Designing stem cell niches for differentiation and self-renewal

Mesenchymal stem cells, characterized by their ability to differentiate into skeletal tissues and self-renew, hold great promise for both regenerative medicine and novel therapeutic discovery. However, their regenerative capacity is retained only when in contact with their specialized microenvironment, termed the stem cell niche. Niches provide structural and functional cues that are both biochemical and biophysical, stem cells integrate this complex array of signals with intrinsic regulatory networks to meet physiological demands. Although, some of these regulatory mechanisms remain poorly understood or difficult to harness with traditional culture systems. Biomaterial strategies are being developed that aim to recapitulate stem cell niches, by engineering microenvironments with physiological-like niche properties that aim to elucidate stem cell-regulatory mechanisms, and to harness their regenerative capacity in vitro. In the future, engineered niches will prove important tools for both regenerative medicine and therapeutic discoveries

Disease Ontology: improving and unifying disease annotations across species.

Model organisms are vital to uncovering the mechanisms of human disease and developing new therapeutic tools. Researchers collecting and integrating relevant model organism and/or human data often apply disparate terminologies (vocabularies and ontologies), making comparisons and inferences difficult. A unified disease ontology is required that connects data annotated using diverse disease terminologies, and in which the terminology relationships are continuously maintained. The Mouse Genome Database (MGD, http://www.informatics.jax.org), Rat Genome Database (RGD, http://rgd.mcw.edu) and Disease Ontology (DO, http://www.disease-ontology.org) projects are collaborating to augment DO, aligning and incorporating disease terms used by MGD and RGD, and improving DO as a tool for unifying disease annotations across species. Coordinated assessment of MGD\u27s and RGD\u27s disease term annotations identified new terms that enhance DO\u27s representation of human diseases. Expansion of DO term content and cross-references to clinical vocabularies (e.g. OMIM, ORDO, MeSH) has enriched the DO\u27s domain coverage and utility for annotating many types of data generated from experimental and clinical investigations. The extension of anatomy-based DO classification structure of disease improves accessibility of terms and facilitates application of DO for computational research. A consistent representation of disease associations across data types from cellular to whole organism, generated from clinical and model organism studies, will promote the integration, mining and comparative analysis of these data. The coordinated enrichment of the DO and adoption of DO by MGD and RGD demonstrates DO\u27s usability across human data, MGD, RGD and the rest of the model organism database community. Dis Model Mech 2018 Mar 12;11(3):dmm032839

Distilling a visual network of Retinitis Pigmentosa gene-protein interactions to uncover new disease candidates

BACKGROUND: Retinitis pigmentosa (RP) is a highly heterogeneous genetic visual disorder with more than 70 known causative genes, some of them shared with other non-syndromic retinal dystrophies (e.g. Leber congenital amaurosis, LCA). The identification of RP genes has increased steadily during the last decade, and the 30% of the cases that still remain unassigned will soon decrease after the advent of exome/genome sequencing. A considerable amount of genetic and functional data on single RD genes and mutations has been gathered, but a comprehensive view of the RP genes and their interacting partners is still very fragmentary. This is the main gap that needs to be filled in order to understand how mutations relate to progressive blinding disorders and devise effective therapies. METHODOLOGY: We have built an RP-specific network (RPGeNet) by merging data from different sources: high-throughput data from BioGRID and STRING databases, manually curated data for interactions retrieved from iHOP, as well as interactions filtered out by syntactical parsing from up-to-date abstracts and full-text papers related to the RP research field. The paths emerging when known RP genes were used as baits over the whole interactome have been analysed, and the minimal number of connections among the RP genes and their close neighbors were distilled in order to simplify the search space. CONCLUSIONS: In contrast to the analysis of single isolated genes, finding the networks linking disease genes renders powerful etiopathological insights. We here provide an interactive interface, RPGeNet, for the molecular biologist to explore the network centered on the non-syndromic and syndromic RP and LCA causative genes. By integrating tissue-specific expression levels and phenotypic data on top of that network, a more comprehensive biological view will highlight key molecular players of retinal degeneration and unveil new RP disease candidates

Glucocorticoid receptor signalling activates YAP in breast cancer

The Hippo pathway is an oncosuppressor signalling cascade that plays a major role in the control of cell growth, tissue homoeostasis and organ size. Dysregulation of the Hippo pathway leads to aberrant activation of the transcription co-activator YAP (Yes-associated protein) that contributes to tumorigenesis in several tissues. Here we identify glucocorticoids (GCs) as hormonal activators of YAP. Stimulation of glucocorticoid receptor (GR) leads to increase of YAP protein levels, nuclear accumulation and transcriptional activity in vitro and in vivo. Mechanistically, we find that GCs increase expression and deposition of fibronectin leading to the focal adhesion-Src pathway stimulation, cytoskeleton-dependent YAP activation and expansion of chemoresistant cancer stem cells. GR activation correlates with YAP activity in human breast cancer and predicts bad prognosis in the basal-like subtype. Our results unveil a novel mechanism of YAP activation in cancer and open the possibility to target GR to prevent cancer stem cells self-renewal and chemoresistance

Micro- and nanoengineering approaches to control stem cell-biomaterial interactions.

As our population ages, there is a greater need for a suitable supply of engineered tissues to address a range of debilitating ailments. Stem cell based therapies are envisioned to meet this emerging need. Despite significant progress in controlling stem cell differentiation, it is still difficult to engineer human tissue constructs for transplantation. Recent advances in micro- and nanofabrication techniques have enabled the design of more biomimetic biomaterials that may be used to direct the fate of stem cells. These biomaterials could have a significant impact on the next generation of stem cell based therapies. Here, we highlight the recent progress made by micro- and nanoengineering techniques in the biomaterials field in the context of directing stem cell differentiation. Particular attention is given to the effect of surface topography, chemistry, mechanics and micro- and nanopatterns on the differentiation of embryonic, mesenchymal and neural stem cells

At the border: the plasma membrane-cell wall continuum

A strong and flexible cell wall relies on its connection to the plasma membrane and governs controlled cell growth. This mediates plant tissue organization and environmental adaptatio

From access and integration to mining of secure genomic data sets across the grid

The UK Department of Trade and Industry (DTI) funded BRIDGES project (Biomedical Research Informatics Delivered by Grid Enabled Services) has developed a Grid infrastructure to support cardiovascular research. This includes the provision of a compute Grid and a data Grid infrastructure with security at its heart. In this paper we focus on the BRIDGES data Grid. A primary aim of the BRIDGES data Grid is to help control the complexity in access to and integration of a myriad of genomic data sets through simple Grid based tools. We outline these tools, how they are delivered to the end user scientists. We also describe how these tools are to be extended in the BBSRC funded Grid Enabled Microarray Expression Profile Search (GEMEPS) to support a richer vocabulary of search capabilities to support mining of microarray data sets. As with BRIDGES, fine grain Grid security underpins GEMEPS

The segregated connectome of late-life depression: a combined cortical thickness and structural covariance analysis.

Late-life depression (LLD) has been associated with both generalized and focal neuroanatomical changes including gray matter atrophy and white matter abnormalities. However, previous literature has not been consistent and, in particular, its impact on the topology organization of brain networks remains to be established. In this multimodal study, we first examined cortical thickness, and applied graph theory to investigate structural covariance networks in LLD. Thirty-three subjects with LLD and 25 controls underwent T1-weighted, fluid-attenuated inversion recovery and clinical assessments. Freesurfer was used to perform vertex-wise comparisons of cortical thickness, whereas the Graph Analysis Toolbox (GAT) was implemented to construct and analyze the structural covariance networks. LLD showed a trend of lower thickness in the left insular region (p < 0.001 uncorrected). In addition, the structural network of LLD was characterized by greater segregation, particularly showing higher transitivity (i.e., measure of clustering) and modularity (i.e., tendency for a network to be organized into subnetworks). It was also less robust against random failure and targeted attacks. Despite relative cortical preservation, the topology of the LLD network showed significant changes particularly in segregation. These findings demonstrate the potential for graph theoretical approaches to complement conventional structural imaging analyses and provide novel insights into the heterogeneous etiology and pathogenesis of LLD.This work was supported by the NIHR Biomedical Research Unit in Dementia and the Biomedical Research Centre awarded to Cambridge University Hospitals NHS Foundation Trust and the University of Cambridge, and the NIHR Biomedical Research Unit in Dementia and the Biomedical Research Centre awarded to Newcastle upon Tyne Hospitals NHS Foundation Trust and the Newcastle University. Elijah Mak was in receipt of a Gates Cambridge, PhD studentship.This is the author accepted manuscript. It first appeared from Elsevier at http://dx.doi.org/10.1016/j.neurobiolaging.2016.08.013