Data Management Policies and Issues with Life Science Data

Data management and sharing are relatively new concepts in the health and life sciences fields. This presentation will cover some basic policies as well as the impediments to data sharing unique to health and life sciences data.

Upregulation of Reactive Oxygen Species During the Retrovirus Life Cycle and Their Roles in a Mutant of Moloney Murine Leukemia Virus, ts1-Mediated Neurodegeneration

Viral invasion of the central nervous system (CNS) and development of neurological symptoms is a characteristic of many retroviruses. The mechanism by which retrovirus infection causes neurological dysfunction has yet to be fully elucidated. Given the complexity of the retrovirus-mediated neuropathogenesis, studies using small animal models are extremely valuable. Our laboratory has used a mutant moloney murine leukemia retrovirus, ts1-mediated neurodegneration. We hypothesize that astrocytes play an important role in ts1-induced neurodegeneration since they are retroviral reservoirs and supporting cells for neurons. It has been shown that ts1 is able to infect astrocytes in vivo and in vitro. Astrocytes, the dominant cell population in the CNS, extend their end feet to endothelial cells and neuronal synapse to provide neuronal support. Signs of oxidative stress in the ts1-infected CNS have been well-documented from previous studies. After viral infection, retroviral DNA is generated from its RNA genome and integrated into the host genome. In this study, we identified the life cycle of ts1 in the infected astrocytes. During the infection, we observed reactive oxygen species (ROS) upregulations: one at low levels during the early infection phase and another at high levels during the late infection phase. Initially we hypothesized that p53 might play an important role in ts1-mediated astrocytic cell death. Subsequently, we found that p53 is unlikely to be involved in the ts1-mediated astrocytic cell death. Instead, p53 phosphorylation was increased by the early ROS upregulation via ATM, the protein encoded by the ataxia-telangiectasia (A-T) mutated gene. The early upregulation of p53 delayed viral gene expression by suppressing expression of the catalytic subunit of NADPH oxidase (NOX). We further demonstrated that the ROS upregulation induced by NOX activation plays an important role in establishing retroviral genome into the host. Inhibition of NOX decreased viral replication and delayed the onset of pathological symptoms in ts1-infected mice. These observations lead us to conclude that suppression of NOX not only prevents the establishment of the retrovirus but also decreases oxidative stress in the CNS. This study provides us with new perspectives on the retrovirus-host cell interaction and sheds light on retrovirus-induced neurodegeneration as a result of the astrocyte-neuron interaction.

New algorithms for automated phylogenetic reconstruction using artificial intelligence and data mining techniques

Academic and industrial research in the late 90s have brought about an exponential explosion of DNA sequence data. Automated expert systems are being created to help biologists to extract patterns, trends and links from this ever-deepening ocean of information. Two such systems aimed on retrieving and subsequently utilizing phylogenetically relevant information have been developed in this dissertation, the major objective of which was to automate the often difficult and confusing phylogenetic reconstruction process. ^ Popular phylogenetic reconstruction methods, such as distance-based methods, attempt to find an optimal tree topology (that reflects the relationships among related sequences and their evolutionary history) by searching through the topology space. Various compromises between the fast (but incomplete) and exhaustive (but computationally prohibitive) search heuristics have been suggested. An intelligent compromise algorithm that relies on a flexible “beam” search principle from the Artificial Intelligence domain and uses the pre-computed local topology reliability information to adjust the beam search space continuously is described in the second chapter of this dissertation. ^ However, sometimes even a (virtually) complete distance-based method is inferior to the significantly more elaborate (and computationally expensive) maximum likelihood (ML) method. In fact, depending on the nature of the sequence data in question either method might prove to be superior. Therefore, it is difficult (even for an expert) to tell a priori which phylogenetic reconstruction method—distance-based, ML or maybe maximum parsimony (MP)—should be chosen for any particular data set. ^ A number of factors, often hidden, influence the performance of a method. For example, it is generally understood that for a phylogenetically “difficult” data set more sophisticated methods (e.g., ML) tend to be more effective and thus should be chosen. However, it is the interplay of many factors that one needs to consider in order to avoid choosing an inferior method (potentially a costly mistake, both in terms of computational expenses and in terms of reconstruction accuracy.) ^ Chapter III of this dissertation details a phylogenetic reconstruction expert system that selects a superior proper method automatically. It uses a classifier (a Decision Tree-inducing algorithm) to map a new data set to the proper phylogenetic reconstruction method. ^