Cytomegalovirus infection during pregnancy with maternofetal transmission induces a proinflammatory cytokine bias in placenta and amniotic fluid.

Congenital infection with cytomegalovirus (CMV) can induce immune responses and placental damage. By use of immunoassay panels, 27 cytokines were assessed in midtrimester amniotic fluid from 8 patients with congenital CMV, in midtrimester sera from 12 pregnant women with primary CMV infection, and in amniotic fluid and serum from uninfected maternal controls. Levels of the cytokines tumor necrosis factor α, interleukin 1ß, interleukin 12, and interleukin 17; the chemokines CCL2, CCL4, and CXCL10; and the growth factors granulocyte-macrophage colony-stimulating factor and platelet-derived growth factor bb were significantly elevated in amniotic fluid from congenital CMV patients (P < .01). Only CXCL10 was significantly higher in sera from CMV-infected pregnant women. CMV infection during pregnancy is associated with a shift in cytokine expression toward a proinflammatory state.

The methylproteome and the intracellular methylation network.

Since its discovery more than 50 years ago, post-translational modification (PTM) of proteins via methylation has grown in prominence, its involvement having been recognised in a number of central processes in the cell. Of these, the best characterised is its role in the epigenetic code. However, there is increasing evidence that its role extends far beyond this and we propose that it is a key regulator in interactome dynamics. In this review, we focus on the role of methylation in regulating protein-protein interactions and illustrate, by providing a broad-scale summary of our current knowledge of methylation and its impact on systems biology, how this can ultimately affect interactome dynamics. We describe the variety of analytical techniques available for the study of the methylproteome, comment on their advantages and limitations, and consider how these tools can help elucidate how methylation regulates the dynamics of the interactome. The insights gained from methyltransferase-substrate networks will be summarised and the ability of protein methylation to facilitate or block protein-protein interactions as well as their interplay with other post-translational modifications, in particular phosphorylation, is highlighted. Finally, the importance of methylation in pathology-associated protein interaction networks will be discussed using examples involving human diseases and the p53 protein.

Identifying foldable regions in protein sequence from the hydrophobic signal.

Structural genomics initiatives aim to elucidate representative 3D structures for the majority of protein families over the next decade, but many obstacles must be overcome. The correct design of constructs is extremely important since many proteins will be too large or contain unstructured regions and will not be amenable to crystallization. It is therefore essential to identify regions in protein sequences that are likely to be suitable for structural study. Scooby-Domain is a fast and simple method to identify globular domains in protein sequences. Domains are compact units of protein structure and their correct delineation will aid structural elucidation through a divide-and-conquer approach. Scooby-Domain predictions are based on the observed lengths and hydrophobicities of domains from proteins with known tertiary structure. The prediction method employs an A*-search to identify sequence regions that form a globular structure and those that are unstructured. On a test set of 173 proteins with consensus CATH and SCOP domain definitions, Scooby-Domain has a sensitivity of 50% and an accuracy of 29%, which is better than current state-of-the-art methods. The method does not rely on homology searches and, therefore, can identify previously unknown domains.

Online resources for the molecular contextualization of disease.

Searching online resources can provide medical researchers with an efficient means of gathering existing knowledge on the molecular causes of disease. The researcher may choose to explore the following areas, e.g., genetic mutations associated with the disease, function and cellular sub-localization of the associated protein(s) and their protein interaction partners. Using a small case study, examining the disease retinoblastoma, this chapter guides the reader through the relevant information contained within relevant databases. It is shown that the integration of online biological knowledge with genomic and proteomic experimental data provides insights into the understanding of diseases in their molecular context.

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.