Simone S. Li

Sequencing and Validation of the Genome of a Campylobacter concisus Reveals Intra-Species Diversity

Campylobacter concisus is an emerging pathogen of the human gastrointestinal tract. Its role in different diseases remains a subject of debate; this may be due to strain to strain genetic variation. Here, we sequence and analyze the genome of a C. concisus from a biopsy of a child with Crohns disease (UNSWCD); the second such genome for this species. A 1.8 Mb genome was assembled with paired-end reads from a next-generation sequencer. This genome is smaller than the 2.1 Mb C. concisus reference BAA-1457. While 1593 genes were conserved across UNSWCD and BAA-1457, 138 genes from UNSWCD and 281 from BAA-1457 were unique when compared against the other. To further validate the genome assembly and annotation, comprehensive shotgun proteomics was performed. This confirmed 78% of open reading frames in UNSWCD and, importantly, provided evidence of expression for 217 proteins previously defined as ‘hypothetical’ in Campylobacter. Substantial functional differences were observed between the UNSWCD and the reference strain. Enrichment analysis revealed differences in membrane proteins, response to stimulus, molecular transport and electron carriers. Synteny maps for the 281 genes not present in UNSWCD identified seven functionally associated gene clusters. These included one associated with the CRISPR family and another which encoded multiple restriction endonucleases; these genes are all involved in resistance to phage attack. Many of the observed differences are consistent with UNSWCD having adapted to greater surface interaction with host cells, as opposed to BAA-1457 which may prefer a free-living environment.

A Genome-Wide Screen in Yeast Identifies Specific Oxidative Stress Genes Required for the Maintenance of Sub-Cellular Redox Homeostasis

Maintenance of an optimal redox environment is critical for appropriate functioning of cellular processes and cell survival. Despite the importance of maintaining redox homeostasis, it is not clear how the optimal redox potential is sensed and set, and the processes that impact redox on a cellular/organellar level are poorly understood. The genetic bases of cellular redox homeostasis were investigated using a green fluorescent protein (GFP) based redox probe, roGFP2 and a pH sensitive GFP-based probe, pHluorin. The use of roGFP2, in conjunction with pHluorin, enabled determination of pH-adjusted sub-cellular redox potential in a non-invasive and real-time manner. A genome-wide screen using both the non-essential and essential gene collections was carried out in Saccharomyces cerevisiae using cytosolic-roGFP2 to identify factors essential for maintenance of cytosolic redox state under steady-state conditions. 102 genes of diverse function were identified that are required for maintenance of cytosolic redox state. Mutations in these genes led to shifts in the half-cell glutathione redox potential by 75-10 mV. Interestingly, some specific oxidative stress-response processes were identified as over-represented in the data set. Further investigation of the role of oxidative stress-responsive systems in sub-cellular redox homeostasis was conducted using roGFP2 constructs targeted to the mitochondrial matrix and peroxisome and E GSH was measured in cells in exponential and stationary phase. Analyses allowed for the identification of key redox systems on a sub-cellular level and the identification of novel genes involved in the regulation of cellular redox homeostasis.

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.

Visualization and analysis of the complexome network of Saccharomyces cerevisiae.

Most processes in the cell are delivered by protein complexes, rather than individual proteins. While the association of proteins has been studied extensively in protein-protein interaction networks (the interactome), an intuitive and effective representation of complex-complex connections (the complexome) is not yet available. Here, we describe a new representation of the complexome of Saccharomyces cerevisiae. Using the core-module-attachment data of Gavin et al. ( Nature 2006 , 440 , 631 - 6 ), protein complexes in the network are represented as nodes; these are connected by edges that represent shared core and/or module protein subunits. To validate this network, we examined the network topology and its distribution of biological processes. The complexome network showed scale-free characteristics, with a power law-like node degree distribution and clustering coefficient independent of node degree. Connected complexes in the network showed similarities in biological process that were nonrandom. Furthermore, clusters of interacting complexes reflected a higher-level organization of many cellular functions. The strong functional relationships seen in these clusters, along with literature evidence, allowed 44 uncharacterized complexes to be assigned putative functions using guilt-by-association. We demonstrate our network model using the GEOMI visualization platform, on which we have developed capabilities to integrate and visualize complexome data.

Proteins deleterious on overexpression are associated with high intrinsic disorder, specific interaction domains, and low abundance.

In proteomics, there is a major challenge in how the functional significance of overexpressed proteins can be interpreted. This is particularly the case when examining proteins in cells or tissues. Here we have analyzed the physicochemical parameters, abundance level, half-life and degree of intrinsic disorder of proteins previously overexpressed in the yeast Saccharomyces cerevisiae. We also examined the interaction domains present and the manner in which overexpressed proteins are, or are not, associated with known complexes. We found a number of protein characteristics were strongly associated with deleterious phenotypes. These included protein abundance (where low-abundance proteins tend to be deleterious on overexpression), intrinsic disorder (where a striking association was seen between percent disorder and degree of deleterious effect), and the number of likely domain-domain interactions. Furthermore, we found a number of domain types, for example, DUF221 and the ubiquitin interaction motif, that were present predominantly in proteins that are deleterious on overexpression. Together, these results provide strong evidence that particular types of proteins are deleterious on overexpression whereas others are not. These factors can be considered in the interpretation of protein expression differences in proteomic experiments.

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.

Time-Course Proteome Analysis Reveals the Dynamic Response of Cryptococcus gattii Cells to Fluconazole

Cryptococcus gattii is an encapsulated fungus capable of causing fatal disease in immunocompetent humans and animals. As current antifungal therapies are few and limited in efficacy, and resistance is an emerging issue, the development of new treatment strategies is urgently required. The current study undertook a time-course analysis of the proteome of C. gattii during treatment with fluconazole (FLC), which is used widely in prophylactic and maintenance therapies. The aims were to analyze the overall cellular response to FLC, and to find fungal proteins involved in this response that might be useful targets in therapies that augment the antifungal activity of FLC. During FLC treatment, an increase in stress response, ATP synthesis and mitochondrial respiratory chain proteins, and a decrease in most ribosomal proteins was observed, suggesting that ATP-dependent efflux pumps had been initiated for survival and that the maintenance of ribosome synthesis was differentially expressed. Two proteins involved in fungal specific pathways were responsive to FLC. An integrative network analysis revealed co-ordinated processes involved in drug response, and highlighted hubs in the network representing essential proteins that are required for cell viability. This work demonstrates the dynamic cellular response of a typical susceptible isolate of C. gattii to FLC, and identified a number of proteins and pathways that could be targeted to augment the activity of FLC.

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.

The Interactorium: Visualising proteins, complexes and interaction networks in a virtual 3‐D cell

Here, we describe the Interactorium, a tool in which a Virtual Cell is used as the context for the seamless visualisation of the yeast protein interaction network, protein complexes and protein 3‐D structures. The tool has been designed to display very complex networks of up to 40 000 proteins or 6000 multiprotein complexes and has a series of toolboxes and menus to allow real‐time data manipulation and control the manner in which data are displayed. It incorporates new algorithms that reduce the complexity of the visualisation by the generation of putative new complexes from existing data and by the reduction of edges through the use of protein “twins” when they occur in multiple locations. Since the Interactorium permits multi‐level viewing of the molecular biology of the cell, it is a considerable advance over existing approaches. We illustrate its use for Saccharomyces cerevisiae but note that it will also be useful for the analysis of data from simpler prokaryotes and higher eukaryotes, including humans. The Interactorium is available for download at http://www.interactorium.net.