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Biomedical subjects

Per Broberg

Publications and source records attributed to Per Broberg.

6 recordsLinked to original sources

Expression of genes involved in oxidative stress responses in airway epithelial cells of smokers with chronic obstructive pulmonary disease.

RATIONALE: The molecular mechanisms involved in airway oxidative stress responses reported in healthy smokers and in those with chronic obstructive pulmonary disease (COPD) are poorly understood. OBJECTIVES: To assess the expression of genes involved in oxidative stress responses in the bronchial epithelium of smokers with or without COPD and in relation to disease severity. METHODS: Global gene expression was assessed in bronchial brushings in 38 subjects with COPD, 14 healthy nonsmokers, and 18 healthy smokers. RESULTS: Gene expression analysis using Affymetrix arrays revealed mRNAs representing 341 out of 642 oxidative stress genes from two predefined gene sets to be differentially expressed in healthy nonsmokers when compared with healthy smokers, and 200 differentially expressed oxidative genes in subjects with COPD when compared with healthy smokers. Gene set enrichment analysis showed that pathways involved in oxidant/antioxidant responses were among the most differentially expressed gene pathways in smoking individuals, with further differences seen in COPD. Distinct, nonlinear gene expression patterns were identified across the severity spectrum of COPD, which correlated with the presence of certain transcription factor binding sites in their promoters. Significant changes in oxidant response genes observed in vivo were reproduced in vitro using primary bronchial epithelial cells from the same donors cultured at an air-liquid interface and exposed to cigarette smoke extract. CONCLUSIONS: Cigarette smoke induces significant changes in oxidant defense responses; some of these are further amplified, but not in a linear fashion, in individuals who develop COPD.

Adult↗

A comparative review of estimates of the proportion unchanged genes and the false discovery rate.

BACKGROUND: In the analysis of microarray data one generally produces a vector of p-values that for each gene give the likelihood of obtaining equally strong evidence of change by pure chance. The distribution of these p-values is a mixture of two components corresponding to the changed genes and the unchanged ones. The focus of this article is how to estimate the proportion unchanged and the false discovery rate (FDR) and how to make inferences based on these concepts. Six published methods for estimating the proportion unchanged genes are reviewed, two alternatives are presented, and all are tested on both simulated and real data. All estimates but one make do without any parametric assumptions concerning the distributions of the p-values. Furthermore, the estimation and use of the FDR and the closely related q-value is illustrated with examples. Five published estimates of the FDR and one new are presented and tested. Implementations in R code are available. RESULTS: A simulation model based on the distribution of real microarray data plus two real data sets were used to assess the methods. The proposed alternative methods for estimating the proportion unchanged fared very well, and gave evidence of low bias and very low variance. Different methods perform well depending upon whether there are few or many regulated genes. Furthermore, the methods for estimating FDR showed a varying performance, and were sometimes misleading. The new method had a very low error. CONCLUSION: The concept of the q-value or false discovery rate is useful in practical research, despite some theoretical and practical shortcomings. However, it seems possible to challenge the performance of the published methods, and there is likely scope for further developing the estimates of the FDR. The new methods provide the scientist with more options to choose a suitable method for any particular experiment. The article advocates the use of the conjoint information regarding false positive and negative rates as well as the proportion unchanged when identifying changed genes.

Amino Acid Sequence↗

Analysis and understanding of high-dimensionality data by means of multivariate data analysis.

Multivariate analysis such as principal-components analysis (PCA) and partial-least-squares-discriminant analysis (PLS-DA) have been applied to peptidomics data from clinical urine samples subjected to LC/MS analysis. We show that it is possible to use these methods to get information from a complex set of clinical data. The aim of the work is to use this information as a first step in the further search for clinical biomarker data. It is possible to identify peptide-biomarker fingerprints related to disease diagnosis and progression. Further, we review clinical proteomics and pharmacogenomics data analyzed with the same multivariate approach.

Data Interpretation, Statistical↗

Statistical methods for ranking differentially expressed genes.

In the analysis of microarray data the identification of differential expression is paramount. Here I outline a method for finding an optimal test statistic with which to rank genes with respect to differential expression. Tests of the method show that it allows generation of top gene lists that give few false positives and few false negatives. Estimation of the false-negative as well as the false-positive rate lies at the heart of the method.

Computational Biology↗

Ranking genes with respect to differential expression.

BACKGROUND: In the pharmaceutical industry and in academia substantial efforts are made to make the best use of the promising microarray technology. The data generated by microarrays are more complex than most other biological data attracting much attention at this point. A method for finding an optimal test statistic with which to rank genes with respect to differential expression is outlined and tested. At the heart of the method lies an estimate of the false negative and false positive rates. Both investing in false positives and missing true positives lead to a waste of resources. The procedure sets out to minimise these errors. For calculation of the false positive and negative rates a simulation procedure is invoked. RESULTS: The method outperforms commonly used alternatives when applied to simulated data modelled after real cDNA array data as well as when applied to real oligonucleotide array data. In both cases the method comes out as the over-all winner. The simulated data are analysed both exponentiated and on the original scale, thus providing evidence of the ability to cope with normal and lognormal distributions. In the case of the real life data it is shown that the proposed method will tend to push the differentially expressed genes higher up on a test statistic based ranking list than the competitors. CONCLUSIONS: The approach of making use of information concerning both the false positive and false negative rates in the inference adds a useful tool to the toolbox available to scientists in functional genomics.

Gene Expression Profiling↗

Natural allergen exposure does not diminish the sensitivity of cytokine production to glucocorticosteroids in blood cells of seasonal allergic asthma and rhinitis patients.

Glucocorticosteroid (GCS) inhibition of cytokine production is a major anti-inflammatory mechanism. However, increased production of pro-inflammatory cytokines during allergic airway inflammation has been proposed to reduce GCS effects. This study aimed to investigate whether allergic airway inflammation due to natural allergen exposure might decrease the sensitivity of granulocyte-macrophage colony-stimulating factor (GM-CSF) production to GCS in blood cells. Blood samples were collected from patients with seasonal allergic asthma (n = 10) and rhinitis (n = 8) and healthy subjects (n = 9), before, during, and after the birch pollen season. Whole blood cultures were stimulated with LPS (10 ng/ml) and treated with budesonide (10(-11)-10(-7) M) for 20 h. GM-CSF levels were analysed using immunoassay. Birch pollen exposure did not alter LPS-stimulated GM-CSF production, although disease symptoms and blood eosinophils increased in the patients. There were no significant differences in budesonide inhibition of GM-CSF production by blood cells of asthma and rhinitis patients compared with cells of healthy subjects before, during or after the birch pollen season and no change in response to allergen exposure. A concentration of 1 nM budesonide inhibited GM-CSF production by more than 50% at all time points. In conclusion, natural allergen exposure did not reduce the sensitivity of GM-CSF production to GCS inhibition in blood cells of seasonal allergic asthma and rhinitis patients.

Adult↗