Chi Nam Ignatius Pang

Identification of arginine- and lysine-methylation in the proteome of Saccharomyces cerevisiae and its functional implications

BackgroundThe methylation of eukaryotic proteins has been proposed to be widespread, but this has not been conclusively shown to date. In this study, we examined 36,854 previously generated peptide mass spectra from 2,607 Saccharomyces cerevisiae proteins for the presence of arginine and lysine methylation. This was done using the FindMod tool and 5 filters that took advantage of the high number of replicate analysis per protein and the presence of overlapping peptides.ResultsA total of 83 high-confidence lysine and arginine methylation sites were found in 66 proteins. Motif analysis revealed many methylated sites were associated with MK, R GG/R XG/R GX or WXXXR motifs. Functionally, methylated proteins were significantly enriched for protein translation, ribosomal biogenesis and assembly and organellar organisation and were predominantly found in the cytoplasm and ribosome. Intriguingly, methylated proteins were seen to have significantly longer half-life than proteins for which no methylation was found. Some 43% of methylated lysine sites were predicted to be amenable to ubiquitination, suggesting methyl-lysine might block the action of ubiquitin ligase.ConclusionsThis study suggests protein methylation to be quite widespread, albeit associated with specific functions. Large-scale tandem mass spectroscopy analyses will help to further confirm the modifications reported here.

Are protein complexes made of cores, modules and attachments?

It has recently been proposed by Gavin et al. (Nature 2006, 440, 631–636) that protein complexes in the cell exist in different forms. The proteins within each complex were proposed to exist as three different classes, being core, module or attachment proteins. This study investigates whether the core–module–attachment classification of proteins within each complex is supported by other high‐throughput protein data. Core proteins were found to have lower abundance, and shorter half‐life as compared to attachment proteins, whilst the abundance and half‐life of core and module proteins were similar. When the cell was perturbed, core proteins had smaller changes in abundance as compared to module and attachment proteins. Comparisons between six different pairwise interaction types of core, module and attachment proteins within a complex showed interaction types involving core or module proteins were more likely to be mediated by domain–domain interactions (DDIs) than interaction types involving attachment proteins. Interaction types that involve attachment proteins had a relatively higher ratio of abundance and ratio of half‐life. So we conclude that, the core, module and attachment model of protein complexes is supported by data from these proteomic scale datasets, and describe a model for a typical protein complex that considers the above results.

Children With Islet Autoimmunity and Enterovirus Infection Demonstrate a Distinct Cytokine Profile

Cytokines are upregulated in prediabetes, but their relationship with Enterovirus (EV) infection and development of islet autoimmunity is unknown. Cytokines (n = 65) were measured using Luminex xMAP technology in a nested case-control study of 67 children with a first-degree relative with type 1 diabetes: 27 with islet autoantibodies (Ab+) and 40 age-matched persistently autoantibody negative (Ab−) control subjects. Of 74 samples, 37 (50%) were EV-PCR+ in plasma and/or stool (EV+) and the remainder were negative for EV and other viruses (EV−). Fifteen cytokines, chemokines, and growth factors were elevated (P ≤ 0.01) in Ab+ versus Ab− children (interleukin [IL]-1β, IL-5, IL-7, IL-12(p70), IL-16, IL-17, IL-20, IL-21, IL-28A, tumor necrosis factor-α, chemokine C-C motif ligand [CCL]13, CCL26, chemokine C-X-C motif ligand 5, granulocyte-macrophage colony-stimulating factor, and thrombopoietin); most have proinflammatory effects. In EV+ versus EV− children, IL-10 was higher (P = 0.005), while IL-21 was lower (P = 0.008). Cytokine levels did not differ between Ab+EV+ and Ab+EV− children. Heat maps demonstrated clustering of some proinflammatory cytokines in Ab+ children, suggesting they are coordinately regulated. In conclusion, children with islet autoimmunity demonstrate higher levels of multiple cytokines, consistent with an active inflammatory process in the prediabetic state, which is unrelated to coincident EV infection. Apart from differences in IL-10 and IL-21, EV infection was not associated with a specific cytokine profile.

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.

Synergy and antagonism between iron chelators and antifungal drugs in Cryptococcus

Fungal infections remain very difficult to treat, and developing new antifungal drugs is difficult and expensive. Recent approaches therefore seek to augment existing antifungals with synergistic agents that can lower the therapeutic dose, increase efficacy and prevent resistance from developing. Iron limitation can inhibit microbial growth, and iron chelators have been employed to treat fungal infections. In this study, chequerboard testing was used to explore combinations of iron chelators with antifungal agents against pathogenic Cryptococcus spp. with the aim of determining how disruption to iron homeostasis affects antifungal susceptibility. The iron chelators ethylenediaminetetraacetic acid (EDTA), deferoxamine (DFO), deferiprone (DFP), deferasirox (DSX), ciclopirox olamine and lactoferrin (LF) were paired with the antifungal agents amphotericin B (AmB), fluconazole, itraconazole, voriconazole and caspofungin. All chelators except for DFO increased the efficacy of AmB, and significant synergy was seen between AmB and LF for all Cryptococcus strains. Addition of exogenous iron rescued cells from the antifungal effect of LF alone but could not prevent inhibition by AmB + LF, indicating that synergy was not due primarily to iron chelation but to other properties of LF that were potentiated in the presence of AmB. Significant synergy was not seen consistently for other antifungal-chelator combinations, and EDTA, DSX and DFP antagonised the activity of azole drugs in strains of Cryptococcus neoformans var. grubii. This study highlights the range of interactions that can be induced by chelators and indicates that most antifungal drugs are not enhanced by iron limitation in Cryptococcus.

Proteomic Validation of Transcript Isoforms, Including Those Assembled from RNA-Seq Data

Human proteome analysis now requires an understanding of protein isoforms. We recently published the PG Nexus pipeline, which facilitates high confidence validation of exons and splice junctions by integrating genomics and proteomics data. Here we comprehensively explore how RNA-seq transcriptomics data, and proteomic analysis of the same sample, can identify protein isoforms. RNA-seq data from human mesenchymal (hMSC) stem cells were analyzed with our new TranscriptCoder tool to generate a database of protein isoform sequences. MS/MS data from matching hMSC samples were then matched against the TranscriptCoder-derived database, along with Ensembl and the neXtProt database. Querying the TranscriptCoder-derived or Ensembl database could unambiguously identify ∼450 protein isoforms, with isoform-specific proteotypic peptides, including candidate hMSC-specific isoforms for the genes DPYSL2 and FXR1. Where isoform-specific peptides did not exist, groups of nonisoform-specific proteotypic peptides could specifically identify many isoforms. In both the above cases, isoforms will be detectable with targeted MS/MS assays. Unfortunately, our analysis also revealed that some isoforms will be difficult to identify unambiguously as they do not have peptides that are sufficiently distinguishing. We covisualize mRNA isoforms and peptides in a genome browser to illustrate the above situations. Mass spectrometry data is available via ProteomeXchange (PXD001449).

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.

PTMOracle: A Cytoscape App for Covisualizing and Coanalyzing Post-Translational Modifications in Protein Interaction Networks

Post-translational modifications of proteins (PTMs) act as key regulators of protein activity and of protein-protein interactions (PPIs). To date, it has been difficult to comprehensively explore functional links between PTMs and PPIs. To address this, we developed PTMOracle, a Cytoscape app for coanalyzing PTMs within PPI networks. PTMOracle also allows extensive data to be integrated and coanalyzed with PPI networks, allowing the role of domains, motifs, and disordered regions to be considered. For proteins of interest, or a whole proteome, PTMOracle can generate network visualizations to reveal complex PTM-associated relationships. This is assisted by OraclePainter for coloring proteins by modifications, OracleTools for network analytics, and OracleResults for exploring tabulated findings. To illustrate the use of PTMOracle, we investigate PTM-associated relationships and their role in PPIs in four case studies. In the yeast interactome and its rich set of PTMs, we construct and explore histone-associated and domain-domain interaction networks and show how integrative approaches can predict kinases involved in phosphodegrons. In the human interactome, a phosphotyrosine-associated network is analyzed but highlights the sparse nature of human PPI networks and lack of PTM-associated data. PTMOracle is open source and available at the Cytoscape app store: http://apps.cytoscape.org/apps/ptmoracle .

Transcriptome and network analyses in Saccharomyces cerevisiae reveal that amphotericin B and lactoferrin synergy disrupt metal homeostasis and stress response

Invasive fungal infections are difficult to treat. The few available antifungal drugs have problems with toxicity or efficacy, and resistance is increasing. To overcome these challenges, existing therapies may be enhanced by synergistic combination with another agent. Previously, we found amphotericin B (AMB) and the iron chelator, lactoferrin (LF), were synergistic against a range of different fungal pathogens. This study investigates the mechanism of AMB-LF synergy, using RNA-seq and network analyses. AMB treatment resulted in increased expression of genes involved in iron homeostasis and ATP synthesis. Unexpectedly, AMB-LF treatment did not lead to increased expression of iron and zinc homeostasis genes. However, genes involved in adaptive response to zinc deficiency and oxidative stress had decreased expression. The clustering of co-expressed genes and network analysis revealed that many iron and zinc homeostasis genes are targets of transcription factors Aft1p and Zap1p. The aft1Δ and zap1Δ mutants were hypersensitive to AMB and H2O2, suggesting they are key regulators of the drug response. Mechanistically, AMB-LF synergy could involve AMB affecting the integrity of the cell wall and membrane, permitting LF to disrupt intracellular processes. We suggest that Zap1p- and Aft1p-binding molecules could be combined with existing antifungals to serve as synergistic treatments.

High throughput protein-protein interaction data: clues for the architecture of protein complexes

BackgroundHigh-throughput techniques are becoming widely used to study protein-protein interactions and protein complexes on a proteome-wide scale. Here we have explored the potential of these techniques to accurately determine the constituent proteins of complexes and their architecture within the complex.ResultsTwo-dimensional representations of the 19S and 20S proteasome, mediator, and SAGA complexes were generated and overlaid with high quality pairwise interaction data, core-module-attachment classifications from affinity purifications of complexes and predicted domain-domain interactions. Pairwise interaction data could accurately determine the members of each complex, but was unexpectedly poor at deciphering the topology of proteins in complexes. Core and module data from affinity purification studies were less useful for accurately defining the member proteins of these complexes. However, these data gave strong information on the spatial proximity of many proteins. Predicted domain-domain interactions provided some insight into the topology of proteins within complexes, but was affected by a lack of available structural data for the co-activator complexes and the presence of shared domains in paralogous proteins.ConclusionThe constituent proteins of complexes are likely to be determined with accuracy by combining data from high-throughput techniques. The topology of some proteins in the complexes will be able to be clearly inferred. We finally suggest strategies that can be employed to use high throughput interaction data to define the membership and understand the architecture of proteins in novel complexes.