The canonical Scribble polarity complex is implicated in regulation of epithelial junctions and apical polarity. Here, we show that SGEF, a RhoG-specific GEF, forms a ternary complex with Scribble and Dlg1, two members of the Scribble complex. SGEF targets to apical junctions in a Scribble-dependent fashion and functions in the regulation of actomyosin-based contractility and barrier function at tight junctions as well as E-cadherin–mediated formation of adherens junctions. Surprisingly, SGEF does not control the establishment of polarity. However, in 3D cysts, SGEF regulates the formation of a single open lumen. Interestingly, SGEF’s nucleotide exchange activity regulates the formation and maintenance of adherens junctions, and in cysts the number of lumens formed, whereas SGEF’s scaffolding activity is critical for regulation of actomyosin contractility and lumen opening. We propose that SGEF plays a key role in coordinating junctional assembly and actomyosin contractility by bringing together Scribble and Dlg1 and targeting RhoG activation to cell–cell junctions.

Computational protein design (CPD) can address the inverse folding problem, exploring a large space of sequences and selecting ones predicted to fold. CPD was used previously to redesign several proteins, employing a knowledge-based energy function for both the folded and unfolded states. We show that a pDZ domain can be entirely redesigned using a "physics-based" energy for the folded state and a knowledge-based energy for the unfolded state. Thousands of sequences were generated by Monte Carlo simulation. Three were chosen for experimental testing, based on their low energies and several empirical criteria. All three could be overexpressed and had native-like circular dichroism spectra and 1D-NMR spectra typical of folded structures. Two had upshifted thermal denaturation curves when a peptide ligand was present, indicating binding and suggesting folding to a correct, PDZ structure. Evidently, the physical principles that govern folded proteins, with a dash of empirical postfiltering, can allow successful whole-protein redesign. Protein sequences have been selected by evolution to fold into specific structures, stabilized by a subtle balance of interactions involving protein and solvent 1,2. In contrast, random polymers of amino acids are very unlikely to adopt a specific, folded structure 3,4 , and exhibit instead a more disordered structure 5. A powerful approach to understand the evolution of proteins and the basis of folding is to perform computer simulations that mimic evolution. This can be done with computational protein design (CPD), which explores a large set of sequences and selects ones predicted to adopt a given fold 6-8. A typical simulation imposes a specific geometry for the protein backbone, corresponding to the experimental conformation of a natural protein. Side chains are mutated randomly. Variants with a favorable predicted folding free energy are saved. The folded state energy function can be physics-based or knowledge-based 9-11 while the unfolded state energy is knowledge-based. The protein is considered "redesigned" if most of the protein side chains are allowed to mutate during the simulation. The successful redesign of complete proteins was reported in 2003 7,12 and small miniproteins were redesigned even earlier 6,13. Several other successes were obtained 14-17 , including a study where 15000 miniproteins (40-43 amino acids) were redesigned 18. 6% of the designs were shown to be successful; i.e., the designed miniproteins folded into the correct tertiary structure. The others either could not be overexpressed and purified, or did not fold as predicted. All of the applications to proteins described the folded structure with an energy function that was at least partly knowledge-based, or statistical. Statistical energy terms included terms derived from experimental amino acid propensities and evolutionary covariances 17 , terms derived from inter-residue distance distributions in crystal structures 16 , and terms derived from torsion angle and hydrogen-bond distance distributions in cry...

Recent case reports and studies on treating COVID-19 in patients with chronic sarcoidosis describe different treatment modalities ranging from glucocorticoids to biologic medications. This review article summarizes seven case series and reports totaling 46 patients. While one case report suggested that sarcoidosis medications such as glucocorticoids may lengthen the COVID-19 disease course, another study with a larger registry suggests they do not. More studies are needed to elucidate an improvement in outcomes. It is possible that addition of TNF-alpha inhibitors at COVID-19 diagnosis decreases hospitalization rate. Overall, it is difficult to make firm conclusions regarding treatment given the heterogeneity of treatment modalities in the current literature. Our summarized findings are outlined with the opinions of sarcoidosis, pulmonary, and infectious disease experts in a flow chart that provides clinicians with our proposed management algorithm for sarcoidosis patients who develop COVID-19. We emphasize a need for exchange of information regarding management of COVID-19 in the setting of sarcoidosis to further improve treatment in this vulnerable population of patients.

A powerful approach to understand protein structure and evolution is to perform computer simulations that mimic aspects of evolution. In particular, structure-based computational protein design (CPD) can address the inverse folding problem, exploring a large space of amino acid sequences and selecting ones predicted to adopt a given fold. Previously, CPD has been used to entirely redesign several proteins: all or most of the protein sequence was allowed to mutate freely; among sampled sequences, those with low computed folding energy were selected, and a few percent of them did indeed adopt the correct fold. Those studies used an energy function that was partly or largely knowledge-based, with several empirical terms. Here, we show that a PDZ domain can be entirely redesigned using a "physics-based" energy function that combines standard molecular mechanics and a recent, continuum electrostatic solvent model. Many thousands of sequences were generated by Monte Carlo simulation. Among the lowest-energy sequences, three were chosen for experimental testing. All three could be overexpressed and had native-like circular dichroism and 1D-NMR spectra. Two exhibited an increase in their thermal denaturation curves when a peptide ligand was present, indicating binding and suggesting correctly folded proteins. Evidently, the physical principles that govern molecular mechanics and continuum electrostatics are sufficient to perform whole-protein redesign. This is encouraging, since these methods provide physical insights, can be systematically improved, and are transferable to other biopolymers and ligands of medical or technological interest.2