Local Application of Mineral-Coated Microparticles Loaded With VEGF and BMP-2 Induces the Healing of Murine Atrophic Non-Unions

Deficient angiogenesis and disturbed osteogenesis are key factors for the development of nonunions. Mineral-coated microparticles (MCM) represent a sophisticated carrier system for the delivery of vascular endothelial growth factor (VEGF) and bone morphogenetic protein (BMP)-2. In this study, we investigated whether a combination of VEGF- and BMP2-loaded MCM (MCM + VB) with a ratio of 1:2 improves bone repair in non-unions. For this purpose, we applied MCM + VB or unloaded MCM in a murine non-union model and studied the process of bone healing by means of radiological, biomechanical, histomorphometric, immunohistochemical and Western blot techniques after 14 and 70 days. MCM-free non-unions served as controls. Bone defects treated with MCM + VB exhibited osseous bridging, an improved biomechanical stiffness, an increased bone volume within the callus including ongoing mineralization, increased vascularization, and a histologically larger total periosteal callus area consisting predominantly of osseous tissue when compared to defects of the other groups. Western blot analyses on day 14 revealed a higher expression of osteoprotegerin (OPG) and vice versa reduced expression of receptor activator of NF-κB ligand (RANKL) in bone defects treated with MCM + VB. On day 70, these defects exhibited an increased expression of erythropoietin (EPO), EPOreceptor and BMP-4. These findings indicate that the use of MCM for spatiotemporal controlled delivery of VEGF and BMP-2 shows great potential to improve bone healing in atrophic non-unions by promoting angiogenesis and osteogenesis as well as reducing early osteoclast activity

Concomitant cytotoxic effector differentiation of CD4(+) and CD8(+) T cells in response to EBV-Infected B cells

Most people infected by EBV acquire specific immunity, which then controls latent infection throughout their life. Immune surveillance of EBV-infected cells by cytotoxic CD4(+) T cells has been recognized; however, the molecular mechanism of generating cytotoxic effector T cells of the CD4(+) subset remains poorly understood. Here we compared phenotypic features and the transcriptome of EBV-specific effector-memory CD4(+) T cells and CD8(+) T cells in mice and found that both T cell types show cytotoxicity and, to our surprise, widely similar gene expression patterns relating to cytotoxicity. Similar to cytotoxic CD8(+) T cells, EBV-specific cytotoxic CD4(+) T cells from human peripheral blood expressed T-bet, Granzyme B, and Perforin and upregulated the degranulation marker, CD107a, immediately after restimulation. Furthermore, T-bet expression in cytotoxic CD4(+) T cells was highly correlated with Granzyme B and Perforin expression at the protein level. Thus, differentiation of EBV-specific cytotoxic CD4(+) T cells is possibly controlled by mechanisms shared by cytotoxic CD8(+) T cells. T-bet-mediated transcriptional regulation may explain the similarity of cytotoxic effector differentiation between CD4(+) T cells and CD8(+) T cells, implicating that this differentiation pathway may be directed by environmental input rather than T cell subset

HeatMapper: powerful combined visualization of gene expression profile correlations, genotypes, phenotypes and sample characteristics

BACKGROUND: Accurate interpretation of data obtained by unsupervised analysis of large scale expression profiling studies is currently frequently performed by visually combining sample-gene heatmaps and sample characteristics. This method is not optimal for comparing individual samples or groups of samples. Here, we describe an approach to visually integrate the results of unsupervised and supervised cluster analysis using a correlation plot and additional sample metadata. RESULTS: We have developed a tool called the HeatMapper that provides such visualizations in a dynamic and flexible manner and is available from . CONCLUSION: The HeatMapper allows an accessible and comprehensive visualization of the results of gene expression profiling and cluster analysis

Analysis of promoter regions of co-expressed genes identified by microarray analysis

BACKGROUND: The use of global gene expression profiling to identify sets of genes with similar expression patterns is rapidly becoming a widespread approach for understanding biological processes. A logical and systematic approach to study co-expressed genes is to analyze their promoter sequences to identify transcription factors that may be involved in establishing specific profiles and that may be experimentally investigated. RESULTS: We introduce promoter clustering i.e. grouping of promoters with respect to their high scoring motif content, and show that this approach greatly enhances the identification of common and significant transcription factor binding sites (TFBS) in co-expressed genes. We apply this method to two different dataset, one consisting of micro array data from 108 leukemias (AMLs) and a second from a time series experiment, and show that biologically relevant promoter patterns may be obtained using phylogenetic foot-printing methodology. In addition, we also found that 15% of the analyzed promoter regions contained transcription factors start sites for additional genes transcribed in the opposite direction. CONCLUSION: Promoter clustering based on global promoter features greatly improve the identification of shared TFBS in co-expressed genes. We believe that the outlined approach may be a useful first step to identify transcription factors that contribute to specific features of gene expression profiles

The state-of-the-art determination of urinary nucleosides using chromatographic techniques “hyphenated” with advanced bioinformatic methods

Over the last decade metabolomics has gained increasing popularity and significance in life sciences. Together with genomics, transcriptomics and proteomics, metabolomics provides additional information on specific reactions occurring in humans, allowing us to understand some of the metabolic pathways in pathological processes. Abnormal levels of such metabolites as nucleosides in the urine of cancer patients (abnormal in relation to the levels observed in healthy volunteers) seem to be an original potential diagnostic marker of carcinogenesis. However, the expectations regarding the diagnostic value of nucleosides may only be justified once an appropriate analytical procedure has been applied for their determination. The achievement of good specificity, sensitivity and reproducibility of the analysis depends on the right choice of the phases (e.g. sample pretreatment procedure), the analytical technique and the bioinformatic approach. Improving the techniques and methods applied implies greater interest in exploration of reliable diagnostic markers. This review covers the last 11 years of determination of urinary nucleosides conducted with the use of high-performance liquid chromatography in conjunction with various types of detection, sample pretreatment methods as well as bioinformatic data processing procedures

Randomization in Laboratory Procedure Is Key to Obtaining Reproducible Microarray Results

The quality of gene expression microarray data has improved dramatically since the first arrays were introduced in the late 1990s. However, the reproducibility of data generated at multiple laboratory sites remains a matter of concern, especially for scientists who are attempting to combine and analyze data from public repositories. We have carried out a study in which a common set of RNA samples was assayed five times in four different laboratories using Affymetrix GeneChip arrays. We observed dramatic differences in the results across laboratories and identified batch effects in array processing as one of the primary causes for these differences. When batch processing of samples is confounded with experimental factors of interest it is not possible to separate their effects, and lists of differentially expressed genes may include many artifacts. This study demonstrates the substantial impact of sample processing on microarray analysis results and underscores the need for randomization in the laboratory as a means to avoid confounding of biological factors with procedural effects

Randomization in Laboratory Procedure Is Key to Obtaining Reproducible Microarray Results

The quality of gene expression microarray data has improved dramatically since the first arrays were introduced in the late 1990s. However, the reproducibility of data generated at multiple laboratory sites remains a matter of concern, especially for scientists who are attempting to combine and analyze data from public repositories. We have carried out a study in which a common set of RNA samples was assayed five times in four different laboratories using Affymetrix GeneChip arrays. We observed dramatic differences in the results across laboratories and identified batch effects in array processing as one of the primary causes for these differences. When batch processing of samples is confounded with experimental factors of interest it is not possible to separate their effects, and lists of differentially expressed genes may include many artifacts. This study demonstrates the substantial impact of sample processing on microarray analysis results and underscores the need for randomization in the laboratory as a means to avoid confounding of biological factors with procedural effects