Publications by authors named "Andrzej Rzepiela"

Article Synopsis
  • Iron deficiency and anemia are major global health issues, and intravenous iron carbohydrate nanoparticles are vital for effective treatment.
  • Our study used advanced cryogenic Scanning Transmission Electron Microscopy (cryo-STEM) to analyze the physical structure of these nanoparticles, revealing they typically have iron cores about 2 nm in size and distinct cluster-like shapes in various products.
  • By employing this sophisticated imaging technique, we not only preserved the specimens' structural integrity but also contributed insights that could enhance understanding of how these nanoparticles function, including the development of a machine learning tool for better image analysis.
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Cryogenic electron microscopy (cryo-EM) is a pivotal technique for imaging macromolecular structures. However, despite extensive processing of large image sets collected in cryo-EM experiments to amplify the signal-to-noise ratio, the reconstructed 3D protein-density maps are often limited in quality due to residual noise, which in turn affects the accuracy of the macromolecular representation. Here, crefDenoiser is introduced, a denoising neural network model designed to enhance the signal in 3D cryo-EM maps produced with standard processing pipelines.

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Intravenous (IV) iron-carbohydrate complexes are widely used nanoparticles (NPs) to treat iron deficiency anaemia, often associated with medical conditions such as chronic kidney disease, heart failure and various inflammatory conditions. Even though a plethora of physicochemical characterisation data and clinical studies are available for these products, evidence-based correlation between physicochemical properties of iron-carbohydrate complexes and clinical outcome has not fully been elucidated yet. Studies on other metal oxide NPs suggest that early interactions between NPs and blood upon IV injection are key to understanding how differences in physicochemical characteristics of iron-carbohydrate complexes cause variance in clinical outcomes.

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Background: The LDLR (low-density lipoprotein receptor) in the liver is the major determinant of LDL-cholesterol levels in human plasma. The discovery of genes that regulate the activity of LDLR helps to identify pathomechanisms of hypercholesterolemia and novel therapeutic targets against atherosclerotic cardiovascular disease.

Methods: We performed a genome-wide RNA interference screen for genes limiting the uptake of fluorescent LDL into Huh-7 hepatocarcinoma cells.

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Loss of the tumor suppressor tuberous sclerosis complex 1 () in the liver promotes gluconeogenesis and glucose intolerance. We asked whether this could be attributed to aberrant expression of small RNAs. We performed small-RNA sequencing on liver of -knockout mice, and found that miRNAs of the delta-like homolog 1 ()-deiodinase iodothyronine type III () locus are up-regulated in an mTORC1-dependent manner.

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Intercellular distribution of nutrients and coordination of responses to internal and external cues via endogenous signaling molecules are hallmarks of multicellular organisms. Vegetative mycelia of multicellular fungi are syncytial networks of interconnected hyphae resulting from hyphal tip growth, branching, and fusion. Such mycelia can reach considerable dimensions and, thus, different parts can be exposed to quite different environmental conditions.

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miRNAs are small RNAs that regulate gene expression post-transcriptionally. By repressing the translation and promoting the degradation of target mRNAs, miRNAs may reduce the cell-to-cell variability in protein expression, induce correlations between target expression levels, and provide a layer through which targets can influence each other's expression as "competing RNAs" (ceRNAs). However, experimental evidence for these behaviors is limited.

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Eukaryotic cyclic nucleotide-modulated channels perform their diverse physiological roles by opening and closing their pores to ions in response to cyclic nucleotide binding. We here present a structural model for the cyclic nucleotide-modulated potassium channel homolog from Mesorhizobium loti, MloK1, determined from 2D crystals in the presence of lipids. Even though crystals diffract electrons to only ∼10 Å, using cryoelectron microscopy (cryo-EM) and recently developed computational methods, we have determined a 3D map of full-length MloK1 in the presence of cyclic AMP (cAMP) at ∼4.

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Objective: Low- and high-density lipoproteins (LDL and HDL) must pass the endothelial layer to exert pro- and antiatherogenic activities, respectively, within the vascular wall. However, the rate-limiting factors that mediate transendothelial transport of lipoproteins are yet little known. Therefore, we performed a high-throughput screen with kinase drug inhibitors to identify modulators of transendothelial LDL and HDL transport.

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We quantify the strength of miRNA-target interactions with MIRZA, a recently introduced biophysical model. We show that computationally predicted energies of interaction correlate strongly with the energies of interaction estimated from biochemical measurements of Michaelis-Menten constants. We further show that the accuracy of the MIRZA model can be improved taking into account recently emerged experimental data types.

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Article Synopsis
  • The data-driven Langevin equation (dLE) analyzes time series data to estimate the drift and diffusion necessary to mimic the dynamics of the original data, allowing for predictions about biomolecular system behavior from shorter simulations.
  • Unlike traditional methods, dLE relies on local information and does not require continuous or Boltzmann-weighted data, making it more flexible in its applications.
  • The method effectively reconstructs the free energy landscape and conformational changes of a peptide helix during unfolding and refolding from only a few hundred nanoseconds of simulation data, demonstrating its efficiency in capturing long-term dynamics.
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Article Synopsis
  • The data-driven Langevin equation developed by Hegger and Stock aims to create a simplified model for biomolecular dynamics based on time series data.
  • The method can work with local information and does not require the input data to be Boltzmann weighted, making it flexible in various situations.
  • When given adequate sampled data, the Langevin model effectively captures the correct statistical properties and dynamic behavior of the system being studied.
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Hybrid simulations, in which part of the system is represented at atomic resolution and the remaining part at a reduced, coarse-grained, level offer a powerful way to combine the accuracy associated with the atomistic force fields to the sampling speed obtained with coarse-grained (CG) potentials. In this work we introduce a straightforward scheme to perform hybrid simulations, making use of virtual sites to couple the two levels of resolution. With the help of these virtual sites interactions between molecules at different levels of resolution, i.

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Article Synopsis
  • Cell membranes are made up of a mix of lipids and proteins that have a complex organization and behavior.
  • The study uses computer simulations and optical microscopy to look at how transmembrane helices sort themselves into specific regions of model membranes, regardless of how well they match with the lipids.
  • Findings indicate that lipid packing influences the sorting of these helices, while hydrophobic mismatches affect how they cluster, impacting the formation of functional protein complexes in actual cells.
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Antimicrobial peptides (AMPs) comprise a large family of peptides that include small cationic peptides, such as magainins, which permeabilize lipid membranes. Previous atomistic level simulations of magainin-H2 peptides show that they act by forming toroidal transmembrane pores. However, due to the atomistic level of description, these simulations were necessarily limited to small system sizes and sub-microsecond time scales.

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We present an algorithm to reconstruct atomistic structures from their corresponding coarse-grained (CG) representations and its implementation into the freely available molecular dynamics (MD) program package GROMACS. The central part of the algorithm is a simulated annealing MD simulation in which the CG and atomistic structures are coupled via restraints. A number of examples demonstrate the application of the reconstruction procedure to obtain low-energy atomistic structural ensembles from their CG counterparts.

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We present an extension of the Martini coarse-grained force field to carbohydrates. The parametrization follows the same philosophy as was used previously for lipids and proteins, focusing on the reproduction of partitioning free energies of small compounds between polar and nonpolar phases. The carbohydrate building blocks considered are the monosaccharides glucose and fructose and the disaccharides sucrose, trehalose, maltose, cellobiose, nigerose, laminarabiose, kojibiose, and sophorose.

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