Revealing hidden information in osteoblast’s mechanotransduction through analysis of time patterns of critical events

Background Mechanotransduction in bone cells plays a pivotal role in osteoblast differentiation and bone remodelling. Mechanotransduction provides the link between modulation of the extracellular matrix by mechanical load and intracellular activity. By controlling the balance between the intracellular and extracellular domains, mechanotransduction determines the optimum functionality of skeletal dynamics. Failure of this relationship was suggested to contribute to bone-related diseases such as osteoporosis. Results A hybrid mechanical and agent-based model (Mech-ABM), simulating mechanotransduction in a single osteoblast under external mechanical perturbations, was utilised to simulate and examine modulation of the activation dynamics of molecules within mechanotransduction on the cellular response to mechanical stimulation. The number of molecules and their fluctuations have been analysed in terms of recurrences of critical events. A numerical approach has been developed to invert subordination processes and to extract the direction processes from the molecular signals in order to derive the distribution of recurring events. These predict that there are large fluctuations enclosing information hidden in the noise which is beyond the dynamic variations of molecular baselines. Moreover, studying the system under different mechanical load regimes and altered dynamics of feedback loops, illustrate that the waiting time distributions of each molecule are a signature of the system’s state. Conclusions The behaviours of the molecular waiting times change with the changing of mechanical load regimes and altered dynamics of feedback loops, presenting the same variation of patterns for similar interacting molecules and identifying specific alterations for key molecules in mechanotransduction. This methodology could be used to provide a new tool to identify potent molecular candidates to modulate mechanotransduction, hence accelerate drug discovery towards therapeutic targets for bone mass upregulation

Analysis of mechanotransduction dynamics during combined mechanical stimulation and modulation of the extracellular-regulated kinase cascade uncovers hidden information within the signalling noise

Osteoporosis is a bone disease characterized by brittle bone and increased fracture incidence. With ageing societies worldwide, the disease presents a high burden on health systems. Furthermore, there are limited treatments for osteoporosis with just two anabolic pharmacological agents approved by the US Food and Drug Administration. Healthy bones are believed to be maintained via an intricate relationship between dual biochemical and mechanical (bio-mechanical) stimulations. It is widely considered that osteoporosis emerges as a result of disturbances to said relationship. The mechanotransduction process is key to this balance, and disruption of its dynamics in bone cells plays a role in osteoporosis development. Nonetheless, the exact details and mechanisms that drive and secure the health of bones are still elusive at the cellular and molecular scales. This study examined the dual modulation of mechanical stimulation and mechanotransduction activation dynamics in an osteoblast (OB). The aim was to find patterns of mechanotransduction dynamics demonstrating a significant change that can be mapped to alterations in the OB responses, specifically at the level of gene expression and osteogenic markers such as alkaline phosphatase. This was achieved using a three-dimensional hybrid multiscale computational model simulating mechanotransduction in the OB and its interaction with the extracellular matrix, combined with a numerical analytical technique. The model and the analysis method predict that within the noise of mechanotransduction, owing to modulation of the bio-mechanical stimulus and consequent gene expression, there are unique events that provide signatures for a shift in the system's dynamics. Furthermore, the study uncovered molecular interactions that can be potential drug targets

Outcomes of COVID-19 in 79 patients with IBD in Italy : an IG-IBD study

COVID-19 has rapidly become a major health emergency worldwide. Patients with IBD are at increased risk of infection, especially when they have active disease and are taking immunosuppressive therapy. The characteristics and outcomes of COVID-19 in patients with IBD remain unclear. Design: This Italian prospective observational cohort study enrolled consecutive patients with an established IBD diagnosis and confirmed COVID-19. Data regarding age, sex, IBD (type, treatments and clinical activity), other comorbidities (Charlson Comorbidity Index (CCI)), signs and symptoms of COVID-19 and therapies were compared with COVID-19 outcomes (pneumonia, hospitalisation, respiratory therapy and death). Results: Between 11 and 29 March 2020, 79 patients with IBD with COVID-19 were enrolled at 24 IBD referral units. Thirty-six patients had COVID-19-related pneumonia (46%), 22 (28%) were hospitalised, 7 (9%) required non-mechanical ventilation, 9 (11%) required continuous positive airway pressure therapy, 2 (3%) had endotracheal intubation and 6 (8%) died. Four patients (6%) were diagnosed with COVID-19 while they were being hospitalised for a severe flare of IBD. Age over 65 years (p=0.03), UC diagnosis (p=0.03), IBD activity (p=0.003) and a CCI score >1 (p=0.04) were significantly associated with COVID-19 pneumonia, whereas concomitant IBD treatments were not. Age over 65 years (p=0.002), active IBD (p=0.02) and higher CCI score were significantly associated with COVID-19-related death. Conclusions: Active IBD, old age and comorbidities were associated with a negative COVID-19 outcome, whereas IBD treatments were not. Preventing acute IBD flares may avoid fatal COVID-19 in patients with IBD. Further research is needed

Modelling collective cell behaviour

The classical mean-field approach to modelling biological systems makes a number of simplifying assumptions which typically lead to coupled systems of reaction-diffusion partial differential equations. While these models have been very useful in allowing us to gain important insights into the behaviour of many biological systems, recent experimental advances in our ability to track and quantify cell behaviour now allow us to build more realistic models which relax some of the assumptions previously made. This brief review aims to illustrate the type of models obtained using this approach

Building two-dimensional metal-organic networks with tin

We show that Sn atoms combined with organic ligands can be used to build 2D coordination networks on Au(111) surfaces

Stabilization of high-spin Mn ions in tetra-pyrrolic configuration on copper

By means of Mn-Cu transmetalation, we incorporated Mn atoms in an array of TCNQ (7,7,8,8-tetracyanoquinodimethane) grown on Cu(100), forming a long range ordered and commensurate metal\u2013organic coordination network (MOCN). Preliminary Sn alloying of the Cu(100) surface allowed us to control the degree of substrate reactivity, thus preventing the chemical interaction of the Mn-TCNQ MOCN with the substrate. Mn2+ ions are stabilized in an artificial tetra-pyrrolic coordination, which mimics the macrocyle configuration of Mn-phthalocyanines/porphyrins. X-ray absorption spectroscopy at the Mn L2,3-edge indicates that the Mn ions are in a high-spin state (S = 5/2), in agreement with DFT + U calculations which also shows that the electronic structure of this Mn-TCNQ MOCN is very similar to that of the corresponding unsupported MOCN