Novel strong tissue specific promoter for gene expression in human germ cells.

BACKGROUND: Tissue specific promoters may be utilized for a variety of applications, including programmed gene expression in cell types, tissues and organs of interest, for developing different cell culture models or for use in gene therapy. We report a novel, tissue-specific promoter that was identified and engineered from the native upstream regulatory region of the human gene NDUFV1 containing an endogenous retroviral sequence. RESULTS: Among seven established human cell lines and five primary cultures, this modified NDUFV1 upstream sequence (mNUS) was active only in human undifferentiated germ-derived cells (lines Tera-1 and EP2102), where it demonstrated high promoter activity (approximately twice greater than that of the SV40 early promoter, and comparable to the routinely used cytomegaloviral promoter). To investigate the potential applicability of the mNUS promoter for biotechnological needs, a construct carrying a recombinant cytosine deaminase (RCD) suicide gene under the control of mNUS was tested in cell lines of different tissue origin. High cytotoxic effect of RCD with a cell-death rate approximately 60% was observed only in germ-derived cells (Tera-1), whereas no effect was seen in a somatic, kidney-derived control cell line (HEK293). In further experiments, we tested mNUS-driven expression of a hyperactive Sleeping Beauty transposase (SB100X). The mNUS-SB100X construct mediated stable transgene insertions exclusively in germ-derived cells, thereby providing further evidence of tissue-specificity of the mNUS promoter. CONCLUSIONS: We conclude that mNUS may be used as an efficient promoter for tissue-specific gene expression in human germ-derived cells in many applications. Our data also suggest that the 91 bp-long sequence located exactly upstream NDUFV1 transcriptional start site plays a crucial role in the activity of this gene promoter in vitro in the majority of tested cell types (10/12), and an important role--in the rest two cell lines.

Data aggregation at the level of molecular pathways improves stability of experimental transcriptomic and proteomic data.

High throughput technologies opened a new era in biomedicine by enabling massive analysis of gene expression at both RNA and protein levels. Unfortunately, expression data obtained in different experiments are often poorly compatible, even for the same biologic samples. Here, using experimental and bioinformatic investigation of major experimental platforms, we show that aggregation of gene expression data at the level of molecular pathways helps to diminish cross- and intra-platform bias otherwise clearly seen at the level of individual genes. We created a mathematical model of cumulative suppression of data variation that predicts the ideal parameters and the optimal size of a molecular pathway. We compared the abilities to aggregate experimental molecular data for the 5 alternative methods, also evaluated by their capacity to retain meaningful features of biologic samples. The bioinformatic method OncoFinder showed optimal performance in both tests and should be very useful for future cross-platform data analyses.

Molecular pathway activation features linked with transition from normal skin to primary and metastatic melanomas in human.

Melanoma is the most aggressive and dangerous type of skin cancer, but its molecular mechanisms remain largely unclear. For transcriptomic data of 478 primary and metastatic melanoma, nevi and normal skin samples, we performed high-throughput analysis of intracellular molecular networks including 592 signaling and metabolic pathways. We showed that at the molecular pathway level, the formation of nevi largely resembles transition from normal skin to primary melanoma. Using a combination of bioinformatic machine learning algorithms, we identified 44 characteristic signaling and metabolic pathways connected with the formation of nevi, development of primary melanoma, and its metastases. We created a model describing formation and progression of melanoma at the level of molecular pathway activation. We discovered six novel associations between activation of metabolic molecular pathways and progression of melanoma: for allopregnanolone biosynthesis, L-carnitine biosynthesis, zymosterol biosynthesis (inhibited in melanoma), fructose 2, 6-bisphosphate synthesis and dephosphorylation, resolvin D biosynthesis (activated in melanoma), D-myo-inositol hexakisphosphate biosynthesis (activated in primary, inhibited in metastatic melanoma). Finally, we discovered fourteen tightly coordinated functional clusters of molecular pathways. This study helps to decode molecular mechanisms underlying the development of melanoma.

Novel robust biomarkers for human bladder cancer based on activation of intracellular signaling pathways.

We recently proposed a new bioinformatic algorithm called OncoFinder for quantifying the activation of intracellular signaling pathways. It was proved advantageous for minimizing errors of high-throughput gene expression analyses and showed strong potential for identifying new biomarkers. Here, for the first time, we applied OncoFinder for normal and cancerous tissues of the human bladder to identify biomarkers of bladder cancer. Using Illumina HT12v4 microarrays, we profiled gene expression in 17 cancer and seven non-cancerous bladder tissue samples. These experiments were done in two independent laboratories located in Russia and Canada. We calculated pathway activation strength values for the investigated transcriptomes and identified signaling pathways that were regulated differently in bladder cancer (BC) tissues compared with normal controls. We found, for both experimental datasets, 44 signaling pathways that serve as excellent new biomarkers of BC, supported by high area under the curve (AUC) values. We conclude that the OncoFinder approach is highly efficient in finding new biomarkers for cancer. These markers are mathematical functions involving multiple gene products, which distinguishes them from "traditional" expression biomarkers that only assess concentrations of single genes.