Search bioRxiv⌕ Search

bioRxiv · 10.1101/2024.02.07.579082

Engineering an anti-CD206-synNotch receptor: insightsinto the development of novel synthetic receptors

Abstract

Immune cells play a pivotal role in the establishment, growth and progression of tumors at primary and metastatic sites. Macrophages, in particular, play a critical role in suppressing immune responses and promoting an anti-inflammatory environment through both direct and indirect cell-cell interactions. However, our understanding of the mechanisms underlying such interactions is limited due to a lack of reliable tools for studying transient interactions between cancer cells and macrophages within the tumor microenvironment. Recent advances in mammalian synthetic biology have introduced a wide range of synthetic receptors that have been used in diverse biosensing applications. One such synthetic receptor is the synNotch receptor, which can be tailored to sense specific ligands displayed on the surface of target cells. With this study, we aimed at developing a novel CD206-synNotch receptor, targeting CD206+ macrophages, a population of macrophages that play a crucial role in promoting metastatic seeding and persistent growth. Engineered in cancer cells and used in mouse metastasis models, such tool could help monitor and understand the effects cell-cell interactions between macrophages and cancer cells have on metastasis establishment. Here, we report the development of cancer landing pad cells for versatile applications, the engineering of CD206-synNotch cells, report the measurements of their activity and specificity, and discuss the unexpected caveats when considering their in vivo applications.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Semeniuk, S., Qian, B., Cachat, E.. 2024-02-07. Engineering an anti-CD206-synNotch receptor: insightsinto the development of novel synthetic receptors. https://doi.org/10.1101/2024.02.07.579082

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Gene expression noise is reduced in communicating synthetic cell populations

A major goal in bottom-up synthetic biology is the construction of multicellular synthetic systems capable of coordinated and robust collective behaviours. However, robustness is often limited by noise and variability arising from increased molecular complexity. Whilst communication has been implemented in synthetic multi-cellular systems, the ability for communication to suppress cell free gene expression variability in populations of synthetic cells remain unexplored. To address this, we encapsulated the Lux and Las quorum sensing gene circuits in lipid vesicles under cell-free conditions to test the effect of communication on reducing cell-free gene expression variability across the population. Our results show that communication, limiting expression resources, and membrane surface effects can reduce gene expression variability. Resource limited Gillespie simulations for transcription and translation show that communication-mediated coupling reduces population-level expression noise under constrained and excess resource conditions. Together, our work provides simple strategies to reduce gene expression variability and thereby improve robustness in synthetic multicellular systems, an important criteria for the future applications of synthetic cells.

synthetic biology↗

Boolean Logic-responsive FRET Biosensors via Genetically Encoded Autonomous Compilation

Forster resonance energy transfer (FRET) is commonly used to monitor protein-protein interactions in situ. The high spatiotemporal resolution and facile implementation inside complex molecular environments have spearheaded FRET's widespread adoption in biosensing. Despite these advantages, current FRET biosensors are largely restricted to the detection of the presence/absence of individual inputs and are thus unable to sense several multiplexable inputs simultaneously within complex milieu of biological environments. In this work, we introduce a generalizable strategy to construct genetically encoded protein-based FRET biosensors capable of recognizing multiple inputs following Boolean logic-type (YES/OR/AND) operations. These topologically specified FRET sensors powerfully expand the input capacity in sensing protein-protein interactions while providing a user-programmable platform for monitoring heterogeneous biological activities both in vitro and in living cells.

synthetic biology↗

AI-Guided Multi-Objective Engineering of Glucoamylase Enables Acidification-Free Starch Saccharification

Glucoamylase is essential for industrial starch saccharification, but the limited thermostability and near-neutral pH tolerance of fungal glucoamylases necessitate cooling and acidification of liquefied starch. Here, we developed an artificial intelligence-guided strategy to simultaneously improve the thermostability, pH tolerance, and catalytic activity of glucoamylase from Penicillium oxalicum (PoGA). Two property-specific machine-learning models, CASPE-T and CASPE-A, identified substitutions associated with thermostability and pH tolerance, respectively. Experimental screening identified beneficial substitutions in 11 of 21 CASPE-T and 12 of 22 CASPE-A candidates. Folding-energy-guided recombination integrated the two traits while maintaining structural compatibility. The optimal variant, PoGA T513E/Q305N, exhibited 2.21-fold higher specific activity than the wild type, with half-life extended from 22.3 to 57.9 min at 60 degrees C and from 16.6 to 64.7 min at pH 8.0. Molecular dynamics simulations attributed these improvements to reinforcement of high-occupancy hydrogen-bonding networks, suppression of conformational fluctuations in the linker and carbohydrate-binding module, enhanced long-range dynamic coordination, and preservation of a compact catalytic architecture. At 60 degrees C and pH 6.5 without acidification, PoGA T513E/Q305N produced 219.9 g/L glucose and achieved 89.1% starch conversion, 31.4% higher than the wild type. This work provides an efficient framework for multi-objective enzyme engineering and sustainable starch biorefining.

synthetic biology↗