Differential effects on light chain amyloid formation depend on mutations and type of glycosaminoglycans.

Amyloid light chain (AL) amyloidosis is a protein misfolding disease where immunoglobulin light chains sample partially folded states that lead to misfolding and amyloid formation, resulting in organ dysfunction and death. In vivo, amyloid deposits are found in the extracellular space and involve a variety of accessory molecules, such as glycosaminoglycans, one of the main components of the extracellular matrix. Glycosaminoglycans are a group of negatively charged heteropolysaccharides composed of repeating disaccharide units. In this study, we investigated the effect of glycosaminoglycans on the kinetics of amyloid fibril formation of three AL cardiac amyloidosis light chains. These proteins have similar thermodynamic stability but exhibit different kinetics of fibril formation. We also studied single restorative and reciprocal mutants and wild type germ line control protein. We found that the type of glycosaminoglycan has a different effect on the kinetics of fibril formation, and this effect seems to be associated with the natural propensity of each AL protein to form fibrils. Heparan sulfate accelerated AL-12, AL-09, κI Y87H, and AL-103 H92D fibril formation; delayed fibril formation for AL-103; and did not promote any fibril formation for AL-12 R65S, AL-103 delP95aIns, or κI O18/O8. Chondroitin sulfate A, on the other hand, showed a strong fibril formation inhibition for all proteins. We propose that heparan sulfate facilitates the formation of transient amyloidogenic conformations of AL light chains, thereby promoting amyloid formation, whereas chondroitin sulfate A kinetically traps partially unfolded intermediates, and further fibril elongation into fibrils is inhibited, resulting in formation/accumulation of oligomeric/protofibrillar aggregates.

Incorporation of charged residues in the CYP2J2 F-G loop disrupts CYP2J2-lipid bilayer interactions.

CYP2J2 epoxygenase is an extrahepatic, membrane bound cytochrome P450 (CYP) that is primarily found in the heart and mediates endogenous fatty acid metabolism. CYP2J2 interacts with membranes through an N-terminal anchor and various non-contiguous hydrophobic residues. The molecular details of the motifs that mediate membrane interactions are complex and not fully understood. To gain better insights of these complex protein-lipid interactions, we employed molecular dynamics (MD) simulations using a highly mobile membrane mimetic (HMMM) model that enabled multiple independent spontaneous membrane binding events to be captured. Simulations revealed that CYP2J2 engages with the membrane at the F-G loop through hydrophobic residues Trp-235, Ille-236, and Phe-239. To explore the role of these residues, three F-G loop mutants were modeled from the truncated CYP2J2 construct (Δ34) which included Δ34-I236D, Δ34-F239H and Δ34-I236D/F239H. Using the HMMM coordinates of CYP2J2, the simulations were extended to a full POPC membrane which showed a significant decrease in the depth of insertion for each of the F-G loop mutants. The CYP2J2 F-G loop mutants were expressed in E. coli and were shown to be localized to the cytosolic fraction at a greater percentage relative to construct Δ34. Notably, the functional data demonstrated that the double mutant, Δ34-I236D/F239H, maintained native-like enzymatic activity. The membrane insertion characteristics were examined by monitoring CYP2J2 Trp-quenching fluorescence spectroscopy upon binding nanodiscs containing pyrene phospholipids. Relative to the Δ34 construct, the F-G loop mutants exhibited lower Trp quenching and membrane insertion. Taken together, the results suggest that the mutants exhibit a different membrane topology in agreement with the MD simulations and provide important evidence towards the involvement of key residues in the F-G loop of CYP2J2.

An operationally implementable model for predicting the effects of an infectious disease on a comprehensive regional healthcare system.

An operationally implementable predictive model has been developed to forecast the number of COVID-19 infections in the patient population, hospital floor and ICU censuses, ventilator and related supply chain demand. The model is intended for clinical, operational, financial and supply chain leaders and executives of a comprehensive healthcare system responsible for making decisions that depend on epidemiological contingencies. This paper describes the model that was implemented at NorthShore University HealthSystem and is applicable to any communicable disease whose risk of reinfection for the duration of the pandemic is negligible.