Cyanophages represent important models for understanding virus-host interactions, yet high-resolution structural, functional, and dynamical studies remain relatively few due to challenges with preparing enough sample of sufficient quality for cryo-electron microscopy (cryo-EM) and multi-omics studies. Here we developed an integrated methodology for scaling production of the model cyanophage P-SSP7 from laboratory maintenance volumes (5-100 mL) to production scales (up to 40 L) while dramatically improving the quality of phage preparation for structural applications. Our systematic approach integrates host cultivation using adaptation to local seawater to reduce production costs, optimized infection protocols to maximize infectious titer yields, and multi-stage purification workflows specifically designed for cryo-EM quality requirements. The final methodology consistently produces infectious phage titers exceeding 3 × 1012 units/mL with recoverable yields of 1013 total infectious units and >95% purity validated by cryo-EM at each optimization step. Most significantly, this approach achieves a 60-fold reduction in cryo-EM data collection time between the initial and final optimization steps by increasing usable particles per field of view for single particle analysis. Overall, our final preparations demonstrate robust phage stability, retaining 68% infectivity after 3 months and 23% after 6 months at 4 °C. This workflow moves cyanophage culturing and downstream structural studies from specialized, resource-intensive endeavors toward routine research capability and establishes an adaptable framework for scaling production that can be applied to other host-virus systems.

Hyperthermophilic acidification (65–80°C) is a promising technology to overcome hydrolysis limitations during lignocellulosic feedstock processing. Still, significant gaps remain in understanding microbial community assembly and function at these temperatures. In this work, we investigate how temperature affects the assembly, function, and productivity of mixed microbial consortia during co-digestion of dairy manure and wheat straw at 70°C. Applying selective enrichment to two starting inocula derived from mesophilic (37°C) and thermophilic (55°C) municipal AD systems, we identify a convergent path towards a low-diversity community with high hydrolytic and acidogenic potential. Enriched communities from distinct inocula achieve equivalent VFA yields (0.38–0.39 g VFA g VS⁻¹; carbon conversion efficiency 31–32%) with H₂ co-production (13–19 mL g VS⁻¹) at a 7.8-day hydraulic retention time (HRT). Parallel selection of thermophilic inoculum at a 2.6-day HRT reduces total yield but nearly doubles acidogenic productivity (0.11 vs. 0.055 g VFA g VS⁻¹ d⁻¹) relative to the 7.8-day HRT. Across consortia, the core taxa selected at hyperthermophilic temperatures comprised species Thermoclostridium stercorarium, Thermoclostridium caenicola, and genera Acetivibrio and Caldicoprobacter. Multi-omics profiling through 16S amplicon sequencing, metatranscriptomics, and metaproteomics revealed a clear division of hydrolytic labor: distinct community members contributed complementary enzymatic and sugar-assimilation pathways across 348 glycoside hydrolases, collectively enabling efficient lignocellulose deconstruction and acidification. These findings establish a reproducible framework for sourcing and operating hyperthermophilic acidogenic consortia, informing process design for VFA platform bioconversion of lignocellulosic feedstocks.

Photosynthetic microorganisms rely on multiple central-carbon-metabolism pathways to adapt to fluctuating light and energy availability across diel cycles. Mechanistic insight into the regulatory dynamics of this adaptation requires integrating processes that operate across disparate timescales, from rapid redox-dependent post-translational modifications (PTMs) to slower changes in protein expression and metabolic pathway usage. Here, we develop a whole-cell four-dimensional (3D + time) model of the marine cyanobacterium Prochlorococcus marinus MED4 that explicitly represents the spatial, subcellular organization of key carbon fixation enzymes and genetic information processes coupled to a non-spatial genome-scale metabolic model (GSMM). We combine perturbative, time-resolved multi-omics measurements and cryo-ET derived 3D segmented volumes as constraints for this dynamic 4D framework. The integration of experiments and modeling across defined light regimes enables quantitative validation of system-level responses and forecasting under distinct light disturbances. We test the hypothesis that light-dependent redox PTMs regulate carbon fixation by controlling the structural assembly of a protein megacomplex, the "dark complex," at a conserved regulatory node of the Calvin-Benson cycle (CBC) in cyanobacteria. Our model shows that subcellular spatial organization buffers rapid light-induced changes in thylakoid reaction rates, which are followed by redox-PTM-mediated sequestration or release of CBC enzymes in the dark complex, ultimately impacting carbon fixation dynamics within carboxysomes. Comparison with an equivalently parameterized well-mixed stochastic model demonstrates the importance of spatial heterogeneity in understanding phenotypic robustness. Spatiotemporal sequestration creates a timing hierarchy spanning seconds to hours and noise-buffering behavior that cannot be recovered from well-mixed phenomenological models or purely time-resolved descriptions. Constrained by spatial heterogeneity, local enzyme stoichiometry and diffusion-limited assembly/disassembly determine effective stochastically varying control kinetics. Diffusion-driven fluctuations amplify transcription of highly expressed genes, whereas PTM-dependent regulation on enzyme stoichiometry maintains perturbation-driven phenotypic outcomes. 4D whole cell modeling with perturbation-based multi-modal experiments unlocks the ability to probe adaptive, spatiotemporally resolved mechanisms in photosynthetic machinery and light-dependent central carbon metabolism. The outcome of this work addresses a critical gap in genotype-to-phenotype inference and expands modeling and design capabilities for understudied or genetically intractable autotrophs such as P. marinus MED4.

Microbes in natural environments often encounter diverse mixtures of organic compounds, yet how mixed substrate environments and their molecular composition shape microbial phenotypes remains understudied. Here, we examined how a defined fungal exudate mimic (FEM) mixture influences growth and metabolism in Pseudomonas putida KT2440 compared to individual substrates matched for total carbon and nitrogen. Growth on FEM initiated 2 h earlier than growth on glucose alone and exhibited both the lowest lag and shortest time to maximum biomass compared to individual substrates. Fructose was the only individual substrate that supported significantly higher maximum biomass than FEM, but exhibited a nearly 15-fold longer lag phase. Gas chromatography mass spectrometry analysis revealed dynamic temporal patterns of substrate utilization within the FEM mixture, with early preferential utilization of malate, followed by overlapping utilization of multiple substrates between 3 and 8 h. By integrating growth and substrate uptake kinetics with genome-scale metabolic modeling and validating model-predicted pathway activity using temporal proteomics, we show that experimentally constrained model predictions accurately captured substrate utilization dynamics across multiple FEM concentrations, and predicted temporal shifts in the dominant substrates supporting growth. Through this integrative experimental-modeling approach, we demonstrate that the mixed substrate FEM environment elicits an emergent growth phenotype characterized by the lowest lag, shortest time to maximum biomass, and relatively high maximum biomass in Pseudomonas putida, a combination of traits not simultaneously reproduced by any individual substrate. IMPORTANCE:How mixed-nutrient substrate environments influence bacterial growth and metabolism is not well understood and is challenging to predictively model. Using Pseudomonas putida KT2440, we show that growth on a defined mixture of substrates inspired by mycorrhizal fungal hyphal exudates produces a distinct and emergent growth phenotype characterized by a lower lag, shorter time to maximum biomass, and relatively high biomass accumulation when compared to individual substrates alone. This combination of growth traits is not simultaneously reproduced by any individual substrate, even when total carbon and nitrogen are matched. By integrating growth measurements and temporal substrate uptake data with dynamic flux balance analysis and proteomics, we demonstrate that metabolic responses to mixed substrates can be quantitatively interpreted. These findings highlight the importance of studying microbes under chemically realistic conditions and suggest that learning from naturally occurring molecular environments may provide new strategies for engineering microbial growth conditions and improving bioprocess performance.

Abstract The virus P-SSP7 infects the cyanobacterium Prochlorococcus marinus , one of the most abundant photosynthetic microbes in the ocean, making this pairing a useful model for studying host-virus interactions. Infection proceeds through a portal-tail complex that attaches to the viral protein shell at a single specialized vertex, where the two structures have mismatched symmetries. Previous studies described this region only at coarse resolution which continues to limit understanding of how the virus assembles and injects its genome. Using cryo-electron microscopy of high-quality samples, we determined the first near-atomic-resolution structure of the complete P-SSP7 virion and its portal-tail complex. We captured 3D classes where the shell and portal-tail complex connect in five distinct arrangements. Fitting and modeling 2 of these states helped resolve and establish modular attachment in five rotational registers as the basis for capsid-portal symmetry mismatch in P-SSP7 assembly.

Microbially induced calcium carbonate precipitation (MICP) holds potential for soil stabilization and carbon sequestration efforts, with the overall efficiency of the process being a major determinant for its use in many environmental and civil engineering applications. While the biogeochemical pathways and enzymes driving MICP are known, the microbial metabolic networks and community dynamics underlying such processes remain poorly characterized. To address this gap, we interrogated a MICP-capable four-member consortium of soil bacteria (Curtobacterium flaccumfaciens, Rhodococcus qingshengii, Bacillus toyonensis and a Microbacterium species), termed carbon storing consortium - A (CSC-A). Prior work shows that CSC-A yields carbonate at a higher quantity compared to the sum of carbonate individually produced by each member, suggesting MICP is driven by community dynamics. To that end we applied a multi-omic integration approach of genomics, transcriptomics, and metabolomics to investigate potential inter-species interactions that may influence the MICP phenotype. Genomic life history characterizations identified evidence of specialization by B. toyonensis and Microbacterium, while metatranscriptomic perturbation was almost ten times greater in the absence of R. qinshengii than C.flaccumfaciens, suggesting that R. qingshengii is a keystone species when grown in urea, a molecule key to the MICP process. By comparing individual species’ metabolomes to the metabolic profile of a shared well, we identified over 200 metabolites predicted to be produced or consumed by CSC-A members. Integrating both data types and mapping them to the KEGG reactome highlighted over 20 different enriched pathways with reactions related to glutamate metabolism, succinate metabolism, and branched chain amino acid biosynthesis. As succinate metabolism was a major node in this network we applied laboratory assays to confirm that additional added succinate led to increased carbonate precipitation by CSC-A, a critical validation of our modeling approach. By isolating and identifying the interconnected metabolic components underlying MICP in CSC-A, we identified keystone taxa, metabolites, and pathways important for future optimization of the application of this consortium to carbonate precipitation.

Soil organic matter decomposition is a complex process reflecting microbial composition and environmental conditions. Moisture can modulate the connectivity and interactions of microbes. Due to heterogeneity, a deeper understanding of the influence of soil moisture on the dynamics of organic matter decomposition and resultant phenotypes remains a challenge. Soils from a long-term field experiment exposed to high and low moisture treatments were incubated in the laboratory to investigate organic matter decomposition using chitin as a model substrate. By combining enzymatic assays, biomass measurements, and microbial enrichment via activity-based probes, we determined the microbial functional response to chitin amendments and field moisture treatments at both the community and cell scales. Chitinolytic activities showed significant responses to the amendment of chitin, independent of differences in field moisture treatments. However, for other measurements of carbon metabolism and cellular functions, soils from high moisture field treatments had greater potential enzyme activity than soils from low moisture field treatments. A cell tagging approach was used to enrich and quantify bacterial taxa that are actively producing chitin-degrading enzymes. By integrating organism, community, and soil core measurements we show that (i) a small subset of taxa compose the majority (>50%) of chitinase production despite broad functional redundancy, (ii) the identity of key chitin degraders varies with moisture level, and (iii) extracellular enzymes that are not cell-associated account for most potential chitinase activity measured in field soil.

We develop a systems approach based on an energy-landscape concept to differentiate interactions involving redox activities and conformational changes of proteins and nucleic acids interactions in multi-layered protein-DNA regulatory networks under light disturbance. Our approach is a data-driven modeling workflow using a physics-informed machine learning algorithm to train a non-linear mathematical model for interpreting gene expression dynamics and to lead discovery for protein regulators using redox proteome analysis. We distinguish light-responsive elements within central carbon metabolism pathways from independent variables like circadian time using the publicly available transcriptome datasets of Synechococcus elongatus over diel cycles responding to light perturbations. Our approach provides interpretable de novo models for elucidating events of reactions in complex regulatory pathways in response to stressful disturbance from the environment. We discovered protein regulators in response to light disturbance in the proteome analysis involving shifts in protein abundance as well as cysteine redox states under constant illumination and after two hours of darkness. We discovered significant shifts in cysteine redox states in regulatory proteins such as transcription sigma factors and metabolic enzymes in the oxidative pentose phosphate pathway and the Calvin-Benson cycle, while the changes in their protein abundance were minimal. These results indicate that regulatory dynamics in reductant generation link photo-induced electron transport pathways and redox metabolic pathways with circadian rhythms through fast redox-induced conformational changes or slow expression regulations across networks.

BACKGROUND:The industrial feasibility of photosynthetic bioproduction using cyanobacterial platforms remains challenging due to insufficient yields, particularly due to competition between product formation and cellular carbon demands across different temporal phases of growth. This study investigates how circadian clock regulation impacts carbon partitioning between storage, growth, and product synthesis in Synechococcus elongatus PCC 7942, and provides insights that suggest potential strategies for enhanced bioproduction. RESULTS:After entrainment to light-dark cycles, PCC 7942 cultures transitioned to constant light revealed distinct temporal patterns in sucrose production, exhibiting three-fold higher productivity during subjective night compared to subjective day despite moderate down-regulation of genes from the photosynthetic apparatus. This enhanced productivity coincided with reduced glycogen accumulation and halted cell division at subjective night time, suggesting temporal separation of competing processes. Transcriptome analysis revealed coordinated circadian clock-driven adjustment of the cell cycle and rewiring of energy and carbon metabolism, with over 300 genes showing differential expression across four time points. The subjective night was characterized by altered expression of cell division-related genes and reduced expression of genes involved in glycogen synthesis, while showing upregulation of glycogen degradation pathways, alternative electron flow components, the pentose phosphate pathway, and oxidative decarboxylation of pyruvate. These molecular changes created favorable conditions for product formation through enhanced availability of major sucrose precursors (glucose-1-phosphate and fructose-6-phosphate) and maintained redox balance through multiple mechanisms. CONCLUSIONS:Our analysis of circadian regulatory rewiring of carbon metabolism and redox balancing suggests two potential approaches that could be developed for improving cyanobacterial bioproduction: leveraging natural circadian rhythms for optimizing cultivation conditions and timing of pathway induction, and engineering strains that mimic circadian-driven metabolic shifts through controlled carbon flux redistribution and redox rebalancing. While these strategies remain to be tested, they could theoretically improve the efficiency of photosynthetic bioproduction by enabling better temporal separation between cell growth, carbon storage accumulation, and product synthesis phases.

ABSTRACT Soil moisture and porosity regulate microbial metabolism by influencing factors, such as system chemistry, substrate availability, and soil connectivity. However, accurately representing the soil environment and establishing a tractable microbial community that limits confounding variables is difficult. Here, we use a reduced-complexity microbial consortium grown in a glass bead porous media amended with chitin to test the effects of moisture and a structural matrix on microbial phenotypes. Leveraging metagenomes, metatranscriptomes, metaproteomes, and metabolomes, we saw that our porous media system significantly altered microbial phenotypes compared with the liquid incubations, denoting the importance of incorporating pores and surfaces for understanding microbial phenotypes in soils. These phenotypic shifts were mainly driven by differences in expression of Streptomyces and Ensifer, which included a significant decrease in overall chitin degradation between porous media and liquid. Our findings suggest that the success of Ensifer in porous media is likely related to its ability to repurpose carbon via the glyoxylate shunt amidst a lack of chitin degradation byproducts while potentially using polyhydroxyalkanoate granules as a C source. We also identified traits expressed by Ensifer and others, including motility, stress resistance, and carbon conservation, that likely influence the metabolic profiles observed across treatments. Together, these results demonstrate that porous media incubations promote structure-induced microbial phenotypes and are likely a better proxy for soil conditions than liquid culture systems. Furthermore, they emphasize that microbial phenotypes encompass not only the multi-enzyme pathways involved in metabolism but also include the complex interactions with the environment and other community members.IMPORTANCESoil moisture and porosity are critical in shaping microbial metabolism. However, accurately representing the soil environment in tractable laboratory experiments remains a challenging frontier. Through our reduced complexity microbial consortium experiment in porous media, we reveal that predicting microbial metabolism from gene-based pathways alone often falls short of capturing the intricate phenotypes driven by cellular interactions. Our findings highlight that porosity and moisture significantly affect chitin decomposition, with environmental matrix (i.e., glass beads) shifting community metabolism towards stress tolerance, reduced resource acquisition, and increased carbon conservation, ultimately invoking unique microbial strategies not evident in liquid cultures. Moreover, we find evidence that changes in moisture relate to community shifts regarding motility, transporters, and biofilm formation, which likely influence chitin degradation. Ultimately, our incubations showcase how reduced complexity communities can be informative of microbial metabolism and present a useful alternative to liquid cultures for studying soil microbial phenotypes.

Soil moisture and porosity regulate microbial metabolism by influencing factors such as redox conditions, substrate availability, and soil connectivity. However, the inherent biological, chemical, and physical heterogeneity of soil complicates laboratory investigations into microbial phenotypes that mediate community metabolism. This difficulty arises from challenges in accurately representing the soil environment and in establishing a tractable microbial community that limits confounding variables. To address these challenges in our investigation of community metabolism, we use a reduced-complexity microbial consortium grown in a soil analog using a glass-bead matrix amended with chitin. Long-read and short-read metagenomes, metatranscriptomes, metaproteomes, and metabolomes were analyzed to test the effects of soil structure and moisture on chitin degradation. Our soil structure analog system greatly altered microbial expression profiles compared to the liquid-only incubations, emphasizing the importance of incorporating environmental parameters, like pores and surfaces, for understanding microbial phenotypes relevant to soil ecosystems. These changes were mainly driven by differences in overall expression of chitin-degrading Streptomyces species and stress-tolerant Ensifer. Our findings suggest that the success of Ensifer in a structured environment is likely related to its ability to repurpose carbon via the glyoxylate shunt while potentially using polyhydroxyalkanoate granules as a C source. We also identified traits like motility, stress resistance, and biofilm formation that underlie the degradation of chitin across our treatments and inform how they may ultimately alter carbon use efficiency. Together our results demonstrate that community functions like decomposition are sensitive to environmental conditions and more complex than the multi-enzyme pathways involved in depolymerization.