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Styczynski, M. P.

Publications and source records attributed to Styczynski, M. P..

10 recordsLinked to original sources

Timing of metabolomics-driven supplementation strategies affects protein expression in E. coli-based cell-free expression systems

While in vivo synthesis of biologic therapeutics has been broadly successful, it is limited by biological constraints of the cells and by the complexity, time, and cost of implementing the pipeline from discovery through manufacturing. Cell-free expression systems (CFES), which use cellular transcription and translation machinery to express proteins in vitro, offer a promising alternative approach that could improve robustness and modularity in that pipeline. However, current benchmark CFES productivity is well below the theoretical capacity of the input nucleotides and amino acids. Efforts to address this issue are hindered by limited understanding of the extent of enzymatic activity in CFES beyond gene expression, as previous work has shown that metabolic enzymes in cell-free lysates cause substantial background metabolic activity that influences protein expression. Here, we hypothesized that the inflection point of protein expression is a critical timescale for CFES metabolism. We performed metabolomics characterization of CFES reactions, finding significant metabolic changes at the inflection point. Driven by these findings, we sought to identify supplements that could be added to the cell-free reaction to avoid metabolic limitations. We found that amino acid supplementation increased expression productivity and lifetime only when added after the inflection point, and actually hurt expression when added before the inflection point. We found similar supplementation timing impacts for some other metabolites as well. These findings show that endogenous metabolism and supplementation timing are deeply interconnected and are critical considerations in CFES optimization, and that metabolomics-informed fed-batch supplementation is a potentially valuable strategy to improve reaction productivity.

synthetic biology↗

Harnessing Plasmid Crosstalk in Transcription Factor Mediated Cell-Free Biosensors

Plasmid crosstalk--unexpected changes to protein expression levels due to interactions between genetic cassettes in cell-free systems--complicates the creation of multi-plasmid cell-free tools. While the potential underlying mechanisms for crosstalk have been previously investigated, the practical impact of crosstalk on the implementation of cell-free genetic circuits has not been thoroughly examined. Here, we contextualize plasmid crosstalk in genetic circuits by examining its impact on the design and performance of multiple, diverse transcription factor biosensors. Guided by a deeper understanding, we harness crosstalk to enhance cell-free biosensor performance. Together, these findings demonstrate that plasmid crosstalk can serve as a tunable property in the development of cell-free genetic circuits.

synthetic biology↗

A modular cell-free protein biosensor platform using split T7 RNA polymerase

Conventional laboratory protein detection techniques are not suitable for point-of-care (POC) use because they require expensive equipment and laborious protocols, and existing POC assays suffer from long development timescales. Here, we describe a modular cell-free biosensing platform for generalizable protein detection that we call TLISA (T7 RNA polymerase-Linked ImmunoSensing Assay), designed for extreme flexibility and equipment-free use. TLISA uses a split T7 RNA polymerase fused to affinity domains against a protein. The target antigen drives polymerase reassembly, inducing reporter expression. We characterize the platform, then demonstrate its modularity by using 16 affinity domains against four different antigens with minimal protocol optimization. We show TLISA is suitable for POC use by sensing human biomarkers in serum and saliva with a colorimetric readout within one hour and by demonstrating functionality after lyophilization. Altogether, this technology could have potentially revolutionary impacts, enabling truly rapid, reconfigurable, equipment-free detection of virtually any protein.

synthetic biology↗

Point-of-care analyte quantification and digital readout via lysate-based cell-free biosensors interfaced with personal glucose monitors

Field-deployable diagnostics based on cell-free systems have advanced greatly, but on-site quantification of target analytes remains a challenge. Here we demonstrate that Escherichia coli lysate-based cell-free biosensors coupled to a personal glucose monitor (PGM) can enable on-site analyte quantification, with the potential for straightforward reconfigurability to diverse types of analytes. We show that analyte-responsive regulators of transcription and translation can modulate production of the reporter enzyme {beta}-galactosidase, which in turn converts lactose into glucose for PGM quantification. Because glycolysis is active in the lysate and would readily deplete converted glucose, we decoupled enzyme production and glucose conversion to increase endpoint signal output. This lysate metabolism did, however, allow for one-pot removal of glucose present in complex samples (like human serum) without confounding target quantification. Taken together, we show that integrating lysate-based cell-free biosensors with PGMs enables accessible target detection and quantification at the point of need. Table of Contents Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=95 SRC="FIGDIR/small/449464v2_ufig1.gif" ALT="Figure 1"> View larger version (18K): org.highwire.dtl.DTLVardef@100532forg.highwire.dtl.DTLVardef@554ac4org.highwire.dtl.DTLVardef@19fa9d4org.highwire.dtl.DTLVardef@146c729_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

SCOUR: A stepwise machine learning framework for predicting metabolite-dependent regulatory interactions

BackgroundThe topology of metabolic networks is both well-studied and remarkably well-conserved across many species. The regulation of these networks, however, is much more poorly characterized, though it is known to be divergent across organisms - two characteristics that make it difficult to model metabolic networks accurately. While many computational methods have been built to unravel transcriptional regulation, there have been few approaches developed for systems-scale analysis and study of metabolic regulation. Here, we present a stepwise machine learning framework that applies established algorithms to identify regulatory interactions in metabolic systems based on metabolic data: Stepwise Classification Of Unknown Regulation, or SCOUR. ResultsWe evaluated our framework on both noiseless and noisy data, using several models of varying sizes and topologies to show that our approach is generalizable. We found that, when testing on data under the most realistic conditions (low sampling frequency and high noise), SCOUR could identify reaction fluxes controlled only by the concentration of a single metabolite (its primary substrate) with high accuracy. The positive predictive value (PPV) for identifying reactions controlled by the concentration of two metabolites ranged from 32-88% for noiseless data, 9.2-49% for either low sampling frequency/low noise or high sampling frequency/high noise data, and 6.6-27% for low sampling frequency/high noise data, with results typically sufficiently high for lab validation to be a practical endeavor. While the PPVs for reactions controlled by three metabolites were lower, they were still in most cases significantly better than random classification. ConclusionsSCOUR uses a novel approach to synthetically generate the training data needed to identify regulators of reaction fluxes in a given metabolic system, enabling metabolomics and fluxomics data to be leveraged for regulatory structure inference. By identifying and triaging the most likely candidate regulatory interactions, SCOUR can drastically reduce the amount of time needed to identify and experimentally validate metabolic regulatory interactions. As high-throughput experimental methods for testing these interactions are further developed, SCOUR will provide critical impact in the development of predictive metabolic models in new organisms and pathways.

bioinformatics↗

Metabolic Dynamics in Escherichia coli-based Cell-Free Systems

The field of metabolic engineering has yielded remarkable accomplishments in using cells to produce valuable molecules, and cell-free expression (CFE) systems have the potential to push the field even further. However, CFE systems still face some outstanding challenges, including endogenous metabolic activity that is poorly understood yet has a significant impact on CFE productivity. Here, we use metabolomics to characterize the temporal metabolic changes in CFE systems and their constituent components, including significant metabolic activity in central carbon and amino acid metabolism. We find that while changing the reaction starting state via lysate pre-incubation impacts protein production, it has a comparatively small impact on metabolic state. We also demonstrate that changes to lysate preparation have a larger effect on protein yield and temporal metabolic profiles, though general metabolic trends are conserved. Finally, while we improve protein production through targeted supplementation of metabolic enzymes, we show that the endogenous metabolic activity is fairly resilient to these enzymatic perturbations. Overall, this work highlights the robust nature of CFE reaction metabolism as well as the importance of understanding the complex interdependence of metabolites and proteins in CFE systems to guide optimization efforts. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=135 SRC="FIGDIR/small/444339v1_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@1afbfc3org.highwire.dtl.DTLVardef@156a23aorg.highwire.dtl.DTLVardef@b494adorg.highwire.dtl.DTLVardef@18d03dc_HPS_FORMAT_FIGEXP M_FIG Abstract Figure C_FIG

systems biology↗

Improved constraints increase the predictivity and applicability of a linear programming-based dynamic metabolic modeling framework

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, LK-DFBA, that addresses a key gap: capturing metabolite dynamics and regulation while retaining a potentially scalable linear programming structure. Key to this frameworks 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 the optimal constraint approach was generally robust to local perturbations of the system, indicating that just a single wild-type dataset could allow identification of the ideal constraint 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.

systems biology↗

Leveraging the mass balances of cellular metabolism to infer absolute concentrations from relative abundance metabolomics data

MotivationAs the large-scale study of metabolites and a direct readout of a systems metabolic state, metabolomics has significant appeal as a source of information for many metabolic modelling platforms and other metabolic analysis tools. However, metabolomics data are typically reported in terms of relative abundances, which precluding use with tools where absolute concentrations are necessary. While chemical standards can be used to determine the absolute concentrations of metabolites, they are often time-consuming to run, expensive, or unavailable for many metabolites. A computational framework that can infer absolute concentrations without the use of chemical standards would be highly beneficial to the metabolomics community. ResultsWe have developed and characterized MetaboPAC, a computational strategy that leverages the mass balances of a system to infer absolute concentrations in metabolomics datasets. MetaboPAC uses a kinetic equations approach and an optimization approach to predict the most likely response factors that describe the relationship between absolute concentrations and their relative abundances. We determined that MetaboPAC performed significantly better than the other approaches assessed on noiseless data when at least 60% of kinetic equations are known a priori. Under the most realistic conditions (low sampling frequency, high noise data), MetaboPAC significantly outperformed other methods in the majority of cases when 100% of the kinetic equations were known. For metabolomics datasets extracted from systems that are well-studied and have partially known kinetic structures, MetaboPAC can provide valuable insight about their absolute concentration profiles.

systems biology↗

Protocell Arrays for Simultaneous Detection of Diverse Analytes

Simultaneous detection of multiple analytes from a single sample (multiplexing), particularly when at the point of need, can guide complex decision-making without increasing the required sample volume or cost per test. Despite recent advances, multiplexing still typically faces the critical limitation of measuring only one type of molecule per assay platform - for example, only small molecules or only nucleic acids. In this work, we address this bottleneck with a customizable platform that integrates cell-free expression (CFE) with a polymer-based aqueous two-phase system (ATPS) to produce membrane-less "protocells" containing transcription and translation machinery used for analyte detection. Multiple protocells are arrayed in microwells where each protocell droplet performs distinct reactions to detect chemically diverse targets including small molecules, minerals, and nucleic acid sequences, all from the same sample. We demonstrate that these protocell arrays can measure analytes in a human biofluid matrix, maintain function after lyophilization and rehydration, and produce visually interpretable readouts, illustrating its potential for application as a minimal-equipment, field-deployable, multi-analyte detection tool.

synthetic biology↗

Multi-omics Integrative Analysis of Acute and Relapsing Malaria in a Non-Human Primate Model of P. vivax infection

Systems-scale analysis of multiple layers of molecular and cellular data has significant potential for providing novel insights into malaria pathology and immunity. We present here a unique longitudinal multi-omics dataset encompassing Macaca mulatta blood and bone marrow responses to infection by Plasmodium cynomolgi, a non-human primate (NHP) parasite species used to model P. vivax malaria acute and relapsing infections in humans. We analyzed relationships across multiple biological layers using a mutual information-based machine learning approach to integrate heterogeneous longitudinal datasets and constructed an atlas of multi-omics relatedness networks (MORNs). Using this technique, we were able to detect signatures that defined both acute and relapsing infections. Importantly, relapse infections could be distinguished from both acutely-infected and uninfected NHP, suggesting that the host-parasite interactions during relapses are unique compared to acute Plasmodium infections. To our knowledge, this is the first report of large-scale, longitudinal multi-omics analysis of malaria in any system. This dataset, along with the method used to analyze it, provides a unique resource for the malaria research community and demonstrates the power of longitudinal infection study designs, NHP model systems and integrative multi-omics analyses.

systems biology↗