Functional analysis of 1796 differentially expressed mRNAs (FDR 0.1%, p-value≤1.13×10⁻⁴).

<p>Functional analysis of 1796 differentially expressed mRNAs (FDR 0.1%, p-value≤1.13×10⁻⁴).</p

Principle component analysis (PCA) of regulated genes.

<p>The clustering represents the overall expression pattern of significantly regulated mRNAs at FDR 0.1% (p-value ≤1.13×10⁻⁴) in five subsets of precursor B cells. Color codes represent the various maturation stages as indicated under the plot. The Partek® Genomics Suite™ program draws the elipsoids encompassing the individual datapoints. Note the dots for the children (spheres) and adults (angular balls) are tightly grouped together.</p

Normalized expression profiles of the (A) miR-17-92 cluster and its (B) paralogs in five precursor B cell subsets in adults (solid lines) and children (dotted lines).

<p>All 10 microRNAs followed the same expression profile in both age groups and showed a near inverse pattern with age.</p

MicroRNA profiles of precursor B cell subsets.

<p>Principle component analysis (PCA) showing the overall expression pattern of 17 microRNAs (18 assays) that were at least once differentially expressed between the various subsets (FDR 10%, p≤0,004). The color codes indicating differentiation stage (right) and age group (left) are explained below.</p

Functional network of up-regulated mRNAs (red) and down-regulated (green) microRNAs during differentiation to PreBII large cells in adults.

<p>Note connection of miR-125b-5p to ID2 and involvement of the hematopoiesis related miR-181a-5p and miR-196a-5p, and the cell cycle regulating miR-24-3p. Note, all the coloured interacting partners in this network were detected in the present study.</p

Cell sorting of precursor B cells subsets from CD10 positively selected cells.

<p>Immunomagnetic selection and subsequent FACS were used to isolate the five populations from pediatric and adult human BM. Shown are the FACS dot plots with sorting gates to obtain CD34⁺CD19⁻ ProB cells, CD34⁺CD19⁺ PreBI cells, CD34⁻CD19⁺CD20^dim PreBII large cells, CD34⁻CD19⁺CD20⁻ PreBII small cells, and CD34⁻CD19⁺CD20^highIgM⁺ Immature B cells.</p

Distinct DNA methylation profiles in bone and blood of osteoporotic and healthy postmenopausal women

<p>DNA methylation affects expression of associated genes and may contribute to the missing genetic effects from genome-wide association studies of osteoporosis. To improve insight into the mechanisms of postmenopausal osteoporosis, we combined transcript profiling with DNA methylation analyses in bone. RNA and DNA were isolated from 84 bone biopsies of postmenopausal donors varying markedly in bone mineral density (BMD). In all, 2529 CpGs in the top 100 genes most significantly associated with BMD were analyzed. The methylation levels at 63 CpGs differed significantly between healthy and osteoporotic women at 10% false discovery rate (FDR). Five of these CpGs at 5% FDR could explain 14% of BMD variation. To test whether blood DNA methylation reflect the situation in bone (as shown for other tissues), an independent cohort was selected and BMD association was demonstrated in blood for 13 of the 63 CpGs. Four transcripts representing inhibitors of bone metabolism—<i>MEPE, SOST, WIF1</i>, and <i>DKK1</i>—showed correlation to a high number of methylated CpGs, at 5% FDR. Our results link DNA methylation to the genetic influence modifying the skeleton, and the data suggest a complex interaction between CpG methylation and gene regulation. This is the first study in the hitherto largest number of postmenopausal women to demonstrate a strong association among bone CpG methylation, transcript levels, and BMD/fracture. This new insight may have implications for evaluation of osteoporosis stage and susceptibility.</p

Principal component analyses demonstrated clustering of the gene expression of unstored cultures (violet) and cultures stored for one week at 4°C (red) and 12°C (blue).

<p>In contrast, gene expression of cultures stored at 37°C (green) showed a distant clustering compared to the other experimental groups.</p

Validation of microarray results by qPCR.

<p>Validation of microarray results by qPCR.</p

Top ten upregulated genes during storage.

<p>Top ten upregulated genes during storage.</p