Search bioRxiv⌕ Search

bioRxiv · 10.1101/266015

A Model for Resource Competition in CRISPR-Mediated Gene Repression

Abstract

CRISPR-mediated gene regulation is known for its ability to control multiple targets simultaneously due to its modular nature: the same dCas9 effector can target different genes simply by changing the associated gRNA. However, multiplexing requires the sharing of limited amounts of dCas9 proteins among multiple gRNAs, leading to resource competition. In turn, competition between gRNAs for the same resource may hamper network function. In this work, we develop a general model that takes into account the sharing of a limited amount of dCas9 protein for arbitrary CRISPR-mediated gene repression networks. We demonstrate that, as a result of resource competition, hidden interactions appear, that modify the intended network regulations. As a case study, we analyze the effects of these hidden interactions in repression cascades. In particular, we illustrate that perfect adaptation to resource fluctuations can be achieved in cascades with an even number of repressors. In contrast, cascades with an odd number of repressors are substantially impacted by resource competition.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Chen, P.-Y., Qian, Y., Del Vecchio, D.. 2018-02-14. A Model for Resource Competition in CRISPR-Mediated Gene Repression. https://doi.org/10.1101/266015

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

KEEP EXPLORING

Related preprints

Machine learning guided design of strong transcriptional activation domains

Artificial intelligence has accelerated the design of structured proteins, but it remains difficult to design functional intrinsically disordered proteins because of weak structural constraints and poorly defined sequence-to-function relationships1. Activation domains (ADs), transcription factor regions that recruit coactivators, are an excellent test case for the de novo design of intrinsically disordered sequences. ADs are one of the oldest groups of functional intrinsically disordered sequences2 and designed ADs have applications in synthetic biology, biotechnology, and agriculture. We computationally generated 500,000 candidate sequences that resembled known ADs and predicted AD function with extant machine learning models, including three neural networks. From high-scoring sequences, we selected 18,750 for experimental validation in yeast. Surprisingly, >90% of the 16,770 well-measured sequences function as ADs. Sequences predicted to be active by multiple models were stronger, motivating development of a fast and accurate consensus AD predictor, MADRIGAL. A number of our designed ADs retain functionality across fungi, plants, and human cells. This work illustrates a generalizable paradigm for incorporating sequence-to-function models into the design loop to efficiently generate functional intrinsically disordered sequences.

synthetic biology↗

E. coli-produced nanobody fusions as a pan-serotype platform for AAV purification, quantification, and detection

Research, production and application of the recombinant adeno-associated virus (rAAV) require versatile tools for rapid and cost-effective advances. We developed fusion constructs with the pan-serotype, AAV-binding VHH-antibody-fragment named nanobody AAVX and devised procedures for AAV purification, quantification, and detection. Starting from an amino acid sequence, we cytosolically expressed in E. coli nanobody AAVX carrying various tags and fusion partners. Very efficient soluble production followed by a one-step immobilized metal-ion affinity chromatography (IMAC) yielded mostly >90% pure protein. Crosslinking AAVX with an agarose matrix enabled efficient purification of intact AAV2 and AAV5 capsids from cell lysates, as assessed by qPCR and ELISA. AAVX also supported effective AAV quantification by ELISA, as capture reagent and, when fused to luciferase, as detection reagent. Using tagged AAVX and streptavidin biosensors, we developed a rapid and label-free quantification assay of AAV capsids by biolayer interferometry (BLI). Finally, immunostaining with an AAVX-eGFP fusion visualized AAV capsids in fixed HEK-293 cells by fluorescence microscopy. Collectively, these results establish nanobody AAVX fusion proteins as versatile and readily customizable tools for AAV research and bioprocessing.

synthetic biology↗

Duality Vaccination through Nanodesign of Click-Ligated B-Cell Inhibitory Receptor Bridging

Synthesis of precise and robust molecular architectures is critical to harness the power of the immune system. The dimensions, rigidity and multivalency of nanoparticles contribute to eliciting high levels of antibodies from vaccination. However, antibodies vary hugely in quality: some antibodies are highly effective at neutralizing pathogens, while others provide little or no benefit. Here we establish switchable and modular platforms for protein and nanoparticle assembly that program the target of antibody responses. We introduce Duality vaccination: the simultaneous delivery of activating and inhibitory stimuli for focusing immune responses. To construct activatory immunogens, we employ click biology with spontaneous amidation to functionalize plug-and-display self-assembling porous dodecahedral nanocages. We construct inhibitory immunogens using SpyMask protease-gated bispecific assembly, ligating the antigen to binders that bridge the B cell receptor to immune checkpoint receptors. This dual immunogen architecture tailors the elicited antibodies to down-modulate undesired B cell responses. We establish the effect of temporal and spatial separation of activatory and inhibitory immunogen delivery. We further explore sensitivity to valency, linker flexibility and adjuvant delivery, employing model targets and coronavirus antigens. Dual programmable functionalization identifies key nanodesign features to control in vivo outputs, providing logic-gating towards urgent challenges in the creation of protective vaccines.

synthetic biology↗