CALCIUM SIGNALING AND ALG-2 IN THE REGULATION OF CONSTITUTIVE SECRETION

The constitutive secretory pathway influences almost every aspect of cellular function: generation and maintenance of subcellular compartments; cell growth and differentiation; and production and deposition of the extracellular matrix. Classically, constitutive trafficking was thought to be continuous and unregulated—in contrast to regulated secretion, wherein vesicles are stored intracellularly until a Ca2+ signal causes synchronous membrane fusion. However, early studies demonstrated a Ca2+ requirement for endosome fusion, ER to Golgi, and intra- Golgi trafficking, demonstrating that Ca2+ is a regulator of constitutive trafficking steps as well. In Chapter 1, I review recent evidence for Ca2+ involvement and the specific Ca2+-related machinery implicated in trafficking events throughout the constitutive trafficking systems. Notably, I describe a series of Ca2+ pumps, channels, and Ca2+-binding effector proteins—and their trafficking machinery targets—that together regulate the flux of cargo in response to genetic alterations or baseline and agonist-dependent Ca2+ signals. Of the intracellular constitutive trafficking steps, the ER-to-Golgi transport stage is the busiest, transporting up to one-third of all eukaryotic proteins. However, the potential regulation of these steps by Ca2+ signaling events has remained largely uncharacterized. Studies indicated that the PEF protein apoptosis-linked gene 2 (ALG-2) binds Sec31A at ER-exit-sites (ERES) in a Ca2+-dependent manner, but in vitro and intact cell methodologies have been unable to adequately explain the specific role of ALG-2 and its PEF protein binding partner, peflin, in secretion. Chapter 2 describes experimental procedures, while Chapter 3 significantly expands upon the ALG-2- peflin regulatory machine. I find that although both proteins are fully dispensable for secretion, a peflin-ALG-2 heterocomplex binds to ERES via the ALG-2 subunit and confers a low, buffered transport rate, while peflin-lacking ALG-2 complexes can either enhance or inhibit transport of ER cargoes depending on expression level. This apparent bifurcated response indicates that PEF protein regulatory states may tune transport rates through expression level or Ca2+ changes, yet PEF proteins have never been observed regulating transport in response to Ca2+. In Chapter 4, I examine the roles of ALG-2 and peflin in response to physiological Ca2+-mobilizing agonists, and find that ALG-2 depresses ER export in epithelial NRK cells and enhances ER export in neuroendocrine PC12 cells. Furthermore, I find that Ca2+ signaling in the NRK cell model can produce opposing effects on secretion depending on signaling intensity and duration— phenomena that could contribute to cellular growth and intercellular communication following secretory increases or protection from excitotoxicity and infection following secretory decreases. Mechanistically, ALG-2-dependent depression of secretion involves decreased levels of the COPII outer shell and increased peflin targeting to ERES, while ALG-2-dependent enhancement of secretion involves increased COPII outer shell and decreased peflin at ERES. These data provide insights into how PEF protein dynamics affect secretion of important physiological cargoes such as collagen I and significantly impact ER stress

Predicting uncertainty and risk in the natural sciences: bridging the gap between academics and industry

The increase in large-scale disasters in recent years, such as the 2007 floods in the UK, has caused disruptions of livelihood, enormous economic losses and increase in fatalities. Losses from natural hazards are only partially derived from the physical event itself but are also caused by society’s vulnerability to it. In the first three months of 2010, an unprecedented US$16 billion in losses occurred from natural hazards caused by events such as the Haiti and Chilean earthquakes, and the European storm Xynthia. This made it the worst ever first quarter for natural hazard losses and left the insurance industry exposed financially to the more loss-prone third and forth quarters. NERC science has a central role to play in the forecasting and mitigation of natural hazards. Research in this area forms the basis for technological solutions to early warning systems, designing mitigation strategies and providing critical information for decision makers to help save lives and avoid economic losses. Understanding uncertainty is essential if reliable forecasting and risk assessments are to be made. However, the quantification and assessment of uncertainty in natural hazards has in general been limited particularly in terms of model limitations and multiplicity. There are several reasons for this; most notably the fragmented nature of natural hazard research which is split both across science areas and between research, risk management and policy. Because of this, each sector has developed its own concepts and language which has acted as a barrier for effective communication and prevented the production of generic methods that have the potential to be used across sectors. It is clear therefore that by bringing the natural hazard community together significant breakthroughs in the visualisation and understanding of risk and uncertainty could be achieved. To accomplish this, this research programme has 4 prime objectives: 1.To improve communication and networking between researchers and risk managers within the financial services sector 2.To provide a platform for the dissemination of information on uncertainty and risk analysis between a range of researchers and practitioners 3.To generate a portfolio of best practice in uncertainty and risk analysis 4.To act as a focal point between the financial sector and natural hazard research in NERC This paper will discuss how the Natural Environmental Research Council, in partnership with other organisations such as TSB, EA and EPSRC etc, is working with academics and industry to bring about a step change in the way that uncertainty and risk assessments are achieved throughout the natural hazard community

Scalable human-computer collaborative assessment

Human-computer collaborative assessment (HCCA) is an approach to eassessment which emphasises the role of the human expert in making judgements. This approach is embodied in the Assessment21 software; for instance we take a very conservative approach to automatic marking, but provide flexible tools to aid the human marker

Domain-specific formative feedback through domain-independent diagram matching

As part of our Human-Computer Collaborative (HCC) approach to assessment, we seek representations of answers and marking judgements which can be applied to a wide variety of situations. In this paper we introduce such a representation, which we call a gree, and discuss an initial practical application of grees for formative feedback. An experiment was carried out in which students were asked to construct an answer while receiving interactive feedback and then complete a short survey. The results show that it is possible to give effective domain-specific formative feedback based on a domain-independent internal representation or “metaformat”. This work builds on results we have previously presented on domain-independent diagram matching based on heuristic matching of graphs. Grees provide much greater flexibility, with a wide variety of potential applications. We discuss some problems which need to be overcome before we can realise their full potential

Mainstreaming prevention: Prescribing fruit and vegetables as a brief intervention in primary care

This is the author's PDF version of an article published in Public health© 2005.This articles discusses a project at the Castlefields Health Centre in Halton whereby primary care professionals issue a prescription for discounts on fruit and vegetables. The prescription is explicitly linked to the five-a-day message

What students really say

The Assess By Computer (ABC) project (Sargeant et al 2004) follows a Human-Computer Collaborative (HCC) approach to assessment. We focus on constructed answers such as text and diagrams rather than answers requiring mere selection between alternatives. The HCC assessment process is an active collaboration between humans and a software system, where the software does the routine work and the humans make the important judgements. Similar approaches in Artificial Intelligence research are developed in Englebart 1962, Grosz 2004, and Potter et al 2004, among others. Our students, through their answers to questions, also implicitly collaborate in the development of resources. We can develop marking support tools which handle the nature and range of variation found in real exam data, and we can adapt marking judgements and feedback - even, in the longer term, our teaching material - in the light of what students really say. In this paper we focus on the reality of student text answers. We present student data from on-line examinations showing a remarkably wide range of acceptable answers to even the most straightforward of questions. We show how the analysis of these examples is supported by the ABC tools, especially the Keyword Manager and answer clustering options

Diagram matching for human-computer collaborative assessment

Diagrams are an important part of many assessments. When diagrams consisting of boxes joined by connectors are drawn on a computer, the resulting structures can be matched against each other to determine similarity. This paper discusses ways of doing such matching, and its application in the context of human-computer collaborative assessment. Results show that a simple heuristic process is effective in finding similarities in such diagrams. The practical usefulness of this varies in different contexts, as students often produce remarkably dissimilar diagrams

A human-computer collaborative approach to the marking of free text answers

We propose the term Human-Computer Collaborative Assessment (HCCA) for a distinct and currently rather neglected sub-field of CAA. In HCCA answers are constructions rather than selections, and marking is a process of active collaboration between human marker and machine. We present the results of experience with simple tools which demonstrate significant time savings compared to traditional paper marking. Further improvements in both speed and quality of assessment are clearly possible, but require much more sophisticated tools, particularly for free text answers. We review the role which Natural Language Processing techniques can play, particularly in the light of experience from other domains. Analysis of a number of answer sets highlights key issues in HCCA as well as underlining the infeasibility of fully automatic marking in many situations

Light-weight clustering techniques for short text answers in human computer collaborative (HCC) CAA

We first explore the paedogogic value, in assessment, of questions which elicit short text answers (as opposed to either multiple choice questions or essays). Related work attempts to develop deeper processing for fully automatic marking. In contrast, we show that light-weight, robust, generic Language Engineering techniques for text clustering in a human-computer collaborative CAA system can contribute significantly to the speed, accuracy, and consistency of human marking. Examples from real summative assessments demonstrate the potential, and the inherent limitations, of this approach. Its value as a framework for formative feedback is also discussed