Evaluation of predicted network modules in yeast metabolism using NMR-based metabolite profiling

Genome-scale metabolic models promise important insights into cell function. However, the definition of pathways and functional network modules within these models, and in the biochemical literature in general, is often based on intuitive reasoning. Although mathematical methods have been proposed to identify modules, which are defined as groups of reactions with correlated fluxes, there is a need for experimental verification. We show here that multivariate statistical analysis of the NMR-derived intra- and extracellular metabolite profiles of single-gene deletion mutants in specific metabolic pathways in the yeast Saccharomyces cerevisiae identified outliers whose profiles were markedly different from those of the other mutants in their respective pathways. Application of flux coupling analysis to a metabolic model of this yeast showed that the deleted gene in an outlying mutant encoded an enzyme that was not part of the same functional network module as the other enzymes in the pathway. We suggest that metabolomic methods such as this, which do not require any knowledge of how a gene deletion might perturb the metabolic network, provide an empirical method for validating and ultimately refining the predicted network structure.

Molecular basis for estrogen receptor alpha deficiency in BRCA1-linked breast cancer

Background BRCA1-mutant breast tumors are typically estrogen receptor alpha (ER alpha) negative, whereas most sporadic tumors express wild-type BRCA1 and are ER alpha positive. We examined a possible mechanism for the observed ER alpha-negative phenotype of BRCA1-mutant tumors.

Methods We used a breast cancer disease-specific microarray to identify transcripts that were differentially expressed between paraffin-embedded samples of 17 BRCA1-mutant and 14 sporadic breast tumors. We measured the mRNA levels of estrogen receptor 1 (ESR1) ( the gene encoding ER alpha), which was differentially expressed in the tumor samples, by quantitative polymerase chain reaction. Regulation of ESR1 mRNA and ER alpha protein expression was assessed in human breast cancer HCC1937 cells that were stably reconstituted with wild-type BRCA1 expression construct and in human breast cancer T47D and MCF-7 cells transiently transfected with BRCA1-specific short-interfering RNA ( siRNA). Chromatin immunoprecipitation assays were performed to determine if BRCA1 binds the ESR1 promoter and to identify other interacting proteins. Sensitivity to the antiestrogen drug fulvestrant was examined in T47D and MCF-7 cells transfected with BRCA1-specific siRNA. All statistical tests were two-sided.

Results Mean ESR1 gene expression was 5.4-fold lower in BRCA1-mutant tumors than in sporadic tumors ( 95% confidence interval [CI]=2.6-fold to 40.1-fold, P =.0019). The transcription factor Oct-1 recruited BRCA1 to the ESR1 promoter, and both BRCA1 and Oct-1 were required for ER alpha expression. BRCA1-depleted breast cancer cells expressing exogenous ER alpha were more sensitive to fulvestrant than BRCA1-depleted cells transfected with empty vector ( T47D cells, the mean concentration of fulvestrant that inhibited the growth of 40% of the cells [IC40] for empty vector versus ER alpha: > 10(-5) versus 8.0 x 10(-9) M [ 95% CI=3.1x10(-10) to 3.2 x 10(-6) M]; MCF-7 cells, mean IC40 for empty vector versus ER alpha : > 10(-5) versus 4.9 x 10(-8) M [ 95% CI=2.0 x 10(-9) to 3.9 x 10(-6) M]).

Conclusions BRCA1 alters the response of breast cancer cells to antiestrogen therapy by directly modulating ER alpha expression.

Generation of a non-small cell lung cancer transcriptome microarray

Background: Non-small cell lung cancer (NSCLC) is the leading cause of cancer mortality worldwide. At present no reliable biomarkers are available to guide the management of this condition. Microarray technology may allow appropriate biomarkers to be identified but present platforms are lacking disease focus and are thus likely to miss potentially vital information contained in patient tissue samples.

Inhibitor profiling of the Pseudomonas aeruginosa virulence factor LasB using N-alpha mercaptoamide template-based inhibitors

We report on the synthesis and biological evaluation of a focussed library of N-alpha mercaptoamide containing dipeptides as inhibitors of the zinc metallopeptidase Pseudomonas aeruginosa elastase (LasB, EC 3.4.24.26). The aim of the study was to derive an inhibitor profile for LasB with regard to mapping the S´1 binding site of the enzyme. Consequently, a focussed library of 160 members has been synthesised, using standard Fmoc-solid phase methods (on a Rink-amide resin), in which a subset of amino acids including examples of those with basic (Lys, Arg), aromatic (Phe, Trp), large aliphatic (Val, Leu) and acidic (Asp, Glu) side-chains populated the P´2 position of the inhibitor sequence and all 20 natural amino acids were incorporated, in turn, at the P´1 position. The study has revealed a preference for aromatic and/or large aliphatic amino acids at P´1 and a distinct bias against acidic residues at P´2. Ten inhibitor sequences were discovered that exhibited sub to low micromolar Ki values.

A statistical framework for the design of microarray experiments and effective detection of differential gene expression

Motivation: Microarray experiments generate a high data volume. However, often due to financial or experimental considerations, e.g. lack of sample, there is little or no replication of the experiments or hybridizations. These factors combined with the intrinsic variability associated with the measurement of gene expression can result in an unsatisfactory detection rate of differential gene expression (DGE). Our motivation was to provide an easy to use measure of the success rate of DGE detection that could find routine use in the design of microarray experiments or in post-experiment assessment.