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Tuomas Nikula

Publications and source records attributed to Tuomas Nikula.

2 recordsLinked to original sources

Differential expression of IFN regulatory factor 4 gene in human monocyte-derived dendritic cells and macrophages.

In vitro human monocyte differentiation to macrophages or dendritic cells (DCs) is driven by GM-CSF or GM-CSF and IL-4, respectively. IFN regulatory factors (IRFs), especially IRF1 and IRF8, are known to play essential roles in the development and functions of macrophages and DCs. In the present study, we performed cDNA microarray and Northern blot analyses to characterize changes in gene expression of selected genes during cytokine-stimulated differentiation of human monocytes to macrophages or DCs. The results show that the expression of IRF4 mRNA, but not of other IRFs, was specifically up-regulated during DC differentiation. No differences in IRF4 promoter histone acetylation could be found between macrophages and DCs, suggesting that the gene locus was accessible for transcription in both cell types. Computer analysis of the human IRF4 promoter revealed several putative STAT and NF-kappaB binding sites, as well as an IRF/Ets binding site. These sites were found to be functional in transcription factor-binding and chromatin immunoprecipitation experiments. Interestingly, Stat4 and NF-kappaB p50 and p65 mRNAs were expressed at higher levels in DCs as compared with macrophages, and enhanced binding of these factors to their respective IRF4 promoter elements was found in DCs. IRF4, together with PU.1, was also found to bind to the IRF/Ets response element in the IRF4 promoter, suggesting that IRF4 protein provides a positive feedback signal for its own gene expression in DCs. Our results suggest that IRF4 is likely to play an important role in myeloid DC differentiation and gene regulatory functions.

Animals↗

Computational strategies for analyzing data in gene expression microarray experiments.

Microarray analysis has become a widely used method for generating gene expression data on a genomic scale. Microarrays have been enthusiastically applied in many fields of biological research, even though several open questions remain about the analysis of such data. A wide range of approaches are available for computational analysis, but no general consensus exists as to standard for microarray data analysis protocol. Consequently, the choice of data analysis technique is a crucial element depending both on the data and on the goals of the experiment. Therefore, basic understanding of bioinformatics is required for optimal experimental design and meaningful interpretation of the results. This review summarizes some of the common themes in DNA microarray data analysis, including data normalization and detection of differential expression. Algorithms are demonstrated by analyzing cDNA microarray data from an experiment monitoring gene expression in T helper cells. Several computational biology strategies, along with their relative merits, are overviewed and potential areas for additional research discussed. The goal of the review is to provide a computational framework for applying and evaluating such bioinformatics strategies. Solid knowledge of microarray informatics contributes to the implementation of more efficient computational protocols for the given data obtained through microarray experiments.

Algorithms↗