Multiomic ALS signatures highlight subclusters and sex differences suggesting the MAPK pathway as therapeutic target.

Amyotrophic lateral sclerosis (ALS) is a debilitating motor neuron disease and lacks effective disease-modifying treatments. This study utilizes a comprehensive multiomic approach to investigate the early and sex-specific molecular mechanisms underlying ALS. By analyzing the prefrontal cortex of 51 patients with sporadic ALS and 50 control subjects, alongside four transgenic mouse models (C9orf72-, SOD1-, TDP-43-, and FUS-ALS), we have uncovered significant molecular alterations associated with the disease. Here, we show that males exhibit more pronounced changes in molecular pathways compared to females. Our integrated analysis of transcriptomes, (phospho)proteomes, and miRNAomes also identified distinct ALS subclusters in humans, characterized by variations in immune response, extracellular matrix composition, mitochondrial function, and RNA processing. The molecular signatures of human subclusters were reflected in specific mouse models. Our study highlighted the mitogen-activated protein kinase (MAPK) pathway as an early disease mechanism. We further demonstrate that trametinib, a MAPK inhibitor, has potential therapeutic benefits in vitro and in vivo, particularly in females, suggesting a direction for developing targeted ALS treatments.

Quantitative plasma profiling by 1H NMR-based metabolomics: impact of sample treatment.

Introduction: There is evidence that sample treatment of blood-based biosamples may affect integral signals in nuclear magnetic resonance-based metabolomics. The presence of macromolecules in plasma/serum samples makes investigating low-molecular-weight metabolites challenging. It is particularly relevant in the targeted approach, in which absolute concentrations of selected metabolites are often quantified based on the area of integral signals. Since there are a few treatments of plasma/serum samples for quantitative analysis without a universally accepted method, this topic remains of interest for future research. Methods: In this work, targeted metabolomic profiling of 43 metabolites was performed on pooled plasma to compare four methodologies consisting of Carr-Purcell-Meiboom-Gill (CPMG) editing, ultrafiltration, protein precipitation with methanol, and glycerophospholipid solid-phase extraction (g-SPE) for phospholipid removal; prior to NMR metabolomics analysis. The effect of the sample treatments on the metabolite concentrations was evaluated using a permutation test of multiclass and pairwise Fisher scores. Results: Results showed that methanol precipitation and ultrafiltration had a higher number of metabolites with coefficient of variation (CV) values above 20%. G-SPE and CPMG editing demonstrated better precision for most of the metabolites analyzed. However, differential quantification performance between procedures were metabolite-dependent. For example, pairwise comparisons showed that methanol precipitation and CPMG editing were suitable for quantifying citrate, while g-SPE showed better results for 2-hydroxybutyrate and tryptophan. Discussion: There are alterations in the absolute concentration of various metabolites that are dependent on the procedure. Considering these alterations is essential before proceeding with the quantification of treatment-sensitive metabolites in biological samples for improving biomarker discovery and biological interpretations. The study demonstrated that g-SPE and CPMG editing are effective methods for removing proteins and phospholipids from plasma samples for quantitative NMR analysis of metabolites. However, careful consideration should be given to the specific metabolites of interest and their susceptibility to the sample treatment procedures. These findings contribute to the development of optimized sample preparation protocols for metabolomics studies using NMR spectroscopy.

Deep learning-based cell composition analysis from tissue expression profiles.

We present Scaden, a deep neural network for cell deconvolution that uses gene expression information to infer the cellular composition of tissues. Scaden is trained on single-cell RNA sequencing (RNA-seq) data to engineer discriminative features that confer robustness to bias and noise, making complex data preprocessing and feature selection unnecessary. We demonstrate that Scaden outperforms existing deconvolution algorithms in both precision and robustness. A single trained network reliably deconvolves bulk RNA-seq and microarray, human and mouse tissue expression data and leverages the combined information of multiple datasets. Because of this stability and flexibility, we surmise that deep learning will become an algorithmic mainstay for cell deconvolution of various data types. Scaden's software package and web application are easy to use on new as well as diverse existing expression datasets available in public resources, deepening the molecular and cellular understanding of developmental and disease processes.

On the feasibility of the computational modelling of the endoluminal vacuum-assisted closure of an oesophageal anastomotic leakage.

Endoluminal vacuum-assisted closure (E-VAC) is a promising therapy to treat anastomotic leakages of the oesophagus and bowel which are associated with high morbidity and mortality rates. An open-pore polyurethane foam is introduced into the leakage cavity and connected to a device that applies a suction pressure to accelerate the closure of the defect. Computational analysis of this healing process can advance our understanding of the biomechanical mechanisms at play. To this aim, we use a dual-stage finite-element analysis in which (i) the structural problem addresses the cavity reduction caused by the suction and (ii) a new constitutive formulation models tissue healing via permanent deformations coupled to a stiffness increase. The numerical implementation in an in-house code is described and a qualitative example illustrates the basic characteristics of the model. The computational model successfully reproduces the generic closure of an anastomotic leakage cavity, supporting the hypothesis that suction pressure promotes healing by means of the aforementioned mechanisms. However, the current framework needs to be enriched with empirical data to help advance device designs and treatment guidelines. Nonetheless, this conceptual study confirms that computational analysis can reproduce E-VAC of anastomotic leakages and establishes the bases for better understanding the mechanobiology of anastomotic defect healing.

A homeostatic-driven turnover remodelling constitutive model for healing in soft tissues.

Remodelling of soft biological tissue is characterized by interacting biochemical and biomechanical events, which change the tissue's microstructure, and, consequently, its macroscopic mechanical properties. Remodelling is a well-defined stage of the healing process, and aims at recovering or repairing the injured extracellular matrix. Like other physiological processes, remodelling is thought to be driven by homeostasis, i.e. it tends to re-establish the properties of the uninjured tissue. However, homeostasis may never be reached, such that remodelling may also appear as a continuous pathological transformation of diseased tissues during aneurysm expansion, for example. A simple constitutive model for soft biological tissues that regards remodelling as homeostatic-driven turnover is developed. Specifically, the recoverable effective tissue damage, whose rate is the sum of a mechanical damage rate and a healing rate, serves as a scalar internal thermodynamic variable. In order to integrate the biochemical and biomechanical aspects of remodelling, the healing rate is, on the one hand, driven by mechanical stimuli, but, on the other hand, subjected to simple metabolic constraints. The proposed model is formulated in accordance with continuum damage mechanics within an open-system thermodynamics framework. The numerical implementation in an in-house finite-element code is described, particularized for Ogden hyperelasticity. Numerical examples illustrate the basic constitutive characteristics of the model and demonstrate its potential in representing aspects of remodelling of soft tissues. Simulation results are verified for their plausibility, but also validated against reported experimental data.

Analytical and numerical analysis of human dental occlusal contact.

The knowledge of contact forces in teeth surfaces during mastication or para-functional movements can help to understand processes related to friction and wear of human dental enamel. The development of a numerical model for analysis of the occlusal contact between two antagonistic teeth is proposed, which includes three basic steps: the characterisation of the surface roughness, its homogenisation using an assumed distribution function and the numerical determination of the resulting forces. Finite element strain results for the main different asperities are statistically combined, deriving the predicted macroscopic behaviour of the interface. Axisymmetric and 3D numerical models with an elasto-plastic constitutive law are used to simulate micro-indentations and micro-contacts, respectively. The contact is allowed to occur locally in planes not necessarily parallel to the surface's mean plane, a problem for which there is no analytical solution. The three identified parameters, homogenised surface hardness (3.68 GPa), surface yield stress (3.08 GPa) and static friction coefficient (0.23), agree with the experimental values reported in the literature.

An inverse approach for the mechanical characterisation of vascular tissues via a generalised rule of mixtures.

Mechanical factors such as stresses and strains play a major role in the growth and remodelling of soft biological tissues. The main constituents of tissue undergo different processes reacting to mechanical stimulus. Thereby, the characterisation of growth and remodelling requires an accurate estimation of the stresses and strains of their main components. Many soft tissues can be considered as composite materials and can be analysed using an appropriate rule of mixtures. Particularly, arterial tissue can be modelled as an isotropic soft matrix reinforced with preferentially oriented collagen fibres. An inverse approach to obtain the mechanical characterisation of each main component is proposed in this work. The procedure is based on a rule of mixtures raised in a finite deformation framework and generalised to include kinematics and compatibility equations for serial-parallel behaviour. This methodology allows obtaining the stress-strain relationship of the components fitting experimental data.

Evaluación de la vulnerabilidad del Puente Warth en Austria

El Puente Warth fue diseñado y construido a pricipios de los años setenta, con códigos sísmicos ahora obsoletos. Por lo tanto, era necesario una revisión de su comportamiento actual para la toma de futuras decisiones sobre su mantenimiento o rehabilitación. En este artículo se muestra el análisis realizado para definir la vulnerabilidad sísmica de esta estructura, considerando un método simplificado de evaluación del daño máximo y la aplicación de la simulación por Monte Carlo para definir la respuesta probable del puente. En este análisis, se tomaron en cuenta las incertidumbres inherentes a las propiedades estructurales y a la excitación sísmica externa, mediante dos escenarios sísmicos. Los resultados obtenidos muestran que bajo algunas condiciones de carga deberían ser consideradas posibles reparaciones moderadas(AU)