SARS-CoV-2 infection: a possible trigger for the recurrence of IgA nephropathy after kidney transplantation?

Immunoglobulin A nephropathy, the most common primary glomerulonephritis worldwide, is a leading cause of chronic kidney disease and end-stage kidney failure. Several cases of immunoglobulin A nephropathy relapse in native kidneys have been described after COVID-19 vaccination or SARS-CoV-2 infection. Here, we report the case of a 52-year-old kidney transplant recipient who had a stable transplant function for more than 14 years, with a glomerular filtration rate above 30 ml/min/1.73 m2. The patient had been vaccinated against COVID-19 four times with the Pfizer-BioNTech vaccine, most recently in March 2022. Eight weeks after a symptomatic SARS-CoV-2 infection in June 2022, his glomerular filtration rate had decreased by more than 50%, and his proteinuria increased to 17.5 g per day. A renal biopsy indicated highly active immunoglobulin A nephritis. Despite steroid therapy, the function of the transplanted kidney deteriorated, and long-term dialysis became necessary because of recurrence of his underlying renal disease. This case report provides what is, to our knowledge, the first description of recurrent immunoglobulin A nephropathy in a kidney transplant recipient after SARS-CoV-2 infection leading to severe transplant failure and finally graft loss.

MORG1-A Negative Modulator of Renal Lipid Metabolism in Murine Diabetes.

Renal fatty acid (FA) metabolism is severely altered in type 1 and 2 diabetes mellitus (T1DM and T2DM). Increasing evidence suggests that altered lipid metabolism is linked to tubulointerstitial fibrosis (TIF). Our previous work has demonstrated that mice with reduced MORG1 expression, a scaffold protein in HIF and ERK signaling, are protected against TIF in the db/db mouse model. Renal TGF-ß1 expression and EMT-like changes were reduced in mice with single-allele deficiency of MORG1. Given the well-known role of HIF and ERK signaling in metabolic regulation, here we examined whether protection was also associated with a restoration of lipid metabolism. Despite similar features of TIF in T1DM and T2DM, diabetes-associated changes in renal lipid metabolism differ between both diseases. We found that de novo synthesis of FA/cholesterol and ß-oxidation were more strongly disrupted in T1DM, whereas pathological fat uptake into tubular cells mediates lipotoxicity in T2DM. Thus, diminished MORG1 expression exerts renoprotection in the diabetic nephropathy by modulating important factors of TIF and lipid dysregulation to a variable extent in T1DM and T2DM. Prospectively, targeting MORG1 appears to be a promising strategy to reduce lipid metabolic alterations in diabetic nephropathy.

General-Purpose Coarse-Grained Toughened Thermoset Model for 44DDS/DGEBA/PES.

The objective of this work is to predict the morphology and material properties of crosslinking polymers used in aerospace applications. We extend the open-source dybond plugin for HOOMD-Blue to implement a new coarse-grained model of reacting epoxy thermosets and use the 44DDS/DGEBA/PES system as a case study for calibration and validation. We parameterize the coarse-grained model from atomistic solubility data, calibrate reaction dynamics against experiments, and check for size-dependent artifacts. We validate model predictions by comparing glass transition temperatures measurements at arbitrary degree of cure, gel-points, and morphology predictions against experiments. We demonstrate for the first time in molecular simulations the cure-path dependence of toughened thermoset morphologies.

Optimization and Validation of Efficient Models for Predicting Polythiophene Self-Assembly.

We develop an optimized force-field for poly(3-hexylthiophene) (P3HT) and demonstrate its utility for predicting thermodynamic self-assembly. In particular, we consider short oligomer chains, model electrostatics and solvent implicitly, and coarsely model solvent evaporation. We quantify the performance of our model to determine what the optimal system sizes are for exploring self-assembly at combinations of state variables. We perform molecular dynamics simulations to predict the self-assembly of P3HT at ∼350 combinations of temperature and solvent quality. Our structural calculations predict that the highest degrees of order are obtained with good solvents just below the melting temperature. We find our model produces the most accurate structural predictions to date, as measured by agreement with grazing incident X-ray scattering experiments.

Tying Together Multiscale Calculations for Charge Transport in P3HT: Structural Descriptors, Morphology, and Tie-Chains.

Evaluating new, promising organic molecules to make next-generation organic optoelectronic devices necessitates the evaluation of charge carrier transport performance through the semi-conducting medium. In this work, we utilize quantum chemical calculations (QCC) and kinetic Monte Carlo (KMC) simulations to predict the zero-field hole mobilities of ∼100 morphologies of the benchmark polymer poly(3-hexylthiophene), with varying simulation volume, structural order, and chain-length polydispersity. Morphologies with monodisperse chains were generated previously using an optimized molecular dynamics force-field and represent a spectrum of nanostructured order. We discover that a combined consideration of backbone clustering and system-wide disorder arising from side-chain conformations are correlated with hole mobility. Furthermore, we show that strongly interconnected thiophene backbones are required for efficient charge transport. This definitively shows the role "tie-chains" play in enabling mobile charges in P3HT. By marrying QCC and KMC over multiple length- and time-scales, we demonstrate that it is now possible to routinely probe the relationship between molecular nanostructure and device performance.

Enhanced Computational Sampling of Perylene and Perylothiophene Packing with Rigid-Body Models.

Molecular simulations have the potential to advance the understanding of how the structure of organic materials can be engineered through the choice of chemical components but are limited by computational costs. The computational costs can be significantly lowered through the use of modeling approximations that capture the relevant features of a system, while lowering algorithmic complexity or by decreasing the degrees of freedom that must be integrated. Such methods include coarse-graining techniques, approximating long-range electrostatics with short-range potentials, and the use of rigid bodies to replace flexible bonded constraints between atoms. To understand whether and to what degree these techniques can be leveraged to enhance the understanding of planar organic molecules, we investigate the morphologies predicted by molecular dynamic simulations using simplified molecular models of perylene and perylothiophene. Approximately, 10 000 wall-clock hours of graphics processing unit-accelerated simulations are performed using both rigid and flexible models to test their efficiency and predictive capability with the two chemistries. We characterize the 1191 resulting morphologies using simulated X-ray diffraction and cluster analysis to distinguish structural transitions, summarized by four phase diagrams. We find that the morphologies generated by the rigid model of perylene and perylothiophene match with those generated by the flexible model. We find that ordered, hexagonally packed columnar phases are thermodynamically favored over a wide range of densities and temperatures for both molecules, in qualitative agreement with experiments. Furthermore, we find the rigid model to be more computationally efficient for both molecules, providing more samples per second and shorter times to equilibrium. Owing to the structural accuracy and improved computational efficiency of modeling polyaromatic groups as rigid bodies, we recommend this modeling choice for enhancing the sampling in polyaromatic molecular simulations.

Digital colloids: reconfigurable clusters as high information density elements.

Through the design and manipulation of discrete, nanoscale systems capable of encoding massive amounts of information, the basic components of computation are open to reinvention. These components will enable tagging, memory storage, and sensing in unusual environments - elementary functions crucial for soft robotics and "wet computing". Here we show how reconfigurable clusters made of N colloidal particles bound flexibly to a central colloidal sphere have the capacity to store an amount of information that increases as O(N ln(N)). Using Brownian dynamics simulations, we predict dynamical regimes that allow for information to be written, saved, and erased. We experimentally assemble an N = 4 reconfigurable cluster from chemically synthesized colloidal building blocks, and monitor its equilibrium dynamics. We observe state switching in agreement with simulations. This cluster can store one bit of information, and represents the simplest digital colloid.

Self-assembled clusters of spheres related to spherical codes.

We consider the thermodynamically driven self-assembly of spheres onto the surface of a central sphere. This assembly process forms self-limiting, or terminal, anisotropic clusters (N-clusters) with well-defined structures. We use Brownian dynamics to model the assembly of N-clusters varying in size from two to twelve outer spheres and free energy calculations to predict the expected cluster sizes and shapes as a function of temperature and inner particle diameter. We show that the arrangements of outer spheres at finite temperatures are related to spherical codes, an ideal mathematical sequence of points corresponding to the densest possible sphere packings. We demonstrate that temperature and the ratio of the diameters of the inner and outer spheres dictate cluster morphology. We present a surprising result for the equilibrium structure of a 5-cluster, for which the square pyramid arrangement is preferred over a more symmetric structure. We show this result using Brownian dynamics, a Monte Carlo simulation, and a free energy approximation. Our results suggest a promising way to assemble anisotropic building blocks from constituent colloidal spheres.

Thermal and athermal three-dimensional swarms of self-propelled particles.

Swarms of self-propelled particles exhibit complex behavior that can arise from simple models, with large changes in swarm behavior resulting from small changes in model parameters. We investigate the steady-state swarms formed by self-propelled Morse particles in three dimensions using molecular dynamics simulations optimized for graphics processing units. We find a variety of swarms of different overall shape assemble spontaneously and that for certain Morse potential parameters at most two competing structures are observed. We report a rich "phase diagram" of athermal swarm structures observed across a broad range of interaction parameters. Unlike the structures formed in equilibrium self-assembly, we find that the probability of forming a self-propelled swarm can be biased by the choice of initial conditions. We investigate how thermal noise influences swarm formation and demonstrate ways it can be exploited to reconfigure one swarm into another. Our findings validate and extend previous observations of self-propelled Morse swarms and highlight open questions for predictive theories of nonequilibrium self-assembly.

Calculation of partition functions for the self-assembly of patchy particles.

Partition functions encode all the thermodynamics of a system, but for most systems of practical importance, they cannot be calculated exactly. In this work we present a new hierarchical method for calculating partition functions to arbitrary precision. We discuss the algorithmic details of our implementation, including elements of shape-matching and entropy calculation for on-lattice and off-lattice systems. We highlight computational trade-offs between speed and accuracy, showing how this varies with temperature, and demonstrate its utility in studying nanoscale self-assembly for a system of model patchy particles.

Self-assembly and reconfigurability of shape-shifting particles.

Reconfigurability of two-dimensional colloidal crystal structures assembled by anisometric particles capable of changing their shape were studied by molecular dynamics computer simulation. We show that when particles change shape on cue, the assembled structures reconfigure into different ordered structures, structures with improved order, or more densely packed disordered structures, on faster time scales than can be achieved via self-assembly from an initially disordered arrangement. These results suggest that reconfigurable building blocks can be used to assemble reconfigurable materials, as well as to assemble structures not possible otherwise, and that shape shifting could be a promising mechanism to engineer assembly pathways to ordered and disordered structures.