Identification of domains in apoA-I susceptible to proteolysis by mast cell chymase. Implications for HDL function.

When stimulated, rat serosal mast cells degranulate and secrete a cytoplasmic neutral protease, chymase. We studied the fragmentation of apolipoprotein (apo) A-I during proteolysis of HDL(3) by chymase, and examined how chymase-dependent proteolysis interfered with the binding of eight murine monoclonal antibodies (Mabs) against functional domains of apoA-I. Size exclusion chromatography of HDL(3) revealed that proteolysis for up to 24 h did not alter the integrity of the alpha-migrating HDL, whereas a minor peak containing particles of smaller size with prebeta mobility disappeared after as little as 15 min of incubation. At the same time, generation of a large (26 kDa) polypeptide containing the N-terminus of apoA-I was detected. This large fragment and other medium-sized fragments of apoA-I produced after prolonged treatment with chymase were found to be associated with the alphaHDL; meanwhile, small lipid-free peptides were rapidly produced. Incubation of HDL(3) with chymase inhibited binding of Mab A-I-9 (specific for prebeta(1)HDL) most rapidly (within 15 min) of the eight studied Mabs. This rapid loss of binding was paralleled by a similar reduction in the ability of HDL(3) to induce high-affinity efflux of cholesterol from macrophage foam cells, indicating that proteolysis had destroyed an epitope that is critical for this function. In sharp contrast, prolonged degradation of HDL(3) by chymase failed to reduce the ability of HDL(3) to activate LCAT, even though it led to modification of three epitopes in the central region of apoA-I that are involved in lecithin cholesterol acyltransferase (LCAT) activation. This differential sensitivity of the two key functions of HDL(3) to the proteolytic action of mast cell chymase is compatible with the notion that, in reverse cholesterol transport, intactness of apoA-I is essential for prebeta(1)HDL to promote the high-affinity efflux of cellular cholesterol, but not for the alpha-migrating HDL particles to activate LCAT

Correlated dynamics in egocentric communication networks

We investigate the communication sequences of millions of people through two different channels and analyze the fine grained temporal structure of correlated event trains induced by single individuals. By focusing on correlations between the heterogeneous dynamics and the topology of egocentric networks we find that the bursty trains usually evolve for pairs of individuals rather than for the ego and his/her several neighbors thus burstiness is a property of the links rather than of the nodes. We compare the directional balance of calls and short messages within bursty trains to the average on the actual link and show that for the trains of voice calls the imbalance is significantly enhanced, while for short messages the balance within the trains increases. These effects can be partly traced back to the technological constrains (for short messages) and partly to the human behavioral features (voice calls). We define a model that is able to reproduce the empirical results and may help us to understand better the mechanisms driving technology mediated human communication dynamics.Comment: 7 pages, 6 figure

Circadian pattern and burstiness in mobile phone communication

The temporal communication patterns of human individuals are known to be inhomogeneous or bursty, which is reflected as the heavy tail behavior in the inter-event time distribution. As the cause of such bursty behavior two main mechanisms have been suggested: a) Inhomogeneities due to the circadian and weekly activity patterns and b) inhomogeneities rooted in human task execution behavior. Here we investigate the roles of these mechanisms by developing and then applying systematic de-seasoning methods to remove the circadian and weekly patterns from the time-series of mobile phone communication events of individuals. We find that the heavy tails in the inter-event time distributions remain robustly with respect to this procedure, which clearly indicates that the human task execution based mechanism is a possible cause for the remaining burstiness in temporal mobile phone communication patterns.Comment: 17 pages, 12 figure

Discovering and Predicting Temporal Patterns of WiFi-interactive Social Populations

Extensive efforts have been devoted to characterizing the rich connectivity patterns among the nodes (components) of such complex networks (systems), and in the course of development of research in this area, people have been prompted to address on a fundamental question: How does the fascinating yet complex topological features of a network affect or determine the collective behavior and performance of the networked system? While elegant attempts to address this core issue have been made, for example, from the viewpoints of synchronization, epidemics, evolutionary cooperation, and the control of complex networks, theoretically or empirically, this widely concerned key question still remains open in the newly emergent field of network science. Such fruitful advances also push the desire to understand (mobile) social networks and characterize human social populations with the interdependent collective dynamics as well as the behavioral patterns. Nowadays, a great deal of digital technologies are unobtrusively embedded into the physical world of human daily activities, which offer unparalleled opportunities to explosively digitize human physical interactions, who is contacting with whom at what time. Such powerful technologies include the Bluetooth, the active Radio Frequency Identification (RFID) technology, wireless sensors and, more close to our interest in this paper, the WiFi technology. As a snapshot of the modern society, a university is in the coverage of WiFi signals, where the WiFi system records the digital access logs of the authorized WiFi users when they access the campus wireless services. Such WiFi access records, as the indirect proxy data, work as the effective proxy of a large-scale population's social interactions.Comment: 11 pages, 10 page

Lysophosphatidylcholine in phospholipase A(2)-modified LDL triggers secretion of angiopoietin 2

Background and aims: Secretory phospholipase A(2) (PLA(2)) hydrolyzes LDL phospholipids generating modified LDL particles (PLA(2)-LDL) with increased atherogenic properties. Exocytosis of Weibel-Palade bodies (WPB) releases angiopoietin 2 (Ang2) and externalizes P-selectin, which both play important roles in vascular inflammation. Here, we investigated the effects of PLA(2)-LDL on exocytosis of WPBs. Methods: Human coronary artery endothelial cells (HCAECs) were stimulated with PLA(2)-LDL, and its uptake and effect on Ang2 release, leukocyte adhesion, and intracellular calcium levels were measured. The effects of PLA(2)-LDL on Ang2 release and WPB exocytosis were measured in and ex vivo in mice. Results: Exposure of HCAECs to PLA(2)-LDL triggered Ang2 secretion and promoted leukocyte-HCAEC interaction. Lysophosphatidylcholine was identified as a critical component of PLA(2)-LDL regulating the WPB exocytosis, which was mediated by cell-surface proteoglycans, phospholipase C, intracellular calcium, and cytoskeletal remodeling. PLA(2)-LDL also induced murine endothelial WPB exocytosis in blood vessels in and ex vivo, as evidenced by secretion of Ang2 in vivo, P-selectin translocation to plasma membrane in intact endothelial cells in thoracic artery and tracheal vessels, and reduced Ang2 staining in tracheal endothelial cells. Finally, in contrast to normal human coronary arteries, in which Ang2 was present only in the endothelial layer, at sites of advanced atherosclerotic lesions, Ang2 was detected also in the intima, media, and adventitia. Conclusions: Our studies reveal PLA(2)-LDL as a potent agonist of endothelial WPB exocytosis, resulting in increased secretion of Ang2 and translocation of P-selectin. The results provide mechanistic insight into PLA(2)-LDL-dependent promotion of vascular inflammation and atherosclerosis.Peer reviewe

Bursty egocentric network evolution in Skype

In this study we analyze the dynamics of the contact list evolution of millions of users of the Skype communication network. We find that egocentric networks evolve heterogeneously in time as events of edge additions and deletions of individuals are grouped in long bursty clusters, which are separated by long inactive periods. We classify users by their link creation dynamics and show that bursty peaks of contact additions are likely to appear shortly after user account creation. We also study possible relations between bursty contact addition activity and other user-initiated actions like free and paid service adoption events. We show that bursts of contact additions are associated with increases in activity and adoption - an observation that can inform the design of targeted marketing tactics.Comment: 7 pages, 6 figures. Social Network Analysis and Mining (2013

Weighted temporal event graphs

The times of temporal-network events and their correlations contain information on the function of the network and they influence dynamical processes taking place on it. To extract information out of correlated event times, techniques such as the analysis of temporal motifs have been developed. We discuss a recently-introduced, more general framework that maps temporal-network structure into static graphs while retaining information on time-respecting paths and the time differences between their consequent events. This framework builds on weighted temporal event graphs: directed, acyclic graphs (DAGs) that contain a superposition of all temporal paths. We introduce the reader to the temporal event-graph mapping and associated computational methods and illustrate its use by applying the framework to temporal-network percolation

APAC treatment limits collar-induced carotid atherosclerotic plaque development in apoE-/- mice

Biopharmaceutic

Spatiotemporal correlations of handset-based service usages

We study spatiotemporal correlations and temporal diversities of handset-based service usages by analyzing a dataset that includes detailed information about locations and service usages of 124 users over 16 months. By constructing the spatiotemporal trajectories of the users we detect several meaningful places or contexts for each one of them and show how the context affects the service usage patterns. We find that temporal patterns of service usages are bound to the typical weekly cycles of humans, yet they show maximal activities at different times. We first discuss their temporal correlations and then investigate the time-ordering behavior of communication services like calls being followed by the non-communication services like applications. We also find that the behavioral overlap network based on the clustering of temporal patterns is comparable to the communication network of users. Our approach provides a useful framework for handset-based data analysis and helps us to understand the complexities of information and communications technology enabled human behavior.Comment: 11 pages, 15 figure

Gene set analysis for longitudinal gene expression data

<p>Abstract</p> <p>Background</p> <p>Gene set analysis (GSA) has become a successful tool to interpret gene expression profiles in terms of biological functions, molecular pathways, or genomic locations. GSA performs statistical tests for independent microarray samples at the level of gene sets rather than individual genes. Nowadays, an increasing number of microarray studies are conducted to explore the dynamic changes of gene expression in a variety of species and biological scenarios. In these longitudinal studies, gene expression is repeatedly measured over time such that a GSA needs to take into account the within-gene correlations in addition to possible between-gene correlations.</p> <p>Results</p> <p>We provide a robust nonparametric approach to compare the expressions of longitudinally measured sets of genes under multiple treatments or experimental conditions. The limiting distributions of our statistics are derived when the number of genes goes to infinity while the number of replications can be small. When the number of genes in a gene set is small, we recommend permutation tests based on our nonparametric test statistics to achieve reliable type I error and better power while incorporating unknown correlations between and within-genes. Simulation results demonstrate that the proposed method has a greater power than other methods for various data distributions and heteroscedastic correlation structures. This method was used for an IL-2 stimulation study and significantly altered gene sets were identified.</p> <p>Conclusions</p> <p>The simulation study and the real data application showed that the proposed gene set analysis provides a promising tool for longitudinal microarray analysis. R scripts for simulating longitudinal data and calculating the nonparametric statistics are posted on the North Dakota INBRE website <url>http://ndinbre.org/programs/bioinformatics.php</url>. Raw microarray data is available in Gene Expression Omnibus (National Center for Biotechnology Information) with accession number GSE6085.</p