ABSTRACT Microbial processes operate at the microscale, which is not resolved by existing ecosystem models. Here, we present a novel model that simulates a 1 mL three-dimensional cube using a hybrid Lagrangian–Eulerian approach, at ecologically relevant timescales. The model simulates individual microbes, including three phytoplankton size classes with healthy, senescent, and dead lifecycle stages; copiotrophic and oligotrophic heterotrophic bacteria; and dissolved organic matter at 50 µm resolution. Diffusion, shear, sedimentation, chemotaxis, and attachment processes are explicitly resolved. The emerging quantitative representation of the ecosystem shows that (1) copiotrophs grow mostly attached to eukaryotic phytoplankters and get almost all of their carbon from them vs. oligotrophs that grow on exudates and lysates of cyanobacteria; (2) contrasting diel patterns in substrate appearance in the phycosphere vs. ambient water and growth of particle-associated copiotrophs vs. free-living oligotrophs; (3) attached bacteria reduce carbon flux from the phycosphere, lowering chemotactic efficiency toward eukaryotes below that toward cyanobacteria; (4) shear reduces chemotactic efficiency and fitness of the copiotroph; and (5) the main benefit of chemotaxis is to locate attachment partners. These patterns are consistent with available observations. Our study provides insights into the microscale ecology of marine bacteria, and the open-source code is a tool for further research in this area. IMPORTANCE A large amount of global CO 2 fixation is performed by marine phytoplankton, and a substantial fraction of that is released as dissolved organic carbon and further processed by heterotrophic bacteria. The interaction between phytoplankton and bacteria, i.e., the carbon flux between them, is therefore an important process in the global carbon and climate system. Some bacteria have developed specialized behavioral traits, like swimming and attachment, to increase their carbon acquisition. These interactions occur at the micrometer scale, for example, the immediate vicinity of phytoplankters (the phycosphere), but existing biogeochemical models typically only simulate down to the 1 meter vertical or ~100 kilometer horizontal scale. We present a new microscale model and use it to predict fluxes and other features in the surface ocean. The model makes important predictions about the fluxes between various types of phytoplankton and bacteria and the role of behavioral traits, and it provides a basis and tool for further research in this area.

Wild-type human glutathione peroxidase 4 (GPX4) was co-expressed with SBP2 (selenocysteine insertion sequence-binding protein 2) in human HEK cells to achieve efficient production of this selenocysteine-containing enzyme on a preparative scale for structural biology. The protein was purified and crystallized, and the crystal structure of the wild-type form of GPX4 was determined at 1.0 Å resolution. The overall fold and the active site are conserved compared with previously determined crystal structures of mutated forms of GPX4. A mass-spectrometry-based approach was developed to monitor the reaction of the active-site selenocysteine Sec46 with covalent inhibitors. This, together with the introduction of a surface mutant (Cys66Ser), enabled the crystal structure determination of GPX4 in complex with the covalent inhibitor ML162 [(S)-enantiomer]. The mass-spectrometry-based approach described here opens the path to further co-complex crystal structures of this potential cancer drug target in complex with covalent inhibitors.

The functioning of microbial ecosystems has important consequences from global climate to human health, but quantitative mechanistic understanding remains elusive. The components of microbial ecosystems can now be observed at high resolution, but interactions still have to be inferred e.g., a time-series may show a bloom of bacteria X followed by virus Y suggesting they interact. Existing inference approaches are mostly empirical, like correlation networks, which are not mechanistically constrained and do not provide quantitative mass fluxes, and thus have limited utility. We developed an inference method, where a mechanistic model with hundreds of species and thousands of parameters is calibrated to time series data. The large scale, nonlinearity and feedbacks pose a challenging optimization problem, which is overcome using a novel procedure that mimics natural speciation or diversification e.g., stepwise increase of bacteria species. The method allows for curation using species-level information from e.g., physiological experiments or genome sequences. The product is a mass-balancing, mechanistically-constrained, quantitative representation of the ecosystem. We apply the method to characterize phytoplankton—heterotrophic bacteria interactions via dissolved organic matter in a marine system. The resulting model predicts quantitative fluxes for each interaction and time point (e.g., 0.16 µmolC/L/d of chrysolaminarin to Polaribacter on April 16, 2009). At the system level, the flux network shows a strong correlation between the abundance of bacteria species and their carbon flux during blooms, with copiotrophs being relatively more important than oligotrophs. However, oligotrophs, like SAR11, are unexpectedly high carbon processors for weeks into blooms, due to their higher biomass. The fraction of exudates (vs. grazing/death products) in the DOM pool decreases during blooms, and they are preferentially consumed by oligotrophs. In addition, functional similarity of phytoplankton i.e., what they produce, decouples their association with heterotrophs. The methodology is applicable to other microbial ecosystems, like human microbiome or wastewater treatment plants.

Water oxidation and concomitant O 2 -formation by the Mn 4 Ca cluster of oxygenic photosynthesis has shaped the biosphere, atmosphere, and geosphere. It has been hypothesized that at an early stage of evolution, before photosynthetic water oxidation became prominent, photosynthetic formation of Mn oxides from dissolved Mn(2+) ions may have played a key role in bioenergetics and possibly facilitated early geological manganese deposits. The biochemical evidence for the ability of photosystems to form extended Mn oxide particles, lacking until now, is provided herein. We tracked the light-driven redox processes in spinach photosystem II (PSII) particles devoid of the Mn 4 Ca clusters by UV-vis and X-ray spectroscopy. We find that oxidation of aqueous Mn(2+) ions results in PSII-bound Mn(III,IV)-oxide nanoparticles of the birnessite type comprising 50-100 Mn ions per PSII. Having shown that even today’s photosystem-II can form birnessite-type oxide particles efficiently, we propose an evolutionary scenario, which involves Mn-oxide production by ancestral photosystems, later followed by down-sizing of protein-bound Mn-oxide nanoparticles to finally yield today’s Mn 4 CaO 5 cluster of photosynthetic water oxidation.

Water oxidation and concomitant dioxygen formation by the manganese-calcium cluster of oxygenic photosynthesis has shaped the biosphere, atmosphere, and geosphere. It has been hypothesized that at an early stage of evolution, before photosynthetic water oxidation became prominent, light-driven formation of manganese oxides from dissolved Mn(2+) ions may have played a key role in bioenergetics and possibly facilitated early geological manganese deposits. Here we report the biochemical evidence for the ability of photosystems to form extended manganese oxide particles. The photochemical redox processes in spinach photosystem-II particles devoid of the manganese-calcium cluster are tracked by visible-light and X-ray spectroscopy. Oxidation of dissolved manganese ions results in high-valent Mn(III,IV)-oxide nanoparticles of the birnessite type bound to photosystem II, with 50-100 manganese ions per photosystem. Having shown that even today’s photosystem II can form birnessite-type oxide particles efficiently, we propose an evolutionary scenario, which involves manganese-oxide production by ancestral photosystems, later followed by down-sizing of protein-bound manganese-oxide nanoparticles to finally yield today’s catalyst of photosynthetic water oxidation.

Solid-state NMR (ssNMR) spectroscopy has evolved into a powerful method to obtain structural information and to study the dynamics of proteins at atomic resolution and under physiological conditions. The method is especially well suited to investigate insoluble and noncrystalline proteins that cannot be investigated easily by X-ray crystallography or solution NMR. To allow for detailed analysis of ssNMR data, the assignment of resonances to the protein atoms is essential. For this purpose, a set of three-dimensional (3D) spectra needs to be acquired. Band-selective homo-nuclear cross-polarization (BSH-CP) is an effective method for magnetization transfer between carbonyl carbon (CO) and alpha carbon (CA) atoms, which is an important transfer step in multidimensional ssNMR experiments. This tutorial describes the detailed procedure for the chemical shift assignment of the backbone atoms of 13 C-15 N-labeled proteins by BSH-CP-based 13 C-detected ssNMR experiments. A set of six 3D experiments is used for unambiguous assignment of the protein backbone as well as certain side-chain resonances. The tutorial especially addresses scientists with little experience in the field of ssNMR and provides all the necessary information for protein assignment in an efficient, time-saving approach.