Transcriptome Remodeling in Gradual Development of Inverse Resistance between Paclitaxel and Cisplatin in Ovarian Cancer Cells.

Resistance to anti-cancer drugs is the main challenge in oncology. In pre-clinical studies, established cancer cell lines are primary tools in deciphering molecular mechanisms of this phenomenon. In this study, we proposed a new, transcriptome-focused approach, utilizing a model of isogenic cancer cell lines with gradually changing resistance. We analyzed trends in gene expression in the aim to find out a scaffold of resistance development process. The ovarian cancer cell line A2780 was treated with stepwise increased concentrations of paclitaxel (PTX) to generate a series of drug resistant sublines. To monitor transcriptome changes we submitted them to mRNA-sequencing, followed by the identification of differentially expressed genes (DEGs), principal component analysis (PCA), and hierarchical clustering. Functional interactions of proteins, encoded by DEGs, were analyzed by building protein-protein interaction (PPI) networks. We obtained human ovarian cancer cell lines with gradually developed resistance to PTX and collateral sensitivity to cisplatin (CDDP) (inverse resistance). In their transcriptomes, we identified two groups of DEGs: (1) With fluctuations in expression in the course of resistance acquiring; and (2) with a consistently changed expression at each stage of resistance development, constituting a scaffold of the process. In the scaffold PPI network, the cell cycle regulator-polo-like kinase 2 (PLK2); proteins belonging to the tumor necrosis factor (TNF) ligand and receptor family, as well as to the ephrin receptor family were found, and moreover, proteins linked to osteo- and chondrogenesis and the nervous system development. Our cellular model of drug resistance allowed for keeping track of trends in gene expression and studying this phenomenon as a process of evolution, reflected by global transcriptome remodeling. This approach enabled us to explore novel candidate genes and surmise that abrogation of the osteomimic phenotype in ovarian cancer cells might occur during the development of inverse resistance between PTX and CDDP.

The Defense Response of Nicotiana benthamiana to Peanut Stunt Virus Infection in the Presence of Symptom Exacerbating Satellite RNA.

Peanut stunt virus (PSV) is a widespread disease infecting legumes. The PSV strains are classified into four subgroups and some are defined by the association of satellite RNAs (satRNAs). In the case of PSV, the presence of satRNAs alters the symptoms of disease in infected plants. In this study, we elucidated the plant response to PSV-G strain, which occurs in natural conditions without satRNA. However, it was found that it might easily acquire satRNA, which exacerbated pathogenesis in Nicotiana benthamiana. To explain the mechanisms underlying PSV infection and symptoms exacerbation caused by satRNA, we carried out transcriptome profiling of N. benthamiana challenged by PSV-G and satRNA using species-specific microarrays. Co-infection of plants with PSV-G + satRNA increased the number of identified differentially expressed genes (DEGs) compared with the number identified in PSV-G-infected plants. In both treatments, the majority of up-regulated DEGs were engaged in translation, ribosome biogenesis, RNA metabolism, and response to stimuli, while the down-regulated DEGs were required for photosynthesis. The presence of satRNA in PSV-G-infected plants caused different trends in expression of DEGs associated with phosphorylation, ATP binding, and plasma membrane.

The c.470 T > C CHEK2 missense variant increases the risk of differentiated thyroid carcinoma in the Great Poland population.

BACKGROUND: Differentiated thyroid carcinoma (DTC) originates from thyroid follicular epithelial cells and belongs to a group of slowly progressing tumors with a relatively good prognosis. However, recurrences and metastases are a serious problem in advanced stages. Furthermore, progression from a well differentiated thyroid carcinoma to an aggressive anaplastic one is possible. The majority of differentiated thyroid carcinomas are sporadic but a few alleles increasing the cancer risk are known. One of them is the c.470 T > C (p.I157T, rs17879961) missense substitution in the CHEK2 gene. AIM OF THE STUDY: The aim of this study was to investigate whether this specific CHEK2 alteration, c.470 T > C, predisposes the Great Poland (Wielkopolska) population to thyroid cancer. METHODS: 602 differentiated thyroid carcinoma patients and 829 controls randomly selected from population were genotyped for the presence of the c.470C allele using pyrosequencing. Hardy-Weinberg Equilibrium (HWE) was tested for both groups by chi-square distribution and Fisher's exact test. The odds ratios (ORs), 95% confidence intervals (CIs), and p-values were calculated using the R software. RESULTS: The results of genotyping showed the presence of the c.470C allele in 51 patients with a frequency of 4.49%, while in a controls in 42 patients with a frequency of 2.53%. We demonstrated that in the Great Poland population the c.470C CHEK2 variant increases the risk of developing differentiated thyroid cancer almost twice (OR = 1.81, p = 0.004). The risk of papillary thyroid carcinoma in female patients homozygous for the c.470C allele was shown to increase almost 13-fold (OR = 12.81, p = 0.019). CONCLUSIONS: Identification of c.470C CHEK2 gene variant ought to be taken into account by healthcare policymakers. Future well-designed and larger population studies are of great value in confirming these findings. Moreover, a combination of genetic factors together with environmental exposures should also be considered.

Analysis of boutique arrays: a universal method for the selection of the optimal data normalization procedure.

DNA microarrays, which are among the most popular genomic tools, are widely applied in biology and medicine. Boutique arrays, which are small, spotted, dedicated microarrays, constitute an inexpensive alternative to whole-genome screening methods. The data extracted from each microarray-based experiment must be transformed and processed prior to further analysis to eliminate any technical bias. The normalization of the data is the most crucial step of microarray data pre-processing and this process must be carefully considered as it has a profound effect on the results of the analysis. Several normalization algorithms have been developed and implemented in data analysis software packages. However, most of these methods were designed for whole-genome analysis. In this study, we tested 13 normalization strategies (ten for double-channel data and three for single-channel data) available on R Bioconductor and compared their effectiveness in the normalization of four boutique array datasets. The results revealed that boutique arrays can be successfully normalized using standard methods, but not every method is suitable for each dataset. We also suggest a universal seven-step workflow that can be applied for the selection of the optimal normalization procedure for any boutique array dataset. The described workflow enables the evaluation of the investigated normalization methods based on the bias and variance values for the control probes, a differential expression analysis and a receiver operating characteristic curve analysis. The analysis of each component results in a separate ranking of the normalization methods. A combination of the ranks obtained from all the normalization procedures facilitates the selection of the most appropriate normalization method for the studied dataset and determines which methods can be used interchangeably.