Cholesterol absorption blocker ezetimibe prevents muscle wasting in severe dysferlin-deficient and mdx mice.

Background: Muscular dystrophy (MD) causes muscle wasting and is often lethal in patients due to a lack of proven therapies. In contrast, mouse models of MD are notoriously mild. We have previously shown severe human-like muscle pathology in mdx [Duchenne MD (DMD)] and dysferlin-deficient limb-girdle MD type 2B (LGMD2B) mice by inactivating the gene encoding for apolipoprotein E (ApoE), a lipid transporter synthesized by the liver, brain and adipocytes to regulate lipid and fat metabolism.

Angiotensin II receptor blocker losartan exacerbates muscle damage and exhibits weak blood pressure-lowering activity in a dysferlin-null model of Limb-Girdle muscular dystrophy type 2B.

There is no cure or beneficial management option for Limb-Girdle muscular dystrophy (MD) type 2B (LGMD2B). Losartan, a blood pressure (BP) lowering angiotensin II (AngII) receptor type 1 (ATR1) blocker (ARB) with unique anti-transforming growth factor-β (TGF-β) properties, can protect muscles in various types of MD such as Duchenne MD, suggesting a potential benefit for LGMD2B patients. Herein, we show in a mild, dysferlin-null mouse model of LGMD2B that losartan increased quadriceps muscle fibrosis (142%; P<0.

A Systems Toxicology Approach for the Prediction of Kidney Toxicity and Its Mechanisms In Vitro.

The failure to predict kidney toxicity of new chemical entities early in the development process before they reach humans remains a critical issue. Here, we used primary human kidney cells and applied a systems biology approach that combines multidimensional datasets and machine learning to identify biomarkers that not only predict nephrotoxic compounds but also provide hints toward their mechanism of toxicity. Gene expression and high-content imaging-derived phenotypical data from 46 diverse kidney toxicants were analyzed using Random Forest machine learning.

MicroRNA-155 deficient mice experience heightened kidney toxicity when dosed with cisplatin.

The development of nephrotoxicity limits the maximum achievable dosage and treatment intervals for cisplatin chemotherapy. Therefore, identifying mechanisms that regulate this toxicity could offer novel methods to optimize cisplatin delivery. MicroRNAs are capable of regulating many different genes, and can influence diverse cellular processes, including cell death and apoptosis.

Properties of cell death models calibrated and compared using Bayesian approaches.

Using models to simulate and analyze biological networks requires principled approaches to parameter estimation and model discrimination. We use Bayesian and Monte Carlo methods to recover the full probability distributions of free parameters (initial protein concentrations and rate constants) for mass-action models of receptor-mediated cell death. The width of the individual parameter distributions is largely determined by non-identifiability but covariation among parameters, even those that are poorly determined, encodes essential information.

Exploring the contextual sensitivity of factors that determine cell-to-cell variability in receptor-mediated apoptosis.

Stochastic fluctuations in gene expression give rise to cell-to-cell variability in protein levels which can potentially cause variability in cellular phenotype. For TRAIL (TNF-related apoptosis-inducing ligand) variability manifests itself as dramatic differences in the time between ligand exposure and the sudden activation of the effector caspases that kill cells. However, the contribution of individual proteins to phenotypic variability has not been explored in detail.

Classic and contemporary approaches to modeling biochemical reactions.

Recent interest in modeling biochemical networks raises questions about the relationship between often complex mathematical models and familiar arithmetic concepts from classical enzymology, and also about connections between modeling and experimental data. This review addresses both topics by familiarizing readers with key concepts (and terminology) in the construction, validation, and application of deterministic biochemical models, with particular emphasis on a simple enzyme-catalyzed reaction. Networks of coupled ordinary differential equations (ODEs) are the natural language for describing enzyme kinetics in a mass action approximation.

Input-output behavior of ErbB signaling pathways as revealed by a mass action model trained against dynamic data.

The ErbB signaling pathways, which regulate diverse physiological responses such as cell survival, proliferation and motility, have been subjected to extensive molecular analysis. Nonetheless, it remains poorly understood how different ligands induce different responses and how this is affected by oncogenic mutations. To quantify signal flow through ErbB-activated pathways we have constructed, trained and analyzed a mass action model of immediate-early signaling involving ErbB1-4 receptors (EGFR, HER2/Neu2, ErbB3 and ErbB4), and the MAPK and PI3K/Akt cascades.

Energetics of protein-DNA interactions.

Protein-DNA interactions are vital for many processes in living cells, especially transcriptional regulation and DNA modification. To further our understanding of these important processes on the microscopic level, it is necessary that theoretical models describe the macromolecular interaction energetics accurately. While several methods have been proposed, there has not been a careful comparison of how well the different methods are able to predict biologically important quantities such as the correct DNA binding sequence, total binding free energy and free energy changes caused by DNA mutation.

All-atom ab initio folding of a diverse set of proteins.

Natural proteins fold to a unique, thermodynamically dominant state. Modeling of the folding process and prediction of the native fold of proteins are two major unsolved problems in biophysics. Here, we show successful all-atom ab initio folding of a representative diverse set of proteins by using a minimalist transferable-energy model that consists of two-body atom-atom interactions, hydrogen bonding, and a local sequence-energy term that models sequence-specific chain stiffness.

A knowledge-based move set for protein folding.

The free energy landscape of protein folding is rugged, occasionally characterized by compact, intermediate states of low free energy. In computational folding, this landscape leads to trapped, compact states with incorrect secondary structure. We devised a residue-specific, protein backbone move set for efficient sampling of protein-like conformations in computational folding simulations.

Entropic stabilization of proteins and its proteomic consequences.

Evolutionary traces of thermophilic adaptation are manifest, on the whole-genome level, in compositional biases toward certain types of amino acids. However, it is sometimes difficult to discern their causes without a clear understanding of underlying physical mechanisms of thermal stabilization of proteins. For example, it is well-known that hyperthermophiles feature a greater proportion of charged residues, but, surprisingly, the excess of positively charged residues is almost entirely due to lysines but not arginines in the majority of hyperthermophilic genomes.

Lessons from the design of a novel atomic potential for protein folding.

We investigate all-atom potentials of mean force for estimating free energies in protein folding and fold recognition. We search through the space potentials and design novel atomic potentials with a random mixing approximation and a contact-correlated Gaussian approximation of decoy states. We show that the two derived potentials are highly correlated, supporting the use of the random energy model as an accurate statistical description of protein conformational states.

Recognition of nucleic acid bases and base-pairs by hydrogen bonding to amino acid side-chains.

Sequence-specific protein-nucleic acid recognition is determined, in part, by hydrogen bonding interactions between amino acid side-chains and nucleotide bases. To examine the repertoire of possible interactions, we have calculated geometrically plausible arrangements in which amino acids hydrogen bond to unpaired bases, such as those found in RNA bulges and loops, or to the 53 possible RNA base-pairs. We find 32 possible interactions that involve two or more hydrogen bonds to the six unpaired bases (including protonated A and C), 17 of which have been observed.