Meta-analysis of microarray data: The case of imatinib resistance in chronic myelogenous leukemia.

With the proliferation of related microarray studies by independent groups, a natural approach to analysis would be to combine the results across studies. In this article, we address a meta-analysis of the gene expression data on imatinib resistance in chronic myelogenous leukemia. First, an analysis of the overlapping among 6 published studies revealed that only 3 genes were coincident between 2 studies. A later reprocessing using different methods on 4 publicly available datasets revealed that 2 extra genes were overlapped between two sets. Both poor overlappings may be due to large differences in the sample source, the microarray platforms used, and a small difference in gene expression between the imatinib non-responder and responder patients. A search of common genes inside 4 public datasets afforded 404 well defined genes. Nevertheless, this necessary condition for meta-analysis caused the loss of many genes of possible interest. The expression signals of the common genes in the four datasets were reanalyzed using three summary statistical methods for combining quantitative information: Fisher, Stouffer and effect-size. Taking the three methods together and using an FDR<0.10 threshold, a gene-list with 33 differentially expressed genes was found. Considering all the reanalysis approaches used in this work, a final gene-list with 38 differentially expressed genes is reported. Despite the important limitations to this microarray meta-analysis, the presented procedures and integrated gene-list may have some potential value as regards imatinib resistance in CML patients since it is the first attempt to integrate evidence about gene-lists in this area.

Modelling the simultaneous processes occurring during the BOD(5) test using synthetic waters as a pattern.

Synthetic waters imitating BOD(5) test conditions have been studied to model the progress curves of cell growth, organic matter consumption and oxygen demand. Glucose and glutamic acid were used as organic matter and Bacillus subtilis, Escherichia coli and Pseudomonas putida as microorganisms. The concentrations of all the products were measured simultaneously over time and were fitted to the Logistic and Monod models. Following this, the goodness of fit was analysed. Discrimination between models allowed us to conclude that the Logistic model has acceptable accuracy to describe the kinetic behaviour inside a BOD(5) bottle, while the extra parameter in the Monod model would not be statistically justified. Thus, the logistic parameters were determined under different conditions by non-linear regression and their biological meaning was interpreted.

Directed enzyme evolution guided by multidimensional analysis of substrate-activity space.

The directed evolution of protein function frequently involves identification of mutants with improved properties from a population of variants obtained by mutagenesis. The selection of clones to parent the subsequent generation is crucial to the continued creation of superior progeny. In the present study, multivariate analysis guided the evolution of human glutathione transferase (GST) T1-1 to 65-fold enhanced alkyltransferase activity. Six alternative substrates monitored the substrate-activity space that characterized a mutant library of enzymes, obtained by recombination of DNA and heterologous expression in Escherichia coli. A subset of mutants was identified by their proximity in the targeted region of six-dimensional factor space. DNA from these mutants was recombined to create a new generation of GST variants from which an improved enzyme was isolated. The multidimensional cluster analysis is applicable to quantitative properties in any population of molecules undergoing evolution and can guide the tailoring of proteins, nucleic acids and other chemical structures to novel and improved functions.