For lake microbes, water column mixing acts as a disturbance because it homogenizes thermal and chemical gradients known to define the distributions of microbial taxa. Our first objective was to isolate hypothesized drivers of lake bacterial response to water column mixing. To accomplish this, we designed an enclosure experiment with three treatments to independently test key biogeochemical changes induced by mixing: oxygen addition to the hypolimnion, nutrient addition to the epilimnion, and full water column mixing. We used molecular fingerprinting to observe bacterial community dynamics in the treatment and control enclosures, and in ambient lake water. We found that oxygen and nutrient amendments simulated the physical-chemical water column environment following mixing and resulted in similar bacterial communities to the mixing treatment, affirming that these were important drivers of community change. These results demonstrate that specific environmental changes can replicate broad disturbance effects on microbial communities. Our second objective was to characterize bacterial community stability by quantifying community resistance, recovery and resilience to an episodic disturbance. The communities in the nutrient and oxygen amendments changed quickly (had low resistance), but generally matched the control composition by the 10th day after treatment, exhibiting resilience. These results imply that aquatic bacterial assemblages are generally stable in the face of disturbance.

The model cyanobacterium, Synechococcus elongatus PCC 7942, is a genetically tractable obligate phototroph that is being developed for the bioproduction of high-value chemicals. Genome-scale models (GEMs) have been successfully used to assess and engineer cellular metabolism; however, GEMs of phototrophic metabolism have been limited by the lack of experimental datasets for model validation and the challenges of incorporating photon uptake. Here, we develop a GEM of metabolism in S. elongatus using random barcode transposon site sequencing (RB-TnSeq) essential gene and physiological data specific to photoautotrophic metabolism. The model explicitly describes photon absorption and accounts for shading, resulting in the characteristic linear growth curve of photoautotrophs. GEM predictions of gene essentiality were compared with data obtained from recent dense-transposon mutagenesis experiments. This dataset allowed major improvements to the accuracy of the model. Furthermore, discrepancies between GEM predictions and the in vivo dataset revealed biological characteristics, such as the importance of a truncated, linear TCA pathway, low flux toward amino acid synthesis from photorespiration, and knowledge gaps within nucleotide metabolism. Coupling of strong experimental support and photoautotrophic modeling methods thus resulted in a highly accurate model of S. elongatus metabolism that highlights previously unknown areas of S. elongatus biology.cyanobacteria | constraint-based modeling | TCA cycle | photosynthesis | Synechococcus elongatus T he unicellular cyanobacterium Synechococcus elongatus PCC 7942 is being developed as a photosynthetic bioproduction platform for an array of industrial products (1-3). This model strain is attractive for this purpose because of its genetic tractability (4) and its reliance on mainly CO 2 , H 2 O, and light for metabolism, reducing the environmental and economic costs of cultivation. For low-cost, high-volume products, such as biofuels, however, one of the biggest challenges is attaining profitable product yields (5, 6). Genome-scale models (GEMs) of metabolism provide a valuable tool for increasing product titers by optimizing yield in silico and then, reproducing the changes in vivo (7). For instance, GEMs were used to select the optimal synthetic pathway for 3-hydroxypropanoate biosynthesis in Saccharomyces cerevisiae (8). In Escherichia coli, GEM optimization was used to realize heterologous production of 1,4-butanediol synthesis and increase titers three orders of magnitude (9). Although there have been numerous modeling efforts in Synechocystis sp. PCC 6803 (here in referred to as PCC 6803), this organism is highly divergent from S. elongatus, where limited modeling has been done (10).This deficit can partially be explained by the lack of in vivo validation datasets, such as 13 C metabolic flux analysis (MFA), for obligate phototrophs (11). Development of metabolic models of S. elongatus with strong experimental support is necessary to exploit the organism as a bioproduc...

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Cyanobacteria are photosynthetic prokaryotes that are influential in global geochemistry and are promising candidates for industrial applications. Because the livelihood of cyanobacteria is directly dependent upon light, a comprehensive understanding of metabolism in these organisms requires taking into account the effects of day-night transitions and circadian regulation. These events synchronize intracellular processes with the solar day. Accordingly, metabolism is controlled and structured differently in cyanobacteria than in heterotrophic bacteria. Thus, the approaches applied to engineering heterotrophic bacteria will need to be revised for cyanobacterial chassis. Here, we summarize important findings related to diurnal metabolism in cyanobacteria and present open questions in the field.