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.

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 investigator's 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.

Developing a DNA variant database.

Disease- and locus-specific variant databases have been a valuable resource to clinical and research geneticists. With the recent rapid developments in technologies, the number of DNA variants detected in a typical molecular genetics laboratory easily exceeds 1,000. To keep track of the growing inventory of DNA variants, many laboratories employ information technology to store the data as well as distributing the data and its associated information to clinicians and researchers via the Web. While it is a valuable resource, the hosting of a web-accessible database requires collaboration between bioinformaticians and biologists and careful planning to ensure its usability and availability. In this chapter, a series of tutorials on building a local DNA variant database out of a sample dataset will be provided. However, this tutorial will not include programming details on building a web interface and on constructing the web application necessary for web hosting. Instead, an introduction to the two commonly used methods for hosting web-accessible variant databases will be described. Apart from the tutorials, this chapter will also consider the resources and planning required for making a variant database project successful.

Modeling and analyzing gene co-expression in hepatocellular carcinoma using actor-semiotic networks and centrality signatures.

Primary hepatocellular carcinoma (HCC) is currently the fifth most common malignancy and the third most common cause of cancer mortality worldwide. Because of its high prevalence in developing nations, there have been numerous efforts made in the molecular characterization of primary HCC. However, a better understanding into the pathology of HCC required software-assisted network modeling and analysis. In this paper, the author presented his first attempt in exploring the biological implication of gene co-expression in HCC using actor-semiotic network modeling and analysis. The network was first constructed by integrating inter-actor relationships, e.g. gene co-expression, microRNA-to-gene, and protein interactions, with semiotic relationships, e.g. gene-to-Gene Ontology Process. Topological features that are highly discriminative of the HCC phenotype were identified by visual inspection. Finally, the author devised a graph signature-based analysis method to supplement the network exploration.

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.

eMelanoBase: an online locus-specific variant database for familial melanoma.

A proportion of melanoma-prone individuals in both familial and non-familial contexts has been shown to carry inactivating mutations in either CDKN2A or, rarely, CDK4. CDKN2A is a complex locus that encodes two unrelated proteins from alternately spliced transcripts that are read in different frames. The alpha transcript (exons 1alpha, 2, and 3) produces the p16INK4A cyclin-dependent kinase inhibitor, while the beta transcript (exons 1beta and 2) is translated as p14ARF, a stabilizing factor of p53 levels through binding to MDM2. Mutations in exon 2 can impair both polypeptides and insertions and deletions in exons 1alpha, 1beta, and 2, which can theoretically generate p16INK4A-p14ARF fusion proteins. No online database currently takes into account all the consequences of these genotypes, a situation compounded by some problematic previous annotations of CDKN2A-related sequences and descriptions of their mutations. As an initiative of the international Melanoma Genetics Consortium, we have therefore established a database of germline variants observed in all loci implicated in familial melanoma susceptibility. Such a comprehensive, publicly accessible database is an essential foundation for research on melanoma susceptibility and its clinical application. Our database serves two types of data as defined by HUGO. The core dataset includes the nucleotide variants on the genomic and transcript levels, amino acid variants, and citation. The ancillary dataset includes keyword description of events at the transcription and translation levels and epidemiological data. The application that handles users' queries was designed in the model-view-controller architecture and was implemented in Java. The object-relational database schema was deduced using functional dependency analysis. We hereby present our first functional prototype of eMelanoBase. The service is accessible via the URL www.wmi.usyd.edu.au:8080/melanoma.html.