Influence of bacterial swimming and hydrodynamics on attachment of phages.

Bacteriophages ("phages") are viruses that infect bacteria. Since they do not actively self-propel, phages rely on thermal diffusion to find target cells-but can also be advected by fluid flows, such as those generated by motile bacteria themselves in bulk fluids. How does the flow field generated by a swimming bacterium influence how it encounters phages? Here, we address this question using coupled molecular dynamics and lattice Boltzmann simulations of flagellated bacteria swimming through a bulk fluid containing uniformly-dispersed phages. We find that while swimming increases the rate at which phages attach to both the cell body and flagellar propeller, hydrodynamic interactions strongly suppress this increase at the cell body, but conversely enhance this increase at the flagellar bundle. Our results highlight the pivotal influence of hydrodynamics on the interactions between bacteria and phages, as well as other diffusible species, in microbial environments.

Development of nitroalkene-based inhibitors to target STING-dependent inflammation.

Stimulator of Interferon Genes (STING) is essential for the inflammatory response to cytosolic DNA. Despite that aberrant activation of STING is linked to an increasing number of inflammatory diseases, the development of inhibitors has been challenging, with no compounds in the pipeline beyond the preclinical stage. We previously identified endogenous nitrated fatty acids as novel reversible STING inhibitors. With the aim of improving the specificity and efficacy of these compounds, we developed and tested a library of nitroalkene-based compounds for in vitro and in vivo STING inhibition. The structure-activity relationship study revealed a robustly improved electrophilicity and reduced degrees of freedom of nitroalkenes by conjugation with an aromatic moiety. The lead compounds CP-36 and CP-45, featuring a ß-nitrostyrene moiety, potently inhibited STING activity in vitro and relieved STING-dependent inflammation in vivo. This validates the potential for nitroalkene compounds as drug candidates for STING modulation to treat STING-driven inflammatory diseases, providing new robust leads for preclinical development.

Modeling microgel swelling: Influence of chain finite extensibility.

Microgels exhibit the ability to undergo reversible swelling in response to shifts in environmental factors that include variations in temperature, concentration, and pH. While several models have been put forward to elucidate specific aspects of microgel swelling and its impact on bulk behavior, a consistent theoretical description that chains throughout the microscopic degrees of freedom with suspension properties and deepens into the full implications of swelling remains a challenge yet to be met. In this work, we extend the mean-field swelling model of microgels from Denton and Tang [J. Chem. Phys. 145, 164901 (2016)] to include the finite extensibility of the polymer chains. The elastic contribution to swelling in the original work is formulated for Gaussian chains. By using the Langevin chain model, we modify this elastic contribution in order to account for finite extensibility effects, which become prominent for microgels containing highly charged polyelectrolytes and short polymer chains. We assess the performance of both elastic models, namely for Gaussian and Langevin chains, comparing against coarse-grained bead-spring simulations of ionic microgels with explicit electrostatic interactions. We examine the applicability scope of the models under a variation of parameters, such as ionization degree, microgel concentration, and salt concentration. The models are also tested against experimental results. This work broadens the applicability of the microgel swelling model toward a more realistic description, which brings advantages when describing the suspensions of nanogels and weak-polyelectrolyte micro-/nanogels.

Generating Minimal Training Sets for Machine Learned Potentials.

This Letter presents a novel approach for identifying uncorrelated atomic configurations from extensive datasets with a nonstandard neural network workflow known as random network distillation (RND) for training machine-learned interatomic potentials (MLPs). This method is coupled with a DFT workflow wherein initial data are generated with cheaper classical methods before only the minimal subset is passed to a more computationally expensive ab initio calculation. This benefits training not only by reducing the number of expensive DFT calculations required but also by providing a pathway to the use of more accurate quantum mechanical calculations. The method's efficacy is demonstrated by constructing machine-learned interatomic potentials for the molten salts KCl and NaCl. Our RND method allows accurate models to be fit on minimal datasets, as small as 32 configurations, reducing the required structures by at least 1 order of magnitude compared to alternative methods. This reduction in dataset sizes not only substantially reduces computational overhead for training data generation but also provides a more comprehensive starting point for active-learning procedures.

Octyl itaconate enhances VSVΔ51 oncolytic virotherapy by multitarget inhibition of antiviral and inflammatory pathways.

The presence of heterogeneity in responses to oncolytic virotherapy poses a barrier to clinical effectiveness, as resistance to this treatment can occur through the inhibition of viral spread within the tumor, potentially leading to treatment failures. Here we show that 4-octyl itaconate (4-OI), a chemical derivative of the Krebs cycle-derived metabolite itaconate, enhances oncolytic virotherapy with VSVΔ51 in various models including human and murine resistant cancer cell lines, three-dimensional (3D) patient-derived colon tumoroids and organotypic brain tumor slices. Furthermore, 4-OI in combination with VSVΔ51 improves therapeutic outcomes in a resistant murine colon tumor model. Mechanistically, we find that 4-OI suppresses antiviral immunity in cancer cells through the modification of cysteine residues in MAVS and IKKß independently of the NRF2/KEAP1 axis. We propose that the combination of a metabolite-derived drug with an oncolytic virus agent can greatly improve anticancer therapeutic outcomes by direct interference with the type I IFN and NF-κB-mediated antiviral responses.

Collaboration on Machine-Learned Potentials with IPSuite: A Modular Framework for Learning-on-the-Fly.

The field of machine learning potentials has experienced a rapid surge in progress, thanks to advances in machine learning theory, algorithms, and hardware capabilities. While the underlying methods are continuously evolving, the infrastructure for their deployment has lagged. The community, due to these rapid developments, frequently finds itself split into groups built around different implementations of machine-learned potentials. In this work, we introduce IPSuite, a Python-driven software package designed to connect different methods and algorithms from the comprehensive field of machine-learned potentials into a single platform while also providing a collaborative infrastructure, helping ensure reproducibility. Furthermore, the data management infrastructure of the IPSuite code enables simple model sharing and deployment in simulations. Currently, IPSuite supports six state-of-the-art machine learning approaches for the fitting of interatomic potentials as well as a variety of methods for the selection of training data, running of ab initio calculations, learning-on-the-fly strategies, model evaluation, and simulation deployment.

Amyloid-ß aggregates activate peripheral monocytes in mild cognitive impairment.

The peripheral immune system is important in neurodegenerative diseases, both in protecting and inflaming the brain, but the underlying mechanisms remain elusive. Alzheimer's Disease is commonly preceded by a prodromal period. Here, we report the presence of large Aß aggregates in plasma from patients with mild cognitive impairment (n = 38). The aggregates are associated with low level Alzheimer's Disease-like brain pathology as observed by 11C-PiB PET and 18F-FTP PET and lowered CD18-rich monocytes. We characterize complement receptor 4 as a strong binder of amyloids and show Aß aggregates are preferentially phagocytosed and stimulate lysosomal activity through this receptor in stem cell-derived microglia. KIM127 integrin activation in monocytes promotes size selective phagocytosis of Aß. Hydrodynamic calculations suggest Aß aggregates associate with vessel walls of the cortical capillaries. In turn, we hypothesize aggregates may provide an adhesion substrate for recruiting CD18-rich monocytes into the cortex. Our results support a role for complement receptor 4 in regulating amyloid homeostasis.

Disentanglement of Surface and Confinement Effects for Diene Metathesis in Mesoporous Confinement.

We study the effects of a planar interface and confinement on a generic catalytically activated ring-closing polymerization reaction near an unstructured catalyst. For this, we employ a coarse-grained polymer model using grand-canonical molecular dynamics simulations with a Monte Carlo reaction scheme. Inspired by recent experiments in the group of M. Buchmeiser that demonstrated an increase in ring-closing selectivity under confinement, we show that both the interface effects, i.e., placing the catalyst near a planar wall, and the confinement effects, i.e., locating the catalyst within a pore, lead to an increase of selectivity. We furthermore demonstrate that curvature effects for cylindrical mesopores (2 nm < d < 12.3 nm) influence the distribution of the chain ends, leading to a further increase in selectivity. This leads us to speculate that specially corrugated surfaces might also help to enhance catalytically activated polymerization processes.

Optimal motility strategies for self-propelled agents to explore porous media.

Microrobots for, e.g., biomedical applications, need to be equipped with motility strategies that enable them to navigate through complex environments. Inspired by biological microorganisms we re-create motility patterns such as run-and-reverse, run-and-tumble, or run-reverse-flick applied to active rodlike particles in silico. We investigate their capability to efficiently explore disordered porous environments with various porosities and mean pore sizes ranging down to the scale of the active particle. By calculating the effective diffusivity for the different patterns, we can predict the optimal one for each porous sample geometry. We find that providing the agent with very basic sensing and decision-making capabilities yields a motility pattern outperforming the biologically inspired patterns for all investigated porous samples.

Explaining Giant Apparent pK_{a} Shifts in Weak Polyelectrolyte Brushes.

Recent experiments on weak polyelectrolyte brushes found marked shifts in the effective pK_{a} that are linear in the logarithm of the salt concentration. Comparing explicit-particle simulations with mean-field calculations we show that for high grafting densities the salt concentration effect can be explained using the ideal Donnan theory, but for low grafting densities the full shift is due to a combination of the Donnan effect and the polyelectrolyte effect. The latter originates from electrostatic correlations that are neglected in the Donnan picture and that are only approximately included in the mean-field theory. Moreover, we demonstrate that the magnitude of the polyelectrolyte effect is almost invariant with respect to salt concentration but depends on the grafting density of the brush. This invariance is due to a complex cancellation of multiple effects. Based on our results, we show how the experimentally determined pK_{a} shifts may be used to infer the grafting density of brushes, a parameter that is difficult to measure directly.

Renormalized charge and dielectric effects in colloidal interactions: a numerical solution of the nonlinear Poisson-Boltzmann equation for unknown boundary conditions.

 The Derjaguin-Landau-Verwey-Overbeek (DLVO) theory, introduced more than 70 years ago, is a hallmark of colloidal particle modeling. For highly charged particles in the dilute regime, it is often supplemented by Alexander's prescription (Alexander et al. in J Chem Phys 80:5776, 1984) for using a renormalized charge. Here, we solve the problem of the interaction between two charged colloids at finite ionic strength, including dielectric mismatch effects, using an efficient numerical scheme to solve the nonlinear Poisson-Boltzmann (NPB) equation with unknown boundary conditions. Our results perfectly match the analytical predictions for the renormalized charge by Trizac and coworkers (Aubouy et al. in J Phys A 36:5835, 2003). Moreover, they allow us to reinterpret previous molecular dynamics (MD) simulation results by Kreer et al. (Phys Rev E 74:021401, 2006), rendering them now in agreement with the expected behavior. We furthermore find that the influence of polarization becomes important only when the Debye layers overlap significantly.

Solvent Effects on Structure and Screening in Confined Electrolytes.

Using classical density functional theory, we investigate the influence of solvent on the structure and ionic screening of electrolytes under slit confinement and in contact with a reservoir. We consider a symmetric electrolyte with implicit and explicit solvent models and find that spatially resolving solvent molecules is essential for the ion structure at confining walls, excess ion adsorption, and the pressure exerted on the walls. Despite this, we observe only moderate differences in the period of oscillations of the pressure with the slit width and virtually coinciding decay lengths as functions of the scaling variable σ_{ion}/λ_{D}, where σ_{ion} is the ion diameter and λ_{D} the Debye length. Moreover, in the electrostatic-dominated regime, this scaling behavior is practically independent of the relative permittivity and its dependence on the ion concentration. In contrast, the crossover to the hard-core-dominated regime depends sensitively on all three factors.

A novel model for biofilm initiation in porous media flow.

Bacteria often form biofilms in porous environments where an external flow is present, such as soil or filtration systems. To understand the initial stages of biofilm formation, one needs to study the interactions between cells, the fluid and the confining geometries. Here, we present an agent based numerical model for bacteria that takes into account the planktonic stage of motile cells as well as surface attachment and biofilm growth in a lattice Boltzmann fluid. In the planktonic stage we show the importance of the interplay between complex flow and cell motility when determining positions of surface attachment. In the growth stage we show the applicability of our model by investigating how external flow and biofilm stiffness determine qualitative colony morphologies as well as quantitative measurements of, e.g., permeability.

A screening of results on the decay length in concentrated electrolytes.

Screening of electrostatic interactions in room-temperature ionic liquids and concentrated electrolytes has recently attracted much attention as surface force balance experiments have suggested the emergence of unanticipated anomalously large screening lengths at high ion concentrations. Termed underscreening, this effect was ascribed to the bulk properties of concentrated ionic systems. However, underscreening under experimentally relevant conditions is not predicted by classical theories and challenges our understanding of electrostatic correlations. Despite the enormous effort in performing large-scale simulations and new theoretical investigations, the origin of the anomalously long-range screening length remains elusive. This contribution briefly summarises the experimental, analytical and simulation results on ionic screening and the scaling behaviour of screening lengths. We then present an atomistic simulation approach that accounts for the solvent and ion exchange with a reservoir. We find that classical density functional theory (DFT) for concentrated electrolytes under confinement reproduces ion adsorption at charged interfaces surprisingly well. With DFT, we study confined electrolytes using implicit and explicit solvent models and the dependence on the solvent's dielectric properties. Our results demonstrate how the absence vs. presence of solvent particles and their discrete nature affect the short and long-range screening in concentrated ionic systems.

Dual intron-targeted CRISPR-Cas9-mediated disruption of the AML RUNX1-RUNX1T1 fusion gene effectively inhibits proliferation and decreases tumor volume in vitro and in vivo.

Oncogenic fusion drivers are common in hematological cancers and are thus relevant targets of future CRISPR-Cas9-based treatment strategies. However, breakpoint-location variation in patients pose a challenge to traditional breakpoint-targeting CRISPR-Cas9-mediated disruption strategies. Here we present a new dual intron-targeting CRISPR-Cas9 treatment strategy, for targeting t(8;21) found in 5-10% of de novo acute myeloid leukemia (AML), which efficiently disrupts fusion genes without prior identification of breakpoint location. We show in vitro growth rate and proliferation reduction by 69 and 94% in AML t(8;21) Kasumi-1 cells, following dual intron-targeted disruption of RUNX1-RUNX1T1 compared to a non t(8;21) AML control. Furthermore, mice injected with RUNX1-RUNX1T1-disrupted Kasumi-1 cells had in vivo tumor growth reduction by 69 and 91% compared to controls. Demonstrating the feasibility of RUNX1-RUNX1T1 disruption, these findings were substantiated in isolated primary cells from a patient diagnosed with AML t(8;21). In conclusion, we demonstrate proof-of-principle of a dual intron-targeting CRISPR-Cas9 treatment strategy in AML t(8;21) without need for precise knowledge of the breakpoint location.

A generalized grand-reaction method for modeling the exchange of weak (polyprotic) acids between a solution and a weak polyelectrolyte phase.

We introduce a Monte-Carlo method that allows for the simulation of a polymeric phase containing a weak polyelectrolyte, which is coupled to a reservoir at a fixed pH, salt concentration, and total concentration of a weak polyprotic acid. The method generalizes the established grand-reaction method by Landsgesell et al. [Macromolecules 53, 3007-3020 (2020)] and, thus, allows for the simulation of polyelectrolyte systems coupled to reservoirs with a more complex chemical composition. In order to set the required input parameters that correspond to a desired reservoir composition, we propose a generalization of the recently published chemical potential tuning algorithm of Miles et al. [Phys. Rev. E 105, 045311 (2022)]. To test the proposed tuning procedure, we perform extensive numerical tests for both ideal and interacting systems. Finally, as a showcase, we apply the method to a simple test system that consists of a weak polybase solution that is coupled to a reservoir containing a small diprotic acid. The complex interplay of the ionization of various species, the electrostatic interactions, and the partitioning of small ions leads to a non-monotonous, stepwise swelling behavior of the weak polybase chains.

Assessing the Validity of NMR Relaxation Rates Obtained from Coarse-Grained Simulations of PEG-Water Mixtures.

NMR relaxometry is a powerful and well-established experimental approach for characterizing dynamic processes in soft matter systems. All-atom (AA) resolved simulations are typically employed to gain further microscopic insights while reproducing the relaxation rates R1. However, such approaches are limited to time and length scales that prevent to model systems such as long polymer chains or hydrogels. Coarse graining (CG) can overcome this barrier at the cost of losing atomistic details that impede the calculation of NMR relaxation rates. Here, we address this issue by performing a systematic characterization of dipolar relaxation rates R1 on a PEG-H2O mixture at two different levels of details: AA and CG. Remarkably, we show that NMR relaxation rates R1 obtained at the CG level obey the same trends when compared to AA calculations but with a systematic offset. This offset is due to, on the one hand, the lack of an intramonomer component and, on the other hand, the inexact positioning of the spin carriers. We show that the offset can be corrected for quantitatively by reconstructing a posteriori the atomistic details for the CG trajectories.