David C. Y. Fung

UBE3A“mutations” in two unrelated and phenotypically different Angelman syndrome patients

Abstract Angelman syndrome (AS) is a rare neurodevelopmental disorder. Recently, several mutations have been found in the E6-AP ubiquitin protein ligase gene (UBE3A) in a group of patients who are nondeleted and do not have uniparental disomy or imprinting defects. Most of the reported mutations cluster within exons 9 or 16 of the UBE3A gene, and nearly all are predicted to give rise to truncated E6-AP ligases. Here, we describe two AS patients with dissimilar phenotypes. At the molecular level, they are both nondeleted, do not display uniparental disomy, and have normal imprint patterns. One has the typical AS phenotype and carries the previously reported 1344delAG de novo mutation involving a functionally significant region of UBE3A. The other expresses an atypical phenotype in that she has less severe ataxia, no inappropriate laughing, or epilepsy, and her EEG was normal at an early age. A 14-bp deletion in the 3’ untranslated region of exon 16 (3’UTRdel14) adjacent to the poly(A) signal was identified. Further investigation revealed that the DNA change was a neutral polymorphism. Haplotype analysis indicated that both the AS patient and her normal sibling had inherited the same maternal UBE3A gene and its 5’ flanking region. Although the 14-bp change has no functional significance, it assists with counseling to determine future risks of recurrence in this family.

Visual Analysis of Overlapping Biological Networks

This paper investigates a new problem of visualizing a set of overlapping networks. We present two methods for constructing visualization of two and three overlapping networks in three dimensions. Our methods aim to achieve both drawing aesthetics (or conventions) for each individual network and exposing the common nodes between the overlapping networks. We evaluated our approaches using biological networks including protein interaction network, metabolic network, and gene regulatory network, from the bacterium Escherichia coli and crop plants to demonstrate their usefulness to support biological analysis.

An online locus-specific mutation database for familial hypertrophic cardiomyopathy.

The aim of this locus‐specific mutation database was to provide an online resource that contains summarised and updated information on familial hypertrophic cardiomyopathy (FHC)‐associated mutations and related data, for researchers and clinicians. It also serves as a means of publishing previously unpublished data, which could be of value in understanding genotype/phenotype correlations. There are 123 FHC‐associated mutations catalogued along with ancillary information. By implementing the cgi/http method, remote users can query the database via the HTML interface on the Web browser and obtain data of relevance to them. The online service is available on http://www.angis.org.au/Databases/Heart. Hum Mutat 14:326–332, 1999.

Capillary electrophoresis: New technology for DNA diagnostics

Summary New innovations in the diagnostic laboratory achieve their full potential when they can be automated. Increasingly molecular biology (DNA) techniques are being utilised in traditional pathology disciplines, as well as the more recent ones of cytogenetics and molecular genetics. Molecular biology was first exploited for diagnostic purposes when Southern blotting became established. However the time‐consuming nature of the methodology, as well as the skills required, made it difficult for Southern blotting to be used routinely in the service laboratory. Subsequently the invention of PCR facilitated the approach to DNA diagnostics. Today PCR in commercial or home‐made kits is used for a range of procedures. The steps required to amplify DNA with PCR can also be fully automated. However the analysis of PCR products, which frequently requires slab gel electrophoresis and toxic chemicals or radioisotopes for visualisation, remains difficult to automate. An alternative way for analysing PCR products is now available through capillary electrophoresis. With this technique, automation can be extended to sample loading, electrophoresis and data analysis. The use of toxic chemicals or radioisotopes can be avoided.Abbreviations: ASO, allele specific oligonucleotides; IVS, intervening sequence; MS‐PCR, mutagenically separated‐polymerase chain reaction; SSCP, single strand conformation polymorphism.

Cardiac and Skeletal Muscles Show Molecularly Distinct Responses to Cancer Cachexia

Cancer cachexia is a systemic, paraneoplastic syndrome seen in patients with advanced cancer. There is growing interest in the altered muscle pathophysiology experienced by cachectic patients. This study reports the microarray analysis of gene expression in cardiac and skeletal muscle in the colon 26 (C26) carcinoma mouse model of cancer cachexia. A total of 268 genes were found to be differentially expressed in cardiac muscle tissue, compared with nontumor-bearing controls. This was fewer than the 1,533 genes that changed in cachectic skeletal muscle. In addition to different numbers of genes changing, different cellular functions were seen to change in each tissue. The cachectic heart showed signs of inflammation, similar to cachectic skeletal muscle, but did not show the upregulation of ubiquitin-dependent protein catabolic processes or downregulation of genes involved in cellular energetics and muscle regeneration that characterizes skeletal muscle cachexia. Quantitative PCR was used to investigate a subset of inflammatory genes in the cardiac and skeletal muscle of independent cachectic samples; this revealed that B4galt1, C1s, Serpina3n, and Vsig4 were significantly upregulated in cardiac tissue, whereas C1s and Serpina3n were significantly upregulated in skeletal tissue. Our skeletal muscle microarray results were also compared with those from three published microarray studies and found to be consistent in terms of the genes differentially expressed and the functional processes affected. Our study highlights that skeletal and cardiac muscles are affected differently in the C26 mouse model of cachexia and that therapeutic strategies cannot assume that both muscle types will show a similar response.

Disturbed protein–protein interaction networks in metastatic melanoma are associated with worse prognosis and increased functional mutation burden

For disseminated melanoma, new prognostic biomarkers and therapeutic targets are urgently needed. The organization of protein–protein interaction networks was assessed via the transcriptomes of four independent studies of metastatic melanoma and related to clinical outcome and MAP‐kinase pathway mutations (BRAF/NRAS). We also examined patient outcome‐related differences in a predicted network of microRNAs and their targets. The 32 hub genes with the most reproducible survival‐related disturbances in co‐expression with their protein partner genes included oncogenes and tumor suppressors, previously known correlates of prognosis, and other proteins not previously associated with melanoma outcome. Notably, this network‐based gene set could classify patients according to clinical outcomes with 67–80% accuracy among cohorts. Reproducibly disturbed networks were also more likely to have a higher functional mutation burden than would be expected by chance. The disturbed regions of networks are therefore markers of clinically relevant, selectable tumor evolution in melanoma which may carry driver mutations.

Visualization of the interactome: what are we looking at?

Network visualization of the interactome has been become routine in systems biology research. Not only does it serve as an illustration on the cellular organization of protein–protein interactions, it also serves as a biological context for gaining insights from high‐throughput data. However, the challenges to produce an effective visualization have been great owing to the fact that the scale, biological context and dynamics of any given interactome are too large and complex to be captured by a single visualization. Visualization design therefore requires a pragmatic trade‐off between capturing biological concept and being comprehensible. In this review, we focus on the biological interpretation of different network visualizations. We will draw on examples predominantly from our experiences but elaborate them in the context of the broader field. A rich variety of networks will be introduced including interactomes and the complexome in 2D, interactomes in 2.5D and 3D and dynamic networks.

2.5D visualisation of overlapping biological networks.

Biological data is often structured in the form of complex interconnected networks such as protein interaction and metabolic networks. In this paper, we investigate a new problem of visualising such overlapping biological networks. Two networks overlap if they share some nodes and edges. We present an approach for constructing visualisations of two overlapping networks, based on a restricted three dimensional representation. More specifically, we use three parallel two dimensional planes placed in three dimensions to represent overlapping networks: one for each network (the top and the bottom planes) and one for the overlapping part (in the middle plane). Our method aims to achieve both drawing aesthetics (or conventions) for each individual network, and highlighting the intersection part by them. Using three biological datasets, we evaluate our visualisation design with the aim to test whether overlapping networks can support the visual analysis of heterogeneous and yet interconnected networks.

Classification of cancer patients using pathway analysis and network clustering

Molecular expression patterns have often been used for patient classification in oncology in an effort to improve prognostic prediction and treatment compatibility. This effort is, however, hampered by the highly heterogeneous data often seen in the molecular analysis of cancer. The lack of overall similarity between expression profiles makes it difficult to partition data using conventional data mining tools. In this chapter, the authors introduce a bioinformatics protocol that uses REACTOME pathways and patient-protein network structure (also called topology) as the basis for patient classification.

Using the clustered circular layout as an informative method for visualizing protein-protein interaction networks.

The force‐directed layout is commonly used in computer‐generated visualizations of protein–protein interaction networks. While it is good for providing a visual outline of the protein complexes and their interactions, it has two limitations when used as a visual analysis method. The first is poor reproducibility. Repeated running of the algorithm does not necessarily generate the same layout, therefore, demanding cognitive readaptation on the investigators part. The second limitation is that it does not explicitly display complementary biological information, e.g. Gene Ontology, other than the protein names or gene symbols. Here, we present an alternative layout called the clustered circular layout. Using the human DNA replication protein–protein interaction network as a case study, we compared the two network layouts for their merits and limitations in supporting visual analysis.