Bacterial community composition and functional potential change subtly across gradients in the surface ocean. In contrast, while there are significant phylogenetic divergences between communities from freshwater and marine habitats, the underlying mechanisms to this phylogenetic structuring yet remain unknown. We hypothesized that the functional potential of natural bacterial communities is linked to this striking divide between microbiomes. To test this hypothesis, metagenomic sequencing of microbial communities along a 1,800 km transect in the Baltic Sea area, encompassing a continuous natural salinity gradient from limnic to fully marine conditions, was explored. Multivariate statistical analyses showed that salinity is the main determinant of dramatic changes in microbial community composition, but also of large scale changes in core metabolic functions of bacteria. Strikingly, genetically and metabolically different pathways for key metabolic processes, such as respiration, biosynthesis of quinones and isoprenoids, glycolysis and osmolyte transport, were differentially abundant at high and low salinities. These shifts in functional capacities were observed at multiple taxonomic levels and within dominant bacterial phyla, while bacteria, such as SAR11, were able to adapt to the entire salinity gradient. We propose that the large differences in central metabolism required at high and low salinities dictate the striking divide between freshwater and marine microbiomes, and that the ability to inhabit different salinity regimes evolved early during bacterial phylogenetic differentiation. These findings significantly advance our understanding of microbial distributions and stress the need to incorporate salinity in future climate change models that predict increased levels of precipitation and a reduction in salinity.

Caspases accomplish initiation and execution of apoptosis, a programmed cell death process specific to metazoans. The existence of prokaryotic caspase homologs, termed metacaspases, has been known for slightly more than a decade. Despite their potential connection to the evolution of programmed cell death in eukaryotes, the phylogenetic distribution and functions of these prokaryotic metacaspase sequences are largely uncharted, while a few experiments imply involvement in programmed cell death. Aiming at providing a more detailed picture of prokaryotic caspase homologs, we applied a computational approach based on Hidden Markov Model search profiles to identify and functionally characterize putative metacaspases in bacterial and archaeal genomes. Out of the total of 1463 analyzed genomes, merely 267 (18%) were identified to contain putative metacaspases, but their taxonomic distribution included most prokaryotic phyla and a few archaea (Euryarchaeota). Metacaspases were particularly abundant in Alphaproteobacteria, Deltaproteobacteria and Cyanobacteria, which harbor many morphologically and developmentally complex organisms, and a distinct correlation was found between abundance and phenotypic complexity in Cyanobacteria. Notably, Bacillus subtilis and Escherichia coli, known to undergo genetically regulated autolysis, lacked metacaspases. Pfam domain architecture analysis combined with operon identification revealed rich and varied configurations among the metacaspase sequences. These imply roles in programmed cell death, but also e.g. in signaling, various enzymatic activities and protein modification. Together our data show a wide and scattered distribution of caspase homologs in prokaryotes with structurally and functionally diverse sub-groups, and with a potentially intriguing evolutionary role. These features will help delineate future characterizations of death pathways in prokaryotes.

Cyanobacteria are model organisms for photosynthesis and are attractive for biotechnology applications. To aid investigation of genotype-phenotype relationships in cyanobacteria, we develop an inducible CRISPRi gene repression library in Synechocystis sp. PCC 6803, where we aim to target all genes for repression. We track the growth of all library members in multiple conditions and estimate gene fitness. The library reveals several clones with increased growth rates, and these have a common upregulation of genes related to cyclic electron flow. We challenge the library with 0.1 M L-lactate and find that repression of peroxiredoxin bcp2 increases growth rate by 49%. Transforming the library into an L-lactatesecreting Synechocystis strain and sorting top lactate producers enriches clones with sgRNAs targeting nutrient assimilation, central carbon metabolism, and cyclic electron flow. In many examples, productivity can be enhanced by repression of essential genes, which are difficult to access by transposon insertion.

An improved kinetic model of the cyanobacterial Calvin cycle employing random sampling identifies probabilistic conditions for metabolome stability and metabolic flux control, agreeing with experimental data and aiding engineering efforts.

Bacteria must balance the different needs for substrate assimilation, growth functions, and resilience in order to thrive in their environment. Of all cellular macromolecules, the bacterial proteome is by far the most important resource and its size is limited. Here, we investigated how the highly versatile 'knallgas' bacterium Cupriavidus necator reallocates protein resources when grown on different limiting substrates and with different growth rates. We determined protein quantity by mass spectrometry and estimated enzyme utilization by resource balance analysis modeling. We found that C. necator invests a large fraction of its proteome in functions that are hardly utilized. Of the enzymes that are utilized, many are present in excess abundance. One prominent example is the strong expression of CBB cycle genes such as Rubisco during growth on fructose. Modeling and mutant competition experiments suggest that CO2-reassimilation through Rubisco does not provide a fitness benefit for heterotrophic growth, but is rather an investment in readiness for autotrophy.

Metacaspases are distant homologs of metazoan caspase proteases, implicated in stress response, and programmed cell death (PCD) in bacteria and phytoplankton. While the few previous studies on metacaspases have relied on cultured organisms and sequenced genomes, no studies have focused on metacaspases in a natural setting. We here present data from the first microbial community-wide metacaspase survey; performed by querying metagenomic and metatranscriptomic datasets from the brackish Baltic Sea, a water body characterized by pronounced environmental gradients and periods of massive cyanobacterial blooms. Metacaspase genes were restricted to ~4% of the bacteria, taxonomically affiliated mainly to Bacteroidetes, Alpha- and Betaproteobacteria and Cyanobacteria. The gene abundance was significantly higher in larger or particle-associated bacteria (>0.8 μm), and filamentous Cyanobacteria dominated metacaspase gene expression throughout the bloom season. Distinct seasonal expression patterns were detected for the three metacaspase genes in Nodularia spumigena, one of the main bloom-formers. Clustering of normalized gene expression in combination with analyses of genomic and assembly data suggest functional diversification of these genes, and possible roles of the metacaspase genes related to stress responses, i.e., sulfur metabolism in connection to oxidative stress, and nutrient stress induced cellular differentiation. Co-expression of genes encoding metacaspases and nodularin toxin synthesis enzymes was also observed in Nodularia spumigena. The study shows that metacaspases represent an adaptation of potentially high importance for several key organisms in the Baltic Sea, most prominently Cyanobacteria, and open up for further exploration of their physiological roles in microbes and assessment of their ecological impact in aquatic habitats.

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Introducing biosynthetic pathways into an organism is both reliant on and challenged by endogenous biochemistry. Here we compared the expansion potential of the metabolic network in the photoautotroph Synechocystis with that of the heterotroph E. coli using the novel workflow POPPY (Prospecting Optimal Pathways with PYthon). First, E. coli and Synechocystis metabolomic and fluxomic data were combined with metabolic models to identify thermodynamic constraints on metabolite concentrations (NET analysis). Then, thousands of automatically constructed pathways were placed within each network and subjected to a network-embedded variant of the max-min driving force analysis (NEM). We found that the networks had different capabilities for imparting thermodynamic driving forces toward certain compounds. Key metabolites were constrained differently in Synechocystis due to opposing flux directions in glycolysis and carbon fixation, the forked tri-carboxylic acid cycle, and photorespiration. Furthermore, the lysine biosynthesis pathway in Synechocystis was identified as thermodynamically constrained, impacting both endogenous and heterologous reactions through low 2-oxoglutarate levels. Our study also identified important yet poorly covered areas in existing metabolomics data and provides a reference for future thermodynamics-based engineering in Synechocystis and beyond. The POPPY methodology represents a step in making optimal pathway-host matches, which is likely to become important as the practical range of host organisms is diversified.