Design and evaluation of Actichip, a thematic microarray for the study of the actin cytoskeleton

Data analysis in complex biomolecular systems

The biomolecular technology progress is directly related to the development of effective methods and algorithms for processing a large amount of information obtained by modern high-throughput experimental equipment. The priority task is the development of promising computational tools for the analysis and interpretation of biophysical information using the methods of big data and computer models. An integrated approach to processing large datasets, which is based on the methods of data analysis and simulation modelling, is proposed. This approach allows to determine the parameters of biophysical and optical processes occurring in complex biomolecular systems. The idea of an integrated approach is to use simulation modelling of biophysical processes occurring in the object of study, comparing simulated and most relevant experimental data selected by dimension reduction methods, determining the characteristics of the investigated processes using data analysis algorithms. The application of the developed approach to the study of bimolecular systems in fluorescence spectroscopy experiments is considered. The effectiveness of the algorithms of the approach was verified by analyzing of simulated and experimental data representing the systems of molecules and proteins. The use of complex analysis increases the efficiency of the study of biophysical systems during the analysis of big data.

The actin filament cross-linker L-plastin confers resistance to TNF-alpha in MCF-7 breast cancer cells in a phosphorylation-dependent manner

We used a tumour necrosis factor (TNF)-α resistant breast adenocarcinoma MCF-7 cell line to investigate the involvement of the actin cytoskeleton in the mechanism of cell resistance to this cytokine. We found that TNF resistance correlates with the loss of cell epithelial properties and the gain of a mesenchymal phenotype, reminiscent of an epithelial-to-mesenchymal transition (EMT). Morphological changes were associated with a profound reorganization of the actin cytoskeleton and with a change in the repertoire of expressed actin cytoskeleton genes and EMT markers, as revealed by DNA microarray-based expression profiling. L-plastin, an F-actin cross-linking and stabilizing protein, was identified as one of the most significantly up-regulated genes in TNF-resistant cells. Knockdown of L-plastin in these cells revealed its crucial role in conferring TNF resistance. Importantly, overexpression of wild-type L-plastin in TNF-sensitive MCF-7 cells was sufficient to protect them against TNF-mediated cell death. Furthermore, we found that this effect is dependent on serine-5 phosphorylation of L-plastin and that non-conventional protein kinase C isoforms and the ceramide pathway may regulate its phosphorylation state. The protective role of L-plastin was not restricted to TNF-α resistant MCF-7 cells because a correlation between the expression of L-plastin and the resistance to TNF-α was observed in other breast cancer cell lines. Together, our study discloses a novel unexpected role of the actin bundling protein L-plastin as a cell protective protein against TNF-cytotoxicity.

Time-resolved analysis of transcriptional events during SNAI1-triggered epithelial to mesenchymal transition

The transcription regulator SNAI1 triggers a transcriptional program leading to epithelial to mesenchymal transition (EMT), providing epithelial cells with mesenchymal features and invasive properties during embryonic development and tumor progression. To identify early transcriptional changes occurring during SNAI1-induced EMT, we performed a time-resolved genome-scale study using human breast carcinoma cells conditionally expressing SNAI1. The approach we developed for microarray data analysis, allowed identifying three distinct EMT stages and the temporal classification of genes. Importantly, we identified unexpected, biphasic expression profiles of EMT-associated genes, supporting their pivotal role during this process. Finally, we established early EMT gene networks by identifying transcription factors and their potential targets which may orchestrate early events of EMT. Collectively, our work provides a framework for the identification and future systematic analysis of novel genes which contribute to SNAI1-triggered EMT.

Advanced spot quality analysis in two-colour microarray experiments

Background

Image analysis of microarrays and, in particular, spot quantification and spot quality control, is one of the most important steps in statistical analysis of microarray data. Recent methods of spot quality control are still in early age of development, often leading to underestimation of true positive microarray features and, consequently, to loss of important biological information. Therefore, improving and standardizing the statistical approaches of spot quality control are essential to facilitate the overall analysis of microarray data and subsequent extraction of biological information.

Findings

We evaluated the performance of two image analysis packages MAIA and GenePix (GP) using two complementary experimental approaches with a focus on the statistical analysis of spot quality factors. First, we developed control microarrays with a priori known fluorescence ratios to verify the accuracy and precision of the ratio estimation of signal intensities. Next, we developed advanced semi-automatic protocols of spot quality evaluation in MAIA and GP and compared their performance with available facilities of spot quantitative filtering in GP. We evaluated these algorithms for standardised spot quality analysis in a whole-genome microarray experiment assessing well-characterised transcriptional modifications induced by the transcription regulator SNAI1. Using a set of RT-PCR or qRT-PCR validated microarray data, we found that the semi-automatic protocol of spot quality control we developed with MAIA allowed recovering approximately 13% more spots and 38% more differentially expressed genes (at FDR = 5%) than GP with default spot filtering conditions.

Conclusion

Careful control of spot quality characteristics with advanced spot quality evaluation can significantly increase the amount of confident and accurate data resulting in more meaningful biological conclusions.