The metabolism of carbohydrates by extremely halophilic bacteria - Glucose metabolism via a modified Entner-Doudoroff pathway
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Tomlinson, G. A.↗
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The metabolism of carbohydrates by extremely halophilic bacteria - Identification of galactonic acid as a product of galactose metabolism
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Hochstein, L. I.↗
The metabolism of carbohydrates by extremely halophilic bacteria - The identification of lactobionic acid as a product of lactose metabolism by Halobacterium saccharovorum
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Tomlinson, G. A.↗
Human in vitro metabolism of an environmental mixture of polycyclic aromatic hydrocarbons (PAH) found at the Portland Harbor Superfund Site
Polycyclic aromatic hydrocarbons (PAHs) are widespread environmental contaminants that pose health risks to humans. Toxicity testing approaches of PAHs have evolved from traditional rodent models to New Approach Methodologies (NAMs), such as high-throughput screening in zebrafish, enabling rapid evaluation of chemical hazards. However, translating toxicity findings from laboratory systems to humans remains difficult due to complexity and species-specific differences. Chemical dosimetry modeling offers a quantitative framework to bridge this gap, but its accuracy depends on robust knowledge of PAH metabolism. The objective of this study was to measure human metabolism rates of Supermix-10, the ten most abundant PAHs found at the Portland Harbor Superfund Site, to support development of human pharmacokinetic models. We incubated individual PAHs from Supermix-10 in pooled human liver microsomes and quantified parent PAH disappearance using high-performance liquid chromatography (HPLC) with UV and florescent detection. To assess the potential of mixture interactions, we also measured metabolism of all 10 compounds in an equimolar mixture and compared rates of parent disappearance to those observed for individual PAHs. All Supermix-10 PAHs demonstrated rapid parent compound disappearance in human hepatic microsomes. PAHs grouped into three metabolism patterns: high metabolism rates and capacity (2-methylnaphthalene, acenaphthylene, fluorene, naphthalene), high affinity metabolism that rapidly achieves low-level saturation (benzo[a]anthracene, chrysene), and moderate metabolism rates and capacity (fluoranthene, pyrene, retene, phenanthrene). Smaller PAHs exhibited faster metabolism, and higher metabolism rates correlated inversely with molecular weight. When incubated in an equimolar mixture, Supermix-10 demonstrated significantly slower metabolism (47–89 %) compared to metabolism of individual PAHs at the same concentration. These findings enhance our understanding of PAH metabolism in humans and demonstrate significant mixture interactions under the conditions tested. Furthermore, our findings offer insights into the metabolic behavior of Supermix-10 and provide critical metabolism rate data to support the development of physiological based pharmacokinetic (PBPK) models. Dosimetry models can translate PAH chemical dosimetry from high-throughput testing platforms, like zebrafish and cellular system assays, to human exposures enhancing the accuracy and reliability of PAH risk assessments.
2-methylnaphthalene↗
Skeletal muscle metabolism in hypokinetic rats
This grant focused on the mechanisms of metabolic changes associated with unweighting atrophy and reduced growth of hind limb muscles of juvenile rats. Metabolic studies included a number of different areas. Amino acid metabolic studies placed particular emphasis on glutamine and branched-chain amino acid metabolism. These studies were an outgrowth of understanding stress effects and the role of glucocorticoids in these animals. Investigations on protein metabolism were largely concerned with selective loss of myofibrillar proteins and the role of muscle proteolysis. These investigations lead to finding important differences from denervation and atrophy and to define the roles of cytosolic versus lysosomal proteolysis in these atrophy models. A major outgrowth of these studies was demonstrating an ability to prevent atrophy of the unweighted muscle for at least 24 hours. A large amount of work concentrated on carbohydrate metabolism and its regulation by insulin and catecholamines. Measurements focused on glucose transport, glycogen metabolism, and glucose oxidation. The grant was used to develop an important new in situ approach for studying protein metabolism, glucose transport, and hormonal effects which involves intramuscular injection of various agents for up to 24 hours. Another important consequence of this project was the development and flight of Physiological-Anatomical Rodent Experiment-1 (PARE-1), which was launched aboard Space Shuttle Discovery in September 1991. Detailed descriptions of these studies can be found in the 30 peer-reviewed publications, 15 non-reviewed publications, 4 reviews and 33 abstracts (total 82 publications) which were or are scheduled to be published as a result of this project. A listing of these publications grouped by area (i.e. amino acid metabolism, protein metabolism, carbohydrate metabolism, and space flight studies) are included.
Tischler, Marc E.↗
Metabolic flux and resource balance in the oleaginous yeast Rhodotorula toruloides
The yeast Rhodotorula toruloides is a promising bioproduction organism due to its high lipid yields and ability to grow on cheap and abundant substrates. Quantitative, systems-level assessment of its metabolic activity is accordingly merited. Resource-balance analysis (RBA) models capture not only reaction stoichiometry but also enzyme requirements for catalysis, providing valuable tools for understanding metabolic trade-offs and optimizing metabolic engineering strategies. Here, in this work, we present systems-level measurements of R. toruloides metabolic flux based on isotope tracing and metabolic flux analysis. In combination with new proteomic measurements, these flux data are used to parameterize a genome-scale resource balance model rtRBA. We find that S. cerevisiae and R. toruloides grow at nearly indistinguishable rates using similar biosynthetic but dramatically different central metabolic programs. R. toruloides consumes one-fifth as much glucose, which it metabolizes primarily via the pentose phosphate pathway and TCA cycle unlike primarily glycolysis in S. cerevisiae . Overall, across these two divergent yeasts, protein abundances aligned more closely than metabolic flux. Resource balance modeling of these metabolic programs predicts superior theoretical yields but lower productivities in R. toruloides than S. cerevisiae for industrial chemicals, highlighting the value of rapid glucose uptake for productivity but respiratory metabolism for yields.
60 APPLIED LIFE SCIENCES↗
Evaluating the limitations of Bayesian metabolic control analysis
AbstractBayesian Metabolic Control Analysis (BMCA) has emerged as a promising framework for inferring metabolic control coefficients in data-limited scenarios by integrating Bayesian inference with linlog rate laws. However, its predictive accuracy and limitations remain underexplored. This study systematically evaluates BMCA’s ability to infer elasticity values, flux control coefficients (FCCs), and concentration control coefficients (CCCs) under varying data availability conditions using three synthetic metabolic network models. Our findings highlight the strengths and weaknesses of BMCA, guiding its application in metabolic engineering and emphasizing the need for methodological refinements.Author summaryUnderstanding how enzymes control metabolic pathways is crucial for optimizing biomanufacturing and synthetic biology applications. Bayesian Metabolic Control Analysis (BMCA) is a promising computational method that integrates Bayesian inference with metabolic control analysis to estimate key control parameters, even in cases with limited experimental data. However, the accuracy and limitations of BMCA remain unclear. In this study, we systematically evaluate BMCA using three synthetic metabolic networks to determine how different types of physiological data impact its predictive performance. We find that BMCA requires flux and enzyme concentration data for accurate predictions, while external metabolite concentrations contribute little. Additionally, BMCA fails to predict elasticity values beyond a magnitude of 1.5 and reliably infer allosteric regulation, even when strong regulatory interactions exist. In addition, BMCA does not accurately rank metabolic control points, which may limit its utility in identifying key enzymes in engineered pathways. Our work provides practical insights into when and how BMCA can be applied, guiding future research in metabolic modeling and control analysis.
Shin, Janis (ORCID:0000000216572455)↗
Engineering of xylose metabolic pathways in Rhodotorula toruloides for sustainable biomanufacturing
Abstract The oleaginous yeast Rhodotorula toruloides is a promising microbial cell factory for the sustainable production of biofuels and value-added chemicals from renewable carbon sources. Unlike the conventional yeast Saccharomyces cerevisiae, R. toruloides can naturally metabolize xylose, the second most abundant sugar in lignocellulosic hydrolysates. However, its native xylose metabolism is inefficient, characterized by slow xylose uptake and accumulation of D-arabitol. Moreover, despite its phenotype, research on the enzymes involved in xylose metabolism has yet to reach a consensus. Therefore, this review provides a comprehensive analysis of the non-canonical xylose metabolism in R. toruloides, focusing on the properties of key enzymes involved in xylose metabolism. Native xylose reductase and xylitol dehydrogenase exhibit broad substrate promiscuity compared to their counterparts in the xylose-fermenting Scheffersomyces stipitis. Additionally, the absence of xylulokinase expression under xylose-utilizing conditions redirects metabolism toward D-arabitol accumulation. Consequently, D-arabitol dehydrogenases and ribulokinase play essential roles in the xylose metabolism of R. toruloides. These findings highlight the fundamental differences between R. toruloides xylose metabolism and the oxidoreductase pathways observed in other xylose-fermenting yeast, providing insights for metabolic engineering strategies to improve xylose utilization and enhance bioconversion of cellulosic hydrolysates to different bioproducts by R. toruloides.
Biotechnology & Applied Microbiology↗
Meta Biome: a multiscale model integrating agent-based and metabolic networks to reveal spatial regulation in gut mucosal microbial communities
ABSTRACT Mucosal microbial communities (MMCs) are complex ecosystems near the mucosal layers of the gut essential for maintaining health and modulating disease states. Despite advances in high-throughput omics technologies, current methodologies struggle to capture the dynamic metabolic interactions and spatiotemporal variations within MMCs. In this work, we presentMetaBiome, a multiscale model integrating agent-based modeling (ABM), finite volume methods, and constraint-based models to explore the metabolic interactions within these communities. Integrating ABM allows for the detailed representation of individual microbial agents each governed by rules that dictate cell growth, division, and interactions with their surroundings. Through a layered approach—encompassing microenvironmental conditions, agent information, and metabolic pathways—we simulated different communities to showcase the potential of the model. Using ourin-silicoplatform, we explored the dynamics and spatiotemporal patterns of MMCs in the proximal small intestine and the cecum, simulating the physiological conditions of the two gut regions. Our findings revealed how specific microbes adapt their metabolic processes based on substrate availability and local environmental conditions, shedding light on spatial metabolite regulation and informing targeted therapies for localized gut diseases.MetaBiome provides a detailed representation of microbial agents and their interactions, surpassing the limitations of traditional grid-based systems. This work marks a significant advancement in microbial ecology, as it offers new insights into predicting and analyzing microbial communities. IMPORTANCE Our study presents a novel multiscale model that combines agent-based modeling, finite volume methods, and genome-scale metabolic models to simulate the complex dynamics of mucosal microbial communities in the gut. This integrated approach allows us to capture spatial and temporal variations in microbial interactions and metabolism that are difficult to study experimentally. Key findings from our model include the following: (i) prediction of metabolic cross-feeding and spatial organization in multi-species communities, (ii) insights into how oxygen gradients and nutrient availability shape community composition in different gut regions, and (iii) identification of spatiallyregulated metabolic pathways and enzymes inE. coli. We believe this work represents a significant advance in computational modeling of microbial communities and provides new insights into the spatial regulation of gut microbiome metabolism. The multiscale modeling approach we have developed could be broadly applicable for studying other complex microbial ecosystems.
Microbiology↗
Metabolic Adaptation to Muscle Ischemia
Although all tissues in the body can adapt to varying physiological/pathological conditions, muscle is the most adaptable. To understand the significance of cellular events and their role in controlling metabolic adaptations in complex physiological systems, it is necessary to link cellular and system levels by means of mechanistic computational models. The main objective of this work is to improve understanding of the regulation of energy metabolism during skeletal/cardiac muscle ischemia by combining in vivo experiments and quantitative models of metabolism. Our main focus is to investigate factors affecting lactate metabolism (e.g., NADH/NAD) and the inter-regulation between carbohydrate and fatty acid metabolism during a reduction in regional blood flow. A mechanistic mathematical model of energy metabolism has been developed to link cellular metabolic processes and their control mechanisms to tissue (skeletal muscle) and organ (heart) physiological responses. We applied this model to simulate the relationship between tissue oxygenation, redox state, and lactate metabolism in skeletal muscle. The model was validated using human data from published occlusion studies. Currently, we are investigating the difference in the responses to sudden vs. gradual onset ischemia in swine by combining in vivo experimental studies with computational models of myocardial energy metabolism during normal and ischemic conditions.
Cabrera, Marco E.↗
Gene network centrality analysis identifies key regulators coordinating day-night metabolic transitions in Synechococcus elongatus PCC 7942 despite limited accuracy in predicting direct regulator-gene interactions
Synechococcus elongatus PCC 7942 is a model organism for studying circadian regulation and bioproduction, where precise temporal control of metabolism significantly impacts photosynthetic efficiency and CO 2 -to-bioproduct conversion. Despite extensive research on core clock components, our understanding of the broader regulatory network orchestrating genome-wide metabolic transitions remains incomplete. We address this gap by applying machine learning tools and network analysis to investigate the transcriptional architecture governing circadian-controlled gene expression. While our approach showed moderate accuracy in predicting individual transcription factor-gene interactions - a common challenge with real expression data - network-level topological analysis successfully revealed the organizational principles of circadian regulation. Our analysis identified distinct regulatory modules coordinating day-night metabolic transitions, with photosynthesis and carbon/nitrogen metabolism controlled by day-phase regulators, while nighttime modules orchestrate glycogen mobilization and redox metabolism. Through network centrality analysis, we identified potentially significant but previously understudied transcriptional regulators: HimA as a putative DNA architecture regulator, and TetR and SrrB as potential coordinators of nighttime metabolism, working alongside established global regulators RpaA and RpaB. This work demonstrates how network-level analysis can extract biologically meaningful insights despite limitations in predicting direct regulatory interactions. The regulatory principles uncovered here advance our understanding of how cyanobacteria coordinate complex metabolic transitions and may inform metabolic engineering strategies for enhanced photosynthetic bioproduction from CO 2 .
59 BASIC BIOLOGICAL SCIENCES↗
Integrative analysis of CAM photosynthesis reveals its impact on primary metabolism in Yucca
Crassulacean Acid Metabolism (CAM) is an adaptation that temporally separates carbon uptake at night from photosynthesis during the day. CAM has evolved repeatedly across vascular plants, as its emergence may depend on simple regulatory changes to deeply conserved metabolic pathways. Modern CAM research relies heavily on interpretation of transcriptomic data, though regulation occurs at multiple levels following transcription. Additionally, while most research to date has focused on a handful of genes and metabolites in the core CAM pathway, the co-option of conserved regulatory and functional genes is bound to have wide-ranging effects on other aspects of primary metabolism. In this study, we integrate transcriptomic, proteomic, and metabolomic data to compare primary metabolism between the CAM species Yucca aloifolia and closely related C 3 species, Y. filamentosa. We observe minimal correlation between protein abundance and mRNA expression, suggesting significant post-transcriptional regulation in CAM species. We also find evidence of shifts in gene expression and metabolite accumulation outside of the central CAM pathway, suggesting that the shift to CAM has cascading effects across primary metabolism, especially nitrogen metabolism. Our findings provide insights into the metabolic shifts associated with CAM evolution, and highlight the complexity of its regulation at multiple biological levels.
59 BASIC BIOLOGICAL SCIENCES↗
Defining Golden Batches in Biomanufacturing Processes From Internal Metabolic Activity to Detect Process Changes That May Affect Product Quality
ABSTRACT Cellular metabolism plays a role in the observed variability of a drug substance's Critical Quality Attributes (CQAs) made by biomanufacturing processes. Therefore, here we describe a new approach for monitoring biomanufacturing processes that measures a set of metabolic reaction rates (named Critical Metabolic Parameters (CMP) in addition to the macroscopic process conditions currently being used as Critical Process Parameters (CPP) for biomanufacturing. Constraint‐based systems biology models like Flux Balance Analysis (FBA) are used to estimate metabolic reaction rates, and metabolic rates are used as inputs for multivariate Batch Evolution Models (BEM). Metabolic activity was reproducible among batches and could be monitored to detect a deliberately induced macroscopic process shift (i.e., temperature change). The CMP approach has the potential to enable “golden batches” in biomanufacturing processes to be defined from the internal metabolic activity and to aid in detecting process changes that may impact the quality of the product. Overall, the data suggested that monitoring of metabolic activity has promise for biomanufacturing process control.
Biotechnology & Applied Microbiology↗
Effects of 5-ion 6-beam sequential irradiation in the presence and absence of hindlimb or control hindlimb unloading on behavioral performances and plasma metabolic pathways of Fischer 344 rats
Introduction Effects and interactions between different spaceflight stressors are expected to be experienced by crew on missions when exposed to microgravity and galactic cosmic rays (GCRs). One of the limitations of previous studies on simulated weightlessness using hindlimb unloading (HU) is that a control HU condition was not included. Methods We characterized the behavioral performance of male Fischer rats 2 months after sham or total body irradiation with a simplified 5-ion 6-mixed-beam exposure representative of GCRs in the absence or presence of HU. Six months later, the plasma, hippocampus, and cortex were processed to determine whether the behavioral effects were associated with long-term alterations in the metabolic pathways. Results In the open field without and with objects, interactions were observed for radiation × HU. In the plasma of animals that were not under the HU or control HU condition, the riboflavin metabolic pathway was affected most for sham irradiation vs. 0.75 Gy exposure. Analysis of the effects of control HU on plasma in the sham-irradiated animals showed that the alanine, aspartate, glutamate, riboflavin, and glutamine metabolisms as well as arginine biosynthesis were affected. The effects of control HU on the hippocampus in the sham-irradiated animals showed that the phenylalanine, tyrosine, and tryptophan pathway was affected the most. Analysis of effects of 0.75 Gy irradiation on the cortex of control HU animals showed that the glutamine and glutamate metabolic pathway was affected similar to the hippocampus, while the riboflavin pathway was affected in animals that were not under the control HU condition. The effects of control HU on the cortex in sham-irradiated animals showed that the riboflavin metabolic pathway was affected. Animals receiving 0.75 Gy of irradiation showed impaired glutamine and glutamate metabolic pathway, whereas animals receiving 1.5 Gy of irradiation showed impaired riboflavin metabolic pathways. A total of 21 plasma metabolites were correlated with the behavioral measures, indicating that plasma and brain biomarkers associated with behavioral performance are dependent on the environmental conditions experienced. Discussion Phenylalanine, tyrosine, and tryptophan metabolism as well as phenylalanine and tryptophan as plasma metabolites are biomarkers that can be considered for spaceflight as they were revealed in both Fischer and WAG/Rij rats exposed to simGCRsim and/or HU.
Physiology↗
Novosphingobium aromaticivorans LigR coordinates transcription of genes involved in metabolism of multiple types of aromatics
Aromatic compounds are a ubiquitous and diverse family of chemicals with functions as biomolecules, natural products, industrial chemicals, and pollutants. Novosphingobium aromaticivorans DSM 12444 uses multiple inducible pathways to catabolize H-, G-, and S-type aromatics that contain zero, one, or two methoxy groups, respectively. Here, we obtain a systems-level view of the transcriptional control of its aromatic metabolic pathways. Several in vitro analyses found that a N. aromaticivorans homolog of the Sphingobium lignivorans SYK-6 transcription factor LigR bound genomic DNA upstream of genes involved in metabolism of multiple aromatic types. We found that a ΔLigR mutant had growth defects on all three types of aromatics as sole carbon sources. Transcriptomic analysis revealed that LigR was required to increase expression of gene products that function in metabolism of all three aromatic types. We also found that, in media containing both glucose and an aromatic carbon source, the ΔLigR mutant directed intermediates through alternative aromatic metabolic pathways. Protein-DNA binding assays showed that N. aromaticivorans LigR binds immediately upstream of promoters of genes involved in aromatic metabolism. We found that N. aromaticivorans LigR coordinates the expression of enzymes that function in the catabolism of H-, G-, and S-type aromatics, and that there are differences in the role of LigR in N. aromaticivorans and S. lignivorans. A comparative genomic analysis predicted that LigR homologs and the aromatic-metabolizing genes that it directly regulates are often co-localized in the genomes of Sphingomonadales, but often not found in this arrangement in many other known aromatic metabolizing bacteria.
Aromatic Compound Degradation↗
Risk of longer-term endocrine and metabolic conditions in the Deepwater Horizon Oil Spill Coast Guard cohort study – five years of follow-up
Abstract Introduction Long-term endocrine and metabolic health risks associated with oil spill cleanup exposures are largely unknown, despite the endocrine-disrupting potential of crude oil and oil dispersant constituents. We aimed to investigate risks of longer-term endocrine and metabolic conditions among U.S. Coast Guard (USCG) responders to the Deepwater Horizon (DWH) oil spill. Methods Our study population included all active duty DWH Oil Spill Coast Guard Cohort members ( N = 45,224). Self-reported spill exposures were ascertained from post-deployment surveys. Incident endocrine and metabolic outcomes were defined using International Classification of Diseases (9th Revision) diagnostic codes from military health encounter records up to 5.5 years post-DWH. Using Cox proportional hazards regression, we estimated adjusted hazard ratios (aHR) and 95% confidence intervals (CIs) for various incident endocrine and metabolic diagnoses (2010–2015, and separately during 2010–2012 and 2013–2015). Results The mean baseline age was 30 years (~ 77% white, ~ 86% male). Compared to non-responders ( n = 39,260), spill responders ( n = 5,964) had elevated risks for simple and unspecified goiter (aHR = 2.09, 95% CI: 1.29–3.38) and disorders of lipid metabolism (aHR = 1.09, 95% CI: 1.00–1.18), including its subcategory other and unspecified hyperlipidemia (aHR = 1.10, 95% CI: 1.01–1.21). The dysmetabolic syndrome X risk was elevated only during 2010–2012 (aHR = 2.07, 95% CI: 1.22–3.51). Responders reporting ever ( n = 1,068) vs. never ( n = 2,424) crude oil inhalation exposure had elevated risks for disorders of lipid metabolism (aHR = 1.24, 95% CI: 1.00–1.53), including its subcategory pure hypercholesterolemia (aHR = 1.71, 95% CI: 1.08–2.72), the overweight, obesity and other hyperalimentation subcategory of unspecified obesity (aHR = 1.52, 95% CI: 1.09–2.13), and abnormal weight gain (aHR = 2.60, 95% CI: 1.04–6.55). Risk estimates for endocrine/metabolic conditions were generally stronger among responders reporting exposure to both crude oil and dispersants (vs. neither) than among responders reporting only oil exposure (vs. neither). Conclusion In this large cohort of active duty USCG responders to the DWH disaster, oil spill cleanup exposures were associated with elevated risks for longer-term endocrine and metabolic conditions.
Denic-Roberts, Hristina↗
Metabolic cost of extravehicular activities
The data on metabolic rates during Skylab extravehicular activities are presented and compared with prior experience during Gemini and Apollo. Difficulties experienced with Gemini extravehicular activities are reviewed. The effect of a pressure suit on metabolic rate is discussed and the life support equipment capabilities of each life support system are reviewed. The methods used to measure metabolic rate, utilizing bioinstrumentation and operational data on the life support system, are described. Metabolic rates are correlated with different activities. Metabolic rates in Skylab were found to be within the capacities of the life support systems and to be similar to the metabolic rates experienced during Apollo lunar 1/6-g extravehicular activities. They were found to range from 100 kcal/h to 500 kcal/h, during both 1/6-g and zero-g extravehicular activities. The average metabolic rates measured during long extravehicular activities were remarkably consistent and appeared to be a function of crew pacing of activity rather than to the effort involved in individual tasks.
Waligora, J. M.↗
Gravity, body mass and composition, and metabolic rate
The scale effects of increased gravitational loading by chronic centrifugation on metabolic rate and body composition in metabolically mature mammals were investigated. Individual oxygen consumption rates in groups of 12 each, 8-month-old, hamster, rats, guinea pigs, and rabbits were measured at weekly intervals at 1.0 g, then 2.0 g for 6 weeks. Metabolic rate was increased significantly in all species, and stabilized after 2 weeks at 2.0 g. Statistical analysis of the data revealed that the larger the animal the greater was the increase in mass-specific metabolic rate, or metabolic intensity, over the 1.0 g value for the same animal, with the result that the interspecies allometric scaling relationship between metabolic rate and total body mass is different at 2.0 g compared 10 1.0 g. Analysis of covariance shows that the postioning constant at 2.0 g is increased by 17% at 2.0 g at the P .001 level, and the exponent is increased by 8% at the P = 0.008 level. Thus, the hypothesis that augmented gravitational loading should shift the allometric relationship between metabolic rate and body size by an increase in both parameters is supported.
Pace, N.↗