BACKGROUND: Pathway enrichment analysis is a crucial method for the biological interpretation of metabolomic data by identifying associations between altered metabolites and biological pathways. However, such traditional approaches often rely on a limited set of predefined metabolic pathways, resulting in a low likelihood of discovering pathways associated with a given metabolic profile. To overcome this limitation, we extended our previously developed iDMET methodology to incorporate a broader range of metabolite sets, including those derived from differential metabolomic profiles. This enhanced approach, termed iDMET+, significantly expands dataset diversity and size, increasing the likelihood of discovering associated metabolite sets for a given metabolic profile, thereby enables more biological insights to be obtained from the metabolic profile. RESULTS: We validated iDMET+ through case studies on three diseases: clear cell renal cell carcinoma, colorectal cancer, and small cell lung cancer. First, using a clear cell renal cell carcinoma study as input, iDMET+ correctly identified another study of the same disease that involved metabolomic analysis. This pair of studies was identified as relevant in our previous iDMET results, showing the consistency between iDMET+ and iDMET. Second, using the metabolomic profile of colorectal cancer as input, iDMET+ identified not only another metabolomic study of the same cancer but, surprisingly, also metabolomic studies on prostate cancer and a high-fat diet. These studies focused on MYC-driven metabolic reprogramming, which was also a major focus of the input study. In both case studies, related studies were enriched because the differential metabolomic profiles of directly associated studies were part of the metabolite set. In contrast, the small cell lung cancer study highlighted limitations in dataset coverage—the absence of directly relevant differential metabolomic profiles resulted in fewer enriched metabolite sets. Nevertheless, the analysis of commonly altered metabolites still yielded some meaningful results. Metabolite alterations associated with inhibition of the purine salvage pathway were observed, suggesting potential involvement in tumor metabolic reprogramming. CONCLUSIONS: These results demonstrate that iDMET+ offers broader biologically relevant information than the conventional pathway-based approaches and has the potential to uncover biologically significant findings by searching across diverse datasets. This work also identifies areas of improvements for iDMET+.

BACKGROUND: Comprehensive metabolomic analyses have been conducted in various institutes and a large amount of metabolomic data are now publicly available. To help fully exploit such data and facilitate their interpretation, metabolomic data obtained from different facilities and different samples should be integrated and compared. However, large-scale integration of such data for biological discovery is challenging given that they are obtained from various types of sample at different facilities and by different measurement techniques, and the target metabolites and sensitivities to detect them also differ from study to study. RESULTS: We developed iDMET, a network-based approach to integrate metabolomic data from different studies based on the differential metabolomic profiles between two groups, instead of the metabolite profiles themselves. As an application, we collected cancer metabolomic data from 27 previously published studies and integrated them using iDMET. A pair of metabolomic changes observed in the same disease from two studies were successfully connected in the network, and a new association between two drugs that may have similar effects on the metabolic reactions was discovered. CONCLUSIONS: We believe that iDMET is an efficient tool for integrating heterogeneous metabolomic data and discovering novel relationships between biological phenomena.

Following recent advances in high-throughput mass spectrometry (MS)-based proteomics, the numbers of identified phosphoproteins and their phosphosites have greatly increased in a wide variety of organisms. Although a critical role of phosphorylation is control of protein signaling, our understanding of the phosphoproteome remains limited. Here, we report unexpected, large-scale connections revealed between the phosphoproteome and protein interactome by integrative data-mining of yeast multi-omics data. First, new phosphoproteome data on yeast cells were obtained by MS-based proteomics and unified with publicly available yeast phosphoproteome data. This revealed that nearly 60% of ∼6,000 yeast genes encode phosphoproteins. We mapped these unified phosphoproteome data on a yeast protein-protein interaction (PPI) network with other yeast multi-omics datasets containing information about proteome abundance, proteome disorders, literature-derived signaling reactomes, and in vitro substratomes of kinases. In the phospho-PPI, phosphoproteins had more interacting partners than nonphosphoproteins, implying that a large fraction of intracellular protein interaction patterns (including those of protein complex formation) is affected by reversible and alternative phosphorylation reactions. Although highly abundant or unstructured proteins have a high chance of both interacting with other proteins and being phosphorylated within cells, the difference between the number counts of interacting partners of phosphoproteins and nonphosphoproteins was significant independently of protein abundance and disorder level. Moreover, analysis of the phospho-PPI and yeast signaling reactome data suggested that co-phosphorylation of interacting proteins by single kinases is common within cells. These multi-omics analyses illuminate how wide-ranging intracellular phosphorylation events and the diversity of physical protein interactions are largely affected by each other.

BACKGROUND: Phosphorylation is a ubiquitous and fundamental regulatory mechanism that controls signal transduction in living cells. The number of identified phosphoproteins and their phosphosites is rapidly increasing as a result of recent mass spectrometry-based approaches. RESULTS: We analyzed time-course phosphoproteome data obtained previously by liquid chromatography mass spectrometry with the stable isotope labeling using amino acids in cell culture (SILAC) method. This provides the relative phosphorylation activities of digested peptides at each of five time points after stimulating HeLa cells with epidermal growth factor (EGF). We initially calculated the correlations between the phosphorylation dynamics patterns of every pair of peptides and connected the strongly correlated pairs to construct a network. We found that peptides extracted from the same intracellular fraction (nucleus vs. cytoplasm) tended to be close together within this phosphorylation dynamics-based network. The network was then analyzed using graph theory and compared with five known signal-transduction pathways. The dynamics-based network was correlated with known signaling pathways in the NetPath and Phospho.ELM databases, and especially with the EGF receptor (EGFR) signaling pathway. Although the phosphorylation patterns of many proteins were drastically changed by the EGF stimulation, our results suggest that only EGFR signaling transduction was both strongly activated and precisely controlled. CONCLUSIONS: The construction of a phosphorylation dynamics-based network provides a useful overview of condition-specific intracellular signal transduction using quantitative time-course phosphoproteome data under specific experimental conditions. Detailed prediction of signal transduction based on phosphoproteome dynamics remains challenging. However, since the phosphorylation profiles of kinase-substrate pairs on the specific pathway were localized in the dynamics-based network, our method will be a complementary strategy to explore new components of protein signaling pathways in combination with previous methods (including software) of predicting direct kinase-substrate relationships.

BACKGROUND: High-throughput methods for detecting protein-protein interactions enable us to obtain large interaction networks, and also allow us to computationally identify the associations of proteins as protein complexes. Although there are methods to extract protein complexes as sets of proteins from interaction networks, the extracted complexes may include false positives because they do not account for the structural limitations of the proteins and thus do not check that the proteins in the extracted complex can simultaneously bind to each other. In addition, there have been few searches for deeper insights into the protein complexes, such as of the topology of the protein-protein interactions or into the domain-domain interactions that mediate the protein interactions. RESULTS: Here, we introduce a combinatorial approach for prediction of protein complexes focusing not only on determining member proteins in complexes but also on the DDI/PPI organization of the complexes. Our method analyzes complex candidates predicted by the existing methods. It searches for optimal combinations of domain-domain interactions in the candidates based on an assumption that the proteins in a candidate can form a true protein complex if each of the domains is used by a single protein interaction. This optimization problem was mathematically formulated and solved using binary integer linear programming. By using publicly available sets of yeast protein-protein interactions and domain-domain interactions, we succeeded in extracting protein complex candidates with an accuracy that is twice the average accuracy of the existing methods, MCL, MCODE, or clustering coefficient. Although the configuring parameters for each algorithm resulted in slightly improved precisions, our method always showed better precision for most values of the parameters. CONCLUSIONS: Our combinatorial approach can provide better accuracy for prediction of protein complexes and also enables to identify both direct PPIs and DDIs that mediate them in complexes.

BACKGROUND: In many bacteria, intragenomic diversity in synonymous codon usage among genes has been reported. However, no quantitative attempt has been made to compare the diversity levels among different genomes. Here, we introduce a mean dissimilarity-based index (Dmean) for quantifying the level of diversity in synonymous codon usage among all genes within a genome. RESULTS: The application of Dmean to 268 bacterial genomes shows that in bacteria with extremely biased genomic G+C compositions there is little diversity in synonymous codon usage among genes. Furthermore, our findings contradict previous reports. For example, a low level of diversity in codon usage among genes has been reported for Helicobacter pylori, but based on Dmean, the diversity level of this species is higher than those of more than half of bacteria tested here. The discrepancies between our findings and previous reports are probably due to differences in the methods used for measuring codon usage diversity. CONCLUSION: We recommend that Dmean be used to measure the diversity level of codon usage among genes. This measure can be applied to other compositional features such as amino acid usage and dinucleotide relative abundance as a genomic signature.

Abstract Motivation: Recent screening techniques have made large amounts of protein–protein interaction data available, from which biologically important information such as the function of uncharacterized proteins, the existence of novel protein complexes, and novel signal-transduction pathways can be discovered. However, experimental data on protein interactions contain many false positives, making these discoveries difficult. Therefore computational methods of assessing the reliability of each candidate protein–protein interaction are urgently needed. Results: We developed a new ‘interaction generality’ measure (IG2) to assess the reliability of protein–protein interactions using only the topological properties of their interaction-network structure. Using yeast protein–protein interaction data, we showed that reliable protein–protein interactions had significantly lower IG2 values than less-reliable interactions, suggesting that IG2 values can be used to evaluate and filter interaction data to enable the construction of reliable protein–protein interaction networks. Availability: The protein–protein interaction data used in this study along with the associated IG2 values are available at http://genome.gsc.riken.go.jp. Contact: [email protected] * To whom correspondence should be addressed. † Present address: Institute for Advanced Biosciences, Keio University, 14-1 Baba-cho, Tsuruoka, Yamagata 997-0035, Japan.

MOTIVATION: Readthrough is an unusual process in which a stop codon is misread or skipped. Recently it has been shown that some translation is regulated by the readthrough reactions although the complete mechanism is not clear. Therefore, the discovery of 'readthrough genes' is important for further investigation of their cellular roles, which may provide additional insights into the mechanism of translational regulation. RESULTS: We constructed a system that lists candidates of readthrough genes based on the existence of a 'protein motif' at the 3' untranslated region (UTR). Using this system, we extracted 85 candidates from 4082 nucleic acid sequences of Drosophila melanogaster in GenBank database. The sequences of these candidates had a slightly more stable secondary structure and different base preferences compared to the non-candidates. As these features are known to have an effect on readthrough events, we would like to suggest that these candidates contain actual readthrough genes. AVAILABILITY: Source code of the system is available upon request.

MOTIVATION: It is well accepted that the 3' end of 16S rRNA is directly involved in prokaryotic translation initiation by pairing with the Shine-Dalgarno (SD) sequence, which is located in the ribosome-binding site of mRNA. According to Shine and Dalgarno, Escherichia coli 's 5' UTR has the pattern of 'AGGAGG' (SD sequence), which is complementary to the 3' end sequence of 16S rRNA. In this work, we systematically calculated free-energy values of the base pairing between the 3' end of 16S rRNA and the 5' UTR of mRNA, in order to analyze the base-pairing potentials in various prokaryotes. The free-energy values were then plotted over distances from the start codon to visualize the free-energy pattern of 5'UTRs. RESULTS: The average free-energy values fell sharply before the start codon in E. coli, which is consistent with the model that the 3' end of 16S rRNA base pairs with the SD sequence. Haemophilus influenzae, Bacillus subtilis and Helicobacter pylori show a similar pattern, suggesting that the organisms have basically the same mechanism of translation initiation as E. coli. Other eubacteria, such as Synechocystis PCC6803, Mycoplasma genitalium, Mycoplasma pneumoniae and Borrelia burgdorferi also show decreases in their free-energy values, although they are less evident. We also did the same analysis with a eukaryote genome as a control; no fall in free-energy values was observed between the 3' end of 18S rRNA and 5' UTRs of Saccharomyces cerevisiae, suggesting that this organism does not base pair in translation initiation. The three archaebacteria A. fulgidus, M. jannaschii and M. thermoautotrophicum show patterns similar to eubacteria, but not to S. cerevisiae, indicating that archaebacteria are closer to eubacteria than to eukaryotes with respect to the mechanism of translation initiation. From these observations, it appears that the shape of the curve produced by the algorithm can be used to predict the mechanism of translation initiation. AVAILABILITY: The C programs used in our analysis are available upon request.