mRNA Levels of BACE1 and its interacting proteins, RTN3 and PPIL2, correlate in human post mortem brain tissue

β-Site amyloid precursor protein cleaving enzyme (BACE1) is the rate-limiting enzyme for production of Aβ peptides, proposed to drive the pathological changes found in Alzheimer’s disease (AD). Reticulon 3 (RTN3) is a negative modulator of BACE1 (β-secretase) proteolytic activity, while peptidylprolyl isomerase (cyclophilin)-like 2 (PPIL2) positively regulated BACE1 gene expression in a cell-based assay. This study aimed to analyze RTN3 and PPIL2 mRNA levels in four brain regions from individuals with AD and controls. BACE1 mRNA had been previously quantified in the samples, as had glial fibrillary acidic protein (GFAP) and neuron-specific enolase (NSE), to track changing cell populations in the tissue. mRNA levels in the human post mortem brain tissue were assayed using quantitative real-time polymerase chain reaction (qPCR) and qbasePLUS, employing validated stably expressed reference genes. No differences in RTN3 or PPIL2 mRNA levels were found in individuals with AD, compared to controls. Both RTN3 and PPIL2 mRNA levels correlated significantly with BACE1 mRNA and all three showed similar disease stage-dependent changes with respect to NSE and GFAP. These findings indicated that the in vitro data demonstrating an effect of PPIL2 on BACE1 expression have functional relevance in vivo. Further research into BACE1-interacting proteins could provide a fruitful approach to the modulation of this protease and consequently Aβ production.

The role of the seed bank in recovery of temperate heath and blanket bog following wildfires

Questions - Are the germinable seed banks of upland heath and blanket bog reduced following wildfires? Are some species at particular risk? Do the impacts of wildfires on seed banks differ between heathlands and blanket bog?

Location - Northern Ireland, United Kingdom.

Methods - Vegetation surveys and seed bank sampling were conducted in 2012 at burned and unburned areas within six upland sites where large wildfires had occurred during spring 2011. Differences in seedling abundance, species richness and Jaccard similarity indices between burned and unburned areas were compared using GLMMs. Differences in the community composition were examined using pRDA.

Results - In total, 24 of the 51 species in the vegetation were detected in the germinable seed bank. Species richness and the abundance of seedlings other than Calluna vulgaris were lower in areas where wildfires had occurred. Species composition of both germinable seed banks and vegetation differed between burned and unburned areas within sites; with negative associations between burned areas and some key indicator species including Drosera rotundifolia, Eriophorum vaginatum, Empetrum nigrum, Narthecium ossifragum and Trichophorum germanicum. We did not find any evidence of significant interactions between burning and habitat, suggesting that wildfires had similar impacts on each species regardless of the habitat in which they occurred.

Conclusions - This study differs from other UK studies in that it examines impacts of wildfires at sites that have not been previously intensively managed by burning. In particular, we highlight potential impacts on N. ossifragum and D. rotundifolia, which are key components of the upland flora and, to our knowledge, were not present in previous UK studies.

Embracing an integromic approach to tissue biomarker research in cancer: Perspectives and lessons learned

Modern approaches to biomedical research and diagnostics targeted towards precision medicine are generating ‘big data’ across a range of high-throughput experimental and analytical platforms. Integrative analysis of this rich clinical, pathological, molecular and imaging data represents one of the greatest bottlenecks in biomarker discovery research in cancer and other diseases. Following on from the publication of our successful framework for multimodal data amalgamation and integrative analysis, Pathology Integromics in Cancer (PICan), this article will explore the essential elements of assembling an integromics framework from a more detailed perspective. PICan, built around a relational database storing curated multimodal data, is the research tool sitting at the heart of our interdisciplinary efforts to streamline biomarker discovery and validation. While recognizing that every institution has a unique set of priorities and challenges, we will use our experiences with PICan as a case study and starting point, rationalizing the design choices we made within the context of our local infrastructure and specific needs, but also highlighting alternative approaches that may better suit other programmes of research and discovery. Along the way, we stress that integromics is not just a set of tools, but rather a cohesive paradigm for how modern bioinformatics can be enhanced. Successful implementation of an integromics framework is a collaborative team effort that is built with an eye to the future and greatly accelerates the processes of biomarker discovery, validation and translation into clinical practice.