Platform-independent approach for cancer detection from gene expression profiles of peripheral blood cells

Peripheral blood gene expression intensity-based methods for distinguishing healthy individuals from cancer patients are limited by sensitivity to batch effects and data normalization and variability between expression profiling assays. To improve the robustness and precision of blood gene expression-based tumour detection, it is necessary to perform molecular diagnostic tests using a more stable approach. Taking breast cancer as an example, we propose a machine learning–based framework that distinguishes breast cancer patients from healthy subjects by pairwise rank transformation of gene expression intensity in each sample. We showed the diagnostic potential of the method by performing RNA-seq for 37 peripheral blood samples from breast cancer patients and by collecting RNA-seq data from healthy donors in Genotype-Tissue Expression project and microarray mRNA expression datasets in Gene Expression Omnibus. The framework was insensitive to experimental batch effects and data normalization, and it can be simultaneously applied to new sample prediction.

Optimizing Recombinant Protein Production in theEscherichia coliPeriplasm Alleviates Stress

ABSTRACTInEscherichia coli, many recombinant proteins are produced in the periplasm. To direct these proteins to this compartment, they are equipped with an N-terminal signal sequence so that they can traverse the cytoplasmic membrane via the protein-conducting Sec translocon. Recently, using the single-chain variable antibody fragment BL1, we have shown that harmonizing the target gene expression intensity with the Sec translocon capacity can be used to improve the production yields of a recombinant protein in the periplasm. Here, we have studied the consequences of improving the production of BL1 in the periplasm by using a proteomics approach. When the target gene expression intensity is not harmonized with the Sec translocon capacity, the impaired translocation of secretory proteins, protein misfolding/aggregation in the cytoplasm, and an inefficient energy metabolism result in poor growth and low protein production yields. The harmonization of the target gene expression intensity with the Sec translocon capacity results in normal growth, enhanced protein production yields, and, surprisingly, a composition of the proteome that is—besides the produced target—the same as that of cells with an empty expression vector. Thus, the single-chain variable antibody fragment BL1 can be efficiently produced in the periplasm without causing any notable detrimental effects to the production host. Finally, we show that under the optimized conditions, a small fraction of the target protein is released into the extracellular milieu via outer membrane vesicles. We envisage that our observations can be used to design strategies to further improve the production of secretory recombinant proteins inE. coli.IMPORTANCEThe bacteriumEscherichia coliis widely used to produce recombinant proteins. Usually, trial-and-error-based screening approaches are used to identify conditions that lead to high recombinant protein production yields. Here, for the production of an antibody fragment in the periplasm ofE. coli, we show that an optimization of its production is accompanied by the alleviation of stress. This indicates that the monitoring of stress responses could be used to facilitate enhanced recombinant protein production yields.

Microarray Probe Expression Measures, Data Normalization and Statistical Validation

DNA microarray technology is a high-throughput method for gaining information on gene function. Microarray technology is based on deposition/synthesis, in an ordered manner, on a solid surface, of thousands of EST sequences/genes/oligonucleotides. Due to the high number of generated datapoints, computational tools are essential in microarray data analysis and mining to grasp knowledge from experimental results. In this review, we will focus on some of the methodologies actually available to define gene expression intensity measures, microarray data normalization, and statistical validation of differential expression.