Biology subjects

Broddrick, J. T.

Publications and source records attributed to Broddrick, J. T..

4 recordsLinked to original sources

bioRxiv2024

Spaceflight alters host-gut microbiota interactions

The rodent habitat on the International Space Station has provided crucial insights into the impact of spaceflight on mammals, including observation of symptoms characteristic of liver disease, insulin resistance, osteopenia and myopathy. Although these physiological responses can involve the microbiome when observed on Earth, changes in host-microbiota interactions during spaceflight are still being elucidated. Here, NASA GeneLab multiomic data from the Rodent Research 6 mission are used to determine changes to gut microbiota and murine host colon and liver gene expression after 29 and 56-days of spaceflight. Using hybrid amplicon and whole metagenome sequencing analysis, significant spaceflight-associated alterations to 42 microbiome species were identified. These included relative reductions of bacteria associated with bile acid and butyrate metabolism, such as Extibacter muris and Dysosmobacter welbionis. Functional prediction suggested over-representation of fatty acid and bile acid metabolism, extracellular matrix interactions, and antibiotic resistance genes within the gut microbiome, while host intestinal and hepatic gene expression described corresponding changes to host bile acid and energy metabolism, and immune suppression from spaceflight. Taken together, these changes imply that interactions at the host-gut microbiome interface contribute to spaceflight pathology and highlight how these interactions might critically influence human health and the feasibility of long-duration spaceflight.

genomics↗

bioRxiv2021

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. In 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. Additionally, our simulations recapitulate the link between mitochondrial dissipation of photosynthetically-derived electrons and the redox state of the photosynthetic electron transport chain. We use this framework to assess engineering strategies for rerouting cellular resources toward bioproducts. Overall, we present a methodology for incorporating a common, informative data type into computational models of light-driven metabolism for characterization, monitoring and engineering of photosynthetic organisms.

systems biology↗

bioRxiv2021

A multiscale model predicts the sensitivity of Chlorella vulgaris to light and nitrogen levels in photobioreactors

The maximization of lipid productivity in microalgae is crucial for the biofuel industry, and it can be achieved by manipulating their metabolism. However, little efforts have been made to apply metabolic models in a dynamic framework to predict possible outcomes to scenarios observed at an industrial scale. Here, we present a dynamic framework for the simulation of large-scale photobioreactors. The framework was generated by merging the genome-scale metabolic model of Chlorella vulgaris (iCZ843) with reactor-scale parameters, thus yielding a multiscale model. This multiscale model was employed to predict the sensitivity of growth and composition variation of C. vulgaris on light and nitrogen levels. Simulations of lipid accumulation quantified the tradeoff between growth and lipid biosynthesis under nitrogen limitation. Moreover, our modeling approach quantitatively predicted the dependence of microalgal metabolism on light intensity and circadian oscillations. Finally, we use the model to design a reactor irradiance profile that maximized lipid accumulation, thus achieving a lipid productivity increase of 46% at a constant intensity of 966 E m-2 s-1. Our modeling framework elucidated how metabolism and external factors can be combined to predict optimized parameters for industrial applications.

bioengineering↗

bioRxiv2017

Multi-scale model of the proteomic and metabolic consequences of reactive oxygen species

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 completely inhibit cell growth when unmanaged and thus elicits an essential stress response that is universal and fundamental in biology. We develop a computable multi-scale description of the ROS stress response in Escherichia coli. We show that this quantitative framework allows for the understanding and prediction of ROS stress responses at three levels: 1) pathways: amino acid auxotrophies, 2) networks: the systemic response to ROS stress, and 3) genetic basis: adaptation to ROS stress during laboratory evolution. These results show that we can now develop fundamental and quantitative genotype-phenotype relationships for stress responses on a genome-wide basis.

systems biology↗