their hosts are unknown. Here, we apply high-throughput chromosome conformation capture (Hi-C) to directly capture phage-host relationships. Some hosts have high centralities in bacterial community co-occurrence networks, suggesting phage infections have an important impact on the soil bacterial community interactions. We observe increased average viral copies per host (VPH) and decreased viral transcriptional activity following a two-week soil-drying incubation, indicating an increase in lysogenic infections. Soil drying also alters the observed phage host range. A significant negative correlation between VPH and host abundance prior to drying indicates more lytic infections result in more host death and inversely influence host abundance. This study provides empirical evidence of phage-mediated bacterial population dynamics in soil by directly capturing specific phage-host interactions.

Tripogon loliiformis tolerates rapid desiccation after metabolic and transcriptional priming during initial drying.

Crop plants and undomesticated resilient species employ different strategies to regulate their energy resources and growth. Most crop species are sensitive to stress and prioritise rapid growth to maximise yield or biomass production. In contrast, resilient plants grow slowly, are small, and allocate their resources for survival in challenging environments. One small group of plants, termed resurrection plants, survive desiccation of their vegetative tissue and regain full metabolic activity upon watering. However, the precise molecular mechanisms underlying this extreme tolerance remain unknown. In this study, we employed a transcriptomics and metabolomics approach, to investigate the mechanisms of desiccation tolerance in Tripogon loliiformis, a modified desiccation-tolerant plant, that survives gradual but not rapid drying. We show that T. loliiformis can survive rapid desiccation if it is gradually dried to 60% relative water content (RWC). Furthermore, the gene expression data showed that T. loliiformis is genetically predisposed for desiccation in the hydrated state, as evidenced by the accumulation of MYB, NAC, bZIP, WRKY transcription factors along with the phytohormones, abscisic acid, salicylic acid, amino acids (e.g., proline) and TCA cycle sugars during initial drying. Through network analysis of co-expressed genes, we observed differential responses to desiccation between T. loliiformis shoots and roots. Dehydrating shoots displayed global transcriptional changes across broad functional categories, although no enrichment was observed during drying. In contrast, dehydrating roots showed distinct network changes with the most significant differences occurring at 40% RWC. The cumulative effects of the early stress responses may indicate the minimum requirements of desiccation tolerance and enable T. loliiformis to survive rapid drying. These findings potentially hold promise for identifying biotechnological solutions aimed at developing drought-tolerant crops without growth and yield penalties.

3' RNA-seq is superior to standard RNA-seq in cases of sparse data but inferior at identifying toxicity pathways in a model organism.

Introduction: The application of RNA-sequencing has led to numerous breakthroughs related to investigating gene expression levels in complex biological systems. Among these are knowledge of how organisms, such as the vertebrate model organism zebrafish (Danio rerio), respond to toxicant exposure. Recently, the development of 3' RNA-seq has allowed for the determination of gene expression levels with a fraction of the required reads compared to standard RNA-seq. While 3' RNA-seq has many advantages, a comparison to standard RNA-seq has not been performed in the context of whole organism toxicity and sparse data. Methods and results: Here, we examined samples from zebrafish exposed to perfluorobutane sulfonamide (FBSA) with either 3' or standard RNA-seq to determine the advantages of each with regards to the identification of functionally enriched pathways. We found that 3' and standard RNA-seq showed specific advantages when focusing on annotated or unannotated regions of the genome. We also found that standard RNA-seq identified more differentially expressed genes (DEGs), but that this advantage disappeared under conditions of sparse data. We also found that standard RNA-seq had a significant advantage in identifying functionally enriched pathways via analysis of DEG lists but that this advantage was minimal when identifying pathways via gene set enrichment analysis of all genes. Conclusions: These results show that each approach has experimental conditions where they may be advantageous. Our observations can help guide others in the choice of 3' RNA-seq vs standard RNA sequencing to query gene expression levels in a range of biological systems.

Graphene oxide exposure alters gut microbial community composition and metabolism in an in vitro human model.

Graphene oxide (GO) nanomaterials have unique physicochemical properties that make them highly promising for biomedical, environmental, and agricultural applications. There is growing interest in the use of GO and extensive in vitro and in vivo studies have been conducted to assess its nanotoxicity. Although it is known that GO can alter the composition of the gut microbiota in mice and zebrafish, studies on the potential impacts of GO on the human gut microbiome are largely lacking. This study addresses an important knowledge gap by investigating the impact of GO exposure- at low (25 mg/L) and high (250 mg/L) doses under both fed (nutrient rich) and fasted (nutrient deplete) conditions- on the gut microbial communitys' structure and function, using an in vitro model. This model includes simulated oral, gastric, small intestinal phase digestion of GO followed by incubation in a colon bioreactor. 16S rRNA amplicon sequencing revealed that GO exposure resulted in a restructuring of community composition. 25 mg/L GO induced a marked decrease in the Bacteroidota phylum and increased the ratio of Firmicutes to Bacteroidota (F/B). Untargeted metabolomics on the supernatants indicated that 25 mg/L GO impaired microbial utilization and metabolism of substrates (amino acids, carbohydrate metabolites) and reduced production of beneficial microbial metabolites such as 5-hydroxyindole-3-acetic acid and GABA. Exposure to 250 mg/L GO resulted in community composition and metabolome profiles that were very similar to the controls that lacked both GO and digestive enzymes. Differential abundance analyses revealed that 3 genera from the phylum Bacteroidota (Bacteroides, Dysgonomonas, and Parabacteroides) were more abundant after 250 mg/L GO exposure, irrespective of feed state. Integrative correlation network analysis indicated that the phylum Bacteroidota showed strong positive correlations to multiple microbial metabolites including GABA and 3-indoleacetic acid, are much larger number of correlations compared to other phyla. These results show that GO exposure has a significant impact on gut microbial community composition and metabolism at both low and high GO concentrations.

Concentration-response gene expression analysis in zebrafish reveals phenotypically-anchored transcriptional responses to retene.

Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous environmental contaminants and are associated with human disease. Canonically, many PAHs induce toxicity via activation of the aryl hydrocarbon receptor (AHR) pathway. While the interaction between PAHs and the AHR is well-established, understanding which AHR-regulated transcriptional effects directly result in observable phenotypes and which are adaptive or benign is important to better understand PAH toxicity. Retene is a frequently detected PAH in environmental sampling and has been associated with AHR2-dependent developmental toxicity in zebrafish, though its mechanism of toxicity has not been fully elucidated. To interrogate transcriptional changes causally associated with retene toxicity, we conducted whole-animal RNA sequencing at 48 h post-fertilization after exposure to eight retene concentrations. We aimed to identify the most sensitive transcriptomic responses and to determine whether this approach could uncover gene sets uniquely differentially expressed at concentrations which induce a phenotype. We identified a concentration-response relationship for differential gene expression in both number of differentially expressed genes (DEGs) and magnitude of expression change. Elevated expression of cyp1a at retene concentrations below the threshold for teratogenicity suggested that while cyp1a expression is a sensitive biomarker of AHR activation, it may be too sensitive to serve as a biomarker of teratogenicity. Genes differentially expressed at only non-teratogenic concentrations were enriched for transforming growth factor-ß (TGF-ß) signaling pathway disruption while DEGs identified at only teratogenic concentrations were significantly enriched for response to xenobiotic stimulus and reduction-oxidation reaction activity. DEGs which spanned both non-teratogenic and teratogenic concentrations showed similar disrupted biological processes to those unique to teratogenic concentrations, indicating these processes were disrupted at low exposure concentrations. Gene co-expression network analysis identified several gene modules, including those associated with PAHs and AHR2 activation. One, Module 7, was strongly enriched for AHR2-associated genes and contained the strongest responses to retene. Benchmark concentration (BMC) of Module seven genes identified a median BMC of 7.5 µM, nearly the highest retene concentration with no associated teratogenicity, supporting the hypothesis that Module seven genes are largely responsible for retene toxicity.

Gene co-expression network analysis in zebrafish reveals chemical class specific modules.

BACKGROUND: Zebrafish is a popular animal model used for high-throughput screening of chemical hazards, however, investigations of transcriptomic mechanisms of toxicity are still needed. Here, our goal was to identify genes and biological pathways that Aryl Hydrocarbon Receptor 2 (AHR2) Activators and flame retardant chemicals (FRCs) alter in developing zebrafish. Taking advantage of a compendium of phenotypically-anchored RNA sequencing data collected from 48-h post fertilization (hpf) zebrafish, we inferred a co-expression network that grouped genes based on their transcriptional response. RESULTS: Genes responding to the FRCs and AHR2 Activators localized to distinct regions of the network, with FRCs inducing a broader response related to neurobehavior. AHR2 Activators centered in one region related to chemical stress responses. We also discovered several highly co-expressed genes in this module, including cyp1a, and we subsequently show that these genes are definitively within the AHR2 signaling pathway. Systematic removal of the two chemical types from the data, and analysis of network changes identified neurogenesis associated with FRCs, and regulation of vascular development associated with both chemical classes. We also identified highly connected genes responding specifically to each class that are potential biomarkers of exposure. CONCLUSIONS: Overall, we created the first zebrafish chemical-specific gene co-expression network illuminating how chemicals alter the transcriptome relative to each other. In addition to our conclusions regarding FRCs and AHR2 Activators, our network can be leveraged by other studies investigating chemical mechanisms of toxicity.

Integrated network modeling approach defines key metabolic responses of soil microbiomes to perturbations.

The soil environment is constantly changing due to shifts in soil moisture, nutrient availability and other conditions. To contend with these changes, soil microorganisms have evolved a variety of ways to adapt to environmental perturbations, including regulation of gene expression. However, it is challenging to untangle the complex phenotypic response of the soil to environmental change, partly due to the absence of predictive modeling frameworks that can mechanistically link molecular-level changes in soil microorganisms to a community's functional phenotypes (or metaphenome). Towards filling this gap, we performed a combined analysis of metabolic and gene co-expression networks to explore how the soil microbiome responded to changes in soil moisture and nutrient conditions and to determine which genes were expressed under a given condition. Our integrated modeling approach revealed previously unknown, but critically important aspects of the soil microbiomes' response to environmental perturbations. Incorporation of metabolomic and transcriptomic data into metabolic reaction networks identified condition-specific signature genes that are uniquely associated with dry, wet, and glycine-amended conditions. A subsequent gene co-expression network analysis revealed that drought-associated genes occupied more central positions in a network model of the soil community, compared to the genes associated with wet, and glycine-amended conditions. These results indicate the occurrence of system-wide metabolic coordination when soil microbiomes cope with moisture or nutrient perturbations. Importantly, the approach that we demonstrate here to analyze large-scale multi-omics data from a natural soil environment is applicable to other microbiome systems for which multi-omics data are available.

Unified feature association networks through integration of transcriptomic and proteomic data.

High-throughput multi-omics studies and corresponding network analyses of multi-omic data have rapidly expanded their impact over the last 10 years. As biological features of different types (e.g. transcripts, proteins, metabolites) interact within cellular systems, the greatest amount of knowledge can be gained from networks that incorporate multiple types of -omic data. However, biological and technical sources of variation diminish the ability to detect cross-type associations, yielding networks dominated by communities comprised of nodes of the same type. We describe here network building methods that can maximize edges between nodes of different data types leading to integrated networks, networks that have a large number of edges that link nodes of different-omic types (transcripts, proteins, lipids etc). We systematically rank several network inference methods and demonstrate that, in many cases, using a random forest method, GENIE3, produces the most integrated networks. This increase in integration does not come at the cost of accuracy as GENIE3 produces networks of approximately the same quality as the other network inference methods tested here. Using GENIE3, we also infer networks representing antibody-mediated Dengue virus cell invasion and receptor-mediated Dengue virus invasion. A number of functional pathways showed centrality differences between the two networks including genes responding to both GM-CSF and IL-4, which had a higher centrality value in an antibody-mediated vs. receptor-mediated Dengue network. Because a biological system involves the interplay of many different types of molecules, incorporating multiple data types into networks will improve their use as models of biological systems. The methods explored here are some of the first to specifically highlight and address the challenges associated with how such multi-omic networks can be assembled and how the greatest number of interactions can be inferred from different data types. The resulting networks can lead to the discovery of new host response patterns and interactions during viral infection, generate new hypotheses of pathogenic mechanisms and confirm mechanisms of disease.

Coupling Genome-wide Transcriptomics and Developmental Toxicity Profiles in Zebrafish to Characterize Polycyclic Aromatic Hydrocarbon (PAH) Hazard.

Polycyclic Aromatic Hydrocarbons (PAHs) are diverse environmental pollutants associated with adverse human health effects. Many studies focus on the carcinogenic effects of a limited number of PAHs and there is an increasing need to understand mechanisms of developmental toxicity of more varied yet environmentally relevant PAHs. A previous study characterized the developmental toxicity of 123 PAHs in zebrafish. Based on phenotypic responses ranging from complete inactivity to acute mortality, we classified these PAHs into eight bins, selected 16 representative PAHs, and exposed developing zebrafish to the concentration of each PAH that induced 80% phenotypic effect. We conducted RNA sequencing at 48 h post fertilization to identify gene expression changes as a result of PAH exposure. Using the Context Likelihood of Relatedness algorithm, we inferred a network that links the PAHs based on coordinated gene responses to PAH exposure. The 16 PAHs formed two broad clusters: Cluster A was transcriptionally more similar to the controls, while Cluster B consisted of PAHs that were generally more developmentally toxic, significantly elevated cyp1a transcript levels, and induced Ahr2-dependent Cyp1a protein expression in the skin confirmed by gene-silencing studies. We found that cyp1a transcript levels were associated with transcriptomic response, but not with PAH developmental toxicity. While all cluster B PAHs predominantly activated Ahr2, they also each enriched unique pathways like ion transport signaling, which likely points to differing molecular events between the PAHs downstream of Ahr2. Thus, using a systems biology approach, we have begun to evaluate, classify, and define mechanisms of PAH toxicity.

Toward a Better Understanding of Species Interactions through Network Biology.

Within the last decade, there has been an explosion of multi-omics data generated for several microbial systems. At the same time, new methods of analysis have emerged that are based on inferring networks that link features both within and between species based on correlation in abundance. These developments prompt two important questions. What can be done with network approaches to better understand microbial species interactions? What challenges remain in applying network approaches to query the more complex systems of natural settings? Here, I briefly describe what has been done and what questions still need to be answered. Over the next 5 to 10 years, we will be in a strong position to infer networks that contain multiple kinds of omic data and describe systems with multiple species. These applications will open the door for a better understanding and use of microbiomes across a variety of fields.

Selection, Succession, and Stabilization of Soil Microbial Consortia.

Soil microorganisms play fundamental roles in cycling of soil carbon, nitrogen, and other nutrients, yet we have a poor understanding of how soil microbiomes are shaped by their nutritional and physical environment. In this study, we investigated the successional dynamics of a soil microbiome during 21 weeks of enrichment on chitin and its monomer, N-acetylglucosamine. We examined succession of the soil communities in a physically heterogeneous soil matrix as well as a homogeneous liquid medium. The guiding hypothesis was that the initial species richness would influence the tendency for the selected consortia to stabilize and maintain a relatively constant community structure over time. We also hypothesized that long-term, substrate-driven growth would result in consortia with reduced species richness compared to the parent microbiome and that this process would be deterministic with relatively little variation between replicates. We found that the initial species richness does influence the long-term community stability in both liquid media and soil and that lower initial richness results in a more rapid convergence to stability. Despite use of the same soil inoculum and access to the same major substrate, the resulting community composition differed greatly in soil from that in liquid medium. Hence, distinct selective pressures in soils relative to homogenous liquid media exist and can control community succession dynamics. This difference is likely related to the fact that soil microbiomes are more likely to thrive, with fewer compositional changes, in a soil matrix than in liquid environments. IMPORTANCE The soil microbiome carries out important ecosystem functions, but interactions between soil microbial communities have been difficult to study due to the high microbial diversity and complexity of the soil habitat. In this study, we successfully obtained stable consortia with reduced complexity that contained species found in the original source soil. These consortia and the methods used to obtain them can be a valuable resource for exploration of specific mechanisms underlying soil microbial community ecology. The results of this study also provide new experimental context to better inform how soil microbial communities are shaped by new environments and how a combination of initial taxonomic structure and physical environment influences stability.

Improving network inference algorithms using resampling methods.

BACKGROUND: Relatively small changes to gene expression data dramatically affect co-expression networks inferred from that data which, in turn, can significantly alter the subsequent biological interpretation. This error propagation is an underappreciated problem that, while hinted at in the literature, has not yet been thoroughly explored. Resampling methods (e.g. bootstrap aggregation, random subspace method) are hypothesized to alleviate variability in network inference methods by minimizing outlier effects and distilling persistent associations in the data. But the efficacy of the approach assumes the generalization from statistical theory holds true in biological network inference applications. RESULTS: We evaluated the effect of bootstrap aggregation on inferred networks using commonly applied network inference methods in terms of stability, or resilience to perturbations in the underlying expression data, a metric for accuracy, and functional enrichment of edge interactions. CONCLUSION: Bootstrap aggregation results in improved stability and, depending on the size of the input dataset, a marginal improvement to accuracy assessed by each method's ability to link genes in the same functional pathway.

Species-specific transcriptomic network inference of interspecies interactions.

The advent of high-throughput 'omics approaches coupled with computational analyses to reconstruct individual genomes from metagenomes provides a basis for species-resolved functional studies. Here, a mutual information approach was applied to build a gene association network of a commensal consortium, in which a unicellular cyanobacterium Thermosynechococcus elongatus BP1 supported the heterotrophic growth of Meiothermus ruber strain A. Specifically, we used the context likelihood of relatedness (CLR) algorithm to generate a gene association network from 25 transcriptomic datasets representing distinct growth conditions. The resulting interspecies network revealed a number of linkages between genes in each species. While many of the linkages were supported by the existing knowledge of phototroph-heterotroph interactions and the metabolism of these two species several new interactions were inferred as well. These include linkages between amino acid synthesis and uptake genes, as well as carbohydrate and vitamin metabolism, terpenoid metabolism and cell adhesion genes. Further topological examination and functional analysis of specific gene associations suggested that the interactions are likely to center around the exchange of energetically costly metabolites between T. elongatus and M. ruber. Both the approach and conclusions derived from this work are widely applicable to microbial communities for identification of the interactions between species and characterization of community functioning as a whole.

Phenotypic responses to interspecies competition and commensalism in a naturally-derived microbial co-culture.

The fundamental question of whether different microbial species will co-exist or compete in a given environment depends on context, composition and environmental constraints. Model microbial systems can yield some general principles related to this question. In this study we employed a naturally occurring co-culture composed of heterotrophic bacteria, Halomonas sp. HL-48 and Marinobacter sp. HL-58, to ask two fundamental scientific questions: 1) how do the phenotypes of two naturally co-existing species respond to partnership as compared to axenic growth? and 2) how do growth and molecular phenotypes of these species change with respect to competitive and commensal interactions? We hypothesized - and confirmed - that co-cultivation under glucose as the sole carbon source would result in competitive interactions. Similarly, when glucose was swapped with xylose, the interactions became commensal because Marinobacter HL-58 was supported by metabolites derived from Halomonas HL-48. Each species responded to partnership by changing both its growth and molecular phenotype as assayed via batch growth kinetics and global transcriptomics. These phenotypic responses depended on nutrient availability and so the environment ultimately controlled how they responded to each other. This simplified model community revealed that microbial interactions are context-specific and different environmental conditions dictate how interspecies partnerships will unfold.

Indirect Interspecies Regulation: Transcriptional and Physiological Responses of a Cyanobacterium to Heterotrophic Partnership.

The mechanisms by which microbes interact in communities remain poorly understood. Here, we interrogated specific interactions between photoautotrophic and heterotrophic members of a model consortium to infer mechanisms that mediate metabolic coupling and acclimation to partnership. This binary consortium was composed of a cyanobacterium, Thermosynechococcus elongatus BP-1, which supported growth of an obligate aerobic heterotroph, Meiothermus ruber strain A, by providing organic carbon, O2, and reduced nitrogen. Species-resolved transcriptomic analyses were used in combination with growth and photosynthesis kinetics to infer interactions and the environmental context under which they occur. We found that the efficiency of biomass production and resistance to stress induced by high levels of dissolved O2 increased, beyond axenic performance, as a result of heterotrophic partnership. Coordinated transcriptional responses transcending both species were observed and used to infer specific interactions resulting from the synthesis and exchange of resources. The cyanobacterium responded to heterotrophic partnership by altering expression of core genes involved with photosynthesis, carbon uptake/fixation, vitamin synthesis, and scavenging of reactive oxygen species (ROS). IMPORTANCE This study elucidates how a cyanobacterial primary producer acclimates to heterotrophic partnership by modulating the expression levels of key metabolic genes. Heterotrophic bacteria can indirectly regulate the physiology of the photoautotrophic primary producers, resulting in physiological changes identified here, such as increased intracellular ROS. Some of the interactions inferred from this model system represent putative principles of metabolic coupling in phototrophic-heterotrophic partnerships.

Network analysis of transcriptomics expands regulatory landscapes in Synechococcus sp. PCC 7002.

Cyanobacterial regulation of gene expression must contend with a genome organization that lacks apparent functional context, as the majority of cellular processes and metabolic pathways are encoded by genes found at disparate locations across the genome and relatively few transcription factors exist. In this study, global transcript abundance data from the model cyanobacterium Synechococcus sp. PCC 7002 grown under 42 different conditions was analyzed using Context-Likelihood of Relatedness (CLR). The resulting network, organized into 11 modules, provided insight into transcriptional network topology as well as grouping genes by function and linking their response to specific environmental variables. When used in conjunction with genome sequences, the network allowed identification and expansion of novel potential targets of both DNA binding proteins and sRNA regulators. These results offer a new perspective into the multi-level regulation that governs cellular adaptations of the fast-growing physiologically robust cyanobacterium Synechococcus sp. PCC 7002 to changing environmental variables. It also provides a methodological high-throughput approach to studying multi-scale regulatory mechanisms that operate in cyanobacteria. Finally, it provides valuable context for integrating systems-level data to enhance gene grouping based on annotated function, especially in organisms where traditional context analyses cannot be implemented due to lack of operon-based functional organization.

Unlocking the Constraints of Cyanobacterial Productivity: Acclimations Enabling Ultrafast Growth.

UNLABELLED: Harnessing the metabolic potential of photosynthetic microbes for next-generation biotechnology objectives requires detailed scientific understanding of the physiological constraints and regulatory controls affecting carbon partitioning between biomass, metabolite storage pools, and bioproduct synthesis. We dissected the cellular mechanisms underlying the remarkable physiological robustness of the euryhaline unicellular cyanobacterium Synechococcus sp. strain PCC 7002 (Synechococcus 7002) and identify key mechanisms that allow cyanobacteria to achieve unprecedented photoautotrophic productivities (~2.5-h doubling time). Ultrafast growth of Synechococcus 7002 was supported by high rates of photosynthetic electron transfer and linked to significantly elevated transcription of precursor biosynthesis and protein translation machinery. Notably, no growth or photosynthesis inhibition signatures were observed under any of the tested experimental conditions. Finally, the ultrafast growth in Synechococcus 7002 was also linked to a 300% expansion of average cell volume. We hypothesize that this cellular adaptation is required at high irradiances to support higher cell division rates and reduce deleterious effects, corresponding to high light, through increased carbon and reductant sequestration. IMPORTANCE: Efficient coupling between photosynthesis and productivity is central to the development of biotechnology based on solar energy. Therefore, understanding the factors constraining maximum rates of carbon processing is necessary to identify regulatory mechanisms and devise strategies to overcome productivity constraints. Here, we interrogate the molecular mechanisms that operate at a systems level to allow cyanobacteria to achieve ultrafast growth. This was done by considering growth and photosynthetic kinetics with global transcription patterns. We have delineated putative biological principles that allow unicellular cyanobacteria to achieve ultrahigh growth rates through photophysiological acclimation and effective management of cellular resource under different growth regimes.

Multi-Omic Dynamics Associate Oxygenic Photosynthesis with Nitrogenase-Mediated H2 Production in Cyanothece sp. ATCC 51142.

To date, the proposed mechanisms of nitrogenase-driven photosynthetic H2 production by the diazotrophic unicellular cyanobacterium Cyanothece sp. ATCC 51142 have assumed that reductant and ATP requirements are derived solely from glycogen oxidation and cyclic-electron flow around photosystem I. Through genome-scale transcript and protein profiling, this study presents and tests a new hypothesis on the metabolic relationship between oxygenic photosynthesis and nitrogenase-mediated H2 production in Cyanothece 51142. Our results show that net-positive rates of oxygenic photosynthesis and increased expression of photosystem II reaction centers correspond and are synchronized with nitrogenase expression and H2 production. These findings provide a new and more complete view on the metabolic processes contributing to the energy budget of photosynthetic H2 production and highlight the role of concurrent photocatalytic H2O oxidation as a participating process.

Integrated in silico Analyses of Regulatory and Metabolic Networks of Synechococcus sp. PCC 7002 Reveal Relationships between Gene Centrality and Essentiality.

Cyanobacteria dynamically relay environmental inputs to intracellular adaptations through a coordinated adjustment of photosynthetic efficiency and carbon processing rates. The output of such adaptations is reflected through changes in transcriptional patterns and metabolic flux distributions that ultimately define growth strategy. To address interrelationships between metabolism and regulation, we performed integrative analyses of metabolic and gene co-expression networks in a model cyanobacterium, Synechococcus sp. PCC 7002. Centrality analyses using the gene co-expression network identified a set of key genes, which were defined here as "topologically important." Parallel in silico gene knock-out simulations, using the genome-scale metabolic network, classified what we termed as "functionally important" genes, deletion of which affected growth or metabolism. A strong positive correlation was observed between topologically and functionally important genes. Functionally important genes exhibited variable levels of topological centrality; however, the majority of topologically central genes were found to be functionally essential for growth. Subsequent functional enrichment analysis revealed that both functionally and topologically important genes in Synechococcus sp. PCC 7002 are predominantly associated with translation and energy metabolism, two cellular processes critical for growth. This research demonstrates how synergistic network-level analyses can be used for reconciliation of metabolic and gene expression data to uncover fundamental biological principles.