One of the aims of high-throughput gene/protein profiling experiments is the identification of biological processes altered between two or more conditions. Pathway analysis is an umbrella term for a multitude of computational approaches used for this purpose. While in the beginning pathway analysis relied on enrichment-based approaches, a newer generation of methods is now available, exploiting pathway topologies in addition to gene/protein expression levels. However, little effort has been invested in their critical assessment with respect to their performance in different experimental setups. Here, we assessed the performance of seven representative methods identifying differentially expressed pathways between two groups of interest based on gene expression data with prior knowledge of pathway topologies: SPIA, PRS, CePa, TAPPA, TopologyGSA, Clipper and DEGraph. We performed a number of controlled experiments that investigated their sensitivity to sample and pathway size, threshold-based filtering of differentially expressed genes, ability to detect target pathways, ability to exploit the topological information and the sensitivity to different pre-processing strategies. We also verified type I error rates and described the influence of overexpression of single genes, gene sets and topological motifs of various sizes on the detection of a pathway as differentially expressed. The results of our experiments demonstrate a wide variability of the tested methods. We provide a set of recommendations for an informed selection of the proper method for a given data analysis task.

Current prognostic factors are insufficient for precise risk-discrimination in breast cancer patients with low grade breast tumors, which, in disagreement with theoretical prognosis, occasionally form early lymph node metastasis. To identify markers for this group of patients, we employed iTRAQ-2DLC-MS/MS proteomics to 24 lymph node positive and 24 lymph node negative grade 1 luminal A primary breast tumors. Another group of 48 high-grade tumors (luminal B, triple negative, Her-2 subtypes) was also analyzed to investigate marker specificity for grade 1 luminal A tumors. From the total of 4405 proteins identified (FDR<5%), the top 65 differentially expressed together with 30 previously identified and control markers were analyzed also at transcript level. Increased levels of carboxypeptidase B1 (CPB1), PDZ and LIM domain protein 2 (PDLIM2), and ring finger protein 25 (RNF25) were associated specifically with lymph node positive grade 1 tumors, whereas stathmin 1 (STMN1) and thymosin beta 10 (TMSB10) associated with aggressive tumor phenotype also in high grade tumors at both protein and transcript level. For CPB1, these differences were also observed by immunohistochemical analysis on tissue microarrays. Up-regulation of putative biomarkers in lymph node positive (versus negative) luminal A tumors was validated by gene expression analysis of an independent published data set (n = 343) for CPB1 (p = 0.00155), PDLIM2 (p = 0.02027) and RELA (p = 0.00015). Moreover, statistically significant connections with patient survival were identified in another public data set (n = 1678). Our findings indicate unique pro-metastatic mechanisms in grade 1 tumors that can include up-regulation of CPB1, activation of NF-κB pathway and changes in cell survival and cytoskeleton. These putative biomarkers have potential to identify the specific minor subpopulation of breast cancer patients with low grade tumors who are at higher than expected risk of recurrence and who would benefit from more intensive follow-up and may require more personalized therapy.

Pathway analysis methods, in which differentially expressed genes are mapped to databases of reference pathways and relative enrichment is assessed, help investigators to propose biologically relevant hypotheses. The last generation of pathway analysis methods takes into account the topological structure of a pathway, which helps to increase both specificity and sensitivity of the findings. Simultaneously, the RNA-Seq technology is gaining popularity and becomes widely used for gene expression profiling. Unfortunately, majority of topological pathway analysis methods remains without implementation and if an implementation exists, it is limited in various factors. We developed a new R/Bioconductor package ToPASeq offering uniform interface to seven distinct topology-based pathway analysis methods, of which three we implemented de-novo and four were adjusted from existing implementations. Apart this, ToPASeq offers a set of tailored visualization functions and functions for importing and manipulating pathways and their topologies, facilitating the application of the methods on different species. The package can be used to compare the differential expression of pathways between two conditions on both gene expression microarray and RNA-Seq data. The package is written in R and is available from Bioconductor 3.2 using AGPL-3 license. ToPASeq is a novel package that offers seven distinct methods for topology-based pathway analysis, which are easily applicable on microarray as well as RNA-Seq data, both in human and other species. At the same time, it provides specific tools for visualization of the results.

Background: Multiple myeloma (MM) is a low proliferative tumor of postgerminal center plasma cell (PC). Centrosome amplification (CA) is supposed to be one of the mechanisms leading to chromosomal instability. Also, CA is associated with deregulation of cell cycle, mitosis, DNA repair and proliferation. The aim of our study was to evaluate the prognostic significance and possible role of CA in pathogenesis and analysis of mitotic genes as mitotic disruption markers.Design and methods: A total of 173 patients were evaluated for this study. CD138+ cells were separated by MACS. Immunofluorescent labeling of centrin was used for evaluation of centrosome amplification in PCs. Interphase FISH with cytoplasmic immunoglobulin light chain staining (cIg FISH) and qRT-PCR were performed on PCs.Results: Based on the immunofluorescent staining results, all patients were divided into two groups: CA positive (38.2%) and CA negative (61.8%). Among the newly diagnosed patients, worse overall survival was indicated in the CA negative group (44/74) in comparison to the CA positive group (30/74) (P = 0.019).Gene expression was significantly down-regulated in the CA positive group in comparison to CA negative in the following genes:AURKB, PLK4, TUBG1(P < 0.05). Gene expression was significantly down-regulated in newly diagnosed in comparison to relapsed patients in the following genes:AURKA, AURKB, CCNB1, CCNB2, CETN2, HMMR, PLK4, PCNT,andTACC3(P < 0.05).Conclusions: Our findings indicate better prognosis for CA positive newly diagnosed patients. Considering revealed clinical and gene expression heterogeneity between CA negative and CA positive patients, there is a possibility to characterize centrosome amplification as a notable event in multiple myeloma pathogenesis.

A high degree of developmental plasticity enables plants to adapt to continuous, often unfavorable and unpredictable changes in their environment. At the molecular level, adaptive advantages for plants are primarily provided by epigenetic machinery including DNA methylation, histone modifications, and the activity of noncoding RNA molecules. Using a mass spectrometry-based proteomic approach, we examined the levels of acetylated histone peptide forms in Arabidopsis plants with a loss of function of histone deacetylase 6 (HDA6), and in plants germinated in the presence of HDA inhibitors trichostatin A (TSA) and sodium butyrate (NaB). Our analyses revealed particular lysine sites at histone sequences targeted by the HDA6 enzyme, and by TSA- and NaB-sensitive HDAs. Compared with plants exposed to drugs, more dramatic changes in the overall profiles of histone post-translational modifications were identified in hda6 mutants. However, loss of HDA6 was not sufficient by itself to induce hyperacetylation to the maximum degree, implying complementary activities of other HDAs. In contrast to hda6 mutants that did not exhibit any obvious phenotypic defects, the phenotypes of seedlings exposed to HDA inhibitors were markedly affected, showing that the effect of these drugs on early plant development is not limited to the modulation of histone acetylation levels.