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Bats have unique characteristics compared to other mammals, including increased longevity and higher resistance to cancer and infectious disease. While previous studies have analyzed the metabolic requirements for flight, it is still unclear how bat metabolism supports these unique features, and no study has integrated metabolomics, transcriptomics, and proteomics to characterize bat metabolism. In this work, we performed a multi-omics data analysis using a computational model of metabolic fluxes to identify fundamental differences in central metabolism between primary lung fibroblast cell lines from the black flying fox fruit bat (Pteropus alecto) and human. Bat cells showed higher expression levels of Complex I components of electron transport chain (ETC), but, remarkably, a lower rate of oxygen consumption. Computational modeling interpreted these results as indicating that Complex II activity may be low or reversed, similar to an ischemic state. An ischemic-like state of bats was also supported by decreased levels of central metabolites and increased ratios of succinate to fumarate in bat cells. Ischemic states tend to produce reactive oxygen species (ROS), which would be incompatible with the longevity of bats. However, bat cells had higher antioxidant reservoirs (higher total glutathione and higher ratio of NADPH to NADP) despite higher mitochondrial ROS levels. In addition, bat cells were more resistant to glucose deprivation and had increased resistance to ferroptosis, one of the characteristics of which is oxidative stress. Thus, our studies revealed distinct differences in the ETC regulation and metabolic stress responses between human and bat cells.

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Therapeutic protein productivity and glycosylation pattern highly rely on cell metabolism. Cell culture medium composition and feeding strategy are critical to regulate cell metabolism. In this study, the relationship between toxic metabolic inhibitors and their nutrient precursors was explored to identify the critical medium components toward cell growth and generation of metabolic by-products. Generic CHO metabolic model was tailored and integrated with CHO fed-batch metabolomic data to obtain a cell line- and process-specific model. Flux balance analysis study was conducted on toxic metabolites cytidine monophosphate, guanosine monophosphate and n-acetylputrescine-all of which were previously reported to generate from endogenous cell metabolism-by mapping them to a compartmentalized carbon utilization network. Using this approach, the study projected high level of inhibitory metabolites accumulation when comparing three industrially relevant fed-batch feeding conditions one against another, from which the results were validated via a dose-dependent amino acids spiking study. In the end, a medium optimization design was employed to lower the amount of supplemented nutrients, of which improvements in critical process performance were realized at 40% increase in peak viable cell density (VCD), 15% increase in integral VCD, and 37% increase in growth rate. Tight control of toxic by-products was also achieved, as the study measured decreased inhibitory metabolites accumulation across all conditions. Overall, the study successfully presented a digital twin approach to investigate the intertwined relationship between supplemented medium constituents and downstream toxic metabolites generated through host cell metabolism, further elucidating different control strategies capable of improving cellular phenotypes and regulating toxic inhibitors.

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Computational models of plants have identified gaps in our understanding of biological systems, and have revealed ways to optimize cellular processes or organ-level architecture to increase productivity. Thus, computational models are learning tools that help direct experimentation and measurements. Models are simplifications of complex systems, and often simulate specific processes at single scales (e.g. temporal, spatial, organizational, etc.). Consequently, single-scale models are unable to capture the critical cross-scale interactions that result in emergent properties of the system. In this perspective article, we contend that to accurately predict how a plant will respond in an untested environment, it is necessary to integrate mathematical models across biological scales. Computationally mimicking the flow of biological information from the genome to the phenome is an important step in discovering new experimental strategies to improve crops. A key challenge is to connect models across biological, temporal and computational (e.g. CPU versus GPU) scales, and then to visualize and interpret integrated model outputs. We address this challenge by describing the efforts of the international Crops in silico consortium.

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Fructo-oligosaccharides (FOS) are linear fructans comprising 2-5 fructose units linked to a terminal glucose residue. They are widely used as food and feed additives due to their sweetness, low calorific value, and prebiotic properties. Here we describe the synthesis of FOS catalyzed by a cell-free crude enzyme solution containing recombinant fructosyltransferase (1-FFT) produced in the yeast Kluyveromyces lactis. During the enzyme catalysis, glucose accumulates as a by-product and eventually inhibits FOS production. We therefore used an enzyme membrane reactor (EMR) to achieve the continuous removal of glucose and the simultaneous replenishment of sucrose. We observed a loss of flux during the reaction with the characteristics of complete pore blocking, probably caused by a combination of proteins (enzyme molecules) and polysaccharides (FOS). Such complex fouling mechanisms must be overcome to achieve the efficient production of FOS using EMR systems.

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The ribulose monophosphate (RuMP) cycle is a highly efficient route for the assimilation of reduced one-carbon compounds. Despite considerable research, the RuMP cycle has not been fully implemented in model biotechnological organisms such as Escherichia coli, mainly since the heterologous establishment of the pathway requires addressing multiple challenges: sufficient formaldehyde production, efficient formaldehyde assimilation, and sufficient regeneration of the formaldehyde acceptor, ribulose 5-phosphate. Here, by efficiently producing formaldehyde from sarcosine oxidation and ribulose 5-phosphate from exogenous xylose, we set aside two of these concerns, allowing us to focus on the particular challenge of establishing efficient formaldehyde assimilation via the RuMP shunt, the linear variant of the RuMP cycle. We have generated deletion strains whose growth depends, to different extents, on the activity of the RuMP shunt, thus incrementally increasing the selection pressure for the activity of the synthetic pathway. Our final strain depends on the activity of the RuMP shunt for providing the cell with almost all biomass and energy needs, presenting an absolute coupling between growth and activity of key RuMP cycle components. This study shows the value of a stepwise problem solving approach when establishing a difficult but promising pathway, and is a strong basis for future engineering, selection, and evolution of model organisms for growth via the RuMP cycle.

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Models are important tools in microbial ecology. They can be used to advance understanding by helping to interpret observations and test hypotheses, and to predict the effects of ecosystem management actions or a different climate. Over the past decades, biological knowledge and ecosystem observations have advanced to the molecular and in particular gene level. However, microbial ecology models have changed less and a current challenge is to make them utilize the knowledge and observations at the genetic level. We review published models that explicitly consider genes and make predictions at the population or ecosystem level. The models can be grouped into three general approaches, i.e., metabolic flux, gene-centric and agent-based. We describe and contrast these approaches by applying them to a hypothetical ecosystem and discuss their strengths and weaknesses. An important distinguishing feature is how variation between individual cells (individuality) is handled. In microbial ecosystems, individual heterogeneity is generated by a number of mechanisms including stochastic interactions of molecules (e.g., gene expression), stochastic and deterministic cell division asymmetry, small-scale environmental heterogeneity, and differential transport in a heterogeneous environment. This heterogeneity can then be amplified and transferred to other cell properties by several mechanisms, including nutrient uptake, metabolism and growth, cell cycle asynchronicity and the effects of age and damage. For example, stochastic gene expression may lead to heterogeneity in nutrient uptake enzyme levels, which in turn results in heterogeneity in intracellular nutrient levels. Individuality can have important ecological consequences, including division of labor, bet hedging, aging and sub-optimality. Understanding the importance of individuality and the mechanism(s) underlying it for the specific microbial system and question investigated is essential for selecting the optimal modeling strategy.

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Photorespiration is an essential high flux metabolic pathway that is found in all oxygen-producing photosynthetic organisms. It is often viewed as a closed metabolic repair pathway that serves to detoxify 2-phosphoglycolic acid and to recycle carbon to fuel the Calvin-Benson cycle. However, this view is too simplistic since the photorespiratory cycle is known to interact with several primary metabolic pathways, including photosynthesis, nitrate assimilation, amino acid metabolism, C1 metabolism and the Krebs (TCA) cycle. Here we will review recent advances in photorespiration research and discuss future priorities to better understand (i) the metabolic integration of the photorespiratory cycle within the complex network of plant primary metabolism and (ii) the importance of photorespiration in response to abiotic and biotic stresses.

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High oxygen:carbon dioxide ratios may have a negative effect on growth and productivity of microalgae. To investigate the effect of O2 and CO2 concentrations and the ratio between these on the metabolism of Chlamydomonas reinhardtii we performed turbidostat experiments at different O2:CO2 ratios. These experiments showed that elevated O2 concentrations and the corresponding increase in the ratio of O2:CO2 common in photobioreactors led to a reduction of growth and biomass yield on light with 20-30%. This is most probably related to the oxygenase activity of Rubisco and the resulting process of photorespiration. Using metabolic flux modeling with measured rates for each experiment we were able to quantify the ratio of the oxygenase reaction to the carboxylase reaction of Rubisco and could demonstrate that photorespiration indeed can cause the reduction in biomass yield on light. The calculated ratio of the oxygenase reaction to the carboxylase reaction was 16.6% and 20.5% for air with 2% CO2 and 1% CO2, respectively. Thus photorespiration has a significant impact on the biomass yield on light already at conditions common in photobioreactors (air with 2% CO2).

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