Proteomic analysis of breast tumors confirms the mRNA intrinsic molecular subtypes using different classifiers: a large-scale analysis of fresh frozen tissue samples.

BACKGROUND: Breast cancer is a complex and heterogeneous disease that is usually characterized by histological parameters such as tumor size, cellular arrangements/rearrangments, necrosis, nuclear grade and the mitotic index, leading to a set of around twenty subtypes. Together with clinical markers such as hormone receptor status, this classification has considerable prognostic value but there is a large variation in patient response to therapy. Gene expression profiling has provided molecular profiles characteristic of distinct subtypes of breast cancer that reflect the divergent cellular origins and degree of progression. METHODS: Here we present a large-scale proteomic and transcriptomic profiling study of 477 sporadic and hereditary breast cancer tumors with matching mRNA expression analysis. Unsupervised hierarchal clustering was performed and selected proteins from large-scale tandem mass spectrometry (MS/MS) analysis were transferred into a highly multiplexed targeted selected reaction monitoring assay to classify tumors using a hierarchal cluster and support vector machine with leave one out cross-validation. RESULTS: The subgroups formed upon unsupervised clustering agree very well with groups found at transcriptional level; however, the classifiers (genes or their respective protein products) differ almost entirely between the two datasets. In-depth analysis shows clear differences in pathways unique to each type, which may lie behind their different clinical outcomes. Targeted mass spectrometry analysis and supervised clustering correlate very well with subgroups determined by RNA classification and show convincing agreement with clinical parameters. CONCLUSIONS: This work demonstrates the merits of protein expression profiling for breast cancer stratification. These findings have important implications for the use of genomics and expression analysis for the prediction of protein expression, such as receptor status and drug target expression. The highly multiplexed MS assay is easily implemented in standard clinical chemistry practice, allowing rapid and cheap characterization of tumor tissue suitable for directing the choice of treatment.

Changes in glycoprotein expression between primary breast tumour and synchronous lymph node metastases or asynchronous distant metastases.

BACKGROUND: Breast cancer is a very heterogeneous disease and some patients are cured by the surgical removal of the primary tumour whilst other patients suffer from metastasis and spreading of the disease, despite adjuvant therapy. A number of prognostic and treatment predictive factors have been identified such as tumour size, oestrogen (ER) and progesterone (PgR) receptor status, human epidermal growth factor receptor type 2 (HER2) status, histological grade, Ki67 and age. Lymph node involvement is also assessed during surgery to determine if the tumour has spread which requires dissection of the axilla and adjuvant treatment. The prognostic and treatment predictive factors assessing the nature of the tumour are all routinely based on the status of the primary tumour. RESULTS: We have analysed a unique tumour set of fourteen primary breast cancer tumours with matched synchronous axillary lymph node metastases and a set of nine primary tumours with, later developed, matched distant metastases from different sites in the body. We used a pairwise tumour analysis (from the same individual) since the difference between the same tumour-type in different patients was greater. Glycopeptide capture was used in this study to selectively isolate and quantify N-linked glycopeptides from tumours mixtures and the captured glycopeptides were subjected to label-free quantitative tandem mass spectrometry analysis. Differentially expressed proteins between primary tumours and matched lymph node metastasis and distant metastasis were identified. Two of the top hits, ATPIF1 and tubulin ß-chain were validated by immunohistochemistry to be differentially regulated. CONCLUSIONS: We show that the expression of a large number of glycosylated proteins change between primary tumours and matched lymph node metastases and distant metastases, confirming that cancer cells undergo a molecular transformation during the spread to a secondary site. The proteins are part of important pathways such as cell adhesion, migration pathways and immune response giving insight into molecular changes needed for the tumour to spread. The large difference between primary tumours and lymph node and distant metastases also suggest that treatment should be based on the phenotype of the lymph node and distant metastases.

Confirmation of protein biomarkers of corticosteroids treatment in veal calves sampled under field conditions.

In veal calf production, growth promoters are still illicitly used. Surveillance of misuse of such molecules is necessary to preserve human health. Methods currently adopted for their analysis are based on liquid chromatography-tandem mass spectrometry, but their efficacy can be affected by undetectable residual concentrations in biological matrices due to treatments at low-dosage or based on unknown anabolic compounds. The development of screening methods to identify the indirect biological effects of administration of growth promoters can improve the efficiency of drug residue monitoring. To this purpose, an integrated approach has been used to further validate the set of protein biomarkers defined in a previous controlled study to detect the use of corticosteroids through the changes caused in muscle protein expression. The thymus morphology of 48 samples collected under field conditions was evaluated to assess the presence of potential corticosteroids treatment. Animals were divided on the basis of their thymus characteristics in negative or suspected for illegal corticosteroids treatment. Drug residue analyses were performed on the liver, giving a satisfactory correlation with the histological examination (∼85%). Finally, the proteomics analysis of muscle protein extracts was carried out by 2D differential in gel electrophoresis, and proteins that were differentially expressed between the two animal groups (p value <0.01) were selected for MALDI-MS/MS analysis. This approach allowed us to identify 29 different proteins, and our findings indicate that the altered protein expression pattern can be used as an indirect method for the detection of illicit corticosteroids administration. A subset of the identified proteins was already reported in a previous controlled study, proving that these biomarkers can be used to develop a screening assay to improve the tools currently available for the detection of corticosteroids abuse in bovine meat production.

A synthetic data set to benchmark anti-money laundering methods.

Bank transactions are highly confidential. As a result, there are no real public data sets that can be used to investigate and compare anti-money laundering (AML) methods in banks. This severely limits research on important AML problems such as efficiency, effectiveness, class imbalance, concept drift, and interpretability. To address the issue, we present SynthAML: a synthetic data set to benchmark statistical and machine learning methods for AML. The data set builds on real data from Spar Nord, a systemically important Danish bank, and contains 20,000 AML alerts and over 16 million transactions. Experimental results indicate that performance on SynthAML can be transferred to the real world. As use cases, we present and discuss open problems in the AML literature.

Protein expression changes in ovarian cancer during the transition from benign to malignant.

Epithelial ovarian carcinoma has in general a poor prognosis since the vast majority of tumors are genomically unstable and clinically highly aggressive. This results in rapid progression of malignancy potential while still asymptomatic and thus in late diagnosis. It is therefore of critical importance to develop methods to diagnose epithelial ovarian carcinoma at its earliest developmental stage, that is, to differentiate between benign tissue and its early malignant transformed counterparts. Here we present a shotgun quantitative proteomic screen of benign and malignant epithelial ovarian tumors using iTRAQ technology with LC-MALDI-TOF/TOF and LC-ESI-QTOF MS/MS. Pathway analysis of the shotgun data pointed to the PI3K/Akt signaling pathway as a significant discriminatory pathway. Selected candidate proteins from the shotgun screen were further confirmed in 51 individual tissue samples of normal, benign, borderline or malignant origin using LC-MRM analysis. The MRM profile demonstrated significant differences between the four groups separating the normal tissue samples from all tumor groups as well as perfectly separating the benign and malignant tumors with a ROC-area of 1. This work demonstrates the utility of using a shotgun approach to filter out a signature of a few proteins only that discriminates between the different sample groups.

Generic workflow for quality assessment of quantitative label-free LC-MS analysis.

As high-resolution instruments are becoming standard in proteomics laboratories, label-free quantification using precursor measurements is becoming a viable option, and is consequently rapidly gaining popularity. Several software solutions have been presented for label-free analysis, but to our knowledge no conclusive studies regarding the sensitivity and reliability of each step of the analysis procedure has been described. Here, we use real complex samples to assess the reliability of label-free quantification using four different software solutions. A generic approach to quality test quantitative label-free LC-MS is introduced. Measures for evaluation are defined for feature detection, alignment and quantification. All steps of the analysis could be considered adequately performed by the utilized software solutions, although differences and possibilities for improvement could be identified. The described method provides an effective testing procedure, which can help the user to quickly pinpoint where in the workflow changes are needed.

Protein expression changes in skeletal muscle in response to growth promoter abuse in beef cattle.

The fraudulent treatment of cattle with growth promoting agents (GPAs) is a matter of great concern for the European Union (EU) authorities and consumers. It has been estimated that 10% of animals are being illegally treated in the EU. In contrast, only a much lower percentage of animals (<0.5%) are actually found as being noncompliant by conventional analytical methods. Thus, it has been proposed that methods should be developed that can detect the use of the substances via the biological effects of these substances on target organs, such as the alteration of protein expression profiles. Here we present a study aimed at evaluating if a correlation exists between the treatment with GPAs and alterations in the two-dimensional electrophoresis (2DE) protein pattern obtained from the biceps brachii skeletal muscle from mixed-bred cattle. After image analysis and statistical evaluation, protein spots that differentiate between treated and control groups were selected for analysis by mass spectrometry. A set of proteins could be defined that accurately detect the use of glucocorticoids and ß(2)-agonists as growth promoters through the changes caused in muscle differentiation. As a further validation, we repeated the analysis using an independent set of samples from a strain of pure-bred cattle and verified these proteins by Western blot analysis.

Hunting for protein markers of hypoxia by combining plasma membrane enrichment with a new approach to membrane protein analysis.

Nontransient hypoxia is strongly associated with malignant lesions, resulting in aggressive behavior and resistance to treatment. We present an analysis of mRNA and protein expression changes in neuroblastoma cell lines occurring upon the transition from normoxia to hypoxia. The correlation between mRNA and protein level changes was poor, although some known hypoxia-driven genes and proteins correlated well. We present previously undescribed membrane proteins expressed under hypoxic conditions that are candidates for evaluation as biomarkers.

Dendritic EGFP-STIM1 activation after type I metabotropic glutamate and muscarinic acetylcholine receptor stimulation in hippocampal neuron.

Several signaling pathways in neurons engage the endoplasmic reticulum (ER) calcium store by triggering calcium release. After release, ER calcium levels must be restored. In many non-neuronal cell types, this is mediated by store-operated calcium entry (SOCE), a cellular homeostatic mechanism that activates specialized store-operated calcium channels (SOC). Although much evidence supports the existence of SOCE in neurons, its importance has been difficult to determine because of the abundance of calcium channels expressed and the lack of SOC-specific pharmacological agents. We have explored the function of the SOCE-inducing protein STIM1 in neurons. In EGFP-STIM1-expressing hippocampal neurons, the sarco- and endoplasmic reticulum calcium ATPase (SERCA) inhibitor thapsigargin caused rapid aggregation (i.e., activation) of STIM1 in soma and dendrites. Upon STIM1 activation by thapsigargin, a dramatic reduction in STIM1 mobility was detected by fluorescence recovery after photobleaching (FRAP). By triggering release of ER calcium with 3,5-dihydroxyphenylglycine (DHPG) or carbachol (Cch), agonists of type I metabotropic glutamate receptors (mGluR) and muscarinic acetylcholine receptors (mAChR), respectively, STIM1 was activated, and calcium entry (likely to represent SOCE) occurred in dendrites. It is therefore possible that neuronal SOCE is activated by physiological stimuli, some of which may alter the postsynaptic calcium signaling properties.

The antiproliferative effect of dietary fiber phenolic compounds ferulic acid and p-coumaric acid on the cell cycle of Caco-2 cells.

Epidemiological and animal studies have shown that dietary fiber is protective against the development of colon cancer. Dietary fiber is a rich source of the hydroxycinnamic acids ferulic acid (FA) and p-coumaric acid (p-CA), which both may contribute to the protective effect. We have investigated the effects of FA and p-CA treatment on global gene expression in Caco-2 colon cancer cells. The Caco-2 cells were treated with 150 µM FA or p-CA for 24 h, and gene expression was analyzed with cDNA microarray technique. A total of 517 genes were significantly affected by FA and 901 by p-CA. As we previously have found that FA or p-CA treatment delayed cell cycle progression, we focused on genes involved in proliferation and cell cycle regulation. The expressions of a number of genes involved in centrosome assembly, such as RABGAP1 and CEP2, were upregulated by FA treatment as well as the gene for the S phase checkpoint protein SMC1L1. p-CA treatment upregulated CDKN1A expression and downregulated CCNA2, CCNB1, MYC, and ODC1. Some proteins corresponding to the affected genes were also studied. Taken together, the changes found can partly explain the effects of FA or p-CA treatment on cell cycle progression, specifically in the S phase by FA and G(2)/M phase by p-CA treatment.

Omics analyses reveal a potential link between hormone-sensitive lipase and polyamine metabolism.

Hormone-sensitive lipase (HSL), a key enzyme in fatty acid mobilization from lipid stores, is expressed in the liver and decreased hepatic insulin sensitivity has been reported in our HSL null mouse model. Here, an integrated approach, comprising transcriptomics and proteomics together with targeted metabolite analysis, was used to investigate the liver phenotype of HSL null mice. Oligonucleotide microarray analysis revealed altered expression of genes involved in lipid and polyamine metabolism in HSL null mice compared with wild-type mice and in genes controlling the immune system in mice on high-fat diet versus mice on normal diet. Two-dimensional gel electrophoresis followed by MS and/or MS/MS allowed identification of 52 and 22 unique proteins differentially regulated according to the genotype and diet, respectively. Changes were observed mainly for proteins related to metabolism, including several proteins involved in polyamine metabolism or exhibiting methyl transferase activity. Despite the coordinated changes in mRNA and protein levels in polyamine pathways, no significant differences in levels of key polyamine metabolites were detected between the two genotypes. This study identifies a link between HSL and polyamine metabolism, which deserves further attention in view of the emerging data suggesting that disturbances in polyamine metabolism may affect insulin sensitivity. The present work also describes a limited correlation between mRNA, protein and metabolite levels, thus, underscoring the importance of integrated approaches.

Detection of pancreatic cancer using antibody microarray-based serum protein profiling.

The driving force behind oncoproteomics is to identify protein signatures that are associated with a particular malignancy. Here, we have used a recombinant scFv antibody microarray in an attempt to classify sera derived from pancreatic adenocarcinoma patients versus healthy subjects. Based on analysis of nonfractionated, directly labeled, whole human serum proteomes we have identified a protein signature based on 19 nonredundant analytes, that discriminates between cancer patients and healthy subjects. Furthermore, a potential protein signature, consisting of 21 protein analytes, could be defined that was shown to be associated with cancer patients having a life expectancy of <12 months. Taken together, the data suggest that antibody microarray analysis of complex proteomes will be a useful tool to define disease associated protein signatures.

Gene expression profiling in primary breast cancer distinguishes patients developing local recurrence after breast-conservation surgery, with or without postoperative radiotherapy.

INTRODUCTION: Some patients with breast cancer develop local recurrence after breast-conservation surgery despite postoperative radiotherapy, whereas others remain free of local recurrence even in the absence of radiotherapy. As clinical parameters are insufficient for identifying these two groups of patients, we investigated whether gene expression profiling would add further information. METHODS: We performed gene expression analysis (oligonucleotide arrays, 26,824 reporters) on 143 patients with lymph node-negative disease and tumor-free margins. A support vector machine was employed to build classifiers using leave-one-out cross-validation. RESULTS: Within the estrogen receptor-positive (ER+) subgroup, the gene expression profile clearly distinguished patients with local recurrence after radiotherapy (n = 20) from those without local recurrence (n = 80 with or without radiotherapy). The receiver operating characteristic (ROC) area was 0.91, and 5,237 of 26,824 reporters had a P value of less than 0.001 (false discovery rate = 0.005). This gene expression profile provides substantially added value to conventional clinical markers (for example, age, histological grade, and tumor size) in predicting local recurrence despite radiotherapy. Within the ER- subgroup, a weaker, but still significant, signal was found (ROC area = 0.74). The ROC area for distinguishing patients who develop local recurrence from those who remain local recurrence-free in the absence of radiotherapy was 0.66 (combined ER+/ER-). CONCLUSION: A highly distinct gene expression profile for patients developing local recurrence after breast-conservation surgery despite radiotherapy has been identified. If verified in further studies, this profile might be a most important tool in the decision making for surgery and adjuvant therapy.

Basal-like phenotype is not associated with patient survival in estrogen-receptor-negative breast cancers.

INTRODUCTION: Basal-phenotype or basal-like breast cancers are characterized by basal epithelium cytokeratin (CK5/14/17) expression, negative estrogen receptor (ER) status and distinct gene expression signature. We studied the clinical and biological features of the basal-phenotype tumors determined by immunohistochemistry (IHC) and cDNA microarrays especially within the ER-negative subgroup. METHODS: IHC was used to evaluate the CK5/14 status of 445 stage II breast cancers. The gene expression signature of the CK5/14 immunopositive tumors was investigated within a subset (100) of the breast tumors (including 50 ER-negative tumors) with a cDNA microarray. Survival for basal-phenotype tumors as determined by CK5/14 IHC and gene expression signature was assessed. RESULTS: From the 375 analyzable tumor specimens, 48 (13%) were immunohistochemically positive for CK5/14. We found adverse distant disease-free survival for the CK5/14-positive tumors during the first years (3 years hazard ratio (HR) 2.23, 95% confidence interval (CI) 1.17 to 4.24, p = 0.01; 5 years HR 1.80, 95% CI 1.02 to 3.15, p = 0.04) but the significance was lost at the end of the follow-up period (10 years HR 1.43, 95% CI 0.84 to 2.43, p = 0.19). Gene expression profiles of immunohistochemically determined CK5/14-positive tumors within the ER-negative tumor group implicated 1,713 differently expressed genes (p < 0.05). Hierarchical clustering analysis with the top 500 of these genes formed one basal-like and a non-basal-like cluster also within the ER-negative tumor entity. A highly concordant classification could be constructed with a published gene set (Sorlie's intrinsic gene set, concordance 90%). Both gene sets identified a basal-like cluster that included most of the CK5/14-positive tumors, but also immunohistochemically CK5/14-negative tumors. Within the ER-negative tumor entity there was no survival difference between the non-basal and basal-like tumors as identified by immunohistochemical or gene-expression-based classification. CONCLUSION: Basal cytokeratin-positive tumors have a biologically distinct gene expression signature from other ER-negative tumors. Even if basal cytokeratin expression predicts early relapse among non-selected tumors, the clinical outcome of basal tumors is similar to non-basal ER-negative tumors. Immunohistochemically basal cytokeratin-positive tumors almost always belong to the basal-like gene expression profile, but this cluster also includes few basal cytokeratin-negative tumors.

Somatostatin effects on the proteome of the LNCaP cell-line.

Some clinical results indicate that somatostatin (sms) might be useful in the treatment of advanced prostate cancer (HRPC). Because of its transient in vivo half-life only more stable derivatives of sms are of interest in this context. Recent studies have shown that natural sms can be conjugated to a carbohydrate (smsdx) with preservation of sms-like effects on the prostatic tumor cell proteome. The present study identifies some of the affected proteins in an effort to elucidate pathways and proteins that might be of importance for the potential usefulness of sms treatment in HRPC. After incubating the LNCaP cell-line with sms14/smsdx, comparative proteomics was used for analysing and identifying affected proteins. Protein expression patterns were analysed with two-dimensional polyacrylamide gel electrophoresis and mass spectrometry. Catalytic mitochondrial and mitochondrial-associated proteins were significantly affected (fold change approximately 2 or higher) and they were in general up-regulated. Apoptosis-related proteins were both up-regulated (VDAC1, VDAC2) and down-regulated (PRDX2, TCTP). The fold change was >2 for PRDX2 and <2 for the others. There was a strong agreement between sms and smsdx on the up- and down-regulation of proteins. Sms/smsdx triggered up-regulation of catalytic mitochondrial proteins and seemed to affect apoptosis-related proteins. This could indicate important pathways on which smsdx might be able to curb the progression of HRPC. The results encourage a pending clinical phase II study.

Folding free energies of 5'-UTRs impact post-transcriptional regulation on a genomic scale in yeast.

Using high-throughput technologies, abundances and other features of genes and proteins have been measured on a genome-wide scale in Saccharomyces cerevisiae. In contrast, secondary structure in 5'-untranslated regions (UTRs) of mRNA has only been investigated for a limited number of genes. Here, the aim is to study genome-wide regulatory effects of mRNA 5'-UTR folding free energies. We performed computations of secondary structures in 5'-UTRs and their folding free energies for all verified genes in S. cerevisiae. We found significant correlations between folding free energies of 5'-UTRs and various transcript features measured in genome-wide studies of yeast. In particular, mRNAs with weakly folded 5'-UTRs have higher translation rates, higher abundances of the corresponding proteins, longer half-lives, and higher numbers of transcripts, and are upregulated after heat shock. Furthermore, 5'-UTRs have significantly higher folding free energies than other genomic regions and randomized sequences. We also found a positive correlation between transcript half-life and ribosome occupancy that is more pronounced for short-lived transcripts, which supports a picture of competition between translation and degradation. Among the genes with strongly folded 5'-UTRs, there is a huge overrepresentation of uncharacterized open reading frames. Based on our analysis, we conclude that (i) there is a widespread bias for 5'-UTRs to be weakly folded, (ii) folding free energies of 5'-UTRs are correlated with mRNA translation and turnover on a genomic scale, and (iii) transcripts with strongly folded 5'-UTRs are often rare and hard to find experimentally.

Signal transduction pathway profiling of individual tumor samples.

BACKGROUND: Signal transduction pathways convey information from the outside of the cell to transcription factors, which in turn regulate gene expression. Our objective is to analyze tumor gene expression data from microarrays in the context of such pathways. RESULTS: We use pathways compiled from the TRANSPATH/TRANSFAC databases and the literature, and three publicly available cancer microarray data sets. Variation in pathway activity, across the samples, is gauged by the degree of correlation between downstream targets of a pathway. Two correlation scores are applied; one considers all pairs of downstream targets, and the other considers only pairs without common transcription factors. Several pathways are found to be differentially active in the data sets using these scores. Moreover, we devise a score for pathway activity in individual samples, based on the average expression value of the downstream targets. Statistical significance is assigned to the scores using permutation of genes as null model. Hence, for individual samples, the status of a pathway is given as a sign, + or -, and a p-value. This approach defines a projection of high-dimensional gene expression data onto low-dimensional pathway activity scores. For each dataset and many pathways we find a much larger number of significant samples than expected by chance. Finally, we find that several sample-wise pathway activities are significantly associated with clinical classifications of the samples. CONCLUSION: This study shows that it is feasible to infer signal transduction pathway activity, in individual samples, from gene expression data. Furthermore, these pathway activities are biologically relevant in the three cancer data sets.

Comparing functional annotation analyses with Catmap.

BACKGROUND: Ranked gene lists from microarray experiments are usually analysed by assigning significance to predefined gene categories, e.g., based on functional annotations. Tools performing such analyses are often restricted to a category score based on a cutoff in the ranked list and a significance calculation based on random gene permutations as null hypothesis. RESULTS: We analysed three publicly available data sets, in each of which samples were divided in two classes and genes ranked according to their correlation to class labels. We developed a program, Catmap (available for download at http://bioinfo.thep.lu.se/Catmap), to compare different scores and null hypotheses in gene category analysis, using Gene Ontology annotations for category definition. When a cutoff-based score was used, results depended strongly on the choice of cutoff, introducing an arbitrariness in the analysis. Comparing results using random gene permutations and random sample permutations, respectively, we found that the assigned significance of a category depended strongly on the choice of null hypothesis. Compared to sample label permutations, gene permutations gave much smaller p-values for large categories with many coexpressed genes. CONCLUSIONS: In gene category analyses of ranked gene lists, a cutoff independent score is preferable. The choice of null hypothesis is very important; random gene permutations does not work well as an approximation to sample label permutations.

Discovery-based protein expression profiling identifies distinct subgroups and pathways in leiomyosarcomas.

UNLABELLED: Soft tissue sarcomas (STS) are malignant tumors of mesenchymal origin. A substantial portion of these tumors exhibits complex karyotypes and lack characterized chromosomal aberrations. Owing to such properties, both histopathologic and molecular classification of these tumors has been a significant challenge. This study examines the protein expression of a large number of human STS, including subtype heterogeneity, using two-dimensional gel proteomics. In addition, detailed proteome profiles of a subset of pleomorphic STS specimens using an in-depth mass-spectrometry approach identified subgroups within the leiomyosarcomas with distinct protein expression patterns. Pathways analysis indicates that key biologic nodes like apoptosis, cytoskeleton remodeling, and telomere regulation are differentially regulated among these subgroups. Finally, investigating the similarities between protein expression of leiomyosarcomas and undifferentiated pleomorphic sarcomas (UPS) revealed similar protein expression profiles for these tumors, in comparison with pleomorphic leiomyosarcomas. IMPLICATIONS: These results suggest that UPS tumors share a similar lineage as leiomyosarcomas and are likely to originate from different stages of differentiation from mesenchymal stem cells to smooth muscle cells.

An oat bran meal influences blood insulin levels and related gene sets in peripheral blood mononuclear cells of healthy subjects.

The understanding of how fibre-rich meals regulate molecular events at a gene level is limited. This pilot study aimed to investigate changes in gene expression in peripheral blood mononuclear cells (PBMCs) from healthy subjects after consumption of an oat bran-rich meal. Fifteen subjects (8 men and 7 women, aged 20-28 years) ingested meals with oat bran or a control meal after an overnight fast. Blood samples for analysis of postprandial glucose, insulin and triglyceride concentrations were taken during 3 h, while PBMCs for microarray gene expression profiling from five men and five women were taken before and 2 h after the meal. Analysis of transcriptome data was performed with linear mixed models to determine differentially expressed genes in response either to meal intake or meal content, and enrichment analysis was used to identify functional gene sets responding to meal intake and specifically to oat bran intake. Meal intake as such affected gene expression for genes mainly involved in metabolic stress; indicating increased inflammation due to the switch from fasting to fed state. The oat bran meal affected gene sets associated with a lower insulin level, compared with the control meal. The gene sets included genes involved in insulin secretion and ß-cell development, but also protein synthesis and genes related to cancer diseases. The oat bran meal also significantly lowered postprandial blood insulin IAUC compared to control. Further studies are needed to compare these acute effects with the long-term health effects of oat bran.