Alan R. Willse

Individual odortypes: interaction of MHC and background genes

Genes of the major histocompatibility complex (MHC) influence the urinary odors of mice. Behavioral studies have shown (1) that mice differing only at MHC have distinct urinary odors, suggesting an MHC odor phenotype or odortype; (2) that the MHC odortype can be recognized across different background strains; and (3) that the MHC odortype is not an additive trait. Very little is known about the odorants underlying this behavioral phenotype. We compared urinary volatile profiles of two MHC haplotypes (H2b and H2k) and their heterozygous cross (H2b×H2k) for two different background strains (C57BL/6J and BALB/c) using solid phase micro-extraction (SPME) headspace analysis and gas chromatography/mass spectrometry (GC/MS). Both MHC and background genes substantially influence the volatile profile. Of 148 compounds screened, 108 of them significantly differ between the six genotypes. Surprisingly, for numerous compounds, their MHC associations are moderated by background genes (i.e., there is a significant MHC × background interaction effect in the statistical model relating genotype to relative compound concentration). These interactions account for nearly 30% of the total genetic effect on the volatile profile. MHC heterozygosity further extends the odortype diversity. For many compounds, the volatile expression for the heterozygote is more extreme than the expression for either homozygote, suggesting a heterozygous-specific odortype. The remarkable breadth of effects of MHC variation on concentrations of metabolites and the interaction between MHC and other genetic variation implies the existence of as yet unknown processes by which variation in MHC genes gives rise to variation in volatile molecules in body fluids.

In search of the chemical basis for MHC odourtypes

Mice can discriminate between chemosignals of individuals based solely on genetic differences confined to the major histocompatibility complex (MHC). Two different sets of compounds have been suggested: volatile compounds and non-volatile peptides. Here, we focus on volatiles and review a number of publications that have identified MHC-regulated compounds in inbred laboratory mice. Surprisingly, there is little agreement among different studies as to the identity of these compounds. One recent approach to specifying MHC-regulated compounds is to study volatile urinary profiles in mouse strains with varying MHC types, genetic backgrounds and different diets. An unexpected finding from these studies is that the concentrations of numerous compounds are influenced by interactions among these variables. As a result, only a few compounds can be identified that are consistently regulated by MHC variation alone. Nevertheless, since trained animals are readily able to discriminate the MHC differences, it is apparent that chemical studies are somehow missing important information underlying mouse recognition of MHC odourtypes. To make progress in this area, we propose a focus on the search for behaviourally relevant odourants rather than a random search for volatiles that are regulated by MHC variation. Furthermore, there is a need to consider a ‘combinatorial odour recognition’ code whereby patterns of volatile metabolites (the basis for odours) specify MHC odourtypes.

Network Analysis of Epidermal Growth Factor Signaling Using Integrated Genomic, Proteomic and Phosphorylation Data

To understand how integration of multiple data types can help decipher cellular responses at the systems level, we analyzed the mitogenic response of human mammary epithelial cells to epidermal growth factor (EGF) using whole genome microarrays, mass spectrometry-based proteomics and large-scale western blots with over 1000 antibodies. A time course analysis revealed significant differences in the expression of 3172 genes and 596 proteins, including protein phosphorylation changes measured by western blot. Integration of these disparate data types showed that each contributed qualitatively different components to the observed cell response to EGF and that varying degrees of concordance in gene expression and protein abundance measurements could be linked to specific biological processes. Networks inferred from individual data types were relatively limited, whereas networks derived from the integrated data recapitulated the known major cellular responses to EGF and exhibited more highly connected signaling nodes than networks derived from any individual dataset. While cell cycle regulatory pathways were altered as anticipated, we found the most robust response to mitogenic concentrations of EGF was induction of matrix metalloprotease cascades, highlighting the importance of the EGFR system as a regulator of the extracellular environment. These results demonstrate the value of integrating multiple levels of biological information to more accurately reconstruct networks of cellular response.

Automated Microarray Image Analysis Toolbox for MATLAB

UNLABELLED The Automated Microarray Image Analysis (AMIA) Toolbox for MATLAB is a flexible, open-source, microarray image analysis tool that allows the user to customize analyses of microarray image sets. This tool provides several methods to identify and quantify spot statistics, as well as extensive diagnostic statistics and images to evaluate data quality and array processing. The open, modular nature of AMIA provides access to implementation details and encourages modification and extension of AMIAs capabilities. AVAILABILITY The AMIA Toolbox is freely available at http://www.pnl.gov/statistics/amia. The AMIA Toolbox requires MATLAB 6.5 (R13) (MathWorks, Inc. Natick, MA), as well as the Statistics Toolbox 4.1 and Image Processing Toolbox 4.1 for MATLAB or more recent versions. CONTACT amanda.white@pnl.gov

Diagnostic Oligonucleotide Microarray Fingerprinting of Bacillus Isolates

ABSTRACT A genome-independent microarray and new statistical techniques were used to genotype Bacillus strains and quantitatively compare DNA fingerprints with the known taxonomy of the genus. A synthetic DNA standard was used to understand process level variability and lead to recommended standard operating procedures for microbial forensics and clinical diagnostics.

Differentiation of gram-negative bacterial aerosol exposure using detected markers in bronchial-alveolar lavage fluid.

The identification of biosignatures of aerosol exposure to pathogens has the potential to provide useful diagnostic information. In particular, markers of exposure to different types of respiratory pathogens may yield diverse sets of markers that can be used to differentiate exposure. We examine a mouse model of aerosol exposure to known Gram negative bacterial pathogens, Francisella tularensis novicida and Pseudomonas aeruginosa. Mice were subjected to either a pathogen or control exposure and bronchial alveolar lavage fluid (BALF) was collected at four and twenty four hours post exposure. Small protein and peptide markers within the BALF were detected by matrix assisted laser desorption/ionization (MALDI) mass spectrometry (MS) and analyzed using both exploratory and predictive data analysis methods; principle component analysis and degree of association. The markers detected were successfully used to accurately identify the four hour exposed samples from the control samples. This report demonstrates the potential for small protein and peptide marker profiles to identify aerosol exposure in a short post-exposure time frame.