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Candida albicans represents the most frequent isolated yeast from bloodstream infections. Despite the remarkable progress in diagnostic and therapeutic approaches, these infections continue to be a critical challenge in intensive care units worldwide. The economic cost of bloodstream fungal infections and its associated mortality, especially in debilitated patients, remains unacceptably high. Candida albicans is a highly adaptable microorganism, being able to develop resistance following prolonged exposure to antifungals. Formation of biofilms, which diminish the accessibility of the antifungal, selection of spontaneous mutations that increase expression or decreased susceptibility of the target, altered chromosome abnormalities, overexpression of multidrug efflux pumps and the ability to escape host immune defenses are some of the factors that can contribute to antifungal tolerance and resistance. The knowledge of the antifungal resistance mechanisms can allow the design of alternative therapeutically options in order to modulate or revert the resistance. We have focused this review on the main factors that are involved in antifungal resistance and tolerance in patients with C. albicans bloodstream infections.

Interactions between macromolecules, such as proteins and nucleic acids, are essential for cellular functions. Experimental methods can fail to provide all the information required to fully model biomolecular complexes at atomic resolution, particularly for large and heterogeneous assemblies. Integrative computational approaches have, therefore, gained popularity, complementing traditional experimental methods in structural biology. Here, we introduce HADDOCK2.4, an integrative modeling platform, and its updated web interface ( https://wenmr.science.uu.nl/haddock2.4 ). The platform seamlessly integrates diverse experimental and theoretical data to generate high-quality models of macromolecular complexes. The user-friendly web server offers automated parameter settings, access to distributed computing resources, and pre- and post-processing steps that enhance the user experience. To present the web server’s various interfaces and features, we demonstrate two different applications: (i) we predict the structure of an antibody–antigen complex by using NMR data for the antigen and knowledge of the hypervariable loops for the antibody, and (ii) we perform coarse-grained modeling of PRC1 with a nucleosome particle guided by mutagenesis and functional data. The described protocols require some basic familiarity with molecular modeling and the Linux command shell. This new version of our widely used HADDOCK web server allows structural biologists and non-experts to explore intricate macromolecular assemblies encompassing various molecule types. The HADDOCK2.4 web server is a modeling platform that can integrate experimental and theoretical data for guiding 3D prediction of biomolecular complexes. Key points HADDOCK is an integrative modeling approach that can combine experimental and predicted information to guide the structure prediction of biomolecular complexes, including protein–protein, protein–ligand, protein–oligosaccharide and protein–nucleic acid complexes, starting from the individual structures of their components. HADDOCK uses ambiguous interaction restraints to drive the docking process and supports various experimental data, including mutagenesis, NMR and cryo-EM data.

Candida parapsilosis is the second most common Candida species isolated in Asia, Southern Europe, and Latin America and is often involved in invasive infections that seriously impact human health. This pathogen is part of the psilosis complex, which also includes Candida orthopsilosis and Candida metapsilosis. C. parapsilosis infections are particularly prevalent among neonates with low birth weights, individuals who are immunocompromised, and patients who require prolonged use of a central venous catheter or other indwelling devices, whose surfaces C. parapsilosis exhibits an enhanced capacity to adhere to and form biofilms. Despite this well-acknowledged prevalence, the biology of C. parapsilosis has not been as extensively explored as that of Candida albicans. In this paper, we describe the molecular mechanistic pathways of virulence in C. parapsilosis and show how they differ from those of C. albicans. We also describe the mode of action of antifungal drugs used for the treatment of Candida infections, namely, polyenes, echinocandins, and azoles, as well as the resistance mechanisms developed by C. parapsilosis to overcome them. Finally, we stress the importance of the ongoing search for species-specific features that may aid the development of effective control strategies and thus reduce the burden on patients and healthcare costs.

Numerous studies have explored the antibacterial properties of different types of honey from all around the world. However, the data available describing how honey acts against bacteria are few. The aim of this study was to apply a flow cytometry (FC) protocol to examine and characterize the primary effects of three varieties of honey (avocado, chestnut and polyfloral) upon physiological status of Staphylococcus aureus and Escherichia coli cells to reveal their antibacterial action mechanisms. The effects of honey samples on membrane potential, membrane integrity, and metabolic activity were assessed using different fluorochromes, in a 180 min time course assay. Time-kill experiments were also carried out under similar conditions. Exposure of S. aureus and E. coli to the distinct honey samples resulted in physiological changes related to membrane polarization and membrane integrity. Moreover, honey induced a remarkable metabolic disruption as primary physiological effect upon S. aureus. The different honey samples induced quite similar effects on both bacteria. However, the depth of bacteria response throughout the treatment varied depending on the concentration tested and among honey varieties, probably due to compositional differences in the honey.

Which factors lead states to apply for observer status in the Arctic Council (AC)? What explains the acceptance of those applications? In 2013, the AC underwent its most significant change since its foundation in 1996, with its formalization through the creation of a secretariat, the confirmation of eight observers, and acceptance of other five states, including China, with the same status. This study explores geopolitical and economic interests of actors of regional environmental governance that impact both applications and their acceptance as observer states. Based on probit models and case studies, we identify that states that mostly increase their carbon-equivalent emissions through consumption and production are less likely to join the AC as observers and to be accepted as such. Models also yield statistically significant correlations between states that import a high amount of goods from China and the pursuit of observer status in the AC. Models that disregard the impact of Beijing on observership in the intergovernmental organization reveal that applicants tend to have higher international status than the average and tend to be accepted as observer states for increasing the AC’s prestige. Unsuccessful attempts of joining the AC as observers also suggest that concerns with the environment, science, and technology impact observership demand and supply. Yet, acceptance of observer states may also be contingent on geopolitical and economic considerations by member states of the AC.

Salmeterol is a partial agonist for the β2 adrenergic receptor (β2AR) and the first long-acting β2AR agonist to be widely used clinically for the treatment of asthma and chronic obstructive pulmonary disease. Salmeterol’s safety and mechanism of action have both been controversial. To understand its unusual pharmacological action and partial agonism, we obtained the crystal structure of salmeterol-bound β2AR in complex with an active-state-stabilizing nanobody. The structure reveals the location of the salmeterol exosite, where sequence differences between β1AR and β2AR explain the high receptor-subtype selectivity. A structural comparison with the β2AR bound to the full agonist epinephrine reveals differences in the hydrogen-bond network involving residues Ser2045.43 and Asn2936.55. Mutagenesis and biophysical studies suggested that these interactions lead to a distinct active-state conformation that is responsible for the partial efficacy of G-protein activation and the limited β-arrestin recruitment for salmeterol.

Protein–protein interactions are fundamental to many biological processes. Experimental screens have identified tens of thousands of interactions, and structural biology has provided detailed functional insight for select 3D protein complexes. An alternative rich source of information about protein interactions is the evolutionary sequence record. Building on earlier work, we show that analysis of correlated evolutionary sequence changes across proteins identifies residues that are close in space with sufficient accuracy to determine the three-dimensional structure of the protein complexes. We evaluate prediction performance in blinded tests on 76 complexes of known 3D structure, predict protein–protein contacts in 32 complexes of unknown structure, and demonstrate how evolutionary couplings can be used to distinguish between interacting and non-interacting protein pairs in a large complex. With the current growth of sequences, we expect that the method can be generalized to genome-wide elucidation of protein–protein interaction networks and used for interaction predictions at residue resolution. DNA is often referred to as the ‘blueprint of life’, as this molecule contains the instructions that are required to build a living organism from a single cell. But these instructions largely play out through the proteins that DNA encodes; and most proteins do not work alone. Instead they come together in different combinations, or complexes, and a single protein may participate in many complexes with different activities. Proteins are so small that it is difficult to get clear information about what they look like. Visualizing protein complexes is even harder. Most protein–protein interactions remain poorly understood, even in the best-studied organisms such as humans, yeast, and bacteria. Proteins are made from smaller molecules, called amino acids, strung together one after the other. The order in which different amino acids are arranged in a protein determines the protein’s shape and ultimately its function. Like DNA, protein sequences can change over time. Sometimes, the sequence of one protein changes in a way that prevents it binding to another protein. If these two proteins must work together for an organism to survive, the second protein will often develop a compensating change that allows the protein–protein complex to reform. Identifying pairs of changes in the sequences of pairs of proteins suggests that the two proteins interact and gives some information about how the proteins fit together. Different species can have copies of the same proteins that have slightly different sequences. Since the DNA sequences from many different organisms are already known, there are now many opportunities to find sites in pairs of proteins that have evolved together, or co-evolved, over time. To find sites that seem to have co-evolved, Hopf et al. used a computer program based on an approach from statistical physics to look at pairs of proteins that were already known to form complexes. Co-evolving sites were found in over 300 pairs of proteins; including 76 where the structure of the complex was already known. When sites that were predicted to be co-evolving were then mapped to these known complex structures, the co-evolving sites were remarkably close to the true protein–protein contacts. This indicates that the information from the co-evolved sequences is sufficient to show how two proteins fit together. Hopf et al. then turned their attention to 82 pairs of proteins that were thought to interact, but where a structure was unavailable. For 32 of these pairs, structures of the entire complex could be predicted, showing how the two proteins might interact. Furthermore, when other researchers subsequently worked out the structure of one of these complexes, the prediction was a good match to the solved complex structure. The machinery of life is largely made up of proteins, which must interact in ever-changing but precise ways. The new methods developed by Hopf et al. provide a new way to discover and investigate the details of these interactions.

[...]pilot data from a recent retrospective study, using an algorithm combining spleen stiffness measurement (cut-off >/<40 kPa) with the latest rule in and rule out CSPH criteria (LSM ≤15 kPa + PLT ≥150 g/L to rule out CSPH and LSM >25 kPa to rule in CSPH), reduced the grey zone from 40–60% to 7–15%. All first decompensation events occurred in the “rule-in” zone of the model including spleen stiffness measurement [12]. Besides combining with other tools to refine the patients at risk, these criteria require validation in patients with nonviral causes for cACLD, particularly in obese NASH patients, where LSM is not as accurate [10]. Recent data has shown that a percentage drop in stiffness, 10% or 20%, can help predict liverrelated events [14,15]. [...]date, very few studies have analysed the use of NITs alone or in combination with other markers, to evaluate response to beta-blocker therapy [16,17]. Abbreviations cACLD, compensated advanced chronic liver disease; CSPH, clinically significant portal hypertension; HCV, hepatitis C virus; HVPG, hepatic venous pressure gradient; LSM, liver stiffness measurement; NASH, non-alcoholic steatohepatitis; NIT, noninvasive test; NSBB, non-selective beta-blocker; PLT, platelet count 1.

A bstract In this work, we revisit the non-minimally coupled Higgs Inflation scenario and investigate its observational viability in light of the current Cosmic Microwave Background, Baryon Acoustic Oscillation and type Ia Supernovae data. We explore the effects of the Coleman-Weinberg approximation to the Higgs potential in the primordial universe, connecting the predictions for the Lagrangian parameters at inflationary scales to the electroweak observables through Renormalization Group methods at two-loop order. Initially, we find that electroweak scale measurements may be dissonant to the limits obtained from the cosmological data sets used in the analysis. Specifically, an ≈ 8 σ -discrepancy between the inflationary parameters and the value of the Monte Carlo reconstructed top quark mass is found. However, considering the most recent results obtained by the CMS Collaboration from differential cross-section measurements of the top quark production a good agreement is obtained.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the ongoing global pandemic that has infected more than 31 million people in more than 180 countries worldwide. Like other coronaviruses, SARS-CoV-2 is thought to have been transmitted to humans from wild animals. Given the scale and widespread geographical distribution of the current pandemic and confirmed cases of cross-species transmission, the question of the extent to which this transmission is possible emerges, as well as what molecular features distinguish susceptible from non-susceptible animal species. Here, we investigated the structural properties of several ACE2 orthologs bound to the SARS-CoV-2 spike protein. We found that species known not to be susceptible to SARS-CoV-2 infection have non-conservative mutations in several ACE2 amino acid residues that disrupt key polar and charged contacts with the viral spike protein. Our models also allow us to predict affinity-enhancing mutations that could be used to design ACE2 variants for therapeutic purposes. Finally, our study provides a blueprint for modeling viral-host protein interactions and highlights several important considerations when designing these computational studies and analyzing their results.