Expression of three topologically distinct membrane proteins elicits unique stress response pathways in the yeast Saccharomyces cerevisiae.

Misfolded membrane proteins are retained in the endoplasmic reticulum (ER) and are subject to ER-associated degradation, which clears the secretory pathway of potentially toxic species. While the transcriptional response to environmental stressors has been extensively studied, limited data exist describing the cellular response to misfolded membrane proteins. To this end, we expressed and then compared the transcriptional profiles elicited by the synthesis of three ER retained, misfolded ion channels: The α-subunit of the epithelial sodium channel, ENaC, the cystic fibrosis transmembrane conductance regulator, CFTR, and an inwardly rectifying potassium channel, Kir2.1, which vary in their mass, membrane topologies, and quaternary structures. To examine transcriptional profiles in a null background, the proteins were expressed in yeast, which was previously used to examine the degradation requirements for each substrate. Surprisingly, the proteins failed to induce a canonical unfolded protein response or heat shock response, although messages encoding several cytosolic and ER lumenal protein folding factors rose when αENaC or CFTR was expressed. In contrast, the levels of these genes were unaltered by Kir2.1 expression; instead, the yeast iron regulon was activated. Nevertheless, a significant number of genes that respond to various environmental stressors were upregulated by all three substrates, and compared with previous microarray data we deduced the existence of a group of genes that reflect a novel misfolded membrane protein response. These data indicate that aberrant proteins in the ER elicit profound yet unique cellular responses.

Mechanism of transfer of functional microRNAs between mouse dendritic cells via exosomes.

Dendritic cells (DCs) are the most potent APCs. Whereas immature DCs down-regulate T-cell responses to induce/maintain immunologic tolerance, mature DCs promote immunity. To amplify their functions, DCs communicate with neighboring DCs through soluble mediators, cell-to-cell contact, and vesicle exchange. Transfer of nanovesicles (< 100 nm) derived from the endocytic pathway (termed exosomes) represents a novel mechanism of DC-to-DC communication. The facts that exosomes contain exosome-shuttle miRNAs and DC functions can be regulated by exogenous miRNAs, suggest that DC-to-DC interactions could be mediated through exosome-shuttle miRNAs, a hypothesis that remains to be tested. Importantly, the mechanism of transfer of exosome-shuttle miRNAs from the exosome lumen to the cytosol of target cells is unknown. Here, we demonstrate that DCs release exosomes with different miRNAs depending on the maturation of the DCs. By visualizing spontaneous transfer of exosomes between DCs, we demonstrate that exosomes fused with the target DCs, the latter followed by release of the exosome content into the DC cytosol. Importantly, exosome-shuttle miRNAs are functional, because they repress target mRNAs of acceptor DCs. Our findings unveil a mechanism of transfer of exosome-shuttle miRNAs between DCs and its role as a means of communication and posttranscriptional regulation between DCs.

Differential hippocampal gene expression and pathway analysis in an etiology-based mouse model of major depressive disorder.

We have recently reported the creation and initial characterization of an etiology-based recombinant mouse model of a severe and inherited form of Major Depressive Disorder (MDD). This was achieved by replacing the corresponding mouse DNA sequence with a 6-base DNA sequence from the human CREB1 promoter that is associated with MDD in individuals from families with recurrent, early-onset MDD (RE-MDD). In the current study, we explored the effect of the pathogenic Creb1 allele on gene expression in the mouse hippocampus, a brain region that is altered in structure and function in MDD. Mouse whole-genome profiling was performed using the Illumina MouseWG-6 v2.0 Expression BeadChip microarray. Univariate analysis identified 269 differentially-expressed genes in the hippocampus of the mutant mouse. Pathway analyses highlighted 11 KEGG pathways: the phosphatidylinositol signaling system, which has been widely implicated in MDD, Bipolar Disorder, and the action of mood stabilizers; gap junction and long-term potentiation, which mediate cognition and memory functions often impaired in MDD; cardiac muscle contraction, insulin signaling pathway, and three neurodegenerative brain disorders (Alzheimer's, Parkinson's, and Huntington's Diseases) that are associated with MDD; ribosome and proteasome pathways affecting protein synthesis/degradation; and the oxidative phosphorylation pathway that is key to energy production. These findings illustrate the merit of this congenic C57BL/6 recombinant mouse as a model of RE-MDD, and demonstrate its potential for highlighting molecular and cellular pathways that contribute to the biology of MDD. The results also inform our understanding of the mechanisms that underlie the comorbidity of MDD with other disorders.

Lung tissues in patients with systemic sclerosis have gene expression patterns unique to pulmonary fibrosis and pulmonary hypertension.

OBJECTIVE: Pulmonary complications, including pulmonary fibrosis (PF) and pulmonary arterial hypertension (PAH), are the leading cause of mortality in patients with systemic sclerosis (SSc). The aim of this study was to compare the molecular fingerprint of lung tissue and matching primary fibroblasts from patients with SSc with that of lung tissue and fibroblasts from normal donors, patients with idiopathic pulmonary fibrosis (IPF), and patients with idiopathic pulmonary arterial hypertension (IPAH). METHODS: Lung tissue samples were obtained from 33 patients with SSc who underwent lung transplantation. Tissues and cells from a subgroup of SSc patients with predominantly PF or PAH were compared to those from normal donors, patients with IPF, and patients with IPAH. Microarray data were analyzed using efficiency analysis for determination of the optimal data-processing methods. Real-time polymerase chain reaction and immunohistochemistry were used to confirm differential levels of messenger RNA and protein, respectively. RESULTS: Consensus efficiency analysis identified 242 and 335 genes that were differentially expressed in lungs and primary fibroblasts, respectively. SSc-PF and IPF lungs shared enriched functional groups in genes implicated in fibrosis, insulin-like growth factor signaling, and caveolin-mediated endocytosis. Gene functional groups shared by SSc-PAH and IPAH lungs included those involved in antigen presentation, chemokine activity, and interleukin-17 signaling. CONCLUSION: Using microarray analysis on carefully phenotyped SSc and comparator lung tissues, we demonstrated distinct molecular profiles in tissues and fibroblasts from patients with SSc-associated lung disease compared to idiopathic forms of lung disease. Unique molecular signatures were generated that are disease specific (SSc) and phenotype specific (PF versus PAH). These signatures provide new insights into the pathogenesis and potential therapeutic targets of SSc-related lung disease.

Performance of whole-genome amplified DNA isolated from serum and plasma on high-density single nucleotide polymorphism arrays.

Defining genetic variation associated with complex human diseases requires standards based on high-quality DNA from well-characterized patients. With the development of robust technologies for whole-genome amplification, sample repositories such as serum banks now represent a potentially valuable source of DNA for both genomic studies and clinical diagnostics. We assessed the performance of whole-genome amplified DNA (wgaDNA) derived from stored serum/plasma on high-density single nucleotide polymorphism arrays. Neither storage time nor usage history affected either DNA extraction or whole-genome amplification yields; however, samples that were thawed and refrozen showed significantly lower call rates (73.9 +/- 7.8%) than samples that were never thawed (92.0 +/- 3.3%) (P < 0.001). Genotype call rates did not differ significantly (P = 0.13) between wgaDNA from never-thawed serum/plasma (92.9 +/- 2.6%) and genomic DNA (97.5 +/- 0.3%) isolated from whole blood. Approximately 400,000 genotypes were consistent between wgaDNA and genomic DNA, but the overall discordance rate of 4.4 +/- 3.8% reflected an average of 11,110 +/- 9502 genotyping errors per sample. No distinct patterns of chromosomal clustering were observed for single nucleotide polymorphisms showing discordant genotypes or homozygote conversion. Because the effects of genotyping errors on whole-genome studies are not well defined, we recommend caution when applying wgaDNA from serum/plasma to high-density single nucleotide polymorphism arrays in addition to the use of stringent quality control requirements for the resulting genotype data.

The genomic heritage of lymph node metastases: implications for clinical management of patients with breast cancer.

BACKGROUND: Metastatic breast cancer is an aggressive disease associated with recurrence and decreased survival. To improve outcomes and develop more effective treatment strategies for patients with breast cancer, it is important to understand the molecular mechanisms underlying metastasis. METHODS: We used allelic imbalance (AI) to determine the molecular heritage of primary breast tumors and corresponding metastases to the axillary lymph nodes. Paraffin-embedded samples from primary breast tumors and matched metastases (n = 146) were collected from 26 patients with node-positive breast cancer involving multiple axillary nodes. Hierarchical clustering was used to assess overall differences in the patterns of AI, and phylogenetic analysis inferred the molecular heritage of axillary lymph node metastases. RESULTS: Overall frequencies of AI were significantly higher (P < 0.01) in primary breast tumors (23%) than in lymph node metastases (15%), and there was a high degree of discordance in patterns of AI between primary breast carcinomas and the metastases. Metastatic tumors in the axillary nodes showed different patterns of chromosomal changes, suggesting that multiple molecular mechanisms may govern the process of metastasis in individual patients. Some metastases progressed with few genomic alterations, while others harbored many chromosomal alterations present in the primary tumor. CONCLUSIONS: The extent of genomic heterogeneity in axillary lymph node metastases differs markedly among individual patients. Genomic diversity may be associated with response to adjuvant therapy, recurrence, and survival, and thus may be important in improving clinical management of breast cancer patients.

Functional identity of genes detectable in expression profiling assays following globin mRNA reduction of peripheral blood samples.

OBJECTIVES: Whole-blood RNA for microarray analysis is easily accessible but contains a large proportion of globin mRNA that interferes with the accurate assessment of other genes. This study investigated the biological significance of genes whose expression was unmasked by globin mRNA reduction in peripheral blood. DESIGN AND METHODS: Samples were collected from healthy subjects using the PAXgene Blood RNA System, and globin mRNA was depleted using GLOBINclear. Genes exhibiting consistent changes in expression on Affymetrix HU133A 2.0 arrays were characterized in three main areas of gene ontology--molecular function, biological process, and cellular component. RESULTS: Globin reduction permitted detection of 2652+/-395 additional genes per assay. Genes unmasked by globin reduction include low abundance transcripts that function primarily as molecular binding proteins and catalytic enzymes in biological processes including transcription, replication, and intracellular transport and signalling. Protein products of these genes are preferentially associated with membranes and the nucleus. CONCLUSIONS: Additional genes detectable only after globin reduction in whole-blood RNA function in a variety of biological processes that may be important to diverse fields of study.

Plasma concentration and activity of matrix metalloproteinase 2 and 9 in patients with breast disease, breast cancer and at risk of developing breast cancer.

Matrix metalloproteinases (MMPs) are involved in extracellular matrix modification and associated with invasive and metastatic behavior of human malignant tumors. Specifically, MMP2 and MMP9 are implicated in both early and late processes of tumor development. It is reported that MMPs occur as inactive precursors, active enzymes or enzyme inhibitor complexes in biological samples. However, there is limited knowledge on the role of each form in disease and/or the significance of changes in the plasma concentration and/or activity in breast cancer patients. The aim of this study was to determine if patients with breast cancer, benign disease and at risk for developing breast cancer display characteristic levels of active and/or total MMP2 and MMP9 in plasma. Concentration and activity of MMP2 and MMP9 were determined quantitatively in the plasma of 124 female volunteers diagnosed with breast cancer (n=31), benign disease (n=38), or determined by the Gail Model to be at high risk (n=31) or low risk (controls, n=24) of developing breast cancer. Data obtained was statistically analyzed to search for differences/patterns characteristic of each category. Concentration of total MMP2 was significantly lower in control individuals than benign, high risk (P<0.001 respectively) and breast cancer patients (P=0.002). Activity of total MMP2 was significantly lower in controls compared to cancer, benign and high risk patients (P<0.001 respectively). Attempts to build a predictive/descriptive model using canonical discriminant analysis (utilizing all eight features; concentrations and activity levels of active/total MMP2 and MMP9) enabled the distinction of the controls from the high risk, benign and cancer groups. Our results suggest that preoperative plasma concentration and activity of MMP2 and MMP9 may permit sub-classification of female patients with breast disorders.

Co-occurrence analysis for discovery of novel breast cancer pathology patterns.

To discover novel patterns in pathology co-occurrence, we have developed algorithms to analyze and visualize pathology co-occurrence. With access to a database of pathology reports, collected under a single protocol and reviewed by a single pathologist, we can conduct an analysis greater in its scope than previous studies looking at breast pathology co-occurrence. Because this data set is unique, specialized methods for pathology co-occurrence analysis and visualization are developed. Primary analysis is through a co-occurrence score based on the Jaccard coefficient. Density maps are used to visualize global co-occurrence. When our co-occurrence analysis is applied to a population stratified by menopausal status, we can successfully identify statistically significant differences in pathology co-occurrence patterns between premenopausal and postmenopausal women. Genomic and proteomic experiments are planned to discover biological mechanisms that may underpin differences seen in pathology patterns between populations.

Biomedical informatics: development of a comprehensive data warehouse for clinical and genomic breast cancer research.

The Windber Research Institute is an integrated high-throughput research center employing clinical, genomic and proteomic platforms to produce terabyte levels of data. We use biomedical informatics technologies to integrate all of these operations. This report includes information on a multi-year, multi-phase hybrid data warehouse project currently under development in the Institute. The purpose of the warehouse is to host the terabyte-level of internal experimentally generated data as well as data from public sources. We have previously reported on the phase I development, which integrated limited internal data sources and selected public databases. Currently, we are completing phase II development, which integrates our internal automated data sources and develops visualization tools to query across these data types. This paper summarizes our clinical and experimental operations, the data warehouse development, and the challenges we have faced. In phase III we plan to federate additional manual internal and public data sources and then to develop and adapt more data analysis and mining tools. We expect that the final implementation of the data warehouse will greatly facilitate biomedical informatics research.

Global search for chromosomal abnormalities in infiltrating ductal carcinoma of the breast using array-comparative genomic hybridization.

Array-comparative genomic hybridization (a-CGH) is a molecular cytogenetic technique for detection of multiple chromosomal abnormalities in genomic DNA samples. Using an a-CGH with 287 probes, we examined 14 cases of breast infiltrating ductal carcinoma (IDCA) that had previously been classified by fluorescent in situ hybridization (FISH) as either human epidermal growth factor receptor-2 positive (HER2+) or HER2- and analyzed the data by hierarchical, K-means, and principal component analyses. The aim of the study was to identify the genetic abnormalities that are present in breast IDCAs and determine if the global status of 287 cytogenetic locations could be used as a more objective method for breast IDCA classification. Concordance between FISH and a-CGH at the HER2 locus was 78.6% (11/14). In general, a-CGH detected more abnormalities in HER2+ cases. In HER 2+ cases, chromosomes 1, 2, 3, 7, 9, 17, and 20 had more regions that showed statistically significant (P < or = 0.01) changes in DNA copy number. Among all the aberrant cytogenetic locations detected, 20q13, 7p12.3 approximately p12.1, and 17q23.2 approximately q25.3, which contain among others, genes for TNFRSF6B, EGFR, and TK1 showed statistically significant gains (P < or = 0.01) in 83, 66.7, and 50% of the HER2+ IDCA cases, respectively. Chromosome location 8q24.12 approximately q24.13 was the only region that showed consistent amplification in approximately 50% of the HER2- cases. Unsupervised hierarchical and K-means cluster analyses and principal component analysis using the DNA copy number status of 287 cytogenetic locations or the 177 cytogenetic locations that showed statistically significant differences revealed a cluster consisting of mainly HER2- IDCA cases. Even though this study demonstrates the usefulness of a-CGH in the rapid identification of aberrant DNA regions in tumor samples, we conclude that an array-CGH with more than 287 probes will be needed for a more precise mapping of DNA aberrations at the global level.

Semi-automated literature mining to identify putative biomarkers of disease from multiple biofluids.

BACKGROUND: Computational methods for mining of biomedical literature can be useful in augmenting manual searches of the literature using keywords for disease-specific biomarker discovery from biofluids. In this work, we develop and apply a semi-automated literature mining method to mine abstracts obtained from PubMed to discover putative biomarkers of breast and lung cancers in specific biofluids. METHODOLOGY: A positive set of abstracts was defined by the terms 'breast cancer' and 'lung cancer' in conjunction with 14 separate 'biofluids' (bile, blood, breastmilk, cerebrospinal fluid, mucus, plasma, saliva, semen, serum, synovial fluid, stool, sweat, tears, and urine), while a negative set of abstracts was defined by the terms '(biofluid) NOT breast cancer' or '(biofluid) NOT lung cancer.' More than 5.3 million total abstracts were obtained from PubMed and examined for biomarker-disease-biofluid associations (34,296 positive and 2,653,396 negative for breast cancer; 28,355 positive and 2,595,034 negative for lung cancer). Biological entities such as genes and proteins were tagged using ABNER, and processed using Python scripts to produce a list of putative biomarkers. Z-scores were calculated, ranked, and used to determine significance of putative biomarkers found. Manual verification of relevant abstracts was performed to assess our method's performance. RESULTS: Biofluid-specific markers were identified from the literature, assigned relevance scores based on frequency of occurrence, and validated using known biomarker lists and/or databases for lung and breast cancer [NCBI's On-line Mendelian Inheritance in Man (OMIM), Cancer Gene annotation server for cancer genomics (CAGE), NCBI's Genes & Disease, NCI's Early Detection Research Network (EDRN), and others]. The specificity of each marker for a given biofluid was calculated, and the performance of our semi-automated literature mining method assessed for breast and lung cancer. CONCLUSIONS: We developed a semi-automated process for determining a list of putative biomarkers for breast and lung cancer. New knowledge is presented in the form of biomarker lists; ranked, newly discovered biomarker-disease-biofluid relationships; and biomarker specificity across biofluids.

Development and validation of a standardized method for the determination of morpholine residues in fruit commodities by liquid chromatography-mass spectrometry.

An analytical method was developed for the determination of morpholine on apples and citrus. The method utilized acidified methanol extraction, centrifugation, and determination by hydrophilic interaction liquid chromatography with electrospray ionization and tandem mass spectrometry (HILIC-ESI-MS/MS). Validation of the method occurred at the Pacific Agricultural Laboratory (PAL, Portland, OR, USA) and the Trace Analytical Laboratory (TAL, UC Davis, CA, USA). Method validation recoveries from control apple, orange, lemon, and grapefruit samples ranged from 84 to 120% over three levels of fortification (0.01, 0.04, and 0.2 µg/g). The limit of quantitation (LOQ) for all commodities was 0.01 µg/g, and the calculated method detection limit (MDL) ranged from 0.0010 to 0.0040 µg/g.

The effect of selenium enrichment on baker's yeast proteome.

The use of regular yeast (RY) and selenium-enriched yeast (SEY) as dietary supplement is of interest because the Nutritional Prevention of Cancer (NPC) trial revealed that SEY but not RY decreased the incidence of prostate cancer (PC). Using two-dimensional difference in gel electrophoresis (2D-DIGE)-tandem mass spectrometry (MS/MS) approach, we performed proteomic analysis of RY and SEY to identify proteins that are differentially expressed as a result of selenium enrichment. 2D-DIGE revealed 96 candidate protein spots that were differentially expressed (p≤0.05) between SEY and RY. The 96 spots were selected, sequenced by LC/MS/MS and 37 proteins were unequivocally identified. The 37 identified proteins were verified with ProteinProphet software and mapped to existing Gene Ontology categories. Furthermore, the expression profile of 5 of the proteins with validated or putative roles in the carcinogenesis process, and for which antibodies against human forms of the proteins are available commercially was verified by western analysis. This study provides evidence for the first time that SEY contains higher levels of Pyruvate Kinase, HSP70, and Elongation factor 2 and lower levels of Eukaryotic Translation Initiation Factor 5A-2 and Triosephosphate Isomerase than those found in RY.

Optimization of the Use of Consensus Methods for the Detection and Putative Identification of Peptides via Mass Spectrometry Using Protein Standard Mixtures.

Correct identification of peptides and proteins in complex biological samples from proteomic mass-spectra is a challenging problem in bioinformatics. The sensitivity and specificity of identification algorithms depend on underlying scoring methods, some being more sensitive, and others more specific. For high-throughput, automated peptide identification, control over the algorithms' performance in terms of trade-off between sensitivity and specificity is desirable. Combinations of algorithms, called 'consensus methods', have been shown to provide more accurate results than individual algorithms. However, due to the proliferation of algorithms and their varied internal settings, a systematic understanding of relative performance of individual and consensus methods are lacking. We performed an in-depth analysis of various approaches to consensus scoring using known protein mixtures, and evaluated the performance of 2310 settings generated from consensus of three different search algorithms: Mascot, Sequest, and X!Tandem. Our findings indicate that the union of Mascot, Sequest, and X!Tandem performed well (considering overall accuracy), and methods using 80-99.9% protein probability and/or minimum 2 peptides and/or 0-50% minimum peptide probability for protein identification performed better (on average) among all consensus methods tested in terms of overall accuracy. The results also suggest method selection strategies to provide direct control over sensitivity and specificity.

Efficiency analysis of competing tests for finding differentially expressed genes in lung adenocarcinoma.

In this study, we introduce and use Efficiency Analysis to compare differences in the apparent internal and external consistency of competing normalization methods and tests for identifying differentially expressed genes. Using publicly available data, two lung adenocarcinoma datasets were analyzed using caGEDA (http://bioinformatics2.pitt.edu/GE2/GEDA.html) to measure the degree of differential expression of genes existing between two populations. The datasets were randomly split into at least two subsets, each analyzed for differentially expressed genes between the two sample groups, and the gene lists compared for overlapping genes. Efficiency Analysis is an intuitive method that compares the differences in the percentage of overlap of genes from two or more data subsets, found by the same test over a range of testing methods. Tests that yield consistent gene lists across independently analyzed splits are preferred to those that yield less consistent inferences. For example, a method that exhibits 50% overlap in the 100 top genes from two studies should be preferred to a method that exhibits 5% overlap in the top 100 genes. The same procedure was performed using all available normalization and transformation methods that are available through caGEDA. The 'best' test was then further evaluated using internal cross-validation to estimate generalizable sample classification errors using a Naïve Bayes classification algorithm. A novel test, termed D1 (a derivative of the J5 test) was found to be the most consistent, and to exhibit the lowest overall classification error, and highest sensitivity and specificity. The D1 test relaxes the assumption that few genes are differentially expressed. Efficiency Analysis can be misleading if the tests exhibit a bias in any particular dimension (e.g. expression intensity); we therefore explored intensity-scaled and segmented J5 tests using data in which all genes are scaled to share the same intensity distribution range. Efficiency Analysis correctly predicted the 'best' test and normalization method using the Beer dataset and also performed well with the Bhattacharjee dataset based on both efficiency and classification accuracy criteria.

Circulating MMP2 and MMP9 in breast cancer -- potential role in classification of patients into low risk, high risk, benign disease and breast cancer categories.

Matrix metalloproteinase (MMP) 2 and 9 are involved in cancer invasion and metastasis, and increased levels occur in serum and plasma of breast cancer (BC) patients. It is, however, unclear whether changes in serum levels can be exploited for early detection or classification of patients into different risk/disease categories. In our study, we measured concentration and activity of MMP2/9 in sera of 345 donors classified as low risk (Gail score <1.7), high risk (HR) (Gail score > or =1.7), benign disease or BC. Kruskal-Wallis and Mann-Whitney nonparametric tests showed that total-MMP2 concentration is higher in HR compared to control (p = 0.012), benign (p = 0.001) and cancer (p = 0.007). Active MMP2 (aMMP2) concentration is higher in control than benign and cancer (p < 0.001, respectively). Total and aMMP9 concentrations are higher in cancer than benign (p < 0.001, p = 0.002, respectively). Total-MMP2 and total-MMP9 activities are lower in control than benign (p < 0.001, p = 0.002, respectively) and cancer (p < 0.001, respectively). Total-MMP2 and MMP9 activities are also higher in cancer than benign (p = 0.004, p < 0.001) and HR (p = 0.008, p = 0.007, respectively). These results were not affected by age or inclusion/exclusion of donors with noninvasive cancer or atypical hyperplasia. Linear discriminant analysis revealed that HR donors are characterized by lower total-MMP2 and higher aMMP2. Overall group classification accuracy was 64.5%. Independent validation based on the leave-one-out cross validation approach gave an overall classification of 63%. Our study provides evidence supporting the potential role of serum MMP2/9 as biomarkers for breast disease classification.

High-throughput proteomic analysis of human infiltrating ductal carcinoma of the breast.

Large-scale proteomics will play a critical role in the rapid display, identification and validation of new protein targets, and elucidation of the underlying molecular events that are associated with disease development, progression and severity. However, because the proteome of most organisms are significantly more complex than the genome, the comprehensive analysis of protein expression changes will require an analytical effort beyond the capacity of standard laboratory equipment. We describe the first high-throughput proteomic analysis of human breast infiltrating ductal carcinoma (IDCA) using OCT (optimal cutting temperature) embedded biopsies, two-dimensional difference gel electrophoresis (2-D DIGE) technology and a fully automated spot handling workstation. Total proteins from four breast IDCAs (Stage I, IIA, IIB and IIIA) were individually compared to protein from non-neoplastic tissue obtained from a female donor with no personal or family history of breast cancer. We detected differences in protein abundance that ranged from 14.8% in stage I IDCA versus normal, to 30.6% in stage IIB IDCA versus normal. A total of 524 proteins that showed > or = three-fold difference in abundance between IDCA and normal tissue were picked, processed and identified by mass spectrometry. Out of the proteins picked, approximately 80% were unambiguously assigned identities by matrix-assisted laser desorbtion/ionization-time of flight mass spectrometry or liquid chromatography-tandem mass spectrometry in the first pass. Bioinformatics tools were also used to mine databases to determine if the identified proteins are involved in important pathways and/or interact with other proteins. Gelsolin, vinculin, lumican, alpha-1-antitrypsin, heat shock protein-60, cytokeratin-18, transferrin, enolase-1 and beta-actin, showed differential abundance between IDCA and normal tissue, but the trend was not consistent in all samples. Out of the proteins with database hits, only heat shock protein-70 (more abundant) and peroxiredoxin-2 (less abundant) displayed the same trend in all the IDCAs examined. This preliminary study demonstrates quantitative and qualitative differences in protein abundance between breast IDCAs and reveals 2-D DIGE portraits that may be a reflection of the histological and pathological status of breast IDCA.