Cyclin D1 mediates epithelial proliferation and links {\it ras\/} activation to the cell cycle

The studies presented in this thesis focus on two aspects of the involvement of cyclin D1 in epithelial proliferation. Since cyclin D1 has been identified as a target for genetic alterations and deregulation in a variety of human cancers, we studied cyclin D1 expression in two experimental models of epithelial carcinogenesis. These studies provided evidence that cyclin D1 was a potential target of the activating mutation of the Ha-ras gene characteristic of the experimental protocol. In addition, evidence from two independent in vitro models suggested that cyclin D1 was indeed part of the primary cellular response to activated ras, and at least partly responsible for the increase in proliferation observed in ras-transformed cells.^ Cyclin D1 has also been described as a key regulator of the passage through the G1 phase of the cell cycle. Cyclin D1 is induced in response to mitogens in a variety of cell lines, and cells engineered to overexpress cyclin D1 show accelerated G1 transit. In order to study the involvement of cyclin D1 in epithelial cell growth and differentiation, we generated transgenic mice that constitutively overexpress cyclin D1 in stratified epithelia. These mice developed thymic hyperplasia and skin hyperproliferation, providing in vivo evidence of the potential of cyclin D1 to regulate growth of epithelial cells. ^

CD73-generated adenosine restricts lymphocyte migration into draining lymph nodes.

After an inflammatory stimulus, lymphocyte migration into draining lymph nodes increases dramatically to facilitate the encounter of naive T cells with Ag-loaded dendritic cells. In this study, we show that CD73 (ecto-5'-nucleotidase) plays an important role in regulating this process. CD73 produces adenosine from AMP and is expressed on high endothelial venules (HEV) and subsets of lymphocytes. Cd73(-/-) mice have normal sized lymphoid organs in the steady state, but approximately 1.5-fold larger draining lymph nodes and 2.5-fold increased rates of L-selectin-dependent lymphocyte migration from the blood through HEV compared with wild-type mice 24 h after LPS administration. Migration rates of cd73(+/+) and cd73(-/-) lymphocytes into lymph nodes of wild-type mice are equal, suggesting that it is CD73 on HEV that regulates lymphocyte migration into draining lymph nodes. The A(2B) receptor is a likely target of CD73-generated adenosine, because it is the only adenosine receptor expressed on the HEV-like cell line KOP2.16 and it is up-regulated by TNF-alpha. Furthermore, increased lymphocyte migration into draining lymph nodes of cd73(-/-) mice is largely normalized by pretreatment with the selective A(2B) receptor agonist BAY 60-6583. Adenosine receptor signaling to restrict lymphocyte migration across HEV may be an important mechanism to control the magnitude of an inflammatory response.

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. ^

A project to improve the information seeking skills and increase the use of evidence-based research in public health practice

The ability of public health practitioners (PHPs) to work efficiently and effectively is negatively impacted by their lack of knowledge of the broad range of evidence-based practice information resources and tools that can be utilized to guide them in their development of health policies and programs. This project, a three-hour continuing education hands-on workshop with supporting resources, was designed to increase knowledge and skills of these resources. The workshop was presented as a pre-conference continuing education program for the Texas Public Health Association (TPHA) 2008 Annual Conference. Topics included: identification of evidence-based practice resources to aid in the development of policies and programs; identification of sources of publicly available data; utilization of data for community assessments; and accessing and searching the literature through a collection of databases available to all citizens of Texas. Supplemental resources included a blog that served as a gateway to the resources explored during the presentation, a community assessment workbook that incorporates both Healthy People 2010 objectives and links to reliable sources of data, and handouts providing additional instruction on the use of the resources covered during the workshop.^ Before- and after-workshop surveys based on Kirkpatrick's 4-level model of evaluation and the Theory of Planned Behavior were administered. Of the questions related to the trainer, the workshop, and the usefulness of the workshop, participants gave "Good" to "Excellent" responses to all one question. Confidence levels overall increased a statistically significant amount; measurements of attitude, social norms, and control showed no significant differences before and after the workshop. Lastly, participants indicated they were likely to use resources shown during the workshop within a one to three month time period on average. ^ The workshop and creation of supplemental resources served as a pilot for a funded project that will be continued with the development and delivery of four 4-week long webinar-based training sessions to be completed by December 2008. ^