Seán I. O’Donoghue

Comprehensive comparison of large-scale tissue expression datasets

For tissues to carry out their functions, they rely on the right proteins to be present. Several high-throughput technologies have been used to map out which proteins are expressed in which tissues; however, the data have not previously been systematically compared and integrated. We present a comprehensive evaluation of tissue expression data from a variety of experimental techniques and show that these agree surprisingly well with each other and with results from literature curation and text mining. We further found that most datasets support the assumed but not demonstrated distinction between tissue-specific and ubiquitous expression. By developing comparable confidence scores for all types of evidence, we show that it is possible to improve both quality and coverage by combining the datasets. To facilitate use and visualization of our work, we have developed the TISSUES resource (http://tissues.jensenlab.org), which makes all the scored and integrated data available through a single user-friendly web interface.

Unexpected features of the dark proteome

Significance A key remaining frontier in our understanding of biological systems is the “dark proteome”—that is, the regions of proteins where molecular conformation is completely unknown. We systematically surveyed these regions, finding that nearly half of the proteome in eukaryotes is dark and that, surprisingly, most of the darkness cannot be accounted for. We also found that the dark proteome has unexpected features, including an association with secretory tissues, disulfide bonding, low evolutionary conservation, and very few known interactions with other proteins. This work will help future research shed light on the remaining dark proteome, thus revealing molecular processes of life that are currently unknown. We surveyed the “dark” proteome–that is, regions of proteins never observed by experimental structure determination and inaccessible to homology modeling. For 546,000 Swiss-Prot proteins, we found that 44–54% of the proteome in eukaryotes and viruses was dark, compared with only ∼14% in archaea and bacteria. Surprisingly, most of the dark proteome could not be accounted for by conventional explanations, such as intrinsic disorder or transmembrane regions. Nearly half of the dark proteome comprised dark proteins, in which the entire sequence lacked similarity to any known structure. Dark proteins fulfill a wide variety of functions, but a subset showed distinct and largely unexpected features, such as association with secretion, specific tissues, the endoplasmic reticulum, disulfide bonding, and proteolytic cleavage. Dark proteins also had short sequence length, low evolutionary reuse, and few known interactions with other proteins. These results suggest new research directions in structural and computational biology.

SnapShot: Insulin/IGF1 Signaling

The insulin/IGF1signaling pathway (ISP) plays an essential role in long-term health. Some perturbations in this pathway are associated with diseases such as type 2 diabetes; other perturbations extend lifespan in worms, flies, and mice. The ISP regulates many biological processes, including energy storage, apoptosis, transcription, and cellular homeostasis. Such regulation involves precise rewiring of temporal events in protein phosphorylation networks. For an animated version of this Enhanced SnapShot, please visit http://www.cell.com/cell/enhanced/odonoghue.

Homology and Mutagenesis Studies of Hamster Dihydroorotase

The third reaction in de novo pyrimidine biosynthesis is catalyzed by dihydroorotase (for details see Williams et al., in this volume). By screening a variety of structural analogues of N-carbamyl-L-aspartate (CA-asp) and L-dihydroorotate (DHO), Christopherson and Jones (1980) found dihydroorotase to be highly specific for its natural substrates. Orotate and 5-substituted derivatives, such as 5-fluoroorotate, are effective inhibitors, but dihydrouracil and the CA-asp analogues, N-carbamyl-β-alanine, N-carbamyl-α-alanine, and N-acetyl-L-aspartate are not inhibitory. These observations suggest the identity of essential attachment points in the enzyme-substrate complex. Dihydrouracil lacks the carboxylate group at position 4 of dihydroorotase, and N-carbamyl-β-alanine lacks the corresponding α-carboxylate of CA-asp demonstrating that this group is required for substrate binding, possibly by interacting with a positively charged enzymic group (Christopherson and Jones, 1980). N-carbamyl-a-alanine is lacking the β-carboxylate of CA-asp and N-acetyl-L-aspartate does not possess the terminal ureido nitrogen of the substrate, indicating attachments at these locations. The β-carboxylate of CA-asp may form a coordination complex with the active site zinc atom (see Williams et al., in this volume). We have used knowledge of the cDNA sequence of hamster dihydroorotase in combination with site-directed mutagenesis in an attempt to identify the amino acids involved in these substrate attachments.

SnapShot: Phosphoregulation of Mitosis

During mitosis, a cell divides its duplicated genome into two identical daughter cells. This process must occur without errors to prevent proliferative diseases (e.g., cancer). A key mechanism controlling mitosis is the precise timing of more than 32,000 phosphorylation and dephosphorylation events by a network of kinases and counterbalancing phosphatases. The identity, magnitude, and temporal regulation of these events have emerged recently, largely from advances in mass spectrometry. Here, we show phosphoevents currently believed to be key regulators of mitosis. For an animated version of this SnapShot, please see http://www.cell.com/cell/enhanced/odonoghue2.

The Dark Proteome Database

BackgroundRecently we surveyed the dark-proteome, i.e., regions of proteins never observed by experimental structure determination and inaccessible to homology modelling. Surprisingly, we found that most of the dark proteome could not be accounted for by conventional explanations (e.g., intrinsic disorder, transmembrane domains, and compositional bias), and that nearly half of the dark proteome comprised dark proteins, in which the entire sequence lacked similarity to any known structure. In this paper we will present the Dark Proteome Database (DPD) and associated web services that provide access to updated information about the dark proteome.ResultsWe assembled DPD from several external web resources (primarily Aquaria and Swiss-Prot) and stored it in a relational database currently containing ~10 million entries and occupying ~2 GBytes of disk space. This database comprises two key tables: one giving information on the ‘darkness’ of each protein, and a second table that breaks each protein into dark and non-dark regions. In addition, a second version of the database is created using also information from the Protein Model Portal (PMP) to determine darkness. To provide access to DPD, a web server has been implemented giving access to all underlying data, as well as providing access to functional analyses derived from these data.ConclusionsAvailability of this database and its web service will help focus future structural and computational biology efforts to study the dark proteome, thus providing a basis for understanding a wide variety of biological functions that currently remain unknown.Availability and implementationDPD is available at http://darkproteome.ws. The complete database is also available upon request. Data use is permitted via the Creative Commons Attribution-NonCommercial International license (http://creativecommons.org/licenses/by-nc/4.0/).

Visual analytics of signalling pathways using time profiles.

Data visualisation is usually a crucial first step in analysing and exploring large-scale complex data. The visualisation of proteomics time-course data on post-translational modifications presents a particular challenge that is largely unmet by existing tools and methods. To this end, we present Minardo, a novel visualisation strategy tailored for such proteomics data, in which data layout is driven by both cellular topology and temporal order. In this work, we utilised the Minardo strategy to visualise a dataset showing phosphorylation events in response to insulin. We evaluated the visualisation together with experts in diabetes and obesity, which led to new insights into the insulin response pathway. Based on this success, we outline how this layout strategy could be automated into a web-based tool for visualising a broad range of proteomics time-course data. We also discuss how the approach could be extended to include protein 3D structure information, as well as higher dimensional data, such as a range of experimental conditions. We also discuss our entry of Minardo in the international DREAM8 competition.

Visual Analytics of Gene Sets Comparison

During the last decades, the volume of genomic data has increased enormously, as has the complexity of related gene annotation data. As a result, there is an ever-increasing need for methods that can help biologists compare sets of genes efficiently. While several standard tools exist that support such analyses, to date there is no broadly accepted approach for the visual comparison of gene sets, and most tools generate as output flat files with lists of annotations and their associated significance scores. More advanced techniques are required by the visual analytics community to help exploration of results efficiently. Here, we report our preliminary work on adaptable, zoomable, and interactive tag-clouds as one such potential improvement.