Patient serum or plasma is frequently monitored for biochemical markers of disease or physiological status. Many of the rapidly evolving technologies of proteome analysis are being used to find additional clinically informative protein markers. The unusually high abundance of albumin in serum can interfere with the resolution and sensitivity of many proteome profiling techniques. We have used monoclonal antibodies against human serum albumin (HSA) to develop an immunoaffinity resin that is effective in the removal of both full-length HSA and many of the HSA fragments present in serum. This resin shows markedly better performance than dye-based resins in terms of both the efficiency and specificity of albumin removal. Immunoglobulins are another class of highly abundant serum protein. When protein G resin is used together with our immunoaffinity resin, Ig proteins and HSA can be removed in a single step. This strategy could be extended to the removal of any protein for which specific antibodies or affinity reagents are available.

Gene expression profiles are an increasingly common data source that can yield insights into the functions of cells at a system-wide level. The present work considers the limitations in information content of gene expression data for reverse engineering regulatory networks. An in silico genetic regulatory network was constructed for this purpose. Using the in silico network, a formal identifiability analysis was performed that considered the accuracy with which the parameters in the network could be estimated using gene expression data and prior structural knowledge (which transcription factors regulate which genes) as a function of the input perturbation and stochastic gene expression. The analysis yielded experimentally relevant results. It was observed that, in addition to prior structural knowledge, prior knowledge of kinetic parameters, particularly mRNA degradation rate constants, was necessary for the network to be identifiable. Also, with the exception of cases where the noise due to stochastic gene expression was high, complex perturbations were more favorable for identifying the network than simple ones. Although the results may be specific to the network considered, the present study provides a framework for posing similar questions in other systems.

We have developed a bioinformatics tool named PAINT that automates the promoter analysis of a given set of genes for the presence of transcription factor binding sites. Based on coincidence of regulatory sites, this tool produces an interaction matrix that represents a candidate transcriptional regulatory network. This tool currently consists of (1) a database of promoter sequences of known or predicted genes in the Ensembl annotated mouse genome database, (2) various modules that can retrieve and process the promoter sequences for binding sites of known transcription factors, and (3) modules for visualization and analysis of the resulting set of candidate network connections. This information provides a substantially pruned list of genes and transcription factors that can be examined in detail in further experimental studies on gene regulation. Also, the candidate network can be incorporated into network identification methods in the form of constraints on feasible structures in order to render the algorithms tractable for large-scale systems. The tool can also produce output in various formats suitable for use in external visualization and analysis software. In this manuscript, PAINT is demonstrated in two case studies involving analysis of differentially regulated genes chosen from two microarray data sets. The first set is from a neuroblastoma N1E-115 cell differentiation experiment, and the second set is from neuroblastoma N1E-115 cells at different time intervals following exposure to neuropeptide angiotensin II. PAINT is available for use as an agent in BioSPICE simulation and analysis framework (www.biospice.org), and can also be accessed via a WWW interface at www.dbi.tju.edu/dbi/tools/paint/.

A sequenced collection of plasmid-borne random fusions of Escherichia coli DNA to a Photorhabdus luminescens luxCDABE reporter was used as a starting point to select a set of 689 nonredundant functional gene fusions. This group, called LuxArray 1.0, represented 27% of the predicted transcriptional units in E. coli. High-density printing of the LuxArray 1.0 reporter strains to membranes on agar plates was used for simultaneous reporter gene assays of gene expression. The cellular response to nalidixic acid perturbation was analyzed using this format. As expected, fusions to promoters of LexA-controlled SOS-responsive genes dinG, dinB, uvrA, and ydjM were found to be upregulated in the presence of nalidixic acid. In addition, six fusions to genes not previously known to be induced by nalidixic acid were also reproducibly upregulated. The responses of two of these, fusions to oraA and yigN, were induced in a LexA-dependent manner by both nalidixic acid and mitomycin C, identifying these as members of the LexA regulon. The responses of the other four were neither induced by mitomycin C nor dependent on lexA function. Thus, the promoters of ycgH, intG, rihC, and a putative operon consisting of lpxA, lpxB, rnhB, and dnaE were not generally DNA damage responsive and represent a more specific response to nalidixic acid. These results demonstrate that cellular arrays of reporter gene fusions are an important alternative to DNA arrays for genomewide transcriptional analyses.