Biology subjects

Menolascina, F.

Publications and source records attributed to Menolascina, F..

5 recordsLinked to original sources

bioRxiv2025

Design of novel synthetic promoters to tune gene expression in T cells

Transcriptional control of transgene expression can be linked to dynamic changes in cellular states if this is accompanied by differential expression of transcription factors (TFs). Synthetic promoters (SPs) designed to respond to the desired TFs can provide this regulation with compactness, specificity, and orthogonality. T cells display differentially expressed TFs according to the functional state. In solid tumors, the highly immunosuppressive TME and the chronic exposure to antigens lead to a progression of T cells from a functional to a dysfunctional state known as exhaustion (Tex), in which their power against cancer cells is strongly compromised. Importantly, this transition is accompanied by a marked increase of several TFs, among other factors, that drive targeted genetic programs. Strategies to detect and mitigate Tex are extremely needed. Here, we design SPs that respond to TFs differentially expressed in activated and exhausted T cells to enable new classifiers of the functional/dysfunctional states. We developed a library of over 80 SPs responsive to 7 TFs. The SPs showed broad strength of activation of reporter genes or immunomodulatory molecules in HEK293 and Jurkat T cell lines. Moreover, using a transfer learning strategy we show SPs strength predictability. By combining SPs responding to different TFs, we created Boolean logic gates and implemented a feed-forward design that was previously shown to reduce noise in the OFF-state. Finally, as proof of principle, we demonstrate the dynamic activation of the NR4A2-responsive SP according to the T cell state in primary human CD8+ T cells. Collectively we present a sensing platform that provides a versatile tool to study and monitor the dynamic changes occurring in T cells. In perspective the biosensors coupled to therapeutic genes can be used to reprogram the TME and reinvigorate the T cell anti-tumoral functionality, preventing or reverting the exhausted phenotype. Graphical AbstractCreated with BioRender.com. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=108 SRC="FIGDIR/small/632034v1_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@136e147org.highwire.dtl.DTLVardef@22e5org.highwire.dtl.DTLVardef@f2e863org.highwire.dtl.DTLVardef@176c02c_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2022

Optimization for Predictive Gene Circuit Design Automation

Synthetic Biology aims to rationally engineer biological systems. Current methods often employ an initial human designed circuit topology and utilise iterative approaches, e.g. directed evolution, to fine-tune part function. This approach can be extremely time consuming and resource intensive whilst often reaching sub-optimal solutions. A design workflow in which circuits and parts are designed in silico can overcome such limitations. Here we describe a method to automatically design synthetic gene circuits with user-specified dynamics; unlike some previous contributions our algorithm is able to design circuits with analog, not just digital behaviours. We demonstrate the capabilities of our approach benchmarking it on a number of different gene circuits design tasks. We review and compare the performance of our method against state of the art and outline future opportunities for development. Finally, to foster adoption, we make our algorithm available through a web app.

synthetic biology↗

bioRxiv2021

Bacterial chemotaxis to saccharides is governed by a trade-off between sensing and uptake

To swim up gradients of nutrients, E. coli senses nutrient concentrations within its periplasm. For small nutrient molecules, periplasmic concentrations typically match extracellular concentrations. However, this is not necessarily the case for saccharides, such as maltose, which is transported into the periplasm via a specific porin. Previous observations have shown that under various conditions E. coli limits maltoporin abundance so that, for extracellular micromolar concentrations of maltose, there are predicted to be only nanomolar concentrations of free maltose in the periplasm. Thus, in the micromolar regime, the total uptake of maltose from the external environment into the cytoplasm is limited not by the abundance of cytoplasmic transport proteins but by the abundance of maltoporins. Here we present results from experiments and modeling showing that this porin-limited transport enables E. coli to sense micromolar gradients of maltose despite having a high-affinity ABC transport system that is saturated at these micromolar levels. We used microfluidic assays to study chemotaxis of E. coli in various gradients of maltose and methyl-aspartate and leveraged our experimental observations to develop a mechanistic transport-and-sensing chemotaxis model. Incorporating this model into agent-based simulations, we discover a trade-off between uptake and sensing: although high-affinity transport enables higher uptake rates at low nutrient concentrations, it severely limits dynamic sensing range. We thus propose that E. coli may limit periplasmic uptake to increase its chemotactic sensitivity, enabling it to use maltose as an environmental cue. Statement of SignificanceBacterial chemotaxis is among the best-studied systems in biology and is paradigmatic of the mechanisms used by cells to link sensory inputs with regulated responses, thus providing insight into the ecological basis of cellular physiology. Here we present a mechanistic chemotaxis model that describes how the regulation of the transport of a sugar into and out of the cells periplasm affects the cells motile response to that sugar. Based on observations from population-level chemotaxis assays, we uncover an ecologically relevant trade-off between sensing and uptake. The general finding of this work is that, while high-affinity transport allows for higher uptake rates, it can severely limit the cells dynamic sensing range.

microbiology↗

bioRxiv2020

Intein-assisted bisection mapping systematically splits proteins for Boolean logic and inducibility engineering

Split inteins are powerful tools for seamless ligation of synthetic split proteins. Yet, their use remains limited because the already intricate split site identification problem is often complicated by the requirement of extein junction sequences. To address this, we augmented a mini-Mu transposon-based screening approach and devised the intein-assisted bisection mapping (IBM) method. IBM robustly revealed clusters of split sites on five proteins, converting them into AND or NAND logic gates. We further showed that the use of inteins expands functional sequence space for splitting a protein. We also demonstrated the utility of our approach over rational inference of split sites from secondary structure alignment of homologous proteins. Furthermore, the intein inserted at an identified site could be engineered by the transposon again to become partially chemically inducible, and to some extent enabled post-translational tuning on host protein function. Our work offers a generalizable and systematic route towards creating split protein-intein fusions and conditional inteins for protein activity control.

synthetic biology↗

bioRxiv2018

Optimally designed vs intuition-driven inputs: the study case of promoter activity modelling

Synthetic biology is an emerging engineering discipline that aims at synthesising logical circuits into cells to accomplish new functions. Despite a thriving community and some notable successes, the basic task of assembling predictable gene circuits is still a key challenge. Mathematical models are uniquely suited to help solve this issue. Yet in biology they are perceived as expensive and laborious to obtain because low-information experiments have often been used to infer model parameters. How much additional information can be gained using optimally designed experiments? To tackle this question we consider a building block in Synthetic Biology, an inducible promoter in yeast S. cerevisiae. Using in vivo data we re-fit a mathematical model for such a system; we then compare in silico the quality of the parameter estimates when model calibration is done using typical (e.g. step inputs) and optimally designed experiments. We find that Optimal Experimental Design leads to ~70% improvement in the predictive ability of the inferred models. We conclude providing suggestions on how optimally designed experiments can be implemented in vivo.

bioengineering↗