Directional next-generation RNA sequencing and examination of premature termination codon mutations in endoglin/hereditary haemorrhagic telangiectasia

Hereditary haemorrhagic telangiectasia (HHT) is a disease characterised by abnormal vascular structures, and most commonly caused by mutations in ENG, ACVRL1 or SMAD4 encoding endothelial cell-expressed proteins involved in TGF-β superfamily signalling. The majority of mutations reported on the HHT mutation database are predicted to lead to stop codons, either due to frameshifts or direct nonsense substitutions. The proportion is higher for ENG (67%) and SMAD4 (65%) than for ACVRL1 (42%), p < 0.0001. Here, by focussing on ENG, we report why conventional views of these mutations may need to be revised. Of the 111 stop codon-generating ENG mutations, on ExPASy translation, all except one were premature termination codons (PTCs), sited at least 50-55 bp upstream of the final exon-exon boundary of the main endoglin isoform, L-endoglin. This strongly suggests that the mutated RNA species will undergo nonsense-mediated decay. We provide new in vitro expression data to support dominant negative activity of stable truncated endoglin proteins but suggest these will not generate HHT: the single natural stop codon mutation in L-endoglin (sited within 50-55 nucleotides of the final exon-exon boundary) is unlikely to generate functional protein since it replaces the entire transmembrane domain, as would 8 further natural stop codon mutations, if the minor S-endoglin isoform were implicated in HHT pathogenesis. Finally, next-generation RNA sequencing data of 7 different RNA libraries from primary human endothelial cells demonstrate that multiple intronic regions of ENG are transcribed. The potential consequences of heterozygous deletions or duplications of such regions are discussed. These data support the haploinsufficiency model for HHT pathogenesis, explain why final exon mutations have not been detected to date in HHT, emphasise the potential need for functional examination of non-PTC-generating mutations, and lead to proposals for an alternate stratification system of mutational types for HHT genotype-phenotype correlations.

EMAAS: an extensible grid-based rich internet application for microarray data analysis and management

Background

Microarray experimentation requires the application of complex analysis methods as well as the use of non-trivial computer technologies to manage the resultant large data sets. This, together with the proliferation of tools and techniques for microarray data analysis, makes it very challenging for a laboratory scientist to keep up-to-date with the latest developments in this field. Our aim was to develop a distributed e-support system for microarray data analysis and management.

Results

EMAAS (Extensible MicroArray Analysis System) is a multi-user rich internet application (RIA) providing simple, robust access to up-to-date resources for microarray data storage and analysis, combined with integrated tools to optimise real time user support and training. The system leverages the power of distributed computing to perform microarray analyses, and provides seamless access to resources located at various remote facilities. The EMAAS framework allows users to import microarray data from several sources to an underlying database, to pre-process, quality assess and analyse the data, to perform functional analyses, and to track data analysis steps, all through a single easy to use web portal. This interface offers distance support to users both in the form of video tutorials and via live screen feeds using the web conferencing tool EVO. A number of analysis packages, including R-Bioconductor and Affymetrix Power Tools have been integrated on the server side and are available programmatically through the Postgres-PLR library or on grid compute clusters. Integrated distributed resources include the functional annotation tool DAVID, GeneCards and the microarray data repositories GEO, CELSIUS and MiMiR. EMAAS currently supports analysis of Affymetrix 3' and Exon expression arrays, and the system is extensible to cater for other microarray and transcriptomic platforms.

Conclusion

EMAAS enables users to track and perform microarray data management and analysis tasks through a single easy-to-use web application. The system architecture is flexible and scalable to allow new array types, analysis algorithms and tools to be added with relative ease and to cope with large increases in data volume.

An integrated map of human 6q22.3-q24 including a 3-Mb high-resolution BAC/PAC contig encompassing a QTL for fetal hemoglobin

Genetic studies have previously assigned a quantitative trait locus (QTL) for hemoglobin F and F cells to a region of approximately 4 Mb between the markers D6S408 and D6S292 on chromosome 6q23. An initial yeast artificial chromosome contig of 13 clones spanning this region was generated. Further linkage analysis of an extended kindred refined the candidate interval to 1-2 cM, and key recombination events now place the QTL within a region of <800 kb. We describe a high-resolution bacterial clone contig spanning 3 Mb covering this critical region. The map consists of 223 bacterial artificial chromosome (BAC) and 100 P1 artificial chromosome (PAC) clones ordered by sequence-tagged site (STS) content and restriction fragment fingerprinting with a minimum tiling path of 22 BACs and 1 PAC. A total of 194 STSs map to this interval of 3 Mb, giving an average marker resolution of approximately one per 15 kb. About half of the markers were novel and were isolated in the present study, including three CA repeats and 13 single nucleotide polymorphisms. Altogether 24 expressed sequence tags, 6 of which are unique genes, have been mapped to the contig.

Do physiological concentrations of IgG induce a direct aggregation of red blood cells: comparison with fibrinogen

Under physiological conditions, red blood cells (RBCs) form aggregates that allow blood flow in all the circulatory system. RBC aggregation is the result of local flow shear stress, erythrocyte properties and macromolecular interactions between adjacent cells. Plasma proteins like fibrinogen or IgG are considered to promote RBC aggregation by a mechanism that remains to be explained. In the present study, we have examined the precise role of IgG on RBC fast-phase aggregation, in comparison with that of fibrinogen. Under our experimental conditions, we observed no fast-phase aggregating effect for IgG, at either physiological or supraphysiological concentrations, while fibrinogen induces strong aggregation of RBC. We also suspect the other plasma proteins to play a role in the RBC aggregating process.