Deep-Sequencing Analysis Reveals that the miR-199a2/214 Cluster within DNM3os Represents the Vast Majority of Aberrantly Expressed MicroRNAs in Sézary Syndrome

Dermatology-oncolog

Comprehensive Gene-Expression Survey Identifies Wif1 as a Modulator of Cardiomyocyte Differentiation

During chicken cardiac development the proepicardium (PE) forms the epicardium (Epi), which contributes to several non-myocardial lineages within the heart. In contrast to Epi-explant cultures, PE explants can differentiate into a cardiomyocyte phenotype. By temporal microarray expression profiles of PE-explant cultures and maturing Epi cells, we identified genes specifically associated with differentiation towards either of these lineages and genes that are associated with the Epi-lineage restriction. We found a central role for Wnt signaling in the determination of the different cell lineages. Immunofluorescent staining after recombinant-protein incubation in PE-explant cultures indicated that the early upregulated Wnt inhibitory factor-1 (Wif1), stimulates cardiomyocyte differentiation in a similar manner as Wnt stimulation. Concordingly, in the mouse pluripotent embryogenic carcinoma cell line p19cl6, early and late Wif1 exposure enhances and attenuates differentiation, respectively. In ovo exposure of the HH12 chicken embryonic heart to Wif1 increases the Tbx18-positive cardiac progenitor pool. These data indicate that Wif1 enhances cardiomyogenesis

Genome-Wide MicroRNA Expression Analysis of Clear Cell Renal Cell Carcinoma by Next Generation Deep Sequencing

<div><p>MicroRNAs (miRNAs), non-coding RNAs regulating gene expression, are frequently aberrantly expressed in human cancers. Next-generation deep sequencing technology enables genome-wide expression profiling of known miRNAs and discovery of novel miRNAs at unprecedented quantitative and qualitative accuracy. Deep sequencing was performed on 11 fresh frozen clear cell renal cell carcinoma (ccRCC) and adjacent non-tumoral renal cortex (NRC) pairs, 11 additional frozen ccRCC tissues, and 2 ccRCC cell lines (n = 35). The 22 ccRCCs patients belonged to 3 prognostic sub-groups, i.e. those without disease recurrence, with recurrence and with metastatic disease at diagnosis. Thirty-two consecutive samples (16 ccRCC/NRC pairs) were used for stem-loop PCR validation. Novel miRNAs were predicted using 2 distinct bioinformatic pipelines. In total, 463 known miRNAs (expression frequency 1–150,000/million) were identified. We found that 100 miRNA were significantly differentially expressed between ccRCC and NRC. Differential expression of 5 miRNAs was confirmed by stem-loop PCR in the 32 ccRCC/NRC samples. With respect to RCC subgroups, 5 miRNAs discriminated between non-recurrent versus recurrent and metastatic disease, whereas 12 uniquely distinguished non-recurrent versus metastatic disease. Blocking overexpressed miR-210 or miR-27a in cell line SKCR-7 by transfecting specific antagomirs did not result in significant changes in proliferation or apoptosis. Twenty-three previously unknown miRNAs were predicted in silico. Quantitative genome-wide miRNA profiling accurately separated ccRCC from (benign) NRC. Individual differentially expressed miRNAs may potentially serve as diagnostic or prognostic markers or future therapeutic targets in ccRCC. The biological relevance of candidate novel miRNAs is unknown at present.</p> </div

miRNA stem-loop PCR validation.

<p>miRNA expression in paired tissue samples of ccRCC and adjacent normal tissue. Small RNA preparations were analyzed for the expression of five selected miRNAs by stem-loop PCR. The expression level of each miRNA was normalized to a small RNA reference U6. The normalized expression values of RCC tumors were relative to the paired normal tissues, and converted expression fold changes. All miRNAs showed statistical significance, determined by pair-wise student t test with P<0.05. Each bar represents one individual RCC tumor.</p

Differentially expressed miRNAs in ccRCC of which experimental evidence exist for one or more target genes.^*

<p>Of the 70 differentially expressed miRNAs with >50 read counts/million found by us only the 44 miRNA of which a validated target is present in miRecord are reported.</p>*<p>Above experimentally proven targets are listed in miRecord target database version 3. miRecord does not allow appropriate distinction between 3p- ad 5p- forms of each miRNA due to differences in techniques applied in the various reports in the literature.</p>#<p>If more than 9 targets per miRNA are reported, only nine are enlisted in the Table.</p

Expression fold-changes of the >3fold differentially expressed miRNAs.

<p>The expression levels of the significant miRNA in ccRCC were relative to that of matched normal kidney tissue, as expression fold change. Only miRNAs expression fold-change >3 are presented. The red bars indicate fold-increase of miRNAs in ccRCC compared to normal kidney tissues, the blue bars indicated miRNAs fold increase in miRNA expression in normal kidney versus ccRCC.</p

Unsupervised cluster analysis of the 100 differentially expressed miRNA derived from differential expression analysis with 11 ccRCC tumors and matched 11 normal additional 11ccRRC tumors and 2 ccRCC cell lines SKRC andMZ127.

<p>Unsupervised cluster analysis of the 100 differentially expressed miRNA derived from differential expression analysis with 11 ccRCC tumors and matched 11 normal additional 11ccRRC tumors and 2 ccRCC cell lines SKRC andMZ127.</p

miRNA expression according to deep sequencing analysis.

<p>In three RCC patient subgroups with different clinical outcomes (B-D) and adjacent normal kidney tissues (A) derived from 11 of these 25 patients. Statistical significance of miRNA expression in each group was calculated using R package edgeR statistics within 3 RCC subgroups A) no recurrence subgroup (n = 7), C) recurrence subgroup (n = 8) and D) metastatic subgroup (n = 7). The 12 miRNAs depicted were selected based on highly significant difference in expression level in metastatic RCC (D) in comparison to RCC without recurrence (B) (adj. P value <0.05 BH correction). miRNA expression level (read counts/million) is presented as boxplots generated at R.</p

Differentially expressed miRNAs in ccRCC tumors.

<p>Differentially expression was analyzed between 11RCC tumors and their matched 11 normal kidney tissues. Significance was determined by adj. P value <0.05 using edgeR package. Differentially expressed miRNAs categorized into four categories based on level of expression. Expression levels are given as Mean ±SD. In the left panels (A-D) overexpressed miRNAs are shown, each panel representing a 10-fold difference in expression level. In the right panels (E-H), downregulated miRNAs are depicted with a 10-fold difference in maximal miRNA expression in normal tissue in each panel.</p

Applicability of Dickson Charge Pump in Energy Harvesting Systems: Experimental Validation of Energy Harvesting Charge Pump Model

Energy harvesting methods provide very low instantaneous power. Accordingly, available voltage levels are low and must be increased so that an energy harvesting method can be used as a power supply. One approach uses charge pumps to boost low AC voltage from energy harvester to a higher DC voltage. Characterized by very low output current and a wide span of operating frequencies, energy harvesting methods introduce a number of limitations to charge pump operation. This paper describes and models behavior of Dickson charge pump in energy harvesting applications. Proposed Energy Harvesting model is evaluated and compared with Standard and Tanzawa charge pump models and with measurement results. Based on the proposed model, the conditions that need to be satisfied so that a charge pump can reach maximum power point of energy harvesting system are defined. Parameter selection method optimized for maximum power point is presented and is experimentally validated