Defective CFTR Expression and Function Are Detectable in Blood Monocytes: Development of a New Blood Test for Cystic Fibrosis

BACKGROUND: Evaluation of cystic fibrosis transmembrane conductance regulator (CFTR) functional activity to assess new therapies and define diagnosis of cystic fibrosis (CF) is cumbersome. It is known that leukocytes express detectable levels of CFTR but the molecule has not been characterized in these cells. In this study we aim at setting up and validating a blood test to evaluate CFTR expression and function in leukocytes. DESCRIPTION: Western blot, PCR, immunofluorescence and cell membrane depolarization analysis by single-cell fluorescence imaging, using the potential-sensitive DiSBAC(2)(3) probe were utilized. Expression of PKA phosphorylated, cell membrane-localized CFTR was detected in non-CF monocytes, being undetectable or present in truncated form in monocytes derived from CF patients presenting with nonsense mutations. CFTR agonist administration induced membrane depolarization in monocytes isolated from non-CF donors (31 subjects) and, to a lesser extent, obligate CFTR heterozygous carriers (HTZ: 15 subjects), but it failed in monocytes from CF patients (44 subjects). We propose an index, which values in CF patients are significantly (p<0.001) lower than in the other two groups. Nasal Potential Difference, measured in selected subjects had concordant results with monocytes assay (Kappa statistic 0.93, 95%CI: 0.80-1.00). RESULTS AND SIGNIFICANCE: CFTR is detectable and is functional in human monocytes. We also showed that CFTR-associated activity can be evaluated in 5 ml of peripheral blood and devise an index potentially applicable for diagnostic purposes and both basic and translational research: from drug development to evaluation of functional outcomes in clinical trials

One thousand plant transcriptomes and the phylogenomics of green plants

Abstract: Green plants (Viridiplantae) include around 450,000–500,000 species1, 2 of great diversity and have important roles in terrestrial and aquatic ecosystems. Here, as part of the One Thousand Plant Transcriptomes Initiative, we sequenced the vegetative transcriptomes of 1,124 species that span the diversity of plants in a broad sense (Archaeplastida), including green plants (Viridiplantae), glaucophytes (Glaucophyta) and red algae (Rhodophyta). Our analysis provides a robust phylogenomic framework for examining the evolution of green plants. Most inferred species relationships are well supported across multiple species tree and supermatrix analyses, but discordance among plastid and nuclear gene trees at a few important nodes highlights the complexity of plant genome evolution, including polyploidy, periods of rapid speciation, and extinction. Incomplete sorting of ancestral variation, polyploidization and massive expansions of gene families punctuate the evolutionary history of green plants. Notably, we find that large expansions of gene families preceded the origins of green plants, land plants and vascular plants, whereas whole-genome duplications are inferred to have occurred repeatedly throughout the evolution of flowering plants and ferns. The increasing availability of high-quality plant genome sequences and advances in functional genomics are enabling research on genome evolution across the green tree of life

miRNA-target prediction based on transcriptional regulation

<p>Abstract</p> <p>Background</p> <p>microRNAs (miRNAs) are tiny endogenous RNAs that have been discovered in animals and plants, and direct the post-transcriptional regulation of target mRNAs for degradation or translational repression via binding to the 3'UTRs and the coding exons. To gain insight into the biological role of miRNAs, it is essential to identify the full repertoire of mRNA targets (target genes). A number of computer programs have been developed for miRNA-target prediction. These programs essentially focus on potential binding sites in 3'UTRs, which are recognized by miRNAs according to specific base-pairing rules.</p> <p>Results</p> <p>Here, we introduce a novel method for miRNA-target prediction that is entirely independent of existing approaches. The method is based on the hypothesis that transcription of a miRNA and its target genes tend to be co-regulated by common transcription factors. This hypothesis predicts the frequent occurrence of common <it>cis</it>-elements between promoters of a miRNA and its target genes. That is, our proposed method first identifies putative <it>cis</it>-elements in a promoter of a given miRNA, and then identifies genes that contain common putative <it>cis</it>-elements in their promoters. In this paper, we show that a significant number of common <it>cis</it>-elements occur in ~28% of experimentally supported human miRNA-target data. Moreover, we show that the prediction of human miRNA-targets based on our method is statistically significant. Further, we discuss the random incidence of common <it>cis</it>-elements, their consensus sequences, and the advantages and disadvantages of our method.</p> <p>Conclusions</p> <p>This is the first report indicating prevalence of transcriptional regulation of a miRNA and its target genes by common transcription factors and the predictive ability of miRNA-targets based on this property.</p

A null model for Pearson co-expression networks

<p>Presented at the Fondazione Edmund Mach</p