Development of methods to monitor maturation and trafficking of Carboxypeptidase Y (CPY) and its G255R mutant (CPY*)

Protein folding is a vital biological process which underpins many cellular functions in both eukaryotic and prokaryotic cells. This mechanism is a prime example of macromolecular self-assembly which leads to important biological function, such as molecular trafficking to specific cellular parts and cellular differentiation. However, whilst for the majority of cases proteins fold into their correct 3D structure with long-term stability, there is a propensity for proteins to misfold due to insufficient molecular interactions between the amino acids within the polypeptide chain. Once formed, these misfolded proteins have the potential to aggregate and cause pathological or even neurological diseases. Thus, it is of importance to probe the mechanism(s) of protein misfolding to uncover its molecular origins. The yeast species known as baker’s yeast (S. Cerevisiae) is a model organism to probe this mechanism, and carboxypeptidase Y (CPY) has been proposed as a suitable model protein, due to its high abundance within the yeast endoplasmic reticulum (ER), to understand this process. Literature has shown that CPY is a widely used model protein in understanding protein sorting events within the ER of S. Cerevisiae. The advantages, and subsequent choice of this protein, are based upon its structure and role. It plays a part in the C-terminal chemistry of polypeptides, and thus may inform on mis-interactions which contribute to misfolding. Furthermore, CPY trafficking from the ER to the Golgi to the vacuole has provided information on sorting signal events which are like mammalian cellular signals, and thus share similar features with other organisms. It is also a preferable model protein due to its unique catalytic triad (active site). Although classified as a serine protease, it has a much greater pH and temperature range than other proteases, and can thus maintain high activity across environmental changes. Its mutated analogue, carboxypeptidase Y* (CPY*), has also been chosen as a model protein to compare its molecular sorting mechanism with CPY. It is characterised by a glycine-arginine mutation at the 255 amino acid position. The purpose of this project is to uncover the molecular mechanisms of misfolding, namely, post trafficking of CPY & CPY*, whether these misfolded proteins renature and continue trafficking or whether they are degraded by cellular machinery. Alternatively, whether there is evidence of competition between these processes. This would also shed light on the kinetics of these processes and the likelihood of clearance of these misfolded proteins from the ER. To probe these processes, the maturation of pre-cursor forms of both CPY and CPY* have been studied, as they undergo cellular trafficking across the secretory pathway from the ER to the Golgi to the vacuole. The initial experiments have been used to test whether CPY/CPY* can be detected in a western blot through SDS-PAGE gels, and whether CPY/CPY* can be detected in an immunoprecipitate. The final experiment was used to assess whether the dose-dependent cell-cycle regulator 2 (DCR2) plays a role in ER-induced stress, by specifically affecting CPY* degradation. All such experiments employ classical molecular biology techniques. These findings could shed light on whether degradation, by means of disulphide bond breaking and thus slower migration on SDS-PAGE gels, or renaturation, by means of cellular mechanisms, is the dominant mechanism within the ER

On the Chemical Reactivity of Molecules with Membrane Lipids: An Experimental and Theoretical Approach

It is now well-established that the reactivity of membrane associated molecules, such as CADs (cationic amphiphilic drugs), with membrane lipids is influenced by the disposition of each molecule in the membrane rather than its lipophilicity. The reactions involving CADs and phospholipids such as POPC include lipidation and hydrolysis. Some CADs promote lipid hydrolysis, whereas others either decree the rate or have no effect on lipid chemical stability. Lipidation involves the transfer of an acyl group from the glycerol part of the lipid to a reactive group on the CAD. Lipidation and hydrolysis reactions both lead to the formation of lysolipids. This project aims to investigate the factors of drug reactivity towards lipid membranes. This project involves both experimental and theoretical work to understand drug reactivity factors with POPC lipids. A range of isotopically enriched reactive compounds have been synthesised. These compounds vary in the rates of lysolipid formation that they induce in POPC membranes. Furthermore, a novel synthetic methodology has been developed to incorporate an 15N isotope into molecules with aniline functionality, and its applicability to clinically relevant molecules. 1D and 2D solid-state NMR data is presented which enables the interactions of each labelled molecule with POPC lipids to be probed via close contact distance measurements. Atomistic molecular dynamics (MD) simulations provide corroborating information on the preferred depth and orientation of the molecules in the lipid bilayer. QM/MM simulations are used to locate the reactive intermediates and the transition state in each bond forming and bond breaking process along the reaction pathway. These calculations were successful in being able to predict reactive and non-reactive conformations of drugs in the membrane interface. The MD and DFT results correlate well with ssNMR data in showing how orientation and depth of partitioning influence drug-lipid reactivity

Predicting out of intensive care unit cardiopulmonary arrest or death using electronic medical record data

BACKGROUND: Accurate, timely and automated identification of patients at high risk for severe clinical deterioration using readily available clinical information in the electronic medical record (EMR) could inform health systems to target scarce resources and save lives. METHODS: We identified 7,466 patients admitted to a large, public, urban academic hospital between May 2009 and March 2010. An automated clinical prediction model for out of intensive care unit (ICU) cardiopulmonary arrest and unexpected death was created in the derivation sample (50% randomly selected from total cohort) using multivariable logistic regression. The automated model was then validated in the remaining 50% from the total cohort (validation sample). The primary outcome was a composite of resuscitation events, and death (RED). RED included cardiopulmonary arrest, acute respiratory compromise and unexpected death. Predictors were measured using data from the previous 24 hours. Candidate variables included vital signs, laboratory data, physician orders, medications, floor assignment, and the Modified Early Warning Score (MEWS), among other treatment variables. RESULTS: RED rates were 1.2% of patient-days for the total cohort. Fourteen variables were independent predictors of RED and included age, oxygenation, diastolic blood pressure, arterial blood gas and laboratory values, emergent orders, and assignment to a high risk floor. The automated model had excellent discrimination (c-statistic=0.85) and calibration and was more sensitive (51.6% and 42.2%) and specific (94.3% and 91.3%) than the MEWS alone. The automated model predicted RED 15.9 hours before they occurred and earlier than Rapid Response Team (RRT) activation (5.7 hours prior to an event, p=0.003) CONCLUSION: An automated model harnessing EMR data offers great potential for identifying RED and was superior to both a prior risk model and the human judgment-driven RRT

A causal inference approach of monosynapses from spike trains

Neuroscientists have worked on the problem of estimating synaptic properties, such as connectivity and strength, from simultaneously recorded spike trains since the 1960s. Recent years have seen renewed interest in the problem, coinciding with rapid advances in the technology of high-density neural recordings and optogenetics, which can be used to calibrate causal hypotheses about functional connectivity. Here, a rigorous causal inference framework for pairwise excitatory and inhibitory monosynaptic effects between spike trains is developed. Causal interactions are identified by separating spike interactions in pairwise spike trains by their timescales. Fast algorithms for computing accurate estimates of associated quantities are also developed. Through the lens of this framework, the link between biophysical parameters and statistical definitions of causality between spike trains is examined across a spectrum of dynamical systems simulations. In an idealized setting, we demonstrate a correspondence between the synaptic causal metric developed here and the probabilities of causation developed by Tian and Pearl. Since the probabilities of causation are derived under distinct assumptions and include data from experimental randomization, this opens up the possibility of testing the synaptic inference framework's assumptions with juxtacellular or optogenetic stimulation. We simulate such an experiment with a biophysically detailed channelrhodopsin model and show that randomization is not achieved; strong confounding persists even with strong stimulations. A principal goal is to ask how carefully articulated causal assumptions might better inform the design of neural stimulation experiments and, in turn, support experimental tests of those assumptions

Short-term Temporal Dependency Detection under Heterogeneous Event Dynamic with Hawkes Processes

Many event sequence data exhibit mutually exciting or inhibiting patterns. Reliable detection of such temporal dependency is crucial for scientific investigation. The de facto model is the Multivariate Hawkes Process (MHP), whose impact function naturally encodes a causal structure in Granger causality. However, the vast majority of existing methods use direct or nonlinear transform of standard MHP intensity with constant baseline, inconsistent with real-world data. Under irregular and unknown heterogeneous intensity, capturing temporal dependency is hard as one struggles to distinguish the effect of mutual interaction from that of intensity fluctuation. In this paper, we address the short-term temporal dependency detection issue. We show the maximum likelihood estimation (MLE) for cross-impact from MHP has an error that can not be eliminated but may be reduced by order of magnitude, using heterogeneous intensity not of the target HP but of the interacting HP. Then we proposed a robust and computationally-efficient method modified from MLE that does not rely on the prior estimation of the heterogeneous intensity and is thus applicable in a data-limited regime (e.g., few-shot, no repeated observations). Extensive experiments on various datasets show that our method outperforms existing ones by notable margins, with highlighted novel applications in neuroscience.Comment: Conference on Uncertainty in Artificial Intelligence 202

Hospital characteristics associated with highly automated and usable clinical information systems in Texas, United States

<p>Abstract</p> <p>Background</p> <p>A hospital's clinical information system may require a specific environment in which to flourish. This environment is not yet well defined. We examined whether specific hospital characteristics are associated with highly automated and usable clinical information systems.</p> <p>Methods</p> <p>This was a cross-sectional survey of 125 urban hospitals in Texas, United States using the Clinical Information Technology Assessment Tool (CITAT), which measures a hospital's level of automation based on physician interactions with the information system. Physician responses were used to calculate a series of CITAT scores: automation and usability scores, four automation sub-domain scores, and an overall clinical information technology (CIT) score. A multivariable regression analysis was used to examine the relation between hospital characteristics and CITAT scores.</p> <p>Results</p> <p>We received a sufficient number of physician responses at 69 hospitals (55% response rate). Teaching hospitals, hospitals with higher IT operating expenses (>$1 million annually), IT capital expenses (>$75,000 annually) and hospitals with larger IT staff (≥ 10 full-time staff) had higher automation scores than hospitals that did not meet these criteria (p < 0.05 in all cases). These findings held after adjustment for bed size, total margin, and ownership (p < 0.05 in all cases). There were few significant associations between the hospital characteristics tested in this study and usability scores.</p> <p>Conclusion</p> <p>Academic affiliation and larger IT operating, capital, and staff budgets are associated with more highly automated clinical information systems.</p