Biology subjects

Ozdemir, E.

Publications and source records attributed to Ozdemir, E..

7 recordsLinked to original sources

bioRxiv2026

Seven mutations unlock strict synthetic methylotrophy in engineered Pseudomonas putida

Methanol is a reduced, soluble one-carbon (C1) feedstock for sustainable bioproduction, but converting this potential into robust microbial growth remains difficult. Several synthetic C1 assimilation routes depend on autocatalytic cycles, whose operation requires coordinated control of redox balance, toxic intermediates, substrate regeneration, and host regulation. Here, we implemented the serine-threonine cycle (STC) in the soil bacterium Pseudomonas putida and used growth-coupled selection with adaptive laboratory evolution (ALE) to transition from mixotrophic C1 incorporation to strict methylotrophy. The evolved strain grew with methanol as the sole carbon and energy source under atmospheric CO2 with a doubling time of ca. 40 h. Whole-genome sequencing, reverse genetics, biosensors, isotope labelling, and comparative RNA sequencing showed that evolution repeatedly targeted native pyrroloquinoline quinone (PQQ)-dependent methanol oxidation, membrane-bound transhydrogenase activity, glycine regeneration, STC enzyme balance, and global regulatory nodes. Additional ALE under glycine-methanol co-feeding increased growth rates and exposed further targets for improving cycle flux. These results establish P. putida as a chassis for strict synthetic methylotrophy and define actionable engineering routes toward C1 biomanufacturing. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=63 SRC="FIGDIR/small/739708v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@1642d2dorg.highwire.dtl.DTLVardef@1d203d1org.highwire.dtl.DTLVardef@f8a5d7org.highwire.dtl.DTLVardef@4fe272_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2024

Metabolic growth coupling strategies for in vivo enzyme selection systems

Whole-cell biocatalysis facilitates the production of a wide range of industrially and pharmaceutically relevant molecules from sustainable feedstocks such as plastic wastes, carbon dioxide, lignocellulose, or plant-based sugar sources. The identification and use of efficient enzymes in the applied biocatalyst is key to establishing economically feasible production processes. The generation and selection of favorable enzyme variants in adaptive laboratory evolution experiments using growth as a selection criterion is facilitated by tightly coupling enzyme catalytic activity to microbial metabolic activity. Here, we present a computational workflow to design strains that have a severe, growth-limiting metabolic chokepoint through a shared class of enzymes. The resulting chassis cell, termed enzyme selection system (ESS), is a platform for growth-coupling any enzyme from the respective enzyme class, thus offering cross-pathway application for enzyme engineering purposes. By applying the constraint-based modeling workflow, a publicly accessible database of 25,505 potential and experimentally tractable ESS designs was built for Escherichia coli and a broad range of production pathways with biotechnological relevance. Model-based analysis of the generated design database reveals a general design principle that the target enzyme activity is linked to overall microbial metabolic activity, not just the synthesis of one biomass precursor. Furthermore, the use of currently available genome-reduced strains or one preeminent carbon source does not significantly enable or improve growth-coupling of target enzymes. Most importantly, observed trade-offs between the predicted viability of ESSs, the design-inflicted metabolic perturbations, and the coupling strength suggest that a suboptimal coupling has benefits regarding the experimental implementation of ESSs and growth-coupling in general. Overall, the computed design database, which is accessible through https://biosustain.github.io/ESS-Designs/, and its analysis lay the foundation for generating valuable in vivo ESSs for a range of biotechnological applications. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=170 SRC="FIGDIR/small/613188v1_ufig1.gif" ALT="Figure 1"> View larger version (47K): org.highwire.dtl.DTLVardef@15d5a10org.highwire.dtl.DTLVardef@1c4067aorg.highwire.dtl.DTLVardef@856ce9org.highwire.dtl.DTLVardef@1b14cdb_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstractC_FLOATNO C_FIG HighlightsO_LIEnzyme selection system designs link enzyme activities to global metabolic processes. C_LIO_LIEnzymes can be categorized based on growth-couplable reaction stoichiometries. C_LIO_LIA database of strain designs for building enzyme selection systems was created. C_LIO_LIEnzyme selection systems enable optimization of a range product synthesis pathways. C_LIO_LIApplication recommendations: weakly coupled enzyme activities can have practical advantages. C_LI

systems biology↗

bioRxiv2023

Understanding and exploitation of host stress responses to protein production using novel transcriptomic analytics

Predictable expression of heterologous genes in a production host is a fundamental challenge in biotechnology. While traditional methods focus on manipulating expression and the property of the heterologous gene, a systems biology approach can complement with designs to improve the host itself. Previously, Independent Component Analysis (ICA) of the RNAseq data helped reveal independently modulated gene sets (iModulons) in bacteria. This was later applied to identify common stress responses related to heterologous gene expression for Escherichia coli. In this study, we expand this analysis with additional non-enzymatic proteins and apply our findings to design novel protein production optimization. By leveraging the Precise-1K transcriptomics knowledge base, we identify three iModulons as novel transcriptional responses to protein production stress; Cold Shock, gcvB sRNA, and the uncharacterized UC-9 iModulons. By studying the gene membership in the UC-9 iModulon, we discover effective novel design targets for improving protein production. This study demonstrates the value of big data analytics and systems understanding of host responses for designing novel strategies to optimize protein production.

systems biology↗

bioRxiv2023

Quantification of ligand and mutation-induced bias in EGFR phosphorylation in direct response to ligand binding

The 58 receptor tyrosine kinases (RTKs) are critically important for human development, and are implicated in many growth disorders and cancers. Here we introduce a methodology to identify and quantify bias in RTK signal transduction across the plasma membrane, and to quantify phosphorylation efficiencies, without contributions from feedback loops and system bias. We show that RTK biased signaling can occur in the first step of signal transduction not only in response to different ligands, but as a consequence of RTK pathogenic mutations as well. Ligand bias and mutation-induced bias are uncoupled here based on a comprehensive data set of dose response curves acquired for three ligands, EGF, TGF and epiregulin, for wild-type EGFR and for the oncogenic L834R EGFR mutant found in patients with non-small-cell lung cancer (NSCLC). Ligand bias has been extensively studied for GPCRs, and has revolutionized the GPCR field. The demonstration of pathogenic mutation-induced bias in RTK signal transduction across the plasma membrane will open new avenues for the exploration of RTK biased inhibitors as highly specific anti-cancer therapies.

pharmacology and toxicology↗

bioRxiv2022

Plasticity of transmembrane helix interactions in EphA2 dimers and oligomers

Lateral interactions can stabilize different EphA2 receptor assemblies in the plasma membrane in response to different ligands. Here we use two fluorescent techniques, Forster Resonance Energy Transfer (FRET) and Fluorescence Intensity Fluctuations (FIF) spectrometry, to investigate how mutations in the EphA2 transmembrane (TM) helix affect the association between full-length EphA2 molecules in the absence of ligand and in the presence of three ligands: ephrinA1-Fc, m-ephrinA1, and the YSA peptide. The EphA2 mutations we studied have been previously characterized in the context of the isolated EphA2 TM helix. Working with full-length EphA2, we observed modest effects of the mutations on receptor-receptor interaction. Our data do not support the currently accepted model of a switch between two discrete TM helix dimerization motifs corresponding to active or inactive receptor states. Instead, we propose that different dimeric/oligomeric arrangements of the EphA2 extracellular region couple to an ensemble of TM helix dimer interfaces. Plasticity in the arrangements of receptor tyrosine kinase TM helices in active dimers and oligomers may serve to facilitate the cross-phosphorylation of multiple tyrosines in different positions of the intracellular regions.

biophysics↗

bioRxiv2021

Data-Driven Strain Design Using Aggregated Adaptive Laboratory Evolution Mutational Data

Microbes are being engineered for an increasingly large and diverse set of applications. However, the designing of microbial genomes remains challenging due to the general complexity of biological system. Adaptive Laboratory Evolution (ALE) leverages natures problem-solving processes to generate optimized genotypes currently inaccessible to rational methods. The large amount of public ALE data now represents a new opportunity for data-driven strain design. This study presents a novel and first of its kind meta-analysis workflow to derive data-driven strain designs from aggregate ALE mutational data using rich mutation annotations, statistical and structural biology methods. The mutational dataset consolidated and utilized in this study contained 63 Escherichia coli K-12 MG1655 based ALE experiments, described by 93 unique environmental conditions, 357 independent evolutions, and 13,957 observed mutations. High-level trends across the entire dataset were established and revealed that ALE-derived strain designs will largely be gene-centric, as opposed to non-coding, and a relatively small number of variants (approx. 4) can significantly alter cellular states and provide benefits which range from an increase in fitness to a complete necessity for survival. Three novel experimentally validated designs relevant to metabolic engineering applications are presented as use cases for the workflow. Specifically, these designs increased growth rates with glycerol as a carbon source through a point mutation to glpK and a truncation to cyaA or increased tolerance to toxic levels of isobutyric acid through a pykF truncation. These results demonstrate how strain designs can be extracted from aggregated ALE data to enhance strain design efforts. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=131 SRC="FIGDIR/small/452699v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@8e1759org.highwire.dtl.DTLVardef@9ee7d5org.highwire.dtl.DTLVardef@866f04org.highwire.dtl.DTLVardef@1e27b29_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2017

Cameo: A Python Library for Computer Aided Metabolic Engineering and Optimization of Cell Factories

Computational systems biology methods enable rational design of cell factories on a genomescale and thus accelerate the engineering of cells for the production of valuable chemicals and proteins. Unfortunately, for the majority of these methods implementations are either not published, rely on proprietary software, or do not provide documented interfaces, which has precluded their mainstream adoption in the field. In this work we present cameo, a platform-independent software that enables in silico design of cell factories and targets both experienced modelers as well as users new to the field. It is written in Python and implements state-of-the-art methods for enumerating and prioritizing knock-out, knock-in, over-expression, and down-regulation strategies and combinations thereof. Cameo is an open source software project and is freely available under the Apache License 2.0. A dedicated website including documentation, examples, and installation instructions can be found at http://cameo.bio. Users can also give cameo a try at http://try.cameo.bio.

bioengineering↗