Biology subjects

di Bernardo, D.

Publications and source records attributed to di Bernardo, D..

15 recordsLinked to original sources

bioRxiv2024

The CRISPRaTOR: a biomolecular circuit for Automatic Gene Regulation in Mammalian Cells with CRISPR technology.

We introduce the CRISPRaTOR, a biomolecular circuit for precise control of gene expression in mammalian cells. The CRISPRaTOR leverages the stochiometric interaction between the artificial transcription factor VPR-dCas9, and the anti-CRISPR protein AcrIIA4, enhanced with synthetic coiled-coil domains to boost their interaction, to maintain the expression of a reporter protein constant across diverse experimental conditions, including fluctuations in protein degradation rates and plasmid concentrations, by automatically adjusting its mRNA level. This capability, known as Robust Perfect Adaptation (RPA), is crucial for the stable functioning of biological systems and has wide-ranging implications for biotechnological applications. The CRISPRaTOR belongs to a class of biomolecular circuits named antithetic integral controllers, and it can be easily adapted to regulate any endogenous transcription factor thanks to the versatility of CRISPR-Cas system. Finally, we show that RPA holds also in cells genomically integrated with the CRISPRaTOR, thus paving the way for practical applications in biotechnology that require stable cell lines.

synthetic biology↗

bioRxiv2024

A general strategy to engineer high-performance mammalian Whole-Cell Biosensors

Transcription-based whole-cell biosensors (WCBs) are cells engineered with an analyte-responsive promoter driving the transcription of a reporter gene. WCBs can sense and report on bioactive molecules (analytes) relevant to human health. Designing an analyte-sensitive promoter requires a cumbersome trial-and-error approach and usually results in biosensors with poor performance. Here, we integrated Synthetic Biology with Control Engineering to design, computationally model, and experimentally implement high-performance biosensors in mammalian cells. Our approach, unlike traditional methods, does not rely on optimizing individual components such as promoters and transcription factors. Instead, it uses biomolecular circuits to enhance the biosensors performance despite inherent component flaws. We experimentally implemented eight different biosensors by employing CRISPR-Cas systems, then quantitatively compared their performance and identified one configuration, which we named CASense, that overcomes the limitations of current biosensors. Our approach is generalisable and can be adapted to sense any analyte of interest for which there is an analyte-sensitive promoter, making it a versatile tool for multiple applications. As a proof of concept, we engineered a high-performance biosensor of intracellular copper due to the critical role that copper plays in human health and disease, and to the lack of techniques able to measure intracellular copper levels in living cells. The significance of our work lies in its potential to impact the monitoring of bioactive molecules and chemicals both in vitro and in vivo, which is crucial in areas such as toxicology, drug discovery, disease diagnosis and therapy.

synthetic biology↗

bioRxiv2023

The CASwitch: a synthetic biology solution for high-performance inducible gene expression systems in biotechnology

Inducible gene expression systems can be used to control the expression of a gene of interest by means of a small-molecule. One of the most common designs involves engineering a small-molecule responsive transcription factor (TF) and its cognate promoter, which often results in a compromise between minimal uninduced background expression (leakiness) and maximal induced expression. Here, we focussed on an alternative strategy using quantitative synthetic biology to mitigate leakiness while maintaining high expression, without modifying neither the TF nor the promoter. Through mathematical modelling and experimental validations, we designed the CASwitch, a mammalian synthetic gene circuit based on combining two well-known network motifs: the Coherent Feed-Forward Loop (CFFL) and the Mutual Inhibition (MI). The CASwitch combines the CRISPR-Cas endoribonuclease CasRx with the state-of-the-art Tet-On3G inducible gene system to achieve high performances. To demonstrate the potentialities of the CASwitch, we applied it to three different scenarios: enhancing a whole-cell biosensor, controlling expression of a toxic gene and inducible production of Adeno-Associated Virus (AAV) vectors.

synthetic biology↗

bioRxiv2021

The VersaLive platform enables pipette-compatible microfluidic mammalian cell culture for versatile applications

Microfluidic-based cell culture allows for precise spatio-temporal regulation of microenvironment, live cell imaging and better recapitulation of physiological conditions, while minimizing reagents consumption. Despite their usefulness, most microfluidic systems are designed with one specific application in mind and require specialized equipment and expertise for their operation. All these requirements prevent microfluidic-based cell culture to be widely adopted. Here, we designed and implemented a versatile and easy-to-use perfusion cell culture microfluidic platform for multiple application (VersaLive) requiring only standard pipettes. Here, we showcase the multiple uses of VersaLive (e.g., time-lapse live cell imaging, immunostaining, cell recovery, cell lysis) on mammalian cell lines and primary cells. VersaLive can replace standard cell culture formats in several applications, thus decreasing costs and increasing reproducibility across laboratories. The layout, documentation and protocols are open-source and available online at https://versalive.tigem.it/.

bioengineering↗

bioRxiv2021

Molecular phenotyping of breast cancer cell lines at the single-cell level for automated cancer diagnosis and prediction of drug sensitivity

Breast cancer patient stratification is mainly driven by tumour receptor status and histological grading and subtyping, with about twenty percent of patients for which absence of any actionable biomarkers results in no clear therapeutic intervention. Cancer cells within the same tumour have heterogeneous phenotypes and exhibit dynamic plasticity. However, how to evaluate such heterogeneity and its impact on outcome and drug response is still unclear. Here, we transcriptionally profiled 35,276 individual cells from 32 breast cancer cell lines covering all main breast cancer subtypes to yield a breast cancer cell line atlas. We found high degree of heterogeneity in the expression of clinically relevant biomarkers across individual cells within the same cell line; such heterogeneity is non-genetic and dynamic. We computationally mapped single cell transcriptional profiles of patients tumour biopsies to the atlas to determine their composition in terms of cell lines. Each tumour was found to be heterogenous and composed of multiple cell lines mostly, but not exclusively, of the same subtype. We then trained an algorithm on the atlas to determine cell line composition from bulk gene expression profiles of tumour biopsies, thus providing a novel approach to patient stratification. Finally, we linked results from large-scale in vitro drug screening1,2 to the single cell data to computationally predict responses to more than 450 anticancer agents starting from single-cell transcriptional profiles. We thus found that transcriptional heterogeneity enables cells with differential drug sensitivity to co-exist in the same population. Our work provides a unique resource and a novel framework to determine tumour heterogeneity and drug response in breast cancer patients.

cancer biology↗

bioRxiv2020

Cyber-yeast: Automatic synchronisation of the cell cycle in budding yeast through closed-loop feedback control

The cell cycle is the process by which eukaryotic cells replicate. Yeast cells cycle asynchronously with each cell in the population budding at a different time. Although there are several experimental approaches to "synchronise" cells, these work only in the short-term. Here, we built a cyber-genetic system to achieve long-term synchronisation of the cell population, by interfacing genetically modified yeast cells with a computer by means of microfluidics to dynamically change medium, and a microscope to estimate cell cycle phases of individual cells. The computer implements a "controller" algorithm to decide when, and for how long, to change the growth medium to synchronise the cell-cycle across the population. Our work builds upon solid theoretical foundations provided by Control Engineering. In addition to providing a new avenue for yeast cell cycle synchronisation, our work shows that computers can automatically steer complex biological processes towards desired behaviours similarly to what is currently done with robots and autonomous vehicles.

synthetic biology↗

bioRxiv2020

ChipSeg: an automatic tool to segment bacteria and mammalian cells cultured in microfluidic devices

Extracting quantitative measurements from time-lapse images is necessary in external feedback control applications, where segmentation results are used to inform control algorithms. While such image segmentation applications have been previously reported, there is in the literature a lack of open-source and documented code for the community. We describe ChipSeg, a computational tool to segment bacterial and mammalian cells cultured in microfluidic devices and imaged by time-lapse microscopy. The method is based on thresholding and uses the same core functions for both cell types. It allows to segment individual cells in high cell-density microfluidic devices, to quantify fluorescence protein expression over a time-lapse experiment and to track individual cells. ChipSeg enables robust segmentation in external feedback control experiments and can be easily customised for other experimental settings and research aims.

synthetic biology↗

bioRxiv2020

β-catenin perturbations control differentiation programs in mouse embryonic stem cells

The Wnt/{beta}-catenin pathway is involved in development, cancer and embryonic stem cell (ESC) maintenance; its dual role in stem cell self-renewal and differentiation is still controversial. Here, by applying an in vitro system enabling inducible gene expression control, we report that moderate induction of transcriptionally active exogenous {beta}- catenin in {beta}-catenin null mouse ESCs promotes epiblast-like cell (EpiLC) derivation in vitro. Instead, in wild type cells moderate chemical pre-activation of the Wnt/{beta}-catenin pathway promotes EpiLC in vitro derivation. Finally, we suggest that moderate {beta}- catenin levels in {beta}-catenin null mouse ESCs favour early stem cell commitment towards mesoderm if the exogenous protein is induced only in the ground state of pluripotency condition, or endoderm if the induction is maintained during the differentiation. Overall, our results confirm previous findings about the role of {beta}-catenin in pluripotency and differentiation, while indicating a role for its doses in promoting specific differentiation programs.

cell biology↗

bioRxiv2020

Synchronisation of yeast cell cycle through quorum sensing coupling

The cell cycle is present in all cells of all species and it is of fundamental importance in coordinating all the steps required for cell replication, including growth, DNA replication and cell division. Budding yeast is an unicellular organism characterised by a mother cell that buds to generate a daughter cell at each cell cycle. Each cell in a population buds at a different time. Despite its importance in biological applications, such as unravelling cell-cycle machinery mechanisms and production of valuable bioproducts, at present no yeast strain is capable of budding synchronously. To overcome this problem, we used control theory to propose a strategy to modify the yeast cell to endow it with the ability to synchronise its cell cycle across the population. Our strategy relies on a quorum sensing molecule diffusing freely in and out of the cell. The quorum sensing molecule is produced only during a specific phase of the cell cycle and couples the cell-cycle across the cell population. Here we model the proposed strategy with ordinary differential equations and numerically simulate it to demonstrate the feasibility of such an approach.

synthetic biology↗

bioRxiv2020

Automatic identification of small molecules that promote cell conversion and reprogramming

Controlling cell fate has great potential for regenerative medicine, drug discovery, and basic research. Although numerous transcription factors have been discovered that are able to promote cell reprogramming and trans-differentiation, methods based on their up-regulation tend to show low efficiency. The identification of small molecules that can facilitate conversion between cell types can ameliorate this problem working through safe, rapid, and reversible mechanisms. Here we present DECCODE, an unbiased computational method for the identification of such molecules solely based on transcriptional data. DECCODE matches the largest available collection of drug-induced profiles (the LINCS database) for drug treatments against the largest publicly available dataset of primary cell transcriptional profiles (FANTOM5), to identify drugs that either alone or in combination enhance cell reprogramming and cell conversion. Extensive in silico and in vitro validation of DECCODE in the context of human induced pluripotent stem cells (hIPSCs) generation shows that the method is able to prioritize drugs enhancing cell reprogramming. We also generated predictions for cell conversion with single drugs and drug combinations for 145 different cell types and made them available for further studies.

bioinformatics↗

bioRxiv2019

Feedback control promotes synchronisation of the cell-cycle across a population of yeast cells

The periodic process of cell replication by division, known as cell-cycle, is a natural phenomenon occurring asynchronously in any cell population. Here, we consider the problem of synchronising cell-cycles across a population of yeast cells grown in a microfluidics device. Cells were engineered to reset their cell-cycle in response to low methionine levels. Automated syringes enable changing methionine levels (control input) in the microfluidics device. However, the control input resets only those cells that are in a specific phase of the cell-cycle (G1 phase), while the others continue to cycle unperturbed. We devised a simplified dynamical model of the cell-cycle, inferred its parameters from experimental data and then designed two control strategies: (i) an open-loop controller based on the application of periodic stimuli; (ii) a closed-loop model predictive controller (MPC) that selects the sequence of control stimuli which maximises a synchronisation index. Both the proposed control strategies were validated in-silico, together with experimental validation of the open-loop strategy.

synthetic biology↗

bioRxiv2018

Towards feedback control of the cell-cycle across a population of yeast cells

Cells are defined by their unique ability to selfreplicate through cell division. This periodic process is known as the cell-cycle and it happens with a defined period in each cell. The budding yeast divides asymmetrically with a mother cell generating multiple daughter cells. Within the cell population each cell divides with the same period but asynchronously. Here, we investigate the problem of synchronising the cell-cycle across a population of yeast cells through a microfluidics-based feedback control platform. We propose a theoretical and experimental approach for cell-cycle control by considering a yeast strain that can be forced to start the cell-cycle by changing growth medium. The duration of the cell-cycle is strictly linked to the cell volume growth, hence a hard constraint in the controller design is to prevent excessive volume growth. We experimentally characterised the yeast strain and derived a simplified phase-oscillator model of the cell-cycle. We then designed and implemented three impulsive control strategies to achieve maximal synchronisation across the population and assessed their control performance by numerical simulations. The first two controllers are based on event-triggered strategies, while the third uses a model predictive control (MPC) algorithm to select the sequence of control impulses while satisfying built-in constraints on volume growth. We compared the three strategies by computing two cost functions: one quantifying the level of synchronisation across the cell population and the other volume growth during the process. We demonstrated that the proposed control approaches can effectively achieve an acceptable trade-off between two conflicting control objectives: (i) obtaining maximal synchronisation of the cell cycle across the population while (ii) minimizing volume growth. The results can be used to implement effective strategies to unfold the biological mechanisms controlling cell cycle and volume growth in yeast cells.

synthetic biology↗

bioRxiv2018

A tunable dual-input system for on-demand dynamic geneexpression regulation.

Cellular systems have evolved numerous mechanisms to finely control signalling pathway activation and properly respond to changing environmental stimuli. This is underpinned by dynamic spatiotemporal patterns of gene expression. Indeed, in addition to gene transcription and translation regulation, modulation of protein levels, dynamics and localization are also essential checkpoints that govern cell functions. The introduction of tetracycline-inducible promoters has allowed gene expression control using orthogonal small molecules, facilitating rapid and reversible manipulation to study gene function in biological systems. However, differing protein stabilities means this solely transcriptional regulation is insufficient to allow precise ON-OFF dynamics, thus hindering generation of temporal profiles of protein levels seen in vivo. We developed an improved Tet-On based system augmented with conditional destabilising elements at the post-translational level that permits simultaneous control of gene expression and protein stability. Integrating these properties to control expression of a fluorescent protein in mouse Embryonic Stem Cells (mESCs), we found that adding protein stability control allows faster response times to changes in small molecules, fully tunable and enhanced dynamic range, and vastly improved microfluidic-based in-silico feedback control of gene expression. Finally, we highlight the effectiveness of our dual-input system to finely modulate levels of signalling pathway components in stem cells.

synthetic biology↗

bioRxiv2017

gene2drug: a Computational Tool for Pathway-based Rational Drug Repositioning

MotivationDrug repositioning has been proposed as an effective shortcut to drug discovery. The availability of large collections of transcriptional responses to drugs enables computational approaches to drug repositioning directly based on measured molecular effects.\n\nResultsWe introduce a novel computational methodology for rational drug repositioning, which exploits the transcriptional responses following treatment with small molecule. Specifically, given a therapeutic target gene, a prioritisation of potential effective drugs is obtained by assessing their impact on the transcription of genes in the pathway(s) including the target. We performed in silico validation and comparison with a state-of-art technique based on similar principles. We next performed experimental validation in two different real-case drug repositioning scenarios: (i) upregulation of the glutamate-pyruvate transaminase, which has been shown to induce reduction of oxalate levels in a mouse model of primary hyperoxaluria, and (ii) activation of the transcription factor TFEB, a master regulator of lysosomal biogenesis and autophagy, whose modulation may be beneficial in neurodegenerative disorders.\n\nAvailabilityfree at http://gene2drug.tigem.it\n\nContactdibernardo@tigem.it

bioinformatics↗

bioRxiv2017

Integrated Structure-Transcription analysis of small molecules reveals widespread noise in drug-induced transcriptional responses and a transcriptional signature for drug-induced phospholipidosis

We performed an integrated analysis of drug chemical structures and drug-induced transcriptional responses. We demonstrated that a network representing 3D structural similarities among 5,452 compounds can be used to automatically group together drugs with similar scaffolds and mode-of-action. We then compared the structural network to a network representing transcriptional similarities among a subset of 1,309 drugs for which transcriptional response were available in the Connectivity Map dataset. Analysis of structurally similar, but transcriptionally different, drugs sharing the same mode of action (MOA) enabled us to detect and remove weak and noisy transcriptional responses, greatly enhancing the reliability and usefulness of transcription-based approaches to drug discovery and drug repositioning. Analysis of transcriptionally similar, but structurally different drugs with unrelated MOA, led us to the identification of a \"toxic\" transcriptional signature indicative of lysosomal stress (lysosomotropism) and lipid accumulation (phospholipidosis) partially masking the target-specific transcriptional effects of these drugs. We further demonstrated by High Content Screening that this transcriptional signature is caused by the activation of the transcription factor TFEB, a master regulator of lysosomal biogenesis and autophagy. Our results show that chemical structures and transcriptional profiles provide complementary information and that combined analysis can lead to new insights on on- and off-target effects of small molecules.

bioinformatics↗