Showing papers by "Mark P. Styczynski published in 2022"

Untargeted plasma metabolomic analysis of wild bottlenose dolphins (Tursiops truncatus) indicate protein degradation when in poorer health.

Abstract: Cumulative exposure to sub-lethal anthropogenic stressors can affect the health and reproduction of coastal cetaceans and hence their population viability. To date, we do not have a clear understanding of the notion of health for cetaceans in an ecological context; that is, how health status affects the ability of individuals to survive and reproduce. Here, we make use of a unique health-monitoring programme of estuarine bottlenose dolphins in South Carolina and Florida to determine de novo changes in biological pathways, using untargeted plasma metabolomics, depending on the health status of individuals obtained from veterinary screening. We found that individuals that were in a poor health state had lower circulating amino acids pointing towards increased involvement of gluconeogenesis (i.e., new formation of glucose). More mechanistic work is needed to disentangle the interconnection between health and energy metabolism in cetaceans to mediate potential metabolic constraints they may face during periods of stress.

Nucleic Acid Partitioning in PEG-Ficoll Protocells

Abstract: The phase separation of aqueous polymer solutions is a widely used method for producing self-assembled, membraneless droplet protocells. Nonionic synthetic polymers forming an aqueous two-phase system (ATPS) have been shown to reliably form protocells that, when equipped with biological materials, are useful for applications such as analyte detection. Previous characterization of an ATPS-templated protocell did not investigate the effects of its biological components on phase stability. Here we report the phase diagram of a PEG 35k-Ficoll 400k-water ATPS at baseline and in the presence of necessary protocell components. Because the stability of an ATPS can be sensitive to small changes in composition, which in turn impacts solute partitioning, we present partitioning data of a variety of nucleic acids in response to protocell additives. The results show that the additives─particularly a mixture of salts and small organic molecules─have profound positive effects on ATPS stability and nucleic acid partitioning, both of which significantly contribute to protocell function. Our data uncovers several new areas of optimization for future protocell engineering.

Harnessing Escherichia coli's Native Machinery for Detection of Vitamin C (Ascorbate) Deficiency.

Abstract: Vitamin C (l-ascorbate) deficiency is a global public health issue most prevalent in resource-limited regions, creating a need for an inexpensive detection platform. Here, we describe efforts to engineer whole-cell and cell-free ascorbate biosensors. Both sensors used the protein UlaR, which binds to a metabolite of ascorbate and regulates transcription. The whole-cell sensor could detect lower, physiologically relevant concentrations of ascorbate, which we attributed to intact functionality of a phosphotransferase system (PTS) that transports ascorbate across the cell membrane and phosphorylates it to form UlaR's ligand. We used multiple strategies to enhance cell-free PTS functionality (which has received little previous attention), improving the cell-free sensor's performance, but the whole-cell sensor remained more sensitive. These efforts demonstrated an advantage of whole-cell sensors for detection of molecules─like ascorbate─transformed by a PTS, but also proof of principle for cell-free sensors requiring membrane-bound components like the PTS. In addition, the cell-free sensor was functional in plasma, setting the stage for future implementation of ascorbate sensors for clinically relevant biofluids in field-deployable formats.

Metabolomics Analysis of Cell-Free Expression Systems Using Gas Chromatography-Mass Spectrometry.

Abstract: Metabolomics is the systems-scale study of the biochemical intermediates of metabolism. This approach has great potential to uncover how metabolic intermediates are used and generated in cell-free expression systems, something that is to date not well understood. Here, we present a detailed metabolomics protocol for characterization of the small molecules in cell-free systems. We specifically focus on the analysis of Escherichia coli lysate-based cell-free systems using gas chromatography coupled to mass spectrometry. Measuring and monitoring the metabolic changes in cell-free systems can provide insight into the ways that metabolites affect the productivity of cell-free reactions, ultimately allowing for more informed engineering and optimization efforts for cell-free systems.

Towards inferring absolute concentrations from relative abundance in time-course GC-MS metabolomics data.

Abstract: Metabolomics, the large-scale study of metabolites, has significant appeal as a source of information for metabolic modeling and other scientific applications. One common approach for measuring metabolomics data is gas chromatography-mass spectrometry (GC-MS). However, GC-MS metabolomics data are typically reported as relative abundances, precluding their use with approaches and tools where absolute concentrations are necessary. While chemical standards can be used to help provide quantification, their use is time-consuming, expensive, or even impossible due to their limited availability. The ability to infer absolute concentrations from GC-MS metabolomics data without chemical standards would have significant value. We hypothesized that when analyzing time-course metabolomics datasets, the mass balances of metabolism and other biological information could provide sufficient information towards inference of absolute concentrations. To demonstrate this, we developed and characterized MetaboPAC, a computational framework that uses two approaches-one based on kinetic equations and another using biological heuristics-to predict the most likely response factors that allow translation between relative abundances and absolute concentrations. When used to analyze noiseless synthetic data generated from multiple types of kinetic rate laws, MetaboPAC performs significantly better than negative control approaches when 20% of kinetic terms are known a priori. Under conditions of lower sampling frequency and high noise, MetaboPAC is still able to provide significant inference of concentrations in 3 of 4 models studied. This provides a starting point for leveraging biological knowledge to extract concentration information from time-course intracellular GC-MS metabolomics datasets, particularly for systems that are well-studied and have partially known kinetic structures.

Diverse classes of constraints enable broader applicability of a linear programming-based dynamic metabolic modeling framework

Abstract: Abstract Current metabolic modeling tools suffer from a variety of limitations, from scalability to simplifying assumptions, that preclude their use in many applications. We recently created a modeling framework, Linear Kinetics-Dynamic Flux Balance Analysis (LK-DFBA), that addresses a key gap: capturing metabolite dynamics and regulation while retaining a potentially scalable linear programming structure. Key to this framework’s success are the linear kinetics and regulatory constraints imposed on the system. However, while the linearity of these constraints reduces computational complexity, it may not accurately capture the behavior of many biochemical systems. Here, we developed three new classes of LK-DFBA constraints to better model interactions between metabolites and the reactions they regulate. We tested these new approaches on several synthetic and biological systems, and also performed the first-ever comparison of LK-DFBA predictions to experimental data. We found that no single constraint approach was optimal across all systems examined, and systems with the same topological structure but different parameters were often best modeled by different types of constraints. However, we did find that when genetic perturbations were implemented in the systems, the optimal constraint approach typically remained the same as for the wild-type regardless of the model topology or parameterization, indicating that just a single wild-type dataset could allow identification of the ideal constraint to enable model predictivity for a given system. These results suggest that the availability of multiple constraint approaches will allow LK-DFBA to model a wider range of metabolic systems.

Metabolomics Analysis of Cell-Free Systems Using

Abstract: Metabolomics is the systems-scale study of the biochemical intermediates of metabolism. This approach has great potential to uncover how metabolic intermediates are used and generated in cell-free expression systems, something that is to date not well understood. Here, we present a detailed metabolomics protocol for characterization of the small molecules in cell-free systems. We specifically focus on the analysis of Escherichia coli lysate-based cell-free systems using gas chromatography coupled to mass spectrometry. Measuring and monitoring the metabolic changes in cell-free systems can provide insight into the ways that metabolites affect the productivity of cell-free reactions, ultimately allowing for more informed engineering and optimization efforts for cell-free systems.

Corrigendum: Effective use of linear DNA in cell-free expression systems

Abstract: [This corrects the article DOI: 10.3389/fbioe.2021.715328.].