Biology subjects

Benenson, Y.

Publications and source records attributed to Benenson, Y..

5 recordsLinked to original sources

bioRxiv2025

Peptide Barcodes for miRNA activity assessment in mammalian cells

Studies of gene regulation require measurements of mRNA and protein levels of a regulated gene. Recently, parallel reporter assays have been introduced to study the regulation of multiple genes at once. While transcriptional regulation can be probed by next generation sequencing, post-transcriptional regulation requires the ability to measure multiple proteins in the same experiment. Multiplexing with the help of fluorescent proteins limits the addressable diversity of simultaneous measurements due to spectral overlap. Inspired by the utility of proteotypic peptides in targeted proteomics, here we show that genetically encoded peptide reporters (peptide barcodes) can be used to analyze multiple post-transcriptional pathways in parallel. We use RNA interference as an exemplary regulatory mechanism that occurs on both transcriptional and post-transcriptional levels. We measure the activity of multiple microRNAs in parallel using a peptide barcode-based miRNA sensor library. Fluorescent reporters are used to validate the accuracy of the miRNA activities reported via the peptide barcodes. Several assay optimization steps are explored leading to the robust activity profiling of nine miRNAs across three different cell lines. Overall, this study underlines the multiplexing potential of peptide barcodes to rapidly and quantitatively measure the post-transcriptional regulation.

synthetic biology↗

bioRxiv2025

Synthetic Cytosolic Splicing Enables Programmable mRNA-Encoded Receptors

A typical cell therapy product comprises engineered cells that detect disease-related molecular cues in their surrounding or on a surface of a target cell, and activate a response that alleviates disease symptoms or eradicates diseased cells. mRNA as a therapeutic substrate has become prevalent in the last decade across multiple therapeutic areas, and it has also been evaluated as a building block of cell therapies. However, compared to DNA-based building blocks, it is much more challenging to use mRNA in a programmable manner to engineer complex multi-input/multi-output processes that can fully support the next generation of cell and gene therapies. Addressing this challenge requires the exploration of novel post-transcriptional control mechanisms that bridge mRNA regulation with extracellular surroundings. Here, we engineer a family of synthetic mRNA splicing (SMS) receptors by redesigning the Inositol-requiring enzyme 1 (IRE1) to regulate protein synthesis from a precursor mRNA. We design SMS-based receptors that sense diverse intracellular and extracellular inputs, highlighting the versatility and modularity of this platform. We apply this approach to design a cytokine-converter receptor that detects inflammatory cytokines and produces an anti-inflammatory output in response. That receptor is successfully validated in cell lines and primary T cells upon mRNA delivery. These cells generate anti-inflammatory IL-10 upon stimulation by physiological levels of either TNF- or IL-1{beta} secreted by macrophage-like cells, highlighting their potential as a cell therapy for inflammatory diseases. With its modular and programmable architecture, the SMS platform is poised to become an important enabling tool for sophisticated programmable mRNA therapeutics.

synthetic biology↗

bioRxiv2024

Synthetic gene circuits that selectively target RAS-driven cancers

Therapies targeting mutated RAS, the most frequently mutated oncogene in human cancers, could benefit millions of patients. Recently approved RAS inhibitors represent a breakthrough, but are limited to a specific KRASG12C mutation and prone to resistance. Synthetic gene circuits offer a promising alternative by sensing and integrating cancer-specific biomolecular inputs, including mutated RAS, to selectively express therapeutic proteins in cancer cells. A key challenge for these circuits is achieving high cancer selectivity to prevent toxicity in healthy cells. To address this challenge, we present a novel approach combining multiple RAS sensors into RAS-targeting gene circuits, which allowed us to express an output protein in cells with mutated RAS with unprecedented selectivity. We implemented a modular design strategy and modelled the impact of individual circuit components on output expression. This enabled cell-line specific adaptation of the circuits to optimize selectivity and fine-tune expression. We further demonstrate the targeting capabilities of the circuits by employing them in different RAS-driven cancer cells and provide evidence for their therapeutic potential by linking them to the expression of a clinically relevant output protein, which induced robust killing of cancer cells with mutated RAS. This work highlights the potential of synthetic gene circuits as a novel therapeutic strategy for RAS-driven cancers, advancing the application of synthetic biology in oncology.

synthetic biology↗

bioRxiv2021

Discrete-to-analog signal conversion in human pluripotent stem cells

During development, state transitions are coordinated through changes in the identity of molecular regulators in a cell state- and dose specific manner. The ability to rationally engineer such functions in human pluripotent stem cells (hPSC) will enable numerous applications in regenerative medicine. Herein we report the generation of synthetic gene circuits that can detect a discrete cell state, and upon state detection, produce fine-tuned effector proteins in a programmable manner. Effectively, these gene circuits convert a discrete (digital-like) cell state into an analog signal by merging AND-like logic integration of endogenous miRNAs (classifiers) with a miRNA-mediated output fine-tuning technology (miSFITs). Using an automated miRNA identification and model-guided circuit optimization approach, we were able to produce robust cell state specific and graded output production in undifferentiated hPSC. We further finely controlled the levels of endogenous BMP4 secretion, which allowed us to document the effect of endogenous factor secretion in comparison to exogenous factor addition on early tissue development using the hPSC-derived gastruloid system. Our work provides the first demonstration of a discrete-to-analog signal conversion circuit operating in living hPSC, and a platform for customized cell state-specific control of desired physiological factors, laying the foundation for programming cell compositions in hPSC-derived tissues and beyond. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=141 SRC="FIGDIR/small/467377v2_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@150da71org.highwire.dtl.DTLVardef@135554borg.highwire.dtl.DTLVardef@72b5b3org.highwire.dtl.DTLVardef@f70f84_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2020

Large-scale DNA-based phenotypic recording and deep learning enable highly accurate sequence-function mapping

Predicting quantitative effects of gene regulatory elements (GREs) on gene expression is a longstanding challenge in biology. Machine learning models for gene expression prediction may be able to address this challenge, but they require experimental datasets that link large numbers of GREs to their quantitative effect. However, current methods to generate such datasets experimentally are either restricted to specific applications or limited by their technical complexity and error-proneness. Here we introduce DNA-based phenotypic recording as a widely applicable and practical approach to generate very large datasets linking GREs to quantitative functional readouts of high precision, temporal resolution, and dynamic range, solely relying on sequencing. This is enabled by a novel DNA architecture comprising a site-specific recombinase, a GRE that controls recombinase expression, and a DNA substrate modifiable by the recombinase. Both GRE sequence and substrate state can be determined in a single sequencing read, and the frequency of modified substrates amongst constructs harbouring the same GRE is a quantitative, internally normalized readout of this GREs effect on recombinase expression. Using next-generation sequencing, the quantitative expression effect of extremely large GRE sets can be assessed in parallel. As a proof of principle, we apply this approach to record translation kinetics of more than 300,000 bacterial ribosome binding sites (RBSs), collecting over 2.7 million sequence-function pairs in a single experiment. Further, we generalize from these large-scale datasets by a novel deep learning approach that combines ensembling and uncertainty modelling to predict the function of untested RBSs with high accuracy, substantially outperforming state-of-the-art methods. The combination of DNA-based phenotypic recording and deep learning represents a major advance in our ability to predict quantitative function from genetic sequence.

synthetic biology↗