Cell Senescence-Independent Changes of Human Skin Fibroblasts with Age

Skin ageing is defined, in part, by collagen depletion and fragmentation that leads to a loss of mechanical tension. This is currently believed to reflect, in part, the accumulation of senescent cells. We compared the expression of genes and proteins for components of the extracellular matrix (ECM) as well as their regulators and found that in vitro senescent cells produced more matrix metalloproteinases (MMPs) than proliferating cells from adult and neonatal donors. This was consistent with previous reports of senescent cells contributing to increased matrix degradation with age; however, cells from adult donors proved significantly less capable of producing new collagen than neonatal or senescent cells, and they showed significantly lower myofibroblast activation as determined by the marker α-SMA. Functionally, adult cells also showed slower migration than neonatal cells. We concluded that the increased collagen degradation of aged fibroblasts might reflect senescence, the reduced collagen production likely reflects senescence-independent processes

Macro Micro Studio: A Prototype Energy Autonomous Laboratory - supplementary information

Supplementary information including concept development, design drawings, shell geometry and technical data. © The University of Dunde

A systems biology investigation into age-related changes in the maintenance of collagen and extracellular matrix in human skin

PhD ThesisAs skin ages, its capacity for providing an effective barrier between an organism and the outside world diminishes. The dermal extracellular matrix (ECM) is a critical component of skin that provides structural integrity and nourishment to the avascular epidermis. The ability for the dermis to perform its function is dependent on its molecular composition, which is mostly a fibrous mesh of type 1 collagens, elastins, proteoglycans and glycoproteins. Fibroblasts are the resident caretakers of the dermis as they are responsible for its constant remodelling and turnover. With age the composition of the dermis changes. Collagen and elastin fibres gradually become fewer in number and disorganised while matrix metalloproteinases (MMPs) that degrade the ECM become more prevalent. A consequence of these changes is that the aged dermis has reduced mechanical and tensile strength compared to the young dermis. Transforming growth factor β (TGF-β) is a pleotropic cytokine that induces the synthesis and deposition of dermal ECM, amongst other functions. The molecular processes that lead the demise of skin integrity and the contribution of TGF-β to these processes are not well understood. The goal of this thesis is to investigate skin ageing using a systems biology approach to gain insight into the differences between the regulation of young and old fibroblasts by TGF-β. This work describes the results of two high-throughput time series experiments. The first is an Affymetrix microarray experiment that was designed to understand how young fibroblasts transcriptionally respond to short term TGF-β treatment, with an emphasis on identifying transcriptional modes of cross-talk in the control of ECM synthesis. The second is a high-throughput quantitative polymerase chain reaction iii iv (qPCR) experiment that measured 68 hand-selected genes involved in the production of ECM components or TGF-β signalling proteins. By comparing temporal data from neonatal, adult and senescent fibroblasts we provide insight into the transcriptional differences that exist between young and old fibroblasts. The data collected using these high throughput methodologies were used in a combinatorial ordinary differential equation model selection problem that was designed to test the feasibility of four hypotheses concerning the fibroblast response to TGF-β in age and the role of connective tissue growth factor (CTGF) in TGF-β mediated collagen production. This work made use of two custom Python packages that are both available on Python package index (PyPI), PyCoTools (Welsh et., al. 2018) which is a toolbox for automating aspects of COPASI, a commonly used software package in systems modelling, and pytseries which provides a set of useful objects and methods for handling and manipulating time series data.Procter & Gamble (P&G

'Molecular habituation' as a potential mechanism of gradual homeostatic loss with age

The ability of reactive oxygen species (ROS) to cause molecular damage has meant that chronic oxidative stress has been mostly studied from the point of view of being a source of toxicity to the cell. However, the known duality of ROS molecules as both damaging agents and cellular redox signals implies another perspective in the study of sustained oxidative stress. This is a perspective of studying oxidative stress as a constitutive signal within the cell. In this work, we adopt a theoretical perspective as an exploratory and explanatory approach to examine how chronic oxidative stress can interfere with signal processing by redox signalling pathways in the cell. We report that constitutive signals can give rise to a ‘molecular habituation’ effect that can prime for a gradual loss of biological function. This is because a constitutive signal in the environment has the potential to reduce the responsiveness of a signalling pathway through the prolonged activation of negative regulators. Additionally, we demonstrate how this phenomenon is likely to occur in different signalling pathways exposed to persistent signals and furthermore at different levels of biological organisation

Systems modelling ageing: from single senescent cells to simple multi-cellular models

Systems modelling has been successfully used to investigate several key molecular mechanisms of ageing. Modelling frameworks to allow integration of models and methods to enhance confidence in models are now well established. In this article, we discuss these issues and work through the process of building an integrated model for cellular senescence as a single cell and in a simple tissue context

PyCoTools: a Python toolbox for COPASI

Motivation COPASI is an open source software package for constructing, simulating and analyzing dynamic models of biochemical networks. COPASI is primarily intended to be used with a graphical user interface but often it is desirable to be able to access COPASI features programmatically, with a high level interface. Results PyCoTools is a Python package aimed at providing a high level interface to COPASI tasks with an emphasis on model calibration. PyCoTools enables the construction of COPASI models and the execution of a subset of COPASI tasks including time courses, parameter scans and parameter estimations. Additional ‘composite’ tasks which use COPASI tasks as building blocks are available for increasing parameter estimation throughput, performing identifiability analysis and performing model selection. PyCoTools supports exploratory data analysis on parameter estimation data to assist with troubleshooting model calibrations. We demonstrate PyCoTools by posing a model selection problem designed to show case PyCoTools within a realistic scenario. The aim of the model selection problem is to test the feasibility of three alternative hypotheses in explaining experimental data derived from neonatal dermal fibroblasts in response to TGF-β over time. PyCoTools is used to critically analyze the parameter estimations and propose strategies for model improvement. Availability and implementation PyCoTools can be downloaded from the Python Package Index (PyPI) using the command ’pip install pycotools’ or directly from GitHub (https://github.com/CiaranWelsh/pycotools). Documentation at http://pycotools.readthedocs.io. Supplementary information Supplementary data are available at Bioinformatics online