Soluble Beta-Amyloid Precursor Protein Is Related to Disease Progression in Amyotrophic Lateral Sclerosis

Background: Biomarkers of disease progression in amyotrophic lateral sclerosis (ALS) could support the identification of beneficial drugs in clinical trials. We aimed to test whether soluble fragments of beta-amyloid precursor protein (sAPPa and sAPPß) correlated with clinical subtypes of ALS and were of prognostic value. Methodology/Principal Findings: In a cross-sectional study including patients with ALS (N = 68) with clinical follow-up data over 6 months, Parkinson’s disease (PD, N = 20), and age-matched controls (N = 40), cerebrospinal fluid (CSF) levels of sAPPa a, sAPPß and neurofilaments (NfH SMI35) were measured by multiplex assay, Progranulin by ELISA. CSF sAPPa and sAPPß levels were lower in ALS with a rapidly-progressive disease course (p = 0.03, and p = 0.02) and with longer disease duration (p = 0.01 and p = 0.01, respectively). CSF NfH SMI35 was elevated in ALS compared to PD and controls, with highest concentrations found in patients with rapid disease progression (p,0.01). High CSF NfH SMI3 was linked to low CSF sAPPa and sAPPß (p = 0.001, and p = 0.007, respectively). The ratios CSF NfH SMI35 /CSF sAPPa,-ß were elevated in patients with fast progression of disease (p = 0.002 each). CSF Progranulin decreased with ongoing disease (p = 0.04). Conclusions: This study provides new CSF candidate markers associated with progression of disease in ALS. The data suggest that a deficiency of cellular neuroprotective mechanisms (decrease of sAPP) is linked to progressive neuro-axona

The CDK inhibitor CR8 acts as a molecular glue degrader that depletes cyclin K

Molecular glue compounds induce protein-protein interactions that, in the context of a ubiquitin ligase, lead to protein degradation1. Unlike traditional enzyme inhibitors, these molecular glue degraders act substoichiometrically to catalyse the rapid depletion of previously inaccessible targets2. They are clinically effective and highly sought-after, but have thus far only been discovered serendipitously. Here, through systematically mining databases for correlations between the cytotoxicity of 4,518 clinical and preclinical small molecules and the expression levels of E3 ligase components across hundreds of human cancer cell lines3-5, we identify CR8-a cyclin-dependent kinase (CDK) inhibitor6-as a compound that acts as a molecular glue degrader. The CDK-bound form of CR8 has a solvent-exposed pyridyl moiety that induces the formation of a complex between CDK12-cyclin K and the CUL4 adaptor protein DDB1, bypassing the requirement for a substrate receptor and presenting cyclin K for ubiquitination and degradation. Our studies demonstrate that chemical alteration of surface-exposed moieties can confer gain-of-function glue properties to an inhibitor, and we propose this as a broader strategy through which target-binding molecules could be converted into molecular glues

Plasma Neurofilament Heavy Chain Levels Correlate to Markers of Late Stage Disease Progression and Treatment Response in SOD1(G93A) Mice that Model ALS

Background: Amyotrophic lateral sclerosis (ALS) is an incurable neurodegenerative disorder characterised by progressive degeneration of motor neurons leading to death, typically within 3–5 years of symptom onset. The diagnosis of ALS is largely reliant on clinical assessment and electrophysiological findings. Neither specific investigative tools nor reliable biomarkers are currently available to enable an early diagnosis or monitoring of disease progression, hindering the design of treatment trials. Methodology/Principal Findings: In this study, using the well-established SOD1G93A mouse model of ALS and a new in-house ELISA method, we have validated that plasma neurofilament heavy chain protein (NfH) levels correlate with both functional markers of late stage disease progression and treatment response. We detected a significant increase in plasma levels of phosphorylated NfH during disease progression in SOD1G93A mice from 105 days onwards. Moreover, increased plasma NfH levels correlated with the decline in muscle force, motor unit survival and, more significantly, with the loss of spinal motor neurons in SOD1 mice during this critical period of decline. Importantly, mice treated with the disease modifying compound arimoclomol had lower plasma NfH levels, suggesting plasma NfH levels could be validated as an outcome measure for treatment trials. Conclusions/Significance: These results show that plasma NfH levels closely reflect later stages of disease progression and therapeutic response in the SOD1G93A mouse model of ALS and may potentially be a valuable biomarker of later disease progression in ALS

A Method for Efficient Calculation of Diffusion and Reactions of Lipophilic Compounds in Complex Cell Geometry

A general description of effects of toxic compounds in mammalian cells is facing several problems. Firstly, most toxic compounds are hydrophobic and partition phenomena strongly influence their behaviour. Secondly, cells display considerable heterogeneity regarding the presence, activity and distribution of enzymes participating in the metabolism of foreign compounds i.e. bioactivation/biotransformation. Thirdly, cellular architecture varies greatly. Taken together, complexity at several levels has to be addressed to arrive at efficient in silico modelling based on physicochemical properties, metabolic preferences and cell characteristics. In order to understand the cellular behaviour of toxic foreign compounds we have developed a mathematical model that addresses these issues. In order to make the system numerically treatable, methods motivated by homogenization techniques have been applied. These tools reduce the complexity of mathematical models of cell dynamics considerably thus allowing to solve efficiently the partial differential equations in the model numerically on a personal computer. Compared to a compartment model with well-stirred compartments, our model affords a more realistic representation. Numerical results concerning metabolism and chemical solvolysis of a polycyclic aromatic hydrocarbon carcinogen show good agreement with results from measurements in V79 cell culture. The model can easily be extended and refined to include more reactants, and/or more complex reaction chains, enzyme distribution etc, and is therefore suitable for modelling cellular metabolism involving membrane partitioning also at higher levels of complexity

Trehalose-6-phosphate-mediated toxicity determines essentiality of OtsB2 in Mycobacterium tuberculosis in vitro and in mice

Trehalose biosynthesis is considered an attractive target for the development of antimicrobials against fungal, helminthic and bacterial pathogens including Mycobacterium tuberculosis. The most common biosynthetic route involves trehalose-6-phosphate (T6P) synthase OtsA and T6P phosphatase OtsB that generate trehalose from ADP/UDP-glucose and glucose-6-phosphate. In order to assess the drug target potential of T6P phosphatase, we generated a conditional mutant of M. tuberculosis allowing the regulated gene silencing of the T6P phosphatase gene otsB2. We found that otsB2 is essential for growth of M. tuberculosis in vitro as well as for the acute infection phase in mice following aerosol infection. By contrast, otsB2 is not essential for the chronic infection phase in mice, highlighting the substantial remodelling of trehalose metabolism during infection by M. tuberculosis. Blocking OtsB2 resulted in the accumulation of its substrate T6P, which appears to be toxic, leading to the self-poisoning of cells. Accordingly, blocking T6P production in a ΔotsA mutant abrogated otsB2 essentiality. T6P accumulation elicited a global upregulation of more than 800 genes, which might result from an increase in RNA stability implied by the enhanced neutralization of toxins exhibiting ribonuclease activity. Surprisingly, overlap with the stress response caused by the accumulation of another toxic sugar phosphate molecule, maltose-1-phosphate, was minimal. A genome-wide screen for synthetic lethal interactions with otsA identified numerous genes, revealing additional potential drug targets synergistic with OtsB2 suitable for combination therapies that would minimize the emergence of resistance to OtsB2 inhibitors

Systems of Differential Algebraic Equations in Computational Electromagnetics

Starting from space-discretisation of Maxwell's equations, various classical formulations are proposed for the simulation of electromagnetic fields. They differ in the phenomena considered as well as in the variables chosen for discretisation. This contribution presents a literature survey of the most common approximations and formulations with a focus on their structural properties. The differential-algebraic character is discussed and quantified by the differential index concept

Control of neural systems at multiple scales using model-free, deep reinforcement learning.

Recent improvements in hardware and data collection have lowered the barrier to practical neural control. Most of the current contributions to the field have focus on model-based control, however, models of neural systems are quite complex and difficult to design. To circumvent these issues, we adapt a model-free method from the reinforcement learning literature, Deep Deterministic Policy Gradients (DDPG). Model-free reinforcement learning presents an attractive framework because of the flexibility it offers, allowing the user to avoid modeling system dynamics. We make use of this feature by applying DDPG to models of low-level and high-level neural dynamics. We show that while model-free, DDPG is able to solve more difficult problems than can be solved by current methods. These problems include the induction of global synchrony by entrainment of weakly coupled oscillators and the control of trajectories through a latent phase space of an underactuated network of neurons. While this work has been performed on simulated systems, it suggests that advances in modern reinforcement learning may enable the solution of fundamental problems in neural control and movement towards more complex objectives in real systems

Numerical scaling studies of kinetically-limited electrochemical nucleation and growth with accelerated stochastic simulations

A stochastic atomic-scale lattice-based numerical method based on the Exact Lattice First Passage Time method was developed for the simulation of the early stages of kinetically controlled electrochemical nucleation and growth. Electrochemical reaction and surface diffusion on a hexagonal lattice was accounted for in a pristine physical model system that included edge diffusion along steps, and movement over step edges with Ehrlich-Schwöbel barrier. Five cases were investigated: homoexpitaxy, heteroepitaxy, multi-layer growth, terraces, and confined regions. For each, the influence of the physical parameters, deposition conditions, and system geometry on growth morphology was investigated. Simulation based studies of multilayer surface morphology were able to distinguish between layer-by-layer and island growth modes. On stepped terraces, parameter regions associated with he surface diffusion to deposition flux ratio (D/F) and the Ehrlich-Schwöbel barrier were identified under which deposition occurred either at the step edge or by nucleation and growth of islands on the terraces. The probability of growing single crystals in a small confined region was found to scale with D/F and the radius squared. © 2014 The Electrochemical Society

An exact and efficient first passage time algorithm for reaction-diffusion processes on a 2D-lattice

We present an exact and efficient algorithm for reaction-diffusion-nucleation processes on a 2D-lattice. The algorithm makes use of first passage time (FPT) to replace the computationally intensive simulation of diffusion hops in KMC by larger jumps when particles are far away from step-edges or other particles. Our approach computes exact probability distributions of jump times and target locations in a closed-form formula, based on the eigenvectors and eigenvalues of the corresponding 1D transition matrix, maintaining atomic-scale resolution of resulting shapes of deposit islands. We have applied our method to three different test cases of electrodeposition: pure diffusional aggregation for large ranges of diffusivity rates and for simulation domain sizes of up to 4096 × 4096 sites, the effect of diffusivity on island shapes and sizes in combination with a KMC edge diffusion, and the calculation of an exclusion zone in front of a step-edge, confirming statistical equivalence to standard KMC simulations. The algorithm achieves significant speedup compared to standard KMC for cases where particles diffuse over long distances before nucleating with other particles or being captured by larger islands. © 2013 Elsevier Inc