Forward and inverse metabolic engineering strategies for improving polyhydroxybyrate production

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 2008.Includes bibliographical references (p. 165-174).Forward metabolic engineering (FME) is a rational approach to cellular engineering, relying on an understanding of the entire metabolic network to direct perturbations for phenotype improvement. Conversely, inverse metabolic engineering (IME) uses a global, combinatorial approach to identify genetic loci that are important for a given phenotype. These two approaches complement each other in a strain improvement program. FME and IME approaches were applied to poly-3-hydroxybutyrate (PHB)production in Synechocystis PCC6803 [IME] and recombinant E. coli [FME] in this thesis.IME was appropriate for Synechocystis, where metabolic regulation of the native PHB pathway was not well understood. A high throughput screening method was established by developing a staining protocol that quantitatively related nile red fluorescence to PHB content, while maintaining cell viability for both organisms. This was combined with fluorescence activated cell sorting (FACS) to screen for high PHB mutants. A Synechocystis insertion mutagenesis library was screened to identify gene disruptions that increased PHB. Two gene disruptions in proline biosynthesis and an unknown function were identified and characterized.An analogous IME study in E. coli did not find increased PHB mutants, but suggested an FME approach on the PHB pathway. Systematic overexpression of the pathway revealed phaB, acetoacetyl-CoA reductase, limited PHB flux. Beyond this, whole operon overexpression led to even higher PHB fluxes.In a nitrogen-limited chemostat, PHB flux did not change with dilution rate. Unlike prior pleiotropic perturbations, these systematic experiments could clearly conclude that the flux control is in the PHB pathway. At high PHB flux, growth rate was extremely hindered and was accompanied by PHB plasmid genetic instability and rapid PHB productivity loss.(cont.) Tandem gene duplication (TGD) was developed to slow productivity loss caused by "allele segregation," a fast process that propagates a DNA mutation to all copies of a plasmid. By placing the many copies in tandem, rather than on individual plasmids, allele segregation could be avoided, increasing stability significantly.These methods and results should support PHB engineering in higher photosynthetic organisms and better E. coli PHB production in batch or continuous culture.TGD is a broadly applicable technique for high level recombinant expression.by Keith E. J. Tyo.Ph.D

Statistical Analysis of Molecular Signal Recording

A molecular device that records time-varying signals would enable new approaches in neuroscience. We have recently proposed such a device, termed a “molecular ticker tape”, in which an engineered DNA polymerase (DNAP) writes time-varying signals into DNA in the form of nucleotide misincorporation patterns. Here, we define a theoretical framework quantifying the expected capabilities of molecular ticker tapes as a function of experimental parameters. We present a decoding algorithm for estimating time-dependent input signals, and DNAP kinetic parameters, directly from misincorporation rates as determined by sequencing. We explore the requirements for accurate signal decoding, particularly the constraints on (1) the polymerase biochemical parameters, and (2) the amplitude, temporal resolution, and duration of the time-varying input signals. Our results suggest that molecular recording devices with kinetic properties similar to natural polymerases could be used to perform experiments in which neural activity is compared across several experimental conditions, and that devices engineered by combining favorable biochemical properties from multiple known polymerases could potentially measure faster phenomena such as slow synchronization of neuronal oscillations. Sophisticated engineering of DNAPs is likely required to achieve molecular recording of neuronal activity with single-spike temporal resolution over experimentally relevant timescales.United States. Defense Advanced Research Projects Agency. Living Foundries ProgramGoogle (Firm)New York Stem Cell Foundation. Robertson Neuroscience Investigator AwardNational Institutes of Health (U.S.) (EUREKA Award 1R01NS075421)National Institutes of Health (U.S.) (Transformative R01 1R01GM104948)National Institutes of Health (U.S.) (Single Cell Grant 1 R01 EY023173)National Institutes of Health (U.S.) (Grant 1R01DA029639)National Institutes of Health (U.S.) (Grant 1R01NS067199)National Science Foundation (U.S.) (CAREER Award CBET 1053233)National Science Foundation (U.S.) (Grant EFRI0835878)National Science Foundation (U.S.) (Grant DMS1042134)Paul G. Allen Family Foundation (Distinguished Investigator in Neuroscience Award

N-Terminal-Based Targeted, Inducible Protein Degradation in Escherichia coli.

Dynamically altering protein concentration is a central activity in synthetic biology. While many tools are available to modulate protein concentration by altering protein synthesis rate, methods for decreasing protein concentration by inactivation or degradation rate are just being realized. Altering protein synthesis rates can quickly increase the concentration of a protein but not decrease, as residual protein will remain for a while. Inducible, targeted protein degradation is an attractive option and some tools have been introduced for higher organisms and bacteria. Current bacterial tools rely on C-terminal fusions, so we have developed an N-terminal fusion (Ntag) strategy to increase the possible proteins that can be targeted. We demonstrate Ntag dependent degradation of mCherry and beta-galactosidase and reconfigure the Ntag system to perform dynamic, exogenously inducible degradation of a targeted protein and complement protein depletion by traditional synthesis repression. Model driven analysis that focused on rates, rather than concentrations, was critical to understanding and engineering the system. We expect this tool and our model to enable inducible protein degradation use particularly in metabolic engineering, biological study of essential proteins, and protein circuits

Impact of protein uptake and degradation on recombinant protein secretion in yeast

Protein titers, a key bioprocessing metric, depend both on the synthesis of protein and the degradation of protein. Secreted recombinant protein production in Saccharomyces cerevisiae is an attractive platform as minimal media can be used for cultivation, thus reducing fermentation costs and simplifying downstream purification, compared to other systems that require complex media. As such, engineering S. cerevisiae to improve titers has been then the subject of significant attention, but the majority of previous efforts have been focused on improving protein synthesis. Here, we characterize the protein uptake and degradation pathways of S. cerevisiae to better understand its impact on protein secretion titers. We do find that S. cerevisiae can consume significant (in the range of 1 g/L/day) quantities of whole proteins. Characterizing the physiological state and combining metabolomics and transcriptomics, we identify metabolic and regulatory markers that are consistent with uptake of whole proteins by endocytosis, followed by intracellular degradation and catabolism of substituent amino acids. Uptake and degradation of recombinant protein products may be common in S. cerevisiae protein secretion systems, and the current data should help formulate strategies to mitigate product loss

A Glucose-Sensing Toggle Switch for Autonomous, High Productivity Genetic Control

Many biosynthetic strategies are coupled to growth, which is inherently limiting, as (1) excess feedstock (<i>e.g.</i>, sugar) may be converted to biomass, instead of product, (2) essential genes must be maintained, and (3) growth toxicity must be managed. A decoupled growth and production phase strategy could avoid these issues. We have developed a toggle switch that uses glucose sensing to enable this two-phase strategy. Temporary glucose starvation precisely and autonomously activates product pathway expression in rich or minimal media, obviating the requirement for expensive inducers. The switch remains stably in the new state even after reintroduction of glucose. In the context of polyhydroxybutyrate (PHB) biosynthesis, our system enables shorter growth phases and comparable titers to a constitutively expressing PHB strain. This two-phase production strategy, and specifically the glucose toggle switch, should be broadly useful to initiate many types of genetic program for metabolic engineering applications

Bayesian inference of metabolic kinetics from genome-scale multiomics data.

Modern biological tools generate a wealth of data on metabolite and protein concentrations that can be used to help inform new strain designs. However, learning from these data to predict how a cell will respond to genetic changes, a key need for engineering, remains challenging. A promising technique for leveraging omics measurements in metabolic modeling involves the construction of kinetic descriptions of the enzymatic reactions that occur within a cell. Parameterizing these models from biological data can be computationally difficult, since methods must also quantify the uncertainty in model parameters resulting from the observed data. While the field of Bayesian inference offers a wide range of methods for efficiently estimating distributions in parameter uncertainty, such techniques are poorly suited to traditional kinetic models due to their complex rate laws and resulting nonlinear dynamics. In this paper, we employ linear-logarithmic kinetics to simplify the calculation of steady-state flux distributions and enable efficient sampling and inference methods. We demonstrate that detailed information on the posterior distribution of parameters can be obtained efficiently at a variety of problem scales, including nearly genome-scale kinetic models trained on multiomics datasets. These results allow modern Bayesian machine learning tools to be leveraged in understanding biological data and in developing new, efficient strain designs

Nucleotide-time alignment for molecular recorders

Using a DNA polymerase to record intracellular calcium levels has been proposed as a novel neural recording technique, promising massive-scale, single-cell resolution monitoring of large portions of the brain. This technique relies on local storage of neural activity in strands of DNA, followed by offline analysis of that DNA. In simple implementations of this scheme, the time when each nucleotide was written cannot be determined directly by post-hoc DNA sequencing; the timing data must be estimated instead. Here, we use a Dynamic Time Warping-based algorithm to perform this estimation, exploiting correlations between neural activity and observed experimental variables to translate DNA-based signals to an estimate of neural activity over time. This algorithm improves the parallelizability of traditional Dynamic Time Warping, allowing several-fold increases in computation speed. The algorithm also provides a solution to several critical problems with the molecular recording paradigm: determining recording start times and coping with DNA polymerase pausing. The algorithm can generally locate DNA-based records to within <10% of a recording window, allowing for the estimation of unobserved incorporation times and latent neural tunings. We apply our technique to an in silico motor control neuroscience experiment, using the algorithm to estimate both timings of DNA-based data and the directional tuning of motor cortical cells during a center-out reaching task. We also use this algorithm to explore the impact of polymerase characteristics on system performance, determining the precision of a molecular recorder as a function of its kinetic and error-generating properties. We find useful ranges of properties for DNA polymerase-based recorders, providing guidance for future protein engineering attempts. This work demonstrates a useful general extension to dynamic alignment algorithms, as well as direct applications of that extension toward the development of molecular recorders, providing a necessary stepping stone for future biological work

Detection of a Peptide Biomarker by Engineered Yeast Receptors

Directed evolution of membrane receptors is challenging as the evolved receptor must not only accommodate a non-native ligand, but also maintain the ability to transduce the detection of the new ligand to any associated intracellular components. The G-protein coupled receptor (GPCR) superfamily is the largest group of membrane receptors. As members of the GPCR family detect a wide range of ligands, GPCRs are an incredibly useful starting point for directed evolution of user-defined analytical tools and diagnostics. The aim of this study was to determine if directed evolution of the yeast Ste2p GPCR, which natively detects the α-factor peptide, could yield a GPCR that detects Cystatin C, a human peptide biomarker. We demonstrate a generalizable approach for evolving Ste2p to detect peptide sequences. Because the target peptide differs significantly from α-factor, a single evolutionary step was infeasible. We turned to a substrate walking approach and evolved receptors for a series of chimeric intermediates with increasing similarity to the biomarker. We validate our previous model as a tool for designing optimal chimeric peptide steps. Finally, we demonstrate the clinical utility of yeast-based biosensors by showing specific activation by a C-terminally amidated Cystatin C peptide in commercially sourced human urine. To our knowledge, this is the first directed evolution of a peptide GPCR

Correction to “Detection of a Peptide Biomarker by Engineered Yeast Receptors”

Correction to “Detection of a Peptide Biomarker by Engineered Yeast Receptors

Detection of a Peptide Biomarker by Engineered Yeast Receptors

Directed evolution of membrane receptors is challenging as the evolved receptor must not only accommodate a non-native ligand, but also maintain the ability to transduce the detection of the new ligand to any associated intracellular components. The G-protein coupled receptor (GPCR) superfamily is the largest group of membrane receptors. As members of the GPCR family detect a wide range of ligands, GPCRs are an incredibly useful starting point for directed evolution of user-defined analytical tools and diagnostics. The aim of this study was to determine if directed evolution of the yeast Ste2p GPCR, which natively detects the α-factor peptide, could yield a GPCR that detects Cystatin C, a human peptide biomarker. We demonstrate a generalizable approach for evolving Ste2p to detect peptide sequences. Because the target peptide differs significantly from α-factor, a single evolutionary step was infeasible. We turned to a substrate walking approach and evolved receptors for a series of chimeric intermediates with increasing similarity to the biomarker. We validate our previous model as a tool for designing optimal chimeric peptide steps. Finally, we demonstrate the clinical utility of yeast-based biosensors by showing specific activation by a C-terminally amidated Cystatin C peptide in commercially sourced human urine. To our knowledge, this is the first directed evolution of a peptide GPCR