Sialic acid on avian erythrocytes.

Understanding variation in physiological traits across taxa is a central question in evolutionary biology that has wide-ranging implications in biomedicine, disease ecology, and environmental protection. Sialic acid (Sia), and in particular, 5-N-acetylneuraminic acid (Neu5Ac), is chemically bound to galactose and the underlying glycan via α2-3 or α2-6 glycosidic linkage (i.e., Siaα2-3Galactose or Siaα2-6Galactose), conferring two different cell surface structures that affects cell to cell communication and interactions with foreign agents including microparasites and toxins. As an initial step towards understanding variation of Sia across the class Aves, we collected red blood cells (RBCs or erythrocytes) and measured Sia quantity in 76 species and 340 individuals using HPLC-MS/MS and glycosidic linkage type in 24 species and 105 individuals using hemagglutination assay. Although Sia quantity did not, α2-6 glycosidic linkage did exhibit a discernable phylogenetic pattern as evaluated by a phylogenetic signal (λ) value of 0.7. Sia quantity appeared to be higher in after hatch year birds than hatch year birds (P < 0.05); moreover, ~80% of the measured Sia across all individuals or species was expressed by ~20% of the individuals or species. Lastly, as expected, we detected a minimal presence of 5-N-glycolylneuraminic acid in the avian RBCs tested. These data provide novel insights and a large baseline dataset for further study on the variability of Sia in the class Aves which might be useful for understanding Sia dependent processes in birds.

The Biosurveillance Analytics Resource Directory (BARD): Facilitating the Use of Epidemiological Models for Infectious Disease Surveillance.

Epidemiological modeling for infectious disease is important for disease management and its routine implementation needs to be facilitated through better description of models in an operational context. A standardized model characterization process that allows selection or making manual comparisons of available models and their results is currently lacking. A key need is a universal framework to facilitate model description and understanding of its features. Los Alamos National Laboratory (LANL) has developed a comprehensive framework that can be used to characterize an infectious disease model in an operational context. The framework was developed through a consensus among a panel of subject matter experts. In this paper, we describe the framework, its application to model characterization, and the development of the Biosurveillance Analytics Resource Directory (BARD; http://brd.bsvgateway.org/brd/), to facilitate the rapid selection of operational models for specific infectious/communicable diseases. We offer this framework and associated database to stakeholders of the infectious disease modeling field as a tool for standardizing model description and facilitating the use of epidemiological models.

Selecting essential information for biosurveillance--a multi-criteria decision analysis.

The National Strategy for Biosurveillance defines biosurveillance as "the process of gathering, integrating, interpreting, and communicating essential information related to all-hazards threats or disease activity affecting human, animal, or plant health to achieve early detection and warning, contribute to overall situational awareness of the health aspects of an incident, and to enable better decision-making at all levels." However, the strategy does not specify how "essential information" is to be identified and integrated into the current biosurveillance enterprise, or what the metrics qualify information as being "essential". The question of data stream identification and selection requires a structured methodology that can systematically evaluate the tradeoffs between the many criteria that need to be taken in account. Multi-Attribute Utility Theory, a type of multi-criteria decision analysis, can provide a well-defined, structured approach that can offer solutions to this problem. While the use of Multi-Attribute Utility Theoryas a practical method to apply formal scientific decision theoretical approaches to complex, multi-criteria problems has been demonstrated in a variety of fields, this method has never been applied to decision support in biosurveillance.We have developed a formalized decision support analytic framework that can facilitate identification of "essential information" for use in biosurveillance systems or processes and we offer this framework to the global BSV community as a tool for optimizing the BSV enterprise. To demonstrate utility, we applied the framework to the problem of evaluating data streams for use in an integrated global infectious disease surveillance system.

Advancing a framework to enable characterization and evaluation of data streams useful for biosurveillance.

In recent years, biosurveillance has become the buzzword under which a diverse set of ideas and activities regarding detecting and mitigating biological threats are incorporated depending on context and perspective. Increasingly, biosurveillance practice has become global and interdisciplinary, requiring information and resources across public health, One Health, and biothreat domains. Even within the scope of infectious disease surveillance, multiple systems, data sources, and tools are used with varying and often unknown effectiveness. Evaluating the impact and utility of state-of-the-art biosurveillance is, in part, confounded by the complexity of the systems and the information derived from them. We present a novel approach conceptualizing biosurveillance from the perspective of the fundamental data streams that have been or could be used for biosurveillance and to systematically structure a framework that can be universally applicable for use in evaluating and understanding a wide range of biosurveillance activities. Moreover, the Biosurveillance Data Stream Framework and associated definitions are proposed as a starting point to facilitate the development of a standardized lexicon for biosurveillance and characterization of currently used and newly emerging data streams. Criteria for building the data stream framework were developed from an examination of the literature, analysis of information on operational infectious disease biosurveillance systems, and consultation with experts in the area of biosurveillance. To demonstrate utility, the framework and definitions were used as the basis for a schema of a relational database for biosurveillance resources and in the development and use of a decision support tool for data stream evaluation.

Immunophenotyping of chicken peripheral blood lymphocyte subpopulations: individual variability and repeatability.

T-cell lymphocyte populations can be delineated into subsets based on expression of cell surface proteins that can be measured in peripheral blood by monoclonal antibodies and flow cytometry percentages of the lymphocyte subpopulations. In order to accurately assess immunocompetence in birds, natural variability in both avian immune function and the methodology must be understood. Our objectives were to (1) further develop flow cytometry for estimating subpopulations of lymphocytes in peripheral blood from poultry, (2) estimate repeatability and variability in the methodology with respect to poultry in a free-range and environmentally diverse situation, and (3) estimate the best antibody and cell marker combination for estimating lymphocyte subpopulations. This work demonstrated the repeatability of using flow cytometry for measurements of peripheral blood in chickens using anti-chicken antibodies for lymphocyte subpopulations. Immunofluorescence staining of cells isolated from peripheral blood revealed that the CD3(+) antibodies reacted with an average of approximately 12-24% of the lymphoid cells in the blood, depending on the fluorescence type. The CD4(+) and CD8(+) molecules were expressed in a range of 4-31% and 1-10% of the lymphoid cells in the blood, respectively. Both fluorescence label and antibody company contribute to the variability of results and should be considered in future flow cytometry studies in poultry.

Proteomic analysis of beryllium-induced genotoxicity in an Escherichia coli mutant model system.

Beryllium is the second lightest metal, has a high melting point and high strength-to-weight ratio, and is chemically stable. These unique chemical characteristics make beryllium metal an ideal choice as a component material for a wide variety of applications in aerospace, defense, nuclear weapons, and industry. However, inhalation of beryllium dust or fumes induces significant health effects, including chronic beryllium disease and lung cancer. In this study, the mutagenicity of beryllium sulfate (BeSO(4)) and the comutagenicity of beryllium with a known mutagen 1-methyl-3-nitro-1-nitrosoguanidine (MNNG) were evaluated using a forward mutant detection system developed in Escherichia coli. In this system, BeSO(4) was shown to be weakly mutagenic alone and significantly enhanced the mutagenicity of MNNG up to 3.5-fold over MNNG alone. Based on these results a proteomic study was conducted to identify the proteins regulated by BeSO(4). Using the techniques of 2-DE and oMALDI-TOF MS, we successfully identified 32 proteins being differentially regulated by beryllium and/or MNNG in the E. coli test system. This is the first study to describe the proteins regulated by beryllium in vitro, and the results suggest several potential pathways for the focus of further research into the mechanisms underlying beryllium-induced genotoxicity.

Structural studies on the hairpins at the 3' untranslated region of an anthrax toxin gene.

Three proteins, namely, protective antigen (PA), edema factor (EF), and lethal factor (LF), encoded by the pX01 plasmid of Bacillus anthracis play a major role in the pathogenesis of target host cells. PA combines with EF and LF to form bipartite PA-EF and PA-LF toxins and facilitates intracellular delivery of EF and LF both of which cause cytotoxicity to the host. Since the level of PA is crucial to pathogenesis by anthrax toxins, it is important to understand how the host environment regulates the expression of the PA (or pagA) gene by utilizing the 5' and 3' untranslated regions (UTR). The 5' UTR sequence determines the initiation of transcription, whereas the 3' UTR sequence determines the efficient termination and stability of the transcript. Although, the role of the 5'UTR sequence of pagA has been investigated, little is known about the role of the 3' UTR. Since hairpin formation at the 3'UTR of a gene is an established mechanism for efficient termination and stability of the transcript, we carried out structural studies, including gel electrophoresis, circular dichroism, and two-dimensional nuclear magnetic resonance spectroscopy, to determine whether the 3' UTR sequences of pagA also form hairpin structures. Our results unequivocally demonstrate that both the coding and the noncoding 3' UTR sequences form stable hairpin structures. It is quite likely that the hairpins at the 3'UTR may contribute to efficient termination and stability of the pagA transcript.