The chemical neighborhood of cells in a diffusion-limited system.

Microorganisms follow us everywhere, and they will be essential to sustaining long-term human space exploration through applications such as vitamin synthesis, biomining, and more. Establishing a sustainable presence in space therefore requires that we better understand how stress due to the altered physical conditions of spaceflight affects our companion organisms. In microgravity environments such as orbital space stations, microorganisms likely experience the change in gravity primarily through changes in fluid mixing processes. Without sedimentation and density-driven convection, diffusion becomes the primary process governing the movement of growth substrates and wastes for microbial cells in suspension culture. Non-motile cells might therefore develop a substrate-deficient "zone of depletion" and experience stress due to starvation and/or waste build-up. This would in turn impact the concentration-dependent uptake rate of growth substrates and could be the cause of the altered growth rates previously observed in microorganisms in spaceflight and in ground-simulated microgravity. To better understand the extent of these concentration differences and their potential influence on substrate uptake rates, we used both an analytical solution and finite difference method to visualize concentration fields around individual cells. We modeled diffusion, using Fick's Second Law, and nutrient uptake, using Michaelis-Menten kinetics, and assessed how that distribution varies in systems with multiple cells and varied geometries. We determined the radius of the zone of depletion, within which cells had reduced the substrate concentration by 10%, to be 5.04 mm for an individual Escherichia coli cell in the conditions we simulated. However, we saw a synergistic effect with multiple cells near each other: multiple cells in close proximity decreased the surrounding concentration by almost 95% from the initial substrate concentration. Our calculations provide researchers an inside look at suspension culture behavior in the diffusion-limited environment of microgravity at the scale of individual cells.

Integration of physiologically relevant photosynthetic energy flows into whole genome models of light-driven metabolism.

Characterizing photosynthetic productivity is necessary to understand the ecological contributions and biotechnology potential of plants, algae, and cyanobacteria. Light capture efficiency and photophysiology have long been characterized by measurements of chlorophyll fluorescence dynamics. However, these investigations typically do not consider the metabolic network downstream of light harvesting. By contrast, genome-scale metabolic models capture species-specific metabolic capabilities but have yet to incorporate the rapid regulation of the light harvesting apparatus. Here, we combine chlorophyll fluorescence parameters defining photosynthetic and non-photosynthetic yield of absorbed light energy with a metabolic model of the pennate diatom Phaeodactylum tricornutum. This integration increases the model predictive accuracy regarding growth rate, intracellular oxygen production and consumption, and metabolic pathway usage. Through the quantification of excess electron transport, we uncover the sequential activation of non-radiative energy dissipation processes, cross-compartment electron shuttling, and non-photochemical quenching as the rapid photoacclimation strategy in P. tricornutum. Interestingly, the photon absorption thresholds that trigger the transition between these mechanisms were consistent at low and high incident photon fluxes. We use this understanding to explore engineering strategies for rerouting cellular resources and excess light energy towards bioproducts in silico. Overall, we present a methodology for incorporating a common, informative data type into computational models of light-driven metabolism and show its utilization within the design-build-test-learn cycle for engineering of photosynthetic organisms.

Discovery of Chlorophyll d: Isolation and Characterization of a Far-Red Cyanobacterium from the Original Site of Manning and Strain (1943) at Moss Beach, California.

We have isolated a chlorophyll-d-containing cyanobacterium from the intertidal field site at Moss Beach, on the coast of Central California, USA, where Manning and Strain (1943) originally discovered this far-red chlorophyll. Here, we present the cyanobacterium's environmental description, culturing procedure, pigment composition, ultrastructure, and full genome sequence. Among cultures of far-red cyanobacteria obtained from red algae from the same site, this strain was an epiphyte on a brown macroalgae. Its Qyin vivo absorbance peak is centered at 704-705 nm, the shortest wavelength observed thus far among the various known Acaryochloris strains. Its Chl a/Chl d ratio was 0.01, with Chl d accounting for 99% of the total Chl d and Chl a mass. TEM imagery indicates the absence of phycobilisomes, corroborated by both pigment spectra and genome analysis. The Moss Beach strain codes for only a single set of genes for producing allophycocyanin. Genomic sequencing yielded a 7.25 Mbp circular chromosome and 10 circular plasmids ranging from 16 kbp to 394 kbp. We have determined that this strain shares high similarity with strain S15, an epiphyte of red algae, while its distinct gene complement and ecological niche suggest that this strain could be the closest known relative to the original Chl d source of Manning and Strain (1943). The Moss Beach strain is designated Acaryochloris sp. (marina) strain Moss Beach.

Systems biology approach to functionally assess the Clostridioides difficile pangenome reveals genetic diversity with discriminatory power.

Combatting Clostridioides difficile infections, a dominant cause of hospital-associated infections with incidence and resulting deaths increasing worldwide, is complicated by the frequent emergence of new virulent strains. Here, we employ whole-genome sequencing, high-throughput phenotypic screenings, and genome-scale models of metabolism to evaluate the genetic diversity of 451 strains of C. difficile. Constructing the C. difficile pangenome based on this set revealed 9,924 distinct gene clusters, of which 2,899 (29%) are defined as core, 2,968 (30%) are defined as unique, and the remaining 4,057 (41%) are defined as accessory. We develop a strain typing method, sequence typing by accessory genome (STAG), that identifies 176 genetically distinct groups of strains and allows for explicit interrogation of accessory gene content. Thirty-five strains representative of the overall set were experimentally profiled on 95 different nutrient sources, revealing 26 distinct growth profiles and unique nutrient preferences; 451 strain-specific genome scale models of metabolism were constructed, allowing us to computationally probe phenotypic diversity in 28,864 unique conditions. The models create a mechanistic link between the observed phenotypes and strain-specific genetic differences and exhibit an ability to correctly predict growth in 76% of measured cases. The typing and model predictions are used to identify and contextualize discriminating genetic features and phenotypes that may contribute to the emergence of new problematic strains.

High-Quality Genome-Scale Models From Error-Prone, Long-Read Assemblies.

Advances in nanopore-based sequencing techniques have enabled rapid characterization of genomes and transcriptomes. An emerging application of this sequencing technology is point-of-care characterization of pathogenic bacteria. However, genome assessments alone are unable to provide a complete understanding of the pathogenic phenotype. Genome-scale metabolic reconstruction and analysis is a bottom-up Systems Biology technique that has elucidated the phenotypic nuances of antimicrobial resistant (AMR) bacteria and other human pathogens. Combining these genome-scale models (GEMs) with point-of-care nanopore sequencing is a promising strategy for combating the emerging health challenge of AMR pathogens. However, the sequencing errors inherent to the nanopore technique may negatively affect the quality, and therefore the utility, of GEMs reconstructed from nanopore assemblies. Here we describe and validate a workflow for rapid construction of GEMs from nanopore (MinION) derived assemblies. Benchmarking the pipeline against a high-quality reference GEM of Escherichia coli K-12 resulted in nanopore-derived models that were >99% complete even at sequencing depths of less than 10× coverage. Applying the pipeline to clinical isolates of pathogenic bacteria resulted in strain-specific GEMs that identified canonical AMR genome content and enabled simulations of strain-specific microbial growth. Additionally, we show that treating the sequencing run as a mock metagenome did not degrade the quality of models derived from metagenome assemblies. Taken together, this study demonstrates that combining nanopore sequencing with GEM construction pipelines enables rapid, in situ characterization of microbial metabolism.

Evolution and regulation of nitrogen flux through compartmentalized metabolic networks in a marine diatom.

Diatoms outcompete other phytoplankton for nitrate, yet little is known about the mechanisms underpinning this ability. Genomes and genome-enabled studies have shown that diatoms possess unique features of nitrogen metabolism however, the implications for nutrient utilization and growth are poorly understood. Using a combination of transcriptomics, proteomics, metabolomics, fluxomics, and flux balance analysis to examine short-term shifts in nitrogen utilization in the model pennate diatom in Phaeodactylum tricornutum, we obtained a systems-level understanding of assimilation and intracellular distribution of nitrogen. Chloroplasts and mitochondria are energetically integrated at the critical intersection of carbon and nitrogen metabolism in diatoms. Pathways involved in this integration are organelle-localized GS-GOGAT cycles, aspartate and alanine systems for amino moiety exchange, and a split-organelle arginine biosynthesis pathway that clarifies the role of the diatom urea cycle. This unique configuration allows diatoms to efficiently adjust to changing nitrogen status, conferring an ecological advantage over other phytoplankton taxa.

Cellular responses to reactive oxygen species are predicted from molecular mechanisms.

Catalysis using iron-sulfur clusters and transition metals can be traced back to the last universal common ancestor. The damage to metalloproteins caused by reactive oxygen species (ROS) can prevent cell growth and survival when unmanaged, thus eliciting an essential stress response that is universal and fundamental in biology. Here we develop a computable multiscale description of the ROS stress response in Escherichia coli, called OxidizeME. We use OxidizeME to explain four key responses to oxidative stress: 1) ROS-induced auxotrophy for branched-chain, aromatic, and sulfurous amino acids; 2) nutrient-dependent sensitivity of growth rate to ROS; 3) ROS-specific differential gene expression separate from global growth-associated differential expression; and 4) coordinated expression of iron-sulfur cluster (ISC) and sulfur assimilation (SUF) systems for iron-sulfur cluster biosynthesis. These results show that we can now develop fundamental and quantitative genotype-phenotype relationships for stress responses on a genome-wide basis.

Cross-compartment metabolic coupling enables flexible photoprotective mechanisms in the diatom Phaeodactylum tricornutum.

Photoacclimation consists of short- and long-term strategies used by photosynthetic organisms to adapt to dynamic light environments. Observable photophysiology changes resulting from these strategies have been used in coarse-grained models to predict light-dependent growth and photosynthetic rates. However, the contribution of the broader metabolic network, relevant to species-specific strategies and fitness, is not accounted for in these simple models. We incorporated photophysiology experimental data with genome-scale modeling to characterize organism-level, light-dependent metabolic changes in the model diatom Phaeodactylum tricornutum. Oxygen evolution and photon absorption rates were combined with condition-specific biomass compositions to predict metabolic pathway usage for cells acclimated to four different light intensities. Photorespiration, an ornithine-glutamine shunt, and branched-chain amino acid metabolism were hypothesized as the primary intercompartment reductant shuttles for mediating excess light energy dissipation. Additionally, simulations suggested that carbon shunted through photorespiration is recycled back to the chloroplast as pyruvate, a mechanism distinct from known strategies in photosynthetic organisms. Our results suggest a flexible metabolic network in P. tricornutum that tunes intercompartment metabolism to optimize energy transport between the organelles, consuming excess energy as needed. Characterization of these intercompartment reductant shuttles broadens our understanding of energy partitioning strategies in this clade of ecologically important primary producers.

Predicting the metabolic capabilities of Synechococcus elongatus PCC 7942 adapted to different light regimes.

There is great interest in engineering photoautotrophic metabolism to generate bioproducts of societal importance. Despite the success in employing genome-scale modeling coupled with flux balance analysis to engineer heterotrophic metabolism, the lack of proper constraints necessary to generate biologically realistic predictions has hindered broad application of this methodology to phototrophic metabolism. Here we describe a methodology for constraining genome-scale models of photoautotrophy in the cyanobacteria Synechococcus elongatus PCC 7942. Experimental photophysiology parameters coupled to genome-scale flux balance analysis resulted in accurate predictions of growth rates and metabolic reaction fluxes at low and high light conditions. Additionally, by constraining photon uptake fluxes, we characterized the metabolic cost of excess excitation energy. The predicted energy fluxes were consistent with known light-adapted phenotypes in cyanobacteria. Finally, we leveraged the modeling framework to characterize existing photoautotrophic and photomixtotrophic engineering strategies for 2,3-butanediol production in S. elongatus. This methodology, applicable to genome-scale modeling of all phototrophic microorganisms, can facilitate the use of flux balance analysis in the engineering of light-driven metabolism.

Quantitative time-course metabolomics in human red blood cells reveal the temperature dependence of human metabolic networks.

The temperature dependence of biological processes has been studied at the levels of individual biochemical reactions and organism physiology (e.g. basal metabolic rates) but has not been examined at the metabolic network level. Here, we used a systems biology approach to characterize the temperature dependence of the human red blood cell (RBC) metabolic network between 4 and 37 °C through absolutely quantified exo- and endometabolomics data. We used an Arrhenius-type model (Q10) to describe how the rate of a biochemical process changes with every 10 °C change in temperature. Multivariate statistical analysis of the metabolomics data revealed that the same metabolic network-level trends previously reported for RBCs at 4 °C were conserved but accelerated with increasing temperature. We calculated a median Q10 coefficient of 2.89 ± 1.03, within the expected range of 2-3 for biological processes, for 48 individual metabolite concentrations. We then integrated these metabolomics measurements into a cell-scale metabolic model to study pathway usage, calculating a median Q10 coefficient of 2.73 ± 0.75 for 35 reaction fluxes. The relative fluxes through glycolysis and nucleotide metabolism pathways were consistent across the studied temperature range despite the non-uniform distributions of Q10 coefficients of individual metabolites and reaction fluxes. Together, these results indicate that the rate of change of network-level responses to temperature differences in RBC metabolism is consistent between 4 and 37 °C. More broadly, we provide a baseline characterization of a biochemical network given no transcriptional or translational regulation that can be used to explore the temperature dependence of metabolism.

Unique attributes of cyanobacterial metabolism revealed by improved genome-scale metabolic modeling and essential gene analysis.

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.

Red colored IgG4 caused by vitamin B12 from cell culture media combined with disulfide reduction at harvest.

While many antibody therapeutics are formulated at low concentration (~10-20 mg/mL) for intravenous administration, high concentration (> 100 mg/mL) formulations may be required for subcutaneous delivery in certain clinical indications. For such high concentration formulations, product color is more apparent due to the higher molecular density across a given path-length. Color is therefore a product quality attribute that must be well-understood and controlled, to demonstrate process consistency and enable clinical trial blinding. Upon concentration of an IgG4 product at the 2000 L manufacturing scale, variability in product color, ranging from yellow to red, was observed. A small-scale experimental model was developed to assess the effect of processing conditions (medium composition and harvest conditions) on final bulk drug substance (BDS) color. The model was used to demonstrate that, for two distinct IgG4 products, red coloration occurred only in the presence of disulfide reduction-mediated antibody dissociation. The red color-causing component was identified as vitamin B 12, in the hydroxocobalamin form, and the extent of red color was correlated with the cobalt (vitamin B 12) concentration in the final pools. The intensity of redness in the final BDS was modulated by changing the concentration of vitamin B 12 in the cell culture media.