Structural and functional modelling of SARS-CoV-2 entry in animal models

SARS-CoV-2 is the novel coronavirus responsible for the outbreak of COVID-19, a disease that has spread to over 100 countries and, as of the 26th July 2020, has infected over 16 million people. Despite the urgent need to find effective therapeutics, research on SARS-CoV-2 has been affected by a lack of suitable animal models. To facilitate the development of medical approaches and novel treatments, we compared the ACE2 receptor, and TMPRSS2 and Furin proteases usage of the SARS-CoV-2 Spike glycoprotein in human and in a panel of animal models, i.e. guinea pig, dog, cat, rat, rabbit, ferret, mouse, hamster and macaque. Here we showed that ACE2, but not TMPRSS2 or Furin, has a higher level of sequence variability in the Spike protein interaction surface, which greatly influences Spike protein binding mode. Using molecular docking simulations we compared the SARS-CoV and SARS-CoV-2 Spike proteins in complex with the ACE2 receptor and showed that the SARS-CoV-2 Spike glycoprotein is compatible to bind the human ACE2 with high specificity. In contrast, TMPRSS2 and Furin are sufficiently similar in the considered hosts not to drive susceptibility differences. Computational analysis of binding modes and protein contacts indicates that macaque, ferrets and hamster are the most suitable models for the study of inhibitory antibodies and small molecules targeting the SARS-CoV-2 Spike protein interaction with ACE2. Since TMPRSS2 and Furin are similar across species, our data also suggest that transgenic animal models expressing human ACE2, such as the hACE2 transgenic mouse, are also likely to be useful models for studies investigating viral entry

Crystal structures reveal transient <scp>PERK</scp> luminal domain tetramerization in endoplasmic reticulum stress signaling

Stress caused by accumulation of misfolded proteins within the endoplasmic reticulum (ER) elicits a cellular unfolded protein response (UPR) aimed at maintaining protein-folding capacity. PERK, a key upstream component, recognizes ER stress via its luminal sensor/transducer domain, but the molecular events that lead to UPR activation remain unclear. Here, we describe the crystal structures of mammalian PERK luminal domains captured in dimeric state as well as in a novel tetrameric state. Small angle X-ray scattering analysis (SAXS) supports the existence of both crystal structures also in solution. The salient feature of the tetramer interface, a helix swapped between dimers, implies transient association. Moreover, interface mutations that disrupt tetramer formation in vitro reduce phosphorylation of PERK and its target eIF2α in cells. These results suggest that transient conversion from dimeric to tetrameric state may be a key regulatory step in UPR activation. Synopsis Activation of unfolded protein response (UPR) upon ER stress involves key regulatory roles of ER-luminal sensor/transducer domains in UPR signaling factors. Structural and functional analyses of the PERK luminal domain reveal a novel tetrameric arrangement, whose transient formation may be an important step in UPR activation. Crystal structure of human PERK luminal domain shows a novel tetramer arrangement. Crystal structure of mouse PERK luminal domain is captured in dimeric form. Biophysical analysis confirm that both mouse and human proteins exist as dimers as well as tetramers in solution. Mutations that disrupt tetramerization in solution reduce phosphorylation of PERK and its target eIF2α in cells. Structural and functional analyses of the PERK luminal domain reveal a novel tetrameric arrangement, whose transient formation may be an important step in activation of the unfolded protein response

Perspective: Structure determination of protein-ligand complexes at room temperature using X-ray diffraction approaches

The interaction between macromolecular proteins and small molecule ligands is an essential component of cellular function. Such ligands may include enzyme substrates, molecules involved in cellular signalling or pharmaceutical drugs. Together with biophysical techniques used to assess the thermodynamic and kinetic properties of ligand binding to proteins, methodology to determine high-resolution structures that enable atomic level interactions between protein and ligand(s) to be directly visualised is required. Whilst such structural approaches are well established with high throughput X-ray crystallography routinely used in the pharmaceutical sector, they provide only a static view of the complex. Recent advances in X-ray structural biology methods offer several new possibilities that can examine protein-ligand complexes at ambient temperature rather than under cryogenic conditions, enable transient binding sites and interactions to be characterised using time-resolved approaches and combine spectroscopic measurements from the same crystal that the structures themselves are determined. This Perspective reviews several recent developments in these areas and discusses new possibilities for applications of these advanced methodologies to transform our understanding of protein-ligand interactions

Machine learning application for development of a data-driven predictive model able to investigate quality of life scores in a rare disease.

BACKGROUND:Alkaptonuria (AKU) is an ultra-rare autosomal recessive disease caused by a mutation in the homogentisate 1,2-dioxygenase (HGD) gene. One of the main obstacles in studying AKU, and other ultra-rare diseases, is the lack of a standardized methodology to assess disease severity or response to treatment. Quality of Life scores (QoL) are a reliable way to monitor patients' clinical condition and health status. QoL scores allow to monitor the evolution of diseases and assess the suitability of treatments by taking into account patients' symptoms, general health status and care satisfaction. However, more comprehensive tools to study a complex and multi-systemic disease like AKU are needed. In this study, a Machine Learning (ML) approach was implemented with the aim to perform a prediction of QoL scores based on clinical data deposited in the ApreciseKUre, an AKU- dedicated database. METHOD:Data derived from 129 AKU patients have been firstly examined through a preliminary statistical analysis (Pearson correlation coefficient) to measure the linear correlation between 11 QoL scores. The variable importance in QoL scores prediction of 110 ApreciseKUre biomarkers has been then calculated using XGBoost, with K-nearest neighbours algorithm (k-NN) approach. Due to the limited number of data available, this model has been validated using surrogate data analysis. RESULTS:We identified a direct correlation of 6 (age, Serum Amyloid A, Chitotriosidase, Advanced Oxidation Protein Products, S-thiolated proteins and Body Mass Index) out of 110 biomarkers with the QoL health status, in particular with the KOOS (Knee injury and Osteoarthritis Outcome Score) symptoms (Relative Absolute Error (RAE) 0.25). The error distribution of surrogate-model (RAE 0.38) was unequivocally higher than the true-model one (RAE of 0.25), confirming the consistency of our dataset. Our data showed that inflammation, oxidative stress, amyloidosis and lifestyle of patients correlates with the QoL scores for physical status, while no correlation between the biomarkers and patients' mental health was present (RAE 1.1). CONCLUSIONS:This proof of principle study for rare diseases confirms the importance of database, allowing data management and analysis, which can be used to predict more effective treatments

Antibiotics select for novel pathways of resistance in biofilms

Most bacteria in nature exist in aggregated communities known as biofilms. Bacteria within biofilms are inherently highly resistant to antibiotics. Current understanding of the evolution and mechanisms of antibiotic resistance is largely derived from work from cells in liquid culture and it is unclear whether biofilms adapt and evolve in response to sub-inhibitory concentrations of drugs. Here we used a biofilm evolution model to show that biofilms of a model food borne pathogen, Salmonella Typhimurium rapidly evolve in response to exposure to three clinically important antibiotics. Whilst the model strongly selected for improved biofilm formation in the absence of any drug, once antibiotics were introduced the need to adapt to the drug was more important than the selection for improved biofilm formation. Adaptation to antibiotic stress imposed a marked cost in biofilm formation, particularly evident for populations exposed to cefotaxime and azithromycin. We identified distinct resistance phenotypes in biofilms compared to corresponding planktonic control cultures and characterised new mechanisms of resistance to cefotaxime and azithromycin. Novel substitutions within the multidrug efflux transporter, AcrB were identified and validated as impacting drug export as well as changes in regulators of this efflux system. There were clear fitness costs identified and associated with different evolutionary trajectories. Our results demonstrate that biofilms adapt rapidly to low concentrations of antibiotics and the mechanisms of adaptation are novel. This work will be a starting point for studies to further examine biofilm specific pathways of adaptation which inform future antibiotic use

Dimerization of the C-type lectin-like receptor CD93 promotes its binding to Multimerin-2 in endothelial cells.

Blocking the signaling activated by the plasma membrane receptor CD93 has recently been demonstrated a useful tool in antiangiogenic treatment and oncotherapy. In the proliferating endothelium, CD93 regulates cell adhesion, migration, and vascular maturation, yet it is unclear how CD93 interacts with the extracellular matrix activating signaling pathways involved in the vascular remodeling. Here for the first time we show that in endothelial cells CD93 is structured as a dimer and that this oligomeric form is physiologically instrumental for the binding of CD93 to its ligand Multimerin-2. Crystallographic X-ray analysis of recombinant CD93 reveals the crucial role played by the C-type lectin-like and sushi-like domains in arranging as an antiparallel dimer to achieve a functional binding state, providing key information for the future design of new drugs able to hamper CD93 function in neovascular pathologies

UPR Signal Activation by Luminal Sensor Domains

The unfolded protein response (UPR) is a cell-signaling system that detects the accumulation of unfolded protein within the endoplasmic reticulum (ER) and initiates a number of cellular responses to restore ER homeostasis. The presence of unfolded protein is detected by the ER-luminal sensor domains of the three UPR-transducer proteins IRE1, PERK, and ATF6, which then propagate the signal to the cytosol. In this review, we discuss the various mechanisms of action that have been proposed on how the sensor domains detect the presence of unfolded protein to activate downstream UPR signaling. © 2013 by the authors; licensee MDPI, Basel, Switzerland