Investigating the Network Basis of Negative Genetic Interactions in Saccharomyces cerevisiae with Integrated Biological Networks and Triplet Motif Analysis.

Negative genetic interactions in Saccharomyces cerevisiae have been systematically screened to near-completeness, with >500 000 interactions identified. Nevertheless, the biological basis of these interactions remains poorly understood. To investigate this, we analyzed negative genetic interactions within an integrated biological network, being the union of protein-protein, kinase-substrate, and transcription factor-target gene interactions. Network triplets, containing two genes/proteins that show negative genetic interaction and a third protein from the network, were then analyzed. Strikingly, just six out of 15 possible triplet motif types were present, as compared to randomized networks. These were in three clear groups: protein-protein interactions, signaling, and regulatory triplets where the latter two showed no overlap. In the triplets, negative genetic interactions were associated with paralogs and ohnologs; however, these were very rare. Negative genetic interactions among the six triplet motifs did however show strong dosage constraints, with genes being significantly associated with toxicity on overexpression and periodicity in the cell cycle. Negative genetic interactions overlapped with other interaction types in 37% of cases; these were predominantly associated with protein complexes or signaling events. Finally, we highlight regions of "network vulnerability" containing multiple negative genetic interactions; these could be targeted in fungal species for the regulation of cell growth.

Markers in blood and saliva for prediction of orthodontically induced inflammatory root resorption: a retrospective case controlled-study.

BACKGROUND: Hormonal and enzymatic factors may render certain individuals more susceptible to orthodontically induced inflammatory root resorption (OIIRR). The objectives of this study are (1) to identify biochemical key markers in blood and saliva that may be correlated to the trend of extensive OIIRR and (2) to utilise these markers to predict a susceptible patient-receiving orthodontic treatment. METHODS: Nine patients (mean age 23 + 2.9 years) who had moderate to severe OIIRR that assessed via orthopantomograms and met the inclusion criteria were classified as the root resorption group (RRG). Blood chemistry was evaluated using the collection of fasting blood and unstimulated saliva samples. Multiplex enzyme-linked immunosorbent assay (ELISA) arrays were used to screen blood and saliva samples for human cytokines, chemokines and several key enzymes that may play a role in root resorption following orthodontic force application. Biochemical findings from 16 matching subjects were used as the control (CG) for comparative measurements. RESULTS: Patients with moderate to severe OIIRR showed a significant increase in salivary cytokines including interleukin (IL) 7, IL-10, IL-12p70 and interferon-gamma (IFN-γ) level as well as a significant decrease in IL-4 level. Osteocalcin and procollagen type I N-terminal peptide (P1NP) appeared to be the only blood factors that showed a significant difference, more in the CG than the RRG. CONCLUSIONS: Saliva might be a more valuable way of measuring changes in cytokine expression than blood secondary to orthodontic treatment. Although the increased expression of pro-inflammatory and anti-inflammatory cytokines may be determinants in the development of moderate to severe OIIRR, cytokine expression may be affected by several potential inflammations in another part of the body. Future research could investigate the cause/effect relationship of different cytokines, in a larger group of patients and at different time intervals, using digital subtraction radiography techniques and microfluidic biosensors.

Pseudomonas aeruginosa Cell Membrane Protein Expression from Phenotypically Diverse Cystic Fibrosis Isolates Demonstrates Host-Specific Adaptations.

Pseudomonas aeruginosa is a Gram-negative, nosocomial, highly adaptable opportunistic pathogen especially prevalent in immuno-compromised cystic fibrosis (CF) patients. The bacterial cell surface proteins are important contributors to virulence, yet the membrane subproteomes of phenotypically diverse P. aeruginosa strains are poorly characterized. We carried out mass spectrometry (MS)-based proteome analysis of the membrane proteins of three novel P. aeruginosa strains isolated from the sputum of CF patients and compared protein expression to the widely used laboratory strain, PAO1. Microbes were grown in planktonic growth condition using minimal M9 media, and a defined synthetic lung nutrient mimicking medium (SCFM) limited passaging. Two-dimensional LC-MS/MS using iTRAQ labeling enabled quantitative comparisons among 3171 and 2442 proteins from the minimal M9 medium and in the SCFM, respectively. The CF isolates showed marked differences in membrane protein expression in comparison with PAO1 including up-regulation of drug resistance proteins (MexY, MexB, MexC) and down-regulation of chemotaxis and aerotaxis proteins (PA1561, PctA, PctB) and motility and adhesion proteins (FliK, FlgE, FliD, PilJ). Phenotypic analysis using adhesion, motility, and drug susceptibility assays confirmed the proteomics findings. These results provide evidence of host-specific microevolution of P. aeruginosa in the CF lung and shed light on the adaptation strategies used by CF pathogens.

Molecular interaction networks for the analysis of human disease: utility, limitations, and considerations.

High-throughput '-omics' data can be combined with large-scale molecular interaction networks, for example, protein-protein interaction networks, to provide a unique framework for the investigation of human molecular biology. Interest in these integrative '-omics' methods is growing rapidly because of their potential to understand complexity and association with disease; such approaches have a focus on associations between phenotype and "network-type." The potential of this research is enticing, yet there remain a series of important considerations. Here, we discuss interaction data selection, data quality, the relative merits of using data from large high-throughput studies versus a meta-database of smaller literature-curated studies, and possible issues of sociological or inspection bias in interaction data. Other work underway, especially international consortia to establish data formats, quality standards and address data redundancy, and the improvements these efforts are making to the field, is also evaluated. We present options for researchers intending to use large-scale molecular interaction networks as a functional context for protein or gene expression data, including microRNAs, especially in the context of human disease.

Dynamic hubs show competitive and static hubs non-competitive regulation of their interaction partners.

Date hub proteins have 1 or 2 interaction interfaces but many interaction partners. This raises the question of whether all partner proteins compete for the interaction interface of the hub or if the cell carefully regulates aspects of this process? Here, we have used real-time rendering of protein interaction networks to analyse the interactions of all the 1 or 2 interface hubs of Saccharomyces cerevisiae during the cell cycle. By integrating previously determined structural and gene expression data, and visually hiding the nodes (proteins) and their edges (interactions) during their troughs of expression, we predict when interactions of hubs and their partners are likely to exist. This revealed that 20 out of all 36 one- or two- interface hubs in the yeast interactome fell within two main groups. The first was dynamic hubs with static partners, which can be considered as 'competitive hubs'. Their interaction partners will compete for the interaction interface of the hub and the success of any interaction will be dictated by the kinetics of interaction (abundance and affinity) and subcellular localisation. The second was static hubs with dynamic partners, which we term 'non-competitive hubs'. Regulatory mechanisms are finely tuned to lessen the presence and/or effects of competition between the interaction partners of the hub. It is possible that these regulatory processes may also be used by the cell for the regulation of other, non-cell cycle processes.

A multidimensional matrix for systems biology research and its application to interaction networks.

A multidimensional matrix containing 76 parameters from 21 transcriptomics, proteomics, interactomics, phenotypic and sequence-based data sets, in which each data set covered most of the Saccharomyces cerevisiae proteome, was compiled for systems biology research. The maximal information coefficient (MIC) was used to measure correlations between every pair of parameters. Out of 2850 possible comparisons, 340 pairs of variables (12%) showed statistically significant MIC scores. There were 321 relationships that were expected; these included relationships within physicochemical parameters of proteins, between abundance levels of genes/proteins and expression noise, and between different types of intracellular networks. We found 19 potentially novel relationships between different types of "-omics" data. The strongest of these involved genetic interaction networks, which were correlated with pleiotropy and cell-to-cell variability in protein expression. Protein disorder also showed a number of significant relationships with protein abundance, signaling and regulatory networks. Significant cross-talk was seen between the signaling and kinase interaction networks. Investigation of this revealed densely connected kinase clusters and significant signaling between them, along with signaling centers that act as integrators or broadcasters of intracellular information. These centers may allow for redundancy and a means of dampening noise in networks under a variety of genetic or environmental perturbations.

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.

Four-dimensional visualisation and analysis of protein-protein interaction networks.

Protein-protein interaction networks are typically built with interactions collated from many experiments. These networks are thus composite and show all interactions that are currently known to occur in a cell. However, these representations are static and ignore the constant changes in protein-protein interactions. Here we present software for the generation and analysis of dynamic, four-dimensional (4-D) protein interaction networks. In this, time-course-derived abundance data are mapped onto three-dimensional networks to generate network movies. These networks can be navigated, manipulated and queried in real time. Two types of dynamic networks can be generated: a 4-D network that maps expression data onto protein nodes and one that employs 'real-time rendering' by which protein nodes and their interactions appear and disappear in association with temporal changes in expression data. We illustrate the utility of this software by the analysis of singlish interface date hub interactions during the yeast cell cycle. In this, we show that proteins MLC1 and YPT52 show strict temporal control of when their interaction partners are expressed. Since these proteins have one and two interaction interfaces, respectively, it suggests that temporal control of gene expression may be used to limit competition at the interaction interfaces of some hub proteins. The software and movies of the 4-D networks are available at http://www.systemsbiology.org.au/downloads_geomi.html.