Biology subjects

Dunlop, M.

Publications and source records attributed to Dunlop, M..

At least 19 recordsLinked to original sources

bioRxiv2026

Gene circuit-driven amplified selection enables evolution of fast-growing Escherichia coli

The laboratory Escherichia coli K-12 strain has doubled no faster than [~]20 minutes for decades. This plateau could reflect a biophysical limit or simply the way batch culture selects on growth rate. Here we show it can be broken through amplified selection with a Red Queen gene circuit, which takes advantage of growth rate heterogeneity in monoclonal populations to selectively suppress slow-growing cells and creates a tunable mapping from intrinsic growth rate to survival. After 70 days ([~]1,000 generations) of amplified selection in MG1655+FHr and subsequent removal of the circuit, a top evolved clone (RQ70) reached a maximum specific growth rate of 2.61 h-{superscript 1} in shake-flask culture. This corresponds to a doubling time of 15.9 minutes, to our knowledge the shortest reported for E. coli K-12, against 18.1 minutes for evolved controls and 20.3 minutes for the ancestor. The gain came at the cost of a [~]3-fold increase in lag time, indicating that the 20-minute plateau is a multi-trait optimum under conventional batch selection rather than an absolute constraint. We argue that synthetic gene circuits can therefore reshape the evolutionary process itself, pushing performance beyond apparent physiological limits.

synthetic biology↗

bioRxiv2026

Engineering a light-responsive, orthogonal Lon protease in E. coli for targeted protein degradation

Optogenetic methods are powerful tools for synthetic biology, allowing light to control cellular processes. While most bacterial optogenetic systems regulate gene expression at the transcriptional level, relatively few enable post-translational control, which can provide faster and growth-independent regulation of protein activity. Here, we describe the development of a post-translational optogenetic tool in Escherichia coli using the Mesoplasma florum Lon (mfLon) protease, an AAA+ protease that is orthogonal to native E. coli degradation machinery. To engineer a light-responsive mfLon, we constructed a large library in which a blue-light responsive domain, Avena sativa LOV2, was introduced into nearly every codon position in the protease using an unbiased molecular approach. We screened 726 mfLon-LOV variants using fluorescence-activated cell sorting and multi-round enrichment campaigns. We identified a novel dark-active variant (mfLon-LOV-534) that degrades target proteins in the dark and is inactivated upon blue-light exposure. Characterization of this variant demonstrates that its proteolytic response can be tuned by varying blue-light intensity and transcriptional expression levels. Furthermore, we show that mfLon-LOV-534 can degrade a target protein in both exponential and stationary growth phases, which addresses the limitations of division-based protein dilution. This work establishes a scalable approach to engineering allosteric control in complex multimeric enzymes and provides a foundation for orthogonal, growth-independent control of protein stability in synthetic circuits.

Synthetic Biology↗

bioRxiv2026

Synthetic cell-cell adhesion provides benefits of proximity without diffusion-related costs

Multicellularity provides many advantages over unicellularity, but accessing those benefits with synthetic biology approaches is challenging due to potential tradeoffs. The same diffusion limitations that exclude toxins can limit nutrient transport, and small intercellular spaces that facilitate sharing also increase local competition. To isolate when and how cell aggregation, an essential feature of multicellularity, is useful, we compared yeast strains differing only in expression of a single adhesion gene. Unexpectedly, aggregates containing thousands of cells showed no signs of diffusion limitations, growing as well as unicellular strains in both rich and toxic media, likely because reversible cell adhesion allows cells to exchange positions. Aggregating cells also grew better than unicellular strains in the limiting carbon conditions expected to increase diffusion limitations. In mixtures of different genotypes, these benefits were specific to cells expressing the adhesion protein. Together, these results suggest key design principles for engineering aggregates: reformable bonds provide a mechanism to sidestep the fitness costs of increased size, and the benefits within such groups may arise more from nutrient sharing than from diffusion limitations.

synthetic biology↗

bioRxiv2025

The PPARβ/δ-induced mesenchymal stromal cell secretome has cytoprotective effects via ANGPTL4 in a pre-clinical model of acute lung inflammation

RationaleHuman bone marrow-derived mesenchymal stromal cells (hBM-MSCs) are known to exert immunomodulatory and pro-reparative effects in vivo. This makes hBM-MSCs an enticing therapeutic candidate for inflammatory diseases, such as acute respiratory distress syndrome (ARDS). The ARDS microenvironment is complex and contains an abundance of free fatty acids (FFAs); which are known to differentially impact MSC functionality. PPAR{beta}/{delta} is a ubiquitously expressed nuclear receptor that is activated in response to FFA-binding. PPAR{beta}/{delta} has been shown to impact the therapeutic efficacy of mouse MSCs. ObjectiveThis study sought to investigate the impact of PPAR{beta}/{delta}-modulation on human MSC functionality in vitro and in vivo. MethodshBM-MSCs were exposed to a synthetic PPAR{beta}/{delta} agonist/antagonist in the presence or absence of ARDS patient serum and the immunomodulatory and pro-reparative capacity of the MSC secretome was investigated using in vitro assays and a pre-clinical model of LPS-induced acute lung inflammation (ALI). ResultsOur results highlighted enhanced pro-reparative capacity of PPAR{beta}/{delta}-agonised hBM-MSCs secretome in CALU-3 lung epithelial cells, mediated by MSC derived angiopoietin-like 4 (ANGPTL4). PPAR{beta}/{delta}-induced ANGPTL4-high MSC secretome facilitated enhanced endothelial barrier integrity in the lungs of ALI mice. Therapeutic effects of PPAR{beta}/{delta}-agonised hBM-MSCs secretome were further enhanced by licensing MSCs with human ARDS patient serum. ARDS-licensed PPAR{beta}/{delta}-induced ANGPTL4-high MSC secretome had reduced clinical score and weight loss. The role ANGPL4 in these protective effects was confirmed using an anti-ANGPTL4 antibody. ConclusionThese findings conclude that the MSC secretome therapeutic effects can be enhanced both in vitro and in vivo through licensing strategies that upregulate the angiogenic factor ANGPTL4. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=145 SRC="FIGDIR/small/667170v2_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@a03b3dorg.highwire.dtl.DTLVardef@1915253org.highwire.dtl.DTLVardef@1b3fd4aorg.highwire.dtl.DTLVardef@1ba27ff_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2025

Single-cell characterization of bacterial optogenetic Cre recombinases

Microbial optogenetic tools can regulate gene expression with high spatial and temporal precision, offering excellent potential for single-cell resolution studies. However, bacterial optogenetic systems have primarily been deployed for population-level experiments. It is not always clear how these tools perform in single cells, where stochastic effects can be substantial. In this study, we focus on optogenetic Cre recombinase and systematically compare the performance of three variants (OptoCre-REDMAP, OptoCre-Vvd, and PA-Cre) for their population-level and single-cell activity. We quantify recombination efficiency, expression variability, and activation dynamics using reporters which produce changes in fluorescence or antibiotic resistance following light-induced Cre activity. Our results indicate that optogenetic recombinase performance can be reporter-dependent. Further, single-cell analysis revealed highly heterogeneous activity across cells. Although general trends match expectations for light-dependent recombination, we found substantial variation in the efficiency and timing of recombinase activity from cell to cell. These findings suggest critical criteria for selecting optogenetic recombinase systems and indicate areas for optimization to improve single-cell capabilities of bacterial optogenetic tools.

synthetic biology↗

bioRxiv2024

Red light responsive Cre recombinase for bacterial optogenetics

Optogenetic tools have been used in a wide range of microbial engineering applications that benefit from the tunable, spatiotemporal control that light affords. However, the majority of current optogenetic constructs for bacteria respond to blue light, limiting the potential for multichromatic control. In addition, other wavelengths offer potential benefits over blue light, including improved penetration of dense cultures and reduced potential for toxicity. In this study, we introduce OptoCre-REDMAP, a red light inducible Cre recombinase system in Escherichia coli. This system harnesses the plant photoreceptors PhyA and FHY1 and a split version of Cre recombinase to achieve precise control over gene expression and DNA excision. We optimized the design by modifying the start codon of Cre and characterized the impact of different levels of induction to find conditions that produced minimal basal expression in the dark and induced full activation within four hours of red light exposure. We characterized the systems sensitivity to ambient light, red light intensity, and exposure time, finding OptoCre-REDMAP to be reliable and flexible across a range of conditions. In co-culture experiments with OptoCre-REDMAP and the blue light responsive OptoCre-VVD, we found that the systems responded orthogonally to red and blue light inputs. Direct comparisons between red and blue light induction with OptoCre-REDMAP and OptoCre-VVD demonstrated the superior penetration properties of red light. OptoCre-REDMAPs robust and selective response to red light makes it suitable for advanced synthetic biology applications, particularly those requiring precise multichromatic control.

synthetic biology↗

bioRxiv2023

Generating information-dense nucleotide sequences with optimal string packing

Dense arrangements of binding sites within nucleotide sequences can collectively influence downstream transcription rates or initiate biomolecular interactions. For example, natural promoter regions can harbor many overlapping transcription factor binding sites that influence the rate of transcription initiation. Despite the prevalence of overlapping binding sites in nature, rapid design of nucleotide sequences with many overlapping sites remains a challenge. Here, we show that this is an NP-hard problem, coined here as the nucleotide String Packing Problem (SPP). We then introduce a computational technique that efficiently assembles sets of DNA-protein binding sites into dense, contiguous stretches of double-stranded DNA. For the efficient design of nucleotide sequences spanning hundreds of base pairs, we reduce the SPP to an Orienteering Problem with integer distances, and then leverage modern integer linear programming solvers. Our method optimally packs libraries of 20-100 binding sites into dense nucleotide arrays of 50-300 base pairs in 0.05-10 seconds. Unlike approximation algorithms or meta-heuristics, our approach finds provably optimal solutions. We demonstrate how our method can generate large sets of diverse sequences suitable for library generation, where the frequency of binding site usage across the returned sequences can be controlled by modulating the objective function. As an example, we then show how adding additional constraints, like the inclusion of sequence elements with fixed positions, allows for the design of bacterial promoters. The nucleotide string packing approach we present can accelerate the design of sequences with complex DNA-protein interactions. When used in combination with synthesis and high-throughput screening, this design strategy could help interrogate how complex binding site arrangements impact either gene expression or biomolecular mechanisms in varied cellular contexts. Author SummaryThe way protein binding sites are arranged on DNA can control the regulation and transcription of downstream genes. Areas with a high concentration of binding sites can enable complex interplay between transcription factors, a feature that is exploited by natural promoters. However, designing synthetic promoters that contain dense arrangements of binding sites is a challenge. The task involves overlapping many binding sites, each typically about 10 nucleotides long, within a constrained sequence area, which becomes increasingly difficult as sequence length decreases, and binding site variety increases. We introduce an approach to design nucleotide sequences with optimally packed protein binding sites, which we call the nucleotide String Packing Problem (SPP). We show that the SPP can be solved efficiently using integer linear programming to identify the densest arrangements of binding sites for a specified sequence length. We show how adding additional constraints, like the inclusion of sequence elements with fixed positions, allows for the design of bacterial promoters. The presented approach enables the rapid design and study of nucleotide sequences with complex, dense binding site architectures.

systems biology↗

bioRxiv2023

Deep neural networks for predicting single cell responses and probability landscapes

Engineering biology relies on the accurate prediction of cell responses. However, making these predictions is challenging for a variety of reasons, including the stochasticity of biochemical reactions, variability between cells, and incomplete information about underlying biological processes. Machine learning methods, which can model diverse input-output relationships without requiring a priori mechanistic knowledge, are an ideal tool for this task. For example, such approaches can be used to predict gene expression dynamics given time-series data of past expression history. To explore this application, we computationally simulated single cell responses, incorporating different sources of noise and alternative genetic circuit designs. We showed that deep neural networks trained on these simulated data were able to correctly infer the underlying dynamics of a cell response even in the presence of measurement noise and stochasticity in the biochemical reactions. The training set size and the amount of past data provided as inputs both affected prediction quality, with cascaded genetic circuits that introduce delays requiring more past data. We also tested prediction performance on a bistable auto-activation circuit, finding that our initial method for predicting a single trajectory was fundamentally ill-suited for multimodal dynamics. To address this, we updated the network architecture to predict the entire distribution of future states, showing it could accurately predict bimodal expression distributions. Overall, these methods can be readily applied to the diverse prediction tasks necessary to predict and control a variety of biological circuits, a key aspect of many synthetic biology applications.

synthetic biology↗

bioRxiv2023

Comprehensive screening of a light-inducible split Cre recombinase with domain insertion profiling

Splitting proteins with light- or chemically-inducible dimers provides a mechanism for post-translational control of protein function. However, current methods for engineering stimulus-responsive split proteins often require significant protein engineering expertise and laborious screening of individual constructs. To address this challenge, we use a pooled library approach that enables rapid generation and screening of nearly all possible split protein constructs in parallel, where results can be read out using sequencing. We perform our method on Cre recombinase with optogenetic dimers as a proof of concept, resulting in comprehensive data on split sites throughout the protein. To improve accuracy in predicting split protein behavior, we develop a Bayesian computational approach to contextualize errors inherent to experimental procedures. Overall, our method provides a streamlined approach for achieving inducible post-translational control of a protein of interest.

synthetic biology↗

bioRxiv2022

Exploring the association between Body Mass Index, Sex and Gene Expression in human colorectal epithelium

IntroductionColorectal cancer (CRC) is the second most common cause of cancer death globally. Genome-wide association studies have established that cancer risk mediated through common genetic variants can be linked to variation in gene expression. Since obesity and male sex impart substantially elevated CRC risk, we studied transcriptional profiles of normal colorectal mucosa using RNA sequencing to better understand the relationship of these risk factors with gene expression levels. MethodsNormal colorectal mucosa was sampled from 365 participants (208 males, 157 females) either during surgery (n=103) or through endoscopic biopsy (n=262) from cancer patients and patients with other unrelated conditions. In total, 238 samples were used for our discovery dataset and 380 samples were obtained for the validation of our findings. The transcription analysis was done using paired-end total RNA sequencing. Data processing and gene filtering followed the Genotype-Tissue Expression (GTEx) Project pipeline v8. Differential Expression Analysis (DEA) was performed on normalised counts to evaluate effects of sex and body mass index on the total gene expression, as well as possible confounding effects of cancer presence on the gene expression in normal colorectal tissue. ResultsFollowing filtering, there were 15,465 genes available for analysis. DEA identified two genes that were significantly associated with sex and five associated with body mass index. However, whilst these nominal signals are of interest, none of the genes associated with sex remained significant in a replication dataset. Due to the missing BMI information, replication of DEA by BMI was not possible. ConclusionWe found no systematic differences in gene expression in normal colorectal epithelium between males and females, nor did we find a strong association between gene expression and BMI. Although sample size may limit our analysis, the results suggest no or limited confounding effects of BMI and sex on gene expression in normal colorectal mucosa samples.

genomics↗

bioRxiv2022

Deep model predictive control of gene expression in thousands of single cells

Gene expression is inherently dynamic, due to complex regulation and stochastic biochemical events. However, the effects of these dynamics on cell phenotypes can be difficult to determine. Researchers have historically been limited to passive observations of natural dynamics, which can preclude studies of elusive and noisy cellular events where large amounts of data are required to reveal statistically significant effects. Here, using recent advances in the fields of machine learning and control theory, we train a deep neural network to accurately predict the response of an optogenetic system in Escherichia coli cells. We then use the network in a deep model predictive control framework to impose arbitrary and cell-specific gene expression dynamics on thousands of single cells in real time, applying the framework to generate complex time-varying patterns. We also showcase the frameworks ability to link expression patterns to dynamic functional outcomes by controlling expression of the tetA antibiotic resistance gene. This study highlights how deep learning-enabled feedback control can be used to tailor distributions of gene expression dynamics with high accuracy and throughput.

systems biology↗

bioRxiv2022

Visualization of a Limonene Synthesis Metabolon inside Living Bacteria by Hyperspectral SRS Microscopy

Metabolons consisting of cellular structure elements and sequential metabolic enzymes are expected to be involved in diverse biological processes. However, direct visualization of metabolons in prokaryotic cells is still challenging. In this study, we report direct visualization of concentrated subcellular regions of limonene synthesis inside single engineered Escherichia coli by using hyperspectral stimulated Raman scattering (hSRS) microscopy. Equipped with spectral unmixing, hSRS imaging provides a reliable method to quantify intracellular limonene content. In E. coli strains with a complete limonene synthesis pathway, intracellular limonene is found locally concentrated and colocalized with proteins. Furthermore, dual-modality SRS and two-photon fluorescence imaging showed colocalization of limonene and GFP-fused limonene synthase. Significance StatementMonitoring biosynthesis activity at the single-cell level is key to metabolic engineering but is still difficult to achieve in a label-free manner. Using hyperspectral stimulated Raman scattering imaging in the 670-900 cm-1 region, we visualized localized limonene synthesis inside engineered E. coli. The colocalization of limonene and GFP-fused limonene synthase was confirmed by co-registered stimulated Raman scattering and two-photon fluorescence images. Our finding suggests a limonene synthesis metabolon with a polar distribution inside the cells. This finding expands our knowledge of de novo limonene biosynthesis in engineered bacteria and highlights the potential of SRS chemical imaging in metabolic engineering research.

biophysics↗

bioRxiv2021

DeLTA 2.0: A deep learning pipeline for quantifying single-cell spatial and temporal dynamics

Improvements in microscopy software and hardware have dramatically increased the pace of image acquisition, making analysis a major bottleneck in generating quantitative, single-cell data. Although tools for segmenting and tracking bacteria within time-lapse images exist, most require human input, are specialized to the experimental set up, or lack accuracy. Here, we introduce DeLTA 2.0, a purely Python workflow that can rapidly and accurately analyze single cells on two-dimensional surfaces to quantify gene expression and cell growth. The algorithm uses deep convolutional neural networks to extract single-cell information from time-lapse images, requiring no human input after training. DeLTA 2.0 retains all the functionality of the original version, which was optimized for bacteria growing in the mother machine microfluidic device, but extends results to two-dimensional growth environments. Two-dimensional environments represent an important class of data because they are more straightforward to implement experimentally, they offer the potential for studies using co-cultures of cells, and they can be used to quantify spatial effects and multi-generational phenomena. However, segmentation and tracking are significantly more challenging tasks in two-dimensions due to exponential increases in the number of cells that must be tracked. To showcase this new functionality, we analyze mixed populations of antibiotic resistant and susceptible cells, and also track pole age and growth rate across generations. In addition to the two-dimensional capabilities, we also introduce several major improvements to the code that increase accessibility, including the ability to accept many standard microscopy file formats and arbitrary image sizes as inputs. DeLTA 2.0 is rapid, with run times of less than 10 minutes for complete movies with hundreds of cells, and is highly accurate, with error rates around 1%, making it a powerful tool for analyzing time-lapse microscopy data. Author SummaryTime-lapse microscopy can generate large image datasets which track single-cell properties like gene expression or growth rate over time. Deep learning tools are very useful for analyzing these data and can identify the location of cells and track their position over time. In this work, we introduce a new version of our Deep Learning for Time-lapse Analysis (DeLTA) software, which includes the ability to robustly segment and track bacteria that are growing in two dimensions, such as on agarose pads or within microfluidic environments. This capability is essential for experiments where spatial and positional effects are important, such as conditions with microbial co-cultures, cell-to-cell interactions, or spatial patterning. The software also tracks pole age and can be used to analyze replicative aging. These new features join other improvements, such as the ability to work directly with many common microscope file formats. DeLTA 2.0 can reliably track hundreds of cells with low error rates, making it an ideal tool for high throughput analysis of microscopy data.

systems biology↗

bioRxiv2021

Single-cell chemical imaging of engineered strains reveals heterogeneity in fatty acid production

Understanding metabolic heterogeneity is critical for optimizing microbial production of valuable chemicals, but requires tools that can quantify metabolites at the single-cell level over time. Here, we develop longitudinal hyperspectral stimulated Raman scattering (SRS) chemical imaging to directly visualize free fatty acids in engineered Escherichia coli over many cell cycles. We also develop compositional analysis to determine the chain length and unsaturation of the fatty acids in living cells. Our method reveals substantial heterogeneity in fatty acid production among and within colonies that emerges over the course of many generations. Interestingly, the strains display distinct types of production heterogeneity in an enzyme-dependent manner. By pairing time-lapse and SRS imaging, we examine the relationship between growth and production at the single-cell level. Single-cell quantification does not show a significant growth-production tradeoff in a strain that exhibits high production heterogeneity. Our results demonstrate that cell-to-cell production heterogeneity is pervasive and provide a means to link single-cell and population-level production.

synthetic biology↗

bioRxiv2020

Programmable gene regulation for metabolic engineering using decoy transcription factor binding sites

Transcription factor decoy binding sites are short DNA sequences that can serve as "sponges" to titrate a transcription factor away from its natural binding site, therefore regulating gene expression. In this study, we harness decoy sites to develop synthetic transcription factor sponge systems to regulate gene expression for metabolic pathways in Escherichia coli. We show that transcription factor sponges can effectively regulate expression of native and heterologous genes. Tunability of the sponge can be engineered via changes in copy number or modifications to the DNA decoy site sequence. Using arginine biosynthesis as a showcase, we observe a 16-fold increase in arginine production when we introduce the sponge system to steer metabolic flux towards increased arginine biosynthesis, with negligible growth differences compared to the wild type strain. The sponge-based production strain shows high genetic stability; in contrast to a gene knock-out approach where mutations were common, we detected no mutations in the production system using the sponge-based strain. We further show that transcription factor sponges are amenable to multiplexed library screening by demonstrating enhanced tolerance to pinene with a combinatorial sponge library. Our study shows that transcription factor sponges are a powerful and compact tool for metabolic engineering.

bioengineering↗

bioRxiv2019

DeLTA: Automated cell segmentation, tracking, and lineage reconstruction using deep learning

Microscopy image analysis is a major bottleneck in quantification of single-cell microscopy data, typically requiring human supervision and curation, which limit both accuracy and throughput. To address this, we developed a deep learning-based image analysis pipeline that performs segmentation, tracking, and lineage reconstruction. Our analysis focuses on time-lapse movies of Escherichia coli cells trapped in a \"mother machine\" microfluidic device, a scalable platform for long-term single-cell analysis that is widely used in the field. While deep learning has been applied to cell segmentation problems before, our approach is fundamentally innovative in that it also uses machine learning to perform cell tracking and lineage reconstruction. With this framework we are able to get high fidelity results (1% error rate), without human supervision. Further, the algorithm is fast, with complete analysis of a typical frame containing [~]150 cells taking <700msec. The framework is not constrained to a particular experimental set up and has the potential to generalize to time-lapse images of other organisms or different experimental configurations. These advances open the door to a myriad of applications including real-time tracking of gene expression and high throughput analysis of strain libraries at single-cell resolution.\n\nAuthor SummaryAutomated microscopy experiments can generate massive data sets, allowing for detailed analysis of cell physiology and properties such as gene expression. In particular, dynamic measurements of gene expression with time-lapse microscopy have proved invaluable for understanding how gene regulatory networks operate. However, image analysis remains a key bottleneck in the analysis pipeline, typically requiring human supervision and a posteriori processing. Recently, machine learning-based approaches have ushered in a new era of rapid, unsupervised image analysis. In this work, we use and repurpose the U-Net deep learning algorithm to develop an image processing pipeline that can not only accurately identify the location of cells in an image, but also track them over time as they grow and divide. As an application, we focus on multi-hour time-lapse movies of bacteria growing in a microfluidic device. Our algorithm is accurate and fast, with error rates near 1% and requiring less than a second to analyze a typical movie frame. This increase in speed and fidelity has the potential to open new experimental avenues, e.g. where images are analyzed on-the-fly so that experimental conditions can be updated in real time.

systems biology↗

bioRxiv2019

Genetic predisposition to mosaic Y chromosome loss in blood is associated with genomic instability in other tissues and susceptibility to non-haematological cancers

Mosaic loss of chromosome Y (LOY) in circulating white blood cells is the most common form of clonal mosaicism, yet our knowledge of the causes and consequences of this is limited. Using a newly developed approach, we estimate that 20% of the UK Biobank male population (N=205,011) has detectable LOY. We identify 156 autosomal genetic determinants of LOY, which we replicate in 757,114 men of European and Japanese ancestry. These loci highlight genes involved in cell-cycle regulation, cancer susceptibility, somatic drivers of tumour growth and cancer therapy targets. Genetic susceptibility to LOY is associated with non-haematological health outcomes in both men and women, supporting the hypothesis that clonal haematopoiesis is a biomarker of genome instability in other tissues. Single-cell RNA sequencing identifies dysregulated autosomal gene expression in leukocytes with LOY, providing insights into how LOY may confer cellular growth advantage. Collectively, these data highlight the utility of studying clonal mosaicism to uncover fundamental mechanisms underlying cancer and other ageing-related diseases.

genetics↗

bioRxiv2018

Distinct timescales of RNA regulators enable the construction of a genetic pulse generator

To build complex genetic networks with predictable behaviours, synthetic biologists use libraries of modular parts that can be characterized in isolation and assembled together to create programmable higher-order functions. Characterization experiments and computational models for gene regulatory parts operating in isolation are routinely employed to predict the dynamics of interconnected parts and guide the construction of new synthetic devices. Here, we individually characterize two modes of RNA-based transcriptional regulation, using small transcription activating RNAs (STARs) and CRISPR interference (CRISPRi), and show how their distinct regulatory timescales can be used to engineer a composed feedforward loop that creates a pulse of gene expression. We use a cell-free transcription-translation system (TXTL) to rapidly characterize the system, and we apply Bayesian inference to extract kinetic parameters for an ODE-based mechanistic model. We then demonstrate in simulation and verify with TXTL experiments that the simultaneous regulation of a single gene target with STARs and CRISPRi leads to a pulse of gene expression. Our results suggest the modularity of the two regulators in an integrated genetic circuit, and we anticipate that construction and modeling frameworks that can leverage this modularity will become increasingly important as synthetic circuits increase in complexity.

synthetic biology↗