The Detection of Hamster Connexins: A Comparison of Expression Profiles with Wild-Type Mouse and the Cancer-Prone Min Mouse

The open reading frames of 17 connexins from Syrian hamster (using tissues) and 16 connexins from the Chinese hamster cell line V79, were fully (Cx30, Cx31, Cx37, Cx43 and Cx45) or partially sequenced. We have also detected, and partially sequenced, seven rat connexins that previously were unavailable. The expression of connexin genes was examined in some hamster organs and cultured hamster cells, and compared with wild-type mouse and the cancer-prone Min mouse. Although the expression patterns were similar for most organs and connexins in hamster and mouse, there were also some prominent differences (Cx29 and 30.3 in testis; Cx31.1 and 32 in eye; Cx46 in brain, kidney and testis; Cx47 in kidney). This suggests that some connexins have species-specific expression profiles. In contrast, there were minimal differences in expression profiles between wild type and Min mice. Species-specific expression profiles should be considered in attempts to make animal models of human connexin-associated diseases.     

Formation of prostanoids in human umbilical vessels perfused

Four major prostanoids (6-keto-PGF_1α, PGE₂, PGF_2α and TXB₂) were measured by specific radioimmunoassays in the outputs from human umbilical vessels perfussed . As evaluated by scanning electron microscopy (SEM) only few blood platelets were attached to the vessel wall. After an initial flush with decreasing concentrations of all four prostanoids, a stable stage was reached, lasting for 4–5 hours. During this stage the production could be inhibited by indomethacin and only slightly stimulated with arachidonic acid. The TXA₂ synthetase inhibitor UK 38485 depressed the TXB₂ production, while only slightly affecting the other three prostanoids at very high concentrations. The arteries produced relatively more 6-keto-PGF_1α than did the vein.     

Formation of prostanoids in human umbilical vessels perfused in vitro

Four major prostanoids (6-keto-PGF1 alpha, PGE2, PGF2 alpha and TXB2) were measured by specific radioimmunoassays in the outputs from human umbilical vessels perfused in vitro. As evaluated by scanning electron microscopy (SEM) only few blood platelets were attached to the vessel wall. After an initial flush with decreasing concentrations of all four prostanoids, a stable stage was reached, lasting for 4-5 hours. During this stage the production could be inhibited by indomethacin and only slightly stimulated with arachidonic acid. The TXA2 synthetase inhibitor UK 38485 depressed the TXB2 production, while only slightly affecting the other three prostanoids at very high concentrations. The arteries produced relatively more 6-keto-PGF1 alpha than did the vein.     

Screening for mutations in human HPRT cDNA using the polymerase chain reaction (PCR) in combination with constant denaturant gel electrophoresis (CDGE).

Previously, we reported the modification of denaturing gradient gel electrophoresis called constant denaturant gel electrophoresis (CDGE). CDGE separates mutant fragments in specific melting domains. CDGE seems to be a useful tool in mutation detection. Since the hypoxanthine phosphoribosyltransferase (HPRT) gene is widely used as target locus for mutation studies in vitro and in vivo, we have examined the approach of analyzing human HPRT cDNA by polymerase chain reaction (PCR) and CDGE. All nine HPRT exons are included in a 716-bp cDNA fragment obtained by PCR using HPRT cDNA as template. When the full-length cDNA fragment was examined by CDGE, it was possible to detect mutations only in the last part of exon 8 and exon 9. However, digestion of the cDNA fragment with the restriction enzyme AvaI prior to CDGE enabled us to detect point mutations in most of exon 2, the beginning of exon 3, the last part of exon 8 and exon 9. With the use of two internal primer sets, including a GC-rich clamp on one of the primers in each pair, a region containing most of exon 3 through exon 6 was amplified and we were able to resolve fragments with point mutations in this region from wild-type DNA. The approach described here allows for rapid screening of point mutations in about two thirds of the human HPRT cDNA sequence. In a test of this approach, we were able to resolve 12 of 13 known mutants. The mutant panel included one single-base deletion, one two-base deletion and 11 single-base substitutions.     

Performance comparison of four exome capture systems for deep sequencing

Background

Recent developments in deep (next-generation) sequencing technologies are significantly impacting medical research. The global analysis of protein coding regions in genomes of interest by whole exome sequencing is a widely used application. Many technologies for exome capture are commercially available; here we compare the performance of four of them: NimbleGen’s SeqCap EZ v3.0, Agilent’s SureSelect v4.0, Illumina’s TruSeq Exome, and Illumina’s Nextera Exome, all applied to the same human tumor DNA sample.

Results

Each capture technology was evaluated for its coverage of different exome databases, target coverage efficiency, GC bias, sensitivity in single nucleotide variant detection, sensitivity in small indel detection, and technical reproducibility. In general, all technologies performed well; however, our data demonstrated small, but consistent differences between the four capture technologies. Illumina technologies cover more bases in coding and untranslated regions. Furthermore, whereas most of the technologies provide reduced coverage in regions with low or high GC content, the Nextera technology tends to bias towards target regions with high GC content.

Conclusions

We show key differences in performance between the four technologies. Our data should help researchers who are planning exome sequencing to select appropriate exome capture technology for their particular application.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-449) contains supplementary material, which is available to authorized users.     

Disentangling the perturbational effects of amino acid substitutions in the DNA-binding domain of p53

The spectrum of somatic cancer-associated missense mutations in the human TP53 gene was studied in order to assess the potential structural and functional importance of various intra-molecular properties associated with these substitutions. Relating the observed frequency of particular amino acid substitutions in the p53 DNA-binding domain to their expected frequency, as calculated from DNA sequence-dependent mutation rates, yielded estimates of their relative clinical observation likelihood (RCOL). Several biophysical properties were found to display significant covariation with RCOL values. Thus RCOL values were observed to decrease with increasing solvent accessibility of the substituted residue and with increasing distance from the p53 DNA-binding and Zn²⁺-binding sites. The number of adverse steric interactions introduced by an amino acid replacement was found to be positively correlated with its RCOL value, irrespective of the magnitude of the interactions. A gain in hydrogen bond number was found to be only half as likely to come to clinical attention as mutations involving either a reduction or no change in hydrogen bond number. When the difference in potential energy between the wild-type and mutant DNA-binding domains was considered, RCOL values exhibited a minimum around changes of zero. Finally, classification of mutated residues in terms of their protein/solvent environment yielded, for somatic p53 mutations, RCOL values that resembled those previously determined for inherited mutations of human factor IX causing haemophilia B, suggesting that similar mechanisms may be responsible for the mutation-related perturbation of biological function in different protein folds. Received: 31 August 1998 / Accepted: 26 October 1998     

ClusTrack: Feature Extraction and Similarity Measures for Clustering of Genome-Wide Data Sets

Clustering is a popular technique for explorative analysis of data, as it can reveal subgroupings and similarities between data in an unsupervised manner. While clustering is routinely applied to gene expression data, there is a lack of appropriate general methodology for clustering of sequence-level genomic and epigenomic data, e.g. ChIP-based data. We here introduce a general methodology for clustering data sets of coordinates relative to a genome assembly, i.e. genomic tracks. By defining appropriate feature extraction approaches and similarity measures, we allow biologically meaningful clustering to be performed for genomic tracks using standard clustering algorithms. An implementation of the methodology is provided through a tool, ClusTrack, which allows fine-tuned clustering analyses to be specified through a web-based interface. We apply our methods to the clustering of occupancy of the H3K4me1 histone modification in samples from a range of different cell types. The majority of samples form meaningful subclusters, confirming that the definitions of features and similarity capture biological, rather than technical, variation between the genomic tracks. Input data and results are available, and can be reproduced, through a Galaxy Pages document at http://hyperbrowser.uio.no/hb/u/hb-superuser/p/clustrack. The clustering functionality is available as a Galaxy tool, under the menu option "Specialized analyzis of tracks", and the submenu option "Cluster tracks based on genome level similarity", at the Genomic HyperBrowser server: http://hyperbrowser.uio.no/hb/.     

A statistical model of ChIA-PET data for accurate detection of chromatin 3D interactions

Identification of three-dimensional (3D) interactions between regulatory elements across the genome is crucial to unravel the complex regulatory machinery that orchestrates proliferation and differentiation of cells. ChIA-PET is a novel method to identify such interactions, where physical contacts between regions bound by a specific protein are quantified using next-generation sequencing. However, determining the significance of the observed interaction frequencies in such datasets is challenging, and few methods have been proposed. Despite the fact that regions that are close in linear genomic distance have a much higher tendency to interact by chance, no methods to date are capable of taking such dependency into account. Here, we propose a statistical model taking into account the genomic distance relationship, as well as the general propensity of anchors to be involved in contacts overall. Using both real and simulated data, we show that the previously proposed statistical test, based on Fisher''s exact test, leads to invalid results when data are dependent on genomic distance. We also evaluate our method on previously validated cell-line specific and constitutive 3D interactions, and show that relevant interactions are significant, while avoiding over-estimating the significance of short nearby interactions.