ProFITS of maize: a database of protein families involved in the transduction of signalling in the maize genome

<p>Abstract</p> <p>Background</p> <p>Maize (<it>Zea mays </it>ssp. <it>mays </it>L.) is an important model for plant basic and applied research. In 2009, the B73 maize genome sequencing made a great step forward, using clone by clone strategy; however, functional annotation and gene classification of the maize genome are still limited. Thus, a well-annotated datasets and informative database will be important for further research discoveries. Signal transduction is a fundamental biological process in living cells, and many protein families participate in this process in sensing, amplifying and responding to various extracellular or internal stimuli. Therefore, it is a good starting point to integrate information on the maize functional genes involved in signal transduction.</p> <p>Results</p> <p>Here we introduce a comprehensive database 'ProFITS' (Protein Families Involved in the Transduction of Signalling), which endeavours to identify and classify protein kinases/phosphatases, transcription factors and ubiquitin-proteasome-system related genes in the B73 maize genome. Users can explore gene models, corresponding transcripts and FLcDNAs using the three abovementioned protein hierarchical categories, and visualize them using an AJAX-based genome browser (JBrowse) or Generic Genome Browser (GBrowse). Functional annotations such as GO annotation, protein signatures, protein best-hits in the <it>Arabidopsis </it>and rice genome are provided. In addition, pre-calculated transcription factor binding sites of each gene are generated and mutant information is incorporated into ProFITS. In short, ProFITS provides a user-friendly web interface for studies in signal transduction process in maize.</p> <p>Conclusion</p> <p>ProFITS, which utilizes both the B73 maize genome and full length cDNA (FLcDNA) datasets, provides users a comprehensive platform of maize annotation with specific focus on the categorization of families involved in the signal transduction process. ProFITS is designed as a user-friendly web interface and it is valuable for experimental researchers. It is freely available now to all users at <url>http://bioinfo.cau.edu.cn/ProFITS</url>.</p

Combining sequence-based prediction methods and circular dichroism and infrared spectroscopic data to improve protein secondary structure determinations

<p>Abstract</p> <p>Background</p> <p>A number of sequence-based methods exist for protein secondary structure prediction. Protein secondary structures can also be determined experimentally from circular dichroism, and infrared spectroscopic data using empirical analysis methods. It has been proposed that comparable accuracy can be obtained from sequence-based predictions as from these biophysical measurements. Here we have examined the secondary structure determination accuracies of sequence prediction methods with the empirically determined values from the spectroscopic data on datasets of proteins for which both crystal structures and spectroscopic data are available.</p> <p>Results</p> <p>In this study we show that the sequence prediction methods have accuracies nearly comparable to those of spectroscopic methods. However, we also demonstrate that combining the spectroscopic and sequences techniques produces significant overall improvements in secondary structure determinations. In addition, combining the extra information content available from synchrotron radiation circular dichroism data with sequence methods also shows improvements.</p> <p>Conclusion</p> <p>Combining sequence prediction with experimentally determined spectroscopic methods for protein secondary structure content significantly enhances the accuracy of the overall results obtained.</p

Interactome and Gene Ontology provide congruent yet subtly different views of a eukaryotic cell

15 pages, 6 figures.-- 19604360 [PubMed]BACKGROUND: The characterization of the global functional structure of a cell is a major goal in bioinformatics and systems biology. Gene Ontology (GO) and the protein-protein interaction network offer alternative views of that structure. RESULTS: This study presents a comparison of the global structures of the Gene Ontology and the interactome of Saccharomyces cerevisiae. Sensitive, unsupervised methods of clustering applied to a large fraction of the proteome led to establish a GO-interactome correlation value of +0.47 for a general dataset that contains both high and low-confidence interactions and +0.58 for a smaller, high-confidence dataset. CONCLUSION: The structures of the yeast cell deduced from GO and interactome are substantially congruent. However, some significant differences were also detected, which may contribute to a better understanding of cell function and also to a refinement of the current ontologiesResearch supported by grant BIO2008-05067 (Programa Nacional de Biotecnología; Ministerio de Ciencia e Innovación. Spain), awarded to IM. AM was a FPI fellow from Ministerio de Educación y Ciencia (Spain).Peer reviewe

Diffusion Model Based Spectral Clustering for Protein-Protein Interaction Networks

BACKGROUND: A goal of systems biology is to analyze large-scale molecular networks including gene expressions and protein-protein interactions, revealing the relationships between network structures and their biological functions. Dividing a protein-protein interaction (PPI) network into naturally grouped parts is an essential way to investigate the relationship between topology of networks and their functions. However, clear modular decomposition is often hard due to the heterogeneous or scale-free properties of PPI networks. METHODOLOGY/PRINCIPAL FINDINGS: To address this problem, we propose a diffusion model-based spectral clustering algorithm, which analytically solves the cluster structure of PPI networks as a problem of random walks in the diffusion process in them. To cope with the heterogeneity of the networks, the power factor is introduced to adjust the diffusion matrix by weighting the transition (adjacency) matrix according to a node degree matrix. This algorithm is named adjustable diffusion matrix-based spectral clustering (ADMSC). To demonstrate the feasibility of ADMSC, we apply it to decomposition of a yeast PPI network, identifying biologically significant clusters with approximately equal size. Compared with other established algorithms, ADMSC facilitates clear and fast decomposition of PPI networks. CONCLUSIONS/SIGNIFICANCE: ADMSC is proposed by introducing the power factor that adjusts the diffusion matrix to the heterogeneity of the PPI networks. ADMSC effectively partitions PPI networks into biologically significant clusters with almost equal sizes, while being very fast, robust and appealing simple

Metabolome Based Reaction Graphs of M. tuberculosis and M. leprae: A Comparative Network Analysis

BACKGROUND: Several types of networks, such as transcriptional, metabolic or protein-protein interaction networks of various organisms have been constructed, that have provided a variety of insights into metabolism and regulation. Here, we seek to exploit the reaction-based networks of three organisms for comparative genomics. We use concepts from spectral graph theory to systematically determine how differences in basic metabolism of organisms are reflected at the systems level and in the overall topological structures of their metabolic networks. METHODOLOGY/PRINCIPAL FINDINGS: Metabolome-based reaction networks of Mycobacterium tuberculosis, Mycobacterium leprae and Escherichia coli have been constructed based on the KEGG LIGAND database, followed by graph spectral analysis of the network to identify hubs as well as the sub-clustering of reactions. The shortest and alternate paths in the reaction networks have also been examined. Sub-cluster profiling demonstrates that reactions of the mycolic acid pathway in mycobacteria form a tightly connected sub-cluster. Identification of hubs reveals reactions involving glutamate to be central to mycobacterial metabolism, and pyruvate to be at the centre of the E. coli metabolome. The analysis of shortest paths between reactions has revealed several paths that are shorter than well established pathways. CONCLUSIONS: We conclude that severe downsizing of the leprae genome has not significantly altered the global structure of its reaction network but has reduced the total number of alternate paths between its reactions while keeping the shortest paths between them intact. The hubs in the mycobacterial networks that are absent in the human metabolome can be explored as potential drug targets. This work demonstrates the usefulness of constructing metabolome based networks of organisms and the feasibility of their analyses through graph spectral methods. The insights obtained from such studies provide a broad overview of the similarities and differences between organisms, taking comparative genomics studies to a higher dimension

Functional clustering of yeast proteins from the protein-protein interaction network

BACKGROUND: The abundant data available for protein interaction networks have not yet been fully understood. New types of analyses are needed to reveal organizational principles of these networks to investigate the details of functional and regulatory clusters of proteins. RESULTS: In the present work, individual clusters identified by an eigenmode analysis of the connectivity matrix of the protein-protein interaction network in yeast are investigated for possible functional relationships among the members of the cluster. With our functional clustering we have successfully predicted several new protein-protein interactions that indeed have been reported recently. CONCLUSION: Eigenmode analysis of the entire connectivity matrix yields both a global and a detailed view of the network. We have shown that the eigenmode clustering not only is guided by the number of proteins with which each protein interacts, but also leads to functional clustering that can be applied to predict new protein interactions

Interleukin-10 Mediated Autoregulation of Murine B-1 B-Cells and Its Role in Borrelia hermsii Infection

B cells are typically characterized as positive regulators of the immune response, primarily by producing antibodies. However, recent studies indicate that various subsets of B cells can perform regulatory functions mainly through IL-10 secretion. Here we discovered that peritoneal B-1 (B-1P) cells produce high levels of IL-10 upon stimulation with several Toll-like receptor (TLR) ligands. High levels of IL-10 suppressed B-1P cell proliferation and differentiation response to all TLR ligands studied in an autocrine manner in vitro and in vivo. IL-10 that accumulated in cultures inhibited B-1P cells at second and subsequent cell divisions mainly at the G1/S interphase. IL-10 inhibits TLR induced B-1P cell activation by blocking the classical NF-κB pathway. Co-stimulation with CD40 or BAFF abrogated the IL-10 inhibitory effect on B-1P cells during TLR stimulation. Finally, B-1P cells adoptively transferred from the peritoneal cavity of IL-10−/− mice showed better clearance of Borrelia hermsii than wild-type B-1P cells. This study described a novel autoregulatory property of B-1P cells mediated by B-1P cell derived IL-10, which may affect the function of B-1P cells in infection and autoimmunity

Using Entropy Maximization to Understand the Determinants of Structural Dynamics beyond Native Contact Topology

Comparison of elastic network model predictions with experimental data has provided important insights on the dominant role of the network of inter-residue contacts in defining the global dynamics of proteins. Most of these studies have focused on interpreting the mean-square fluctuations of residues, or deriving the most collective, or softest, modes of motions that are known to be insensitive to structural and energetic details. However, with increasing structural data, we are in a position to perform a more critical assessment of the structure-dynamics relations in proteins, and gain a deeper understanding of the major determinants of not only the mean-square fluctuations and lowest frequency modes, but the covariance or the cross-correlations between residue fluctuations and the shapes of higher modes. A systematic study of a large set of NMR-determined proteins is analyzed using a novel method based on entropy maximization to demonstrate that the next level of refinement in the elastic network model description of proteins ought to take into consideration properties such as contact order (or sequential separation between contacting residues) and the secondary structure types of the interacting residues, whereas the types of amino acids do not play a critical role. Most importantly, an optimal description of observed cross-correlations requires the inclusion of destabilizing, as opposed to exclusively stabilizing, interactions, stipulating the functional significance of local frustration in imparting native-like dynamics. This study provides us with a deeper understanding of the structural basis of experimentally observed behavior, and opens the way to the development of more accurate models for exploring protein dynamics

Lipoic acid plays a role in scleroderma: insights obtained from scleroderma dermal fibroblasts

Abstract Introduction Systemic sclerosis (SSc) is a connective tissue disease characterized by fibrosis of the skin and organs. Increase in oxidative stress and platelet-derived growth factor receptor (PDGFR) activation promote type I collagen (Col I) production, leading to fibrosis in SSc. Lipoic acid (LA) and its active metabolite dihydrolipoic acid (DHLA) are naturally occurring thiols that act as cofactors and antioxidants and are produced by lipoic acid synthetase (LIAS). Our goals in this study were to examine whether LA and LIAS were deficient in SSc patients and to determine the effect of DHLA on the phenotype of SSc dermal fibroblasts. N-acetylcysteine (NAC), a commonly used thiol antioxidant, was included as a comparison. Methods Dermal fibroblasts were isolated from healthy subjects and patients with diffuse cutaneous SSc. Matrix metalloproteinase (MMPs), tissue inhibitors of MMPs (TIMP), plasminogen activator inhibitor 1 (PAI-1) and LIAS were measured by enzyme-linked immunosorbent assay. The expression of Col I was measured by immunofluorescence, hydroxyproline assay and quantitative PCR. PDGFR phosphorylation and α-smooth muscle actin (αSMA) were measured by Western blotting. Student’s t-tests were performed for statistical analysis, and P-values less than 0.05 with two-tailed analysis were considered statistically significant. Results The expression of LA and LIAS in SSc dermal fibroblasts was lower than normal fibroblasts; however, LIAS was significantly higher in SSc plasma and appeared to be released from monocytes. DHLA lowered cellular oxidative stress and decreased PDGFR phosphorylation, Col I, PAI-1 and αSMA expression in SSc dermal fibroblasts. It also restored the activities of phosphatases that inactivated the PDGFR. SSc fibroblasts produced lower levels of MMP-1 and MMP-3, and DHLA increased them. In contrast, TIMP-1 levels were higher in SSc, but DHLA had a minimal effect. Both DHLA and NAC increased MMP-1 activity when SSc cells were stimulated with PDGF. In general, DHLA showed better efficacy than NAC in most cases. Conclusions DHLA acts not only as an antioxidant but also as an antifibrotic because it has the ability to reverse the profibrotic phenotype of SSc dermal fibroblasts. Our study suggests that thiol antioxidants, including NAC, LA, or DHLA, could be beneficial for patients with SSc.http://deepblue.lib.umich.edu/bitstream/2027.42/112060/1/13075_2014_Article_411.pd