Showing papers by "Keith A. Baggerly published in 2006"

Synchronous selection of homing peptides for multiple tissues by in vivo phage display

Abstract: In vivo phage display is a technology used to reveal organ-specific vascular ligand-receptor systems in animal models and, recently, in patients, and to validate them as potential therapy targets. Here, we devised an efficient approach to simultaneously screen phage display libraries for peptides homing to any number of tissues without the need for an individual subject for each target tissue. We tested this approach in mice by selecting homing peptides for six different organs in a single screen and prioritizing them by using software compiled for statistical validation of peptide biodistribution specificity. We identified a number of motif-containing biological candidates for ligands binding to organ-selective receptors based on similarity of the selected peptide motifs to mouse proteins. To demonstrate that this methodology can lead to targetable ligand-receptor systems, we validated one of the pancreas-homing peptides as a mimic peptide of natural prolactin receptor ligands. This new comprehensive strategy for screening phage libraries in vivo provides an advantage over the conventional approach because multiple organs internally control for organ selectivity of each other in the successive rounds of selection. It may prove particularly relevant for patient studies, allowing efficient high-throughput selection of targeting ligands for multiple organs in a single screen.

Analysis of Mass Spectrometry Data Using Bayesian Wavelet-Based Functional Mixed Models

Abstract: In this chapter, we demonstrate how to analyze MALDI-TOF/SELDITOF mass spectrometry data using the wavelet-based functional mixed model introduced by Morris and Carroll (2006), which generalizes the linear mixed models to the case of functional data This approach models each spectrum as a function, and is very general, accommodating a broad class of experimental designs and allowing one to model nonparametric functional effects for various factors, which can be conditions of interest (eg cancer/normal) or experimental factors (blocking factors) Inference on these functional effects allows us to identify protein peaks related to various outcomes of interest, including dichotomous outcomes, categorical outcomes, continuous outcomes, and any interactions among factors Functional random effects make it possible to account for correlation between spectra from the same individual or block in a flexible manner After fitting this model using an MCMC, the output can be used to perform peak detection and identify the peaks that are related to factors of interest, while automatically adjusting for nonlinear block effects that are characteristic of these data We apply this method to mass spectrometry data from an University of Texas MD Anderson Cancer Center experiment studying the serum proteome of mice injected with one of two cell lines in one of two organs This methodology ap-

An Introduction to High-Throughput Bioinformatics Data

Abstract: High throughput biological assays supply thousands of measurements per sample, and the sheer amount of related data increases the need for better models to enhance inference. Such models, however, are more effective if they take into account the idiosyncracies associated with the specific methods of measurement: where the numbers come from. We illustrate this point by describing three different measurement platforms: microarrays, serial analysis of gene expression (SAGE), and proteomic mass spectrometry.