Publications related to The United of Professor Barth

Effective removal of the rotifer Brachionus calyciflorus from a Chlorella vulgaris microalgal culture by homogeneous solar photo-Fenton at neutral pH

Jiahua Chen, Jérémie Decker, Stefanos Giannakis, Christian Ludwig, Horst Pick, Adrian Pulgarin, Dominik Refardt

In this study, a citrate-modified photo-Fenton process was successfully applied to decontaminate a Chlorella vulgaris microalgae culture spiked with the rotifer Brachionus calyciflorus (5 individuals mL−1). The applied treatment (1 mg L−1 Fe2+, 20 mg L−1 H2O2, 17.5 mg L−1 citric acid) had only moderate effects on viability and regrowth of the microalgae since, after a short post-treatment delay of a few days, they reached final cell densities similar to that obtained for microalgae cultures that were not spiked. The decontamination was effective as no regrowth of rotifers was observed in the microalgae cultures after treatment. The efficacy of the citrate-modified photo-Fenton treatment was also studied with a higher starting concentration of 20 rotifers mL−1 and was compared with a solar light/H2O2 treatment. Results show that both treatments had similar efficacies on the rotifer elimination, but that the citrate-modified photo-Fenton treatment had a lower negative impact on the regrowth of microalgae than the solar light/H2O2 treatment. However, when microalgae cultures were spiked with 20 rotifers mL−1, rotifers were only partially inactivated and post-treatment regrowth occurred, which highlights the importance to apply the photo-Fenton process at an early stage of a contamination to achieve full rotifer elimination. In any case, a contamination with 5 rotifers mL−1 is already a significant threat as numbers above 1000 rotifers mL−1 were reached after 14 days and caused the microalgae culture to fail. Overall, our treatment suggests that the citrate-modified solar photo-Fenton process is an environmentally friendly solution to support the maintenance of contaminant-free microalgal cultures.

2022

Epithelial-mesenchymal plasticity determines estrogen receptor positive breast cancer dormancy and epithelial reconversion drives recurrence

Giovanna Ambrosini, Patrik Aouad, Cathrin Brisken, Fabio De Martino, Kelly Spry Maggs, Hazel Margaret Quinn, Georgios Sflomos, Céline Marie Victoria Stibolt, Yueyun Zhang

More than 70% of human breast cancers (BCs) are estrogen receptor α-positive (ER+). A clinical challenge of ER+ BC is that they can recur decades after initial treatments. Mechanisms governing latent disease remain elusive due to lack of adequate in vivo models. We compare intraductal xenografts of ER+ and triple-negative (TN) BC cells and demonstrate that disseminated TNBC cells proliferate similarly as TNBC cells at the primary site whereas disseminated ER+ BC cells proliferate slower, they decrease CDH1 and increase ZEB1,2 expressions, and exhibit characteristics of epithelial-mesenchymal plasticity (EMP) and dormancy. Forced E-cadherin expression overcomes ER+ BC dormancy. Cytokine signalings are enriched in more active versus inactive disseminated tumour cells, suggesting microenvironmental triggers for awakening. We conclude that intraductal xenografts model ER + BC dormancy and reveal that EMP is essential for the generation of a dormant cell state and that targeting exit from EMP has therapeutic potential.

2022

Deep learning approaches for conformational flexibility and switching properties in protein design

Patrick Daniel Barth, Mahdi Hijazi

Following the hugely successful application of deep learning methods to protein structure prediction, an increasing number of design methods seek to leverage generative models to design proteins with improved functionality over native proteins or novel structure and function. The inherent flexibility of proteins, from side-chain motion to larger conformational reshuffling, poses a challenge to design methods, where the ideal approach must consider both the spatial and temporal evolution of proteins in the context of their functional capacity. In this review, we highlight existing methods for protein design before discussing how methods at the forefront of deep learning-based design accommodate flexibility and where the field could evolve in the future.

2022

Computational rewiring of allosteric pathways reprograms GPCR selective responses to ligands

Patrick Daniel Barth, Mahdi Hijazi, Dániel Kéri, Aurélien Laurent Jean-Charles Oggier

G-protein-coupled receptors (GPCRs) are the largest class of cell surface receptors and drug targets, and respond to a wide variety of chemical stimuli to activate diverse cellular functions. Understanding and predicting how ligand binding triggers a specific signaling response is critical for drug discovery and design but remains a major challenge. Here, computational design of GPCR allosteric functions is used to uncover the mechanistic relationships between agonist ligand chemistry, receptor sequence, structure, dynamics and allosteric signaling in the dopamine D2 receptor. Designed gain of function D2 variants for dopamine displayed very divergent G-protein signaling responses to other ligand agonists that strongly correlated with ligand structural similarity. Consistent with these observations, computational analysis revealed distinct topologies of allosteric signal transduction pathways for each ligand-bound D2 pair that were perturbed differently by the designs. We leveraged these findings by rewiring ligand-specific pathways and designed receptors with highly selective ligand responses. Overall, our study suggests that distinct ligand agonists can activate a given signaling effector through specific “allosteric activator” moieties that engage partially independent signal transmission networks in GPCRs. The results provide a mechanistic framework for understanding and predicting the impact of sequence polymorphism on receptor pharmacology, informing selective drug design and rationally designing receptors with highly selective ligand responses for basic and therapeutic applications.

bioRxiv2022

Computational design of ultrasensitive flexible peptide:receptor signaling complexes for enhanced chemotaxis

Patrick Daniel Barth, Andreas Füglistaler, Mahdi Hijazi, Robert Everett Jefferson, Aurélien Laurent Jean-Charles Oggier, Ana Paola Rico Villarreal

Engineering biosensors that sensitively recognize specific biomolecules and trigger functional cellular responses is a holy grail of diagnostics and synthetic cell biology. Biosensor design approaches have mostly focused on binding structurally well-defined molecules. Coupling the sensing of flexible compounds to complex cellular responses would considerably expand biosensor applications, but remains challenging. We developed a computational strategy for designing highly sensitive receptor biosensors of flexible peptides. Using the method, we created ultrasensitive chemotactic GPCR:peptide pairs capable of eliciting potent chemotaxis in human primary T cells. Through mutual induced fit, our flexible structure design approach enhanced peptide contacts with binding and allosteric sites in the ligand pocket to achieve unprecedented signaling efficacy and potency. The approach paves the road for designing peptide-sensing receptors and expanding the biosensor toolbox for basic and therapeutic applications.

bioRxiv2022

Method and system for determining intracellular mediators' activity

Thamani Dahoun, Margaux Catherine Marie Duchamp, Philippe Renaud, Horst Vogel

A method for determining the presence of a molecule coupled to the cytoplasmic side of a cellular membrane is disclosed. The method is implemented in a microfluidic setting and is particularly suitable for determining the presence and/or the activity of a G protein or an arrestin protein in a cell.

2022

Computational design of highly signaling active membrane receptors through de novo solvent-mediated allosteric networks

Patrick Daniel Barth, Jian Wang

Protein catalysis and allostery require the atomic-level orchestration and motion of residues, ligand, solvent and protein effector molecules, but the ability to design protein activity through precise protein-solvent cooperative interactions has not been demonstrated. Here, we report the design of a dozen novel membrane receptors catalyzing G-protein nucleotide exchange through diverse de novo engineered allosteric pathways mediated by cooperative networks of intra-protein, protein-ligand and solvent molecule interactions. Consistent with the predictions, designed protein activities strongly correlated with the level of solvent-mediated interaction network plasticity at flexible transmembrane helical interfaces. Several designs displayed considerably enhanced thermostability and activity compared to related natural receptors. The most stable and active variant crystallized in an unforeseen signaling active conformation, in excellent agreement with the design models. The allosteric network topologies of the best designs bear limited similarity to those of natural receptors and reveal a space of allosteric interactions larger than previously inferred from natural proteins. The approach should prove useful for engineering proteins with novel complex protein catalytic and signaling activities. Competing Interest Statement The authors have declared no competing interest.

2021

Computationally-guided tuning of ligand sensitivity in a GPCR-based sensor

Patrick Daniel Barth, Dániel Kéri

Genetically-encoded fluorescent sensors for neuromodulators are increasingly used molecular tools in neuroscience. However, these protein-based biosensors are often limited by the sensitivity of the protein scaffold towards endogenous ligands. Here, we explored the possibility of applying computational design approaches for enhancing sensor sensitivity. Using the dopamine sensor dLight1 as proof of concept, we designed two variants that boost the sensor’s potency (EC50) for dopamine and norepinephrine by up to 5- and 15-fold, respectively. Interestingly, the largest effects were obtained through improved designed allosteric transmission in the transmembrane region of the sensor. Our approach should prove generally useful for enhancing sensing capabilities of a large variety of neuromodulator sensors. Competing Interest Statement T.P. is listed as inventor on a patent application on the sensor technology described in this research. All other authors declare no competing interest.

2021

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Communication across membranes controls critical cellular processes and is achieved by receptors translating extracellular signals into selective cytoplasmic responses. While receptor tertiary structures can now be readily characterized, receptor associations into quaternary structures are very challenging to study and their implications in signal transduction remain poorly understood. Here, we report a computational approach for predicting membrane receptor self-associations, and designing receptor oligomers with various quaternary structures and signaling properties. Using this approach, we designed chemokine receptor CXCR4 dimers with reprogrammed stabilities, conformations, and abilities to activate distinct intracellular signaling proteins. In agreement with our predictions, the designed CXCR4s dimerized through distinct conformations and displayed different quaternary structural changes upon activation. Consistent with the active state models, all engineered CXCR4 oligomers activated the G protein Gi, but only a few specific dimer structures also recruited β-arrestins. Overall, we demonstrate that quaternary structures represent an important unforeseen mechanism of receptor biased signaling and reveal the existence of a conformational switch at the dimer interface of several G protein-coupled receptors including CXCR4, mu-Opioid and type-2 Vasopressin receptors that selectively control the activation of G proteins vs β-arrestin-mediated pathways. The approach should prove useful for predicting and designing receptor associations to uncover and reprogram selective cellular signaling functions.

2021

Rapid Noninvasive Skin Monitoring by Surface Mass Recording and Data Learning

Natalia Gasilova, Hubert Girault, Milica Jovic, Andreas Stephan Lesch, Tzu-En Lin, Horst Pick, Yingdi Zhu

Skin problems are often overlooked due to a lack of robust and patient-friendly monitoring tools. Herein, we report a rapid, noninvasive, and high-throughput analytical chemical methodology, aiming at real-time monitoring of skin conditions and early detection of skin disorders. Within this methodology, adhesive sampling and laser desorption ionization mass spectrometry are coordinated to record skin surface molecular mass in minutes. Automated result interpretation is achieved by data learning, using similarity scoring and machine learning algorithms. Feasibility of the methodology has been demonstrated after testing a total of 117 healthy, benign-disordered, or malignant-disordered skins. Remarkably, skin malignancy, using melanoma as a proof of concept, was detected with 100% accuracy already at early stages when the lesions were submillimeter-sized, far beyond the detection limit of most existing noninvasive diagnosis tools. Moreover, the malignancy development over time has also been monitored successfully, showing the potential to predict skin disorder progression. Capable of detecting skin alterations at the molecular level in a nonsurgical and time-saving manner, this analytical chemistry platform is promising to build personalized skin care.

2021