Posttranscriptional Regulation of the Human LDL Receptor by the U2-Spliceosome.

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. Top hit genes were validated by in vitro experiments as well as analyses of data sets on gene expression and variants in human populations. RESULTS: The knockdown of 54 genes significantly inhibited LDL uptake. Fifteen of them encode for components or interactors of the U2-spliceosome. Knocking down any one of 11 out of 15 genes resulted in the selective retention of intron 3 of LDLR. The translated LDLR fragment lacks 88% of the full length LDLR and is detectable neither in nontransfected cells nor in human plasma. The hepatic expression of the intron 3 retention transcript is increased in nonalcoholic fatty liver disease as well as after bariatric surgery. Its expression in blood cells correlates with LDL-cholesterol and age. Single nucleotide polymorphisms and 3 rare variants of one spliceosome gene, RBM25, are associated with LDL-cholesterol in the population and familial hypercholesterolemia, respectively. Compared with overexpression of wild-type RBM25, overexpression of the 3 rare RBM25 mutants in Huh-7 cells led to lower LDL uptake. CONCLUSIONS: We identified a novel mechanism of posttranscriptional regulation of LDLR activity in humans and associations of genetic variants of RBM25 with LDL-cholesterol levels.

Single-cell mRNA profiling reveals the hierarchical response of miRNA targets to miRNA induction.

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. Combining mathematical modeling with RNA sequencing of individual human embryonic kidney cells in which the expression of two distinct miRNAs was induced over a wide range, we have inferred parameters describing the response of hundreds of miRNA targets to miRNA induction. Individual targets have widely different response dynamics, and only a small proportion of predicted targets exhibit high sensitivity to miRNA induction. Our data reveal for the first time the response parameters of the entire network of endogenous miRNA targets to miRNA induction, demonstrating that miRNAs correlate target expression and at the same time increase the variability in expression of individual targets across cells. The approach is generalizable to other miRNAs and post-transcriptional regulators to improve the understanding of gene expression dynamics in individual cell types.

Quantifying the strength of miRNA-target interactions.

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. In particular, we use chimeric miRNA-mRNA sequences to infer a MIRZA-CHIMERA model and we provide a framework for inferring a similar model from measurements of rate constants of miRNA-mRNA interaction in the context of Argonaute proteins. Finally, based on a simple model of miRNA-based regulation, we discuss the importance of interaction energy and its variability between targets for the modulation of miRNA target expression in vivo.

Communication: Microsecond peptide dynamics from nanosecond trajectories: a Langevin approach.

Based on a given time series, the data-driven Langevin equation (dLE) estimates the drift and the diffusion field of the dynamics, which are then employed to reproduce the essential statistical and dynamical features of the original time series. Because the propagation of the dLE requires only local information, the input data are neither required to be Boltzmann weighted nor to be a continuous trajectory. Similar to a Markov state model, the dLE approach therefore holds the promise of predicting the long-time dynamics of a biomolecular system from relatively short trajectories which can be run in parallel. The practical applicability of the approach is shown to be mainly limited by the initial sampling of the system's conformational space obtained from the short trajectories. Adopting extensive molecular dynamics simulations of the unfolding and refolding of a short peptide helix, it is shown that the dLE approach is able to describe microsecond conformational dynamics from a few hundred nanosecond trajectories. In particular, the dLE quantitatively reproduces the free energy landscape and the associated conformational dynamics along the chosen five-dimensional reaction coordinate.

Lipid packing drives the segregation of transmembrane helices into disordered lipid domains in model membranes.

Cell membranes are comprised of multicomponent lipid and protein mixtures that exhibit a complex partitioning behavior. Regions of structural and compositional heterogeneity play a major role in the sorting and self-assembly of proteins, and their clustering into higher-order oligomers. Here, we use computer simulations and optical microscopy to study the sorting of transmembrane helices into the liquid-disordered domains of phase-separated model membranes, irrespective of peptide-lipid hydrophobic mismatch. Free energy calculations show that the enthalpic contribution due to the packing of the lipids drives the lateral sorting of the helices. Hydrophobic mismatch regulates the clustering into either small dynamic or large static aggregates. These results reveal important molecular driving forces for the lateral organization and self-assembly of transmembrane helices in heterogeneous model membranes, with implications for the formation of functional protein complexes in real cells.

Iron-carbohydrate complexes treating iron anaemia: Understanding the nano-structure and interactions with proteins through orthogonal characterisation.

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. We therefore investigated the core-ligand structure of two clinically relevant iron-carbohydrate complexes, iron sucrose (IS) and ferric carboxymaltose (FCM), and their interactions with two structurally different human plasma proteins, human serum albumin (HSA) and fibrinogen, using a combination of cryo-scanning transmission electron microscopy (cryo-STEM), x-ray diffraction (XRD), small-angle x-ray scattering (SAXS) and small-angle neutron scattering (SANS). Using this orthogonal approach, we defined the nano-structure, individual building blocks and surface morphology for IS and FCM. Importantly, we revealed significant differences in the surface morphology of the iron-carbohydrate complexes. FCM shows a localised carbohydrate shell around its core, in contrast to IS, which is characterised by a diffuse and dynamic layer of carbohydrate ligand surrounding its core. We hypothesised that such differences in carbohydrate morphology determine the interaction between iron-carbohydrate complexes and proteins and therefore investigated the NPs in the presence of HSA and fibrinogen. Intriguingly, IS showed significant interaction with HSA and fibrinogen, forming NP-protein clusters, while FCM only showed significant interaction with fibrinogen. We postulate that these differences could influence bio-response of the two formulations and their clinical outcome. In conclusion, our study provides orthogonal characterisation of two clinically relevant iron-carbohydrate complexes and first hints at their interaction behaviour with proteins in the human bloodstream, setting a prerequisite towards complete understanding of the correlation between physicochemical properties and clinical outcome.

Data driven Langevin modeling of biomolecular dynamics.

Based on a given time series, the data-driven Langevin equation proposed by Hegger and Stock [J. Chem. Phys. 130, 034106 (2009)] aims to construct a low-dimensional dynamical model of the system. Adopting various simple model problems of biomolecular dynamics, this work presents a systematic study of the theoretical virtues and limitations as well as of the practical applicability and performance of the method. As the method requires only local information, the input data need not to be Boltzmann weighted in order to warrant that the Langevin model yields correct Boltzmann-distributed results. Moreover, a delay embedding of the state vector allows for the treatment of memory effects. The robustness of the modeling with respect to wrongly chosen model parameters or low sampling is discussed, as well as the treatment of inertial effects. Given sufficiently sampled input data, the Langevin modeling is shown to successfully recover the correct statistics (such as the probability distribution) and the dynamics (such as the position autocorrelation function) of all considered problems.

Hybrid simulations: combining atomistic and coarse-grained force fields using virtual sites.

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.e. between CG and atomistic molecules, are treated the same way as the pure CG-CG interactions. To test our method, we combine the Gromos atomistic force field with a number of coarse-grained potentials, obtained through several approaches that are designed to obtain CG potentials based on an existing atomistic model, namely iterative Boltzmann inversion, force matching, and a potential of mean force subtraction procedure (SB). We also explore the use of the MARTINI force field for the CG potential. A simple system, consisting of atomistic butane molecules dissolved in CG butane, is used to study the performance of our hybrid scheme. Based on the potentials of mean force for atomistic butane in CG solvent, and the properties of 1:1 mixtures of atomistic and CG butane which should exhibit ideal mixing behavior, we conclude that the MARTINI and SB potentials are particularly suited to be combined with the atomistic force field. The MARTINI potential is subsequently used to perform hybrid simulations of atomistic dialanine peptides in both CG butane and water. Compared to a fully atomistic description of the system, the hybrid description gives similar results provided that the dielectric screening of water is accounted for. Within the field of biomolecules, our method appears ideally suited to study e.g. protein-ligand binding, where the active site and ligand are modeled in atomistic detail and the rest of the protein, together with the solvent, is coarse-grained.

Reconstruction of atomistic details from coarse-grained structures.

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. We reconstructed individual molecules in vacuo (NCQ tripeptide, dipalmitoylphosphatidylcholine, and cholesterol), bulk water, and a WALP transmembrane peptide embedded in a solvated lipid bilayer. The first examples serve to optimize the parameters for the reconstruction procedure, whereas the latter examples illustrate the applicability to condensed-phase biomolecular systems.

High-Resolution Cryoelectron Microscopy Structure of the Cyclic Nucleotide-Modulated Potassium Channel MloK1 in a Lipid Bilayer.

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 ∼4.5 Å isotropic 3D resolution. The structure provides a clear picture of the arrangement of the cyclic nucleotide-binding domains with respect to both the pore and the putative voltage sensor domains when cAMP is bound, and reveals a potential gating mechanism in the context of the lipid-embedded channel.

Membrane poration by antimicrobial peptides combining atomistic and coarse-grained descriptions.

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. Here, we study the long-time relaxation properties of these pores by evolving the systems using a coarse-grain (CG) description. The disordered nature and the topology of the atomistic pores are maintained at the CG level. The peptides sample different orientations but at any given time, only a few peptides insert into the pore. Key states observed at the CG level are subsequently back-transformed to the atomistic level using a resolution-transformation protocol. The configurations sampled at the CG level are stable in the atomistic simulation. The effect of helicity on pore stability is investigated at the CG level and we find that partial helicity is required to form stable pores. We also show that the current CG scheme can be used to study spontaneous poration by magainin-H2 peptides. Overall, our simulations provide a multi-scale view of a fundamental biophysical membrane process involving a complex interplay between peptides and lipids.

Martini Coarse-Grained Force Field: Extension to Carbohydrates.

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. Bonded parameters for these saccharides are optimized by comparison to conformations sampled with an atomistic force field, in particular with respect to the representation of the most populated rotameric state for the glycosidic bond. Application of the new coarse-grained carbohydrate model to the oligosaccharides amylose and Curdlan shows a preservation of the main structural properties with 3 orders of magnitude more efficient sampling than the atomistic counterpart. Finally, we investigate the cryo- and anhydro-protective effect of glucose and trehalose on a lipid bilayer and find a strong decrease of the melting temperature, in good agreement with both experimental findings and atomistic simulation studies.