SpectralNET – an application for spectral graph analysis and visualization

Background  

Graph theory provides a computational framework for modeling a variety of datasets including those emerging from genomics, proteomics, and chemical genetics. Networks of genes, proteins, small molecules, or other objects of study can be represented as graphs of nodes (vertices) and interactions (edges) that can carry different weights. SpectralNET is a flexible application for analyzing and visualizing these biological and chemical networks.     

How advancement in biological network analysis methods empowers proteomics

Proteomics provides important information--that may not be inferable from indirect sources such as RNA or DNA--on key players in biological systems or disease states. However, it suffers from coverage and consistency problems. The advent of network-based analysis methods can help in overcoming these problems but requires careful application and interpretation. This review considers briefly current trends in proteomics technologies and understanding the causes of critical issues that need to be addressed--i.e., incomplete data coverage and inter-sample inconsistency. On the coverage issue, we argue that holistic analysis based on biological networks provides a suitable background on which more robust models and interpretations can be built upon; and we introduce some recently developed approaches. On consistency, group-based approaches based on identified clusters, as well as on properly integrated pathway databases, are particularly useful. Despite that protein interactions and pathway networks are still largely incomplete, given proper quality checks, applications and reasonably sized data sets, they yield valuable insights that greatly complement data generated from quantitative proteomics.     

Proteomics in cancer vaccine development

Proteomics is a new scientific field aimed at the large-scale characterization of the protein constituents of biologic systems. It facilitates comparisons between different protein preparations by searching for minute differences in their protein expression repertoires and the patterns of their post-translational modifications. These attributes make proteomics perfectly suited for searching for proteins and peptides expressed exclusively or preferentially in cancer cells as candidates for cancer vaccines. The main proteomics technologies include 2D polyacrylamide gel electrophoresis, multidimensional high-performance liquid chromatography, mass spectrometry and protein arrays. Proteomics technologies used to analyze cancer culture cells, fresh tumor specimens, human leukocyte antigen peptides, serum and serum antibodies (serologic proteomics) have successfully identified tumor markers. Turning the potential vaccine candidates identified by proteomics technologies into clinical treatments awaits demonstration.     

Clinical proteomics: revolutionizing disease detection and patient tailoring therapy

The evolving discipline of Clinical Proteomics is more than simply describing and enumerating the systematic changes in the protein constituency of a cell, or just generating lists of proteins that increase or decrease in expression as a cause or consequence of disease. Clinical applications of proteomics involve the use of proteomic technologies at the bedside with the ultimate goal to characterize the information flow through the intra- and extracellular molecular protein networks that interconnect organ and circulatory systems together. These networks are both new targets for therapeutics themselves as well as underpin the dynamic changes that give rise to cascades of new diagnostic biomarkers. The analysis of human cancer can be used as a model for how clinical proteomics is having an impact at the bedside for early detection, rational therapeutic targeting, and patient-tailored therapy.     

Approaching Drosophila development through proteomic tools and databases: At the hub of the post-genomic era

The past decade has witnessed an explosion in the growth of proteomics. The completion of numerous genome sequences, the development of powerful protein analytical technologies, as well as the design of innovative bioinformatics tools have marked the beginning of a new post-genomic era. Proteomics, the large-scale analysis of proteins in an organism, organ or organelle encompasses different aspects: (1) the identification, analysis of post-translational modifications and quantification of proteins; (2) the study of protein-protein interactions; and (3) the functional analysis of interactome networks. Here, we briefly summarize the emerging analytical tools and databases that are paving the way for studying Drosophila development by proteomic approaches.     

Hubs and bottlenecks in plant molecular signalling networks

Conditional control of plant cell function and development relies on appropriate signal perception, signal integration and processing. The development of high throughput technologies such as proteomics and interactomics has enabled the identification of protein interaction networks that mediate signal processing from inputs to appropriate outputs. Such networks can be depicted in graphical representations using nodes and edges allowing for the immediate visualization and analysis of the network's topology. Hubs are network elements characterized by many edges (often degree grade k ≥ 5) which confer a degree of topological importance to them. The review introduces the concept of networks, hubs and bottlenecks and describes four examples from plant science in more detail, namely hubs in the redox regulatory network of the chloroplast with ferredoxin, thioredoxin and peroxiredoxin, in mitogen activated protein (MAP) kinase signal processing, in photomorphogenesis with the COP9 signalosome, COP1 and CDD, and monomeric GTPase function. Some guidance is provided to appropriate internet resources, web repositories, databases and their use. Plant networks can be generated from existing public databases and this type of analysis is valuable in support of existing hypotheses, or to allow for the generation of new concepts or ideas. However, intensive manual curating of in silico networks is still always necessary.     

Protein networking: insights into global functional organization of proteomes

The formulation of network models from global protein studies is essential to understand the functioning of organisms. Network models of the proteome enable the application of Complex Network Analysis, a quantitative framework to investigate large complex networks using techniques from graph theory, statistical physics, dynamical systems and other fields. This approach has provided many insights into the functional organization of the proteome so far and will likely continue to do so. Currently, several network concepts have emerged in the field of proteomics. It is important to highlight the differences between these concepts, since different representations allow different insights into functional organization. One such concept is the protein interaction network, which contains proteins as nodes and undirected edges representing the occurrence of binding in large-scale protein-protein interaction studies. A second concept is the protein-signaling network, in which the nodes correspond to levels of post-translationally modified forms of proteins and directed edges to causal effects through post-translational modification, such as phosphorylation. Several other network concepts were introduced for proteomics. Although all formulated as networks, the concepts represent widely different physical systems. Therefore caution should be taken when applying relevant topological analysis. We review recent literature formulating and analyzing such networks.     

蛋白质组学及其技术发展

蛋白质组学产生于20世纪90年代,发展至今已日趋成熟。蛋白质组学是以生物体的全部或部分蛋白为研究对象,研究它们在生命活动过程中的作用、功能。蛋白质组学较之前的基因组学对于生命现象的解释更直接、更准确,近年得到了快速发展,并受到世界各国学者的高度关注。我们简要综述了蛋白质组学及其技术,并简单概述了这项技术在生命科学领域的应用。     

Proteomics for Biological Discovery * Edited by T.D. Veenstra and John R. Yates

An understanding of the roles and interactions of proteins withincells is vital to our understanding of biology. Proteomics hascome a long way, and covers many areas now, since the inventionof 2 dimensional gel electrophoresis in the 1970s, or the coiningof the term proteomics in the 1990s. As more people move intothis important area, there is a need for guides to aid thesemoves. Proteomics for Biological Discovery aims to serve assuch a guide. The format of this multi-author book is well laidout, with fifteen chapters in three main parts: I. Foundationsof Proteomics; II. Functional Proteomics; and III. Novel Approachesin Proteomics. The editors have selected a strong team of respectedexperts in the various proteomics niches to write the fifteenconstituent chapters. The chapters are, for the most part, self-contained,and there is no     

蛋白质组学在结核分枝杆菌研究中的应用

蛋白质组学是在基因组学基础上发展起来的新兴学科, 其基本技术包括样品制备、蛋白质分离和蛋白质鉴定分析, 其中的核心技术是双向凝胶电泳技术(2-Dimensional Electrophoresis, 2-DE)和质谱技术(Mass Spectrometry, MS)。近年来, 蛋白质组学技术已应用于结核分枝杆菌的研究领域。应用蛋白质组学技术分离、鉴定、检测结核分枝杆菌致病株的全菌蛋白及分泌蛋白, 分析其蛋白组成, 可深入解析结核分枝杆菌的致病机理和耐药机制。通过对结核分枝杆菌致病株抗原的分析, 为研制预防结核病的新型疫苗拓展了空间。通过对结核分枝杆菌临床分离株的蛋白组成分析还发现了一些有意义的结核病早期诊断标志物。蛋白质组学技术还应用于寻找新的药物靶标, 在研制和筛选新的抗结核药物等方面展示了一些有价值的研究成果, 为更好地开展结核病的预防、早期诊断及治疗打下了基础。     

Proteomics signature profiling (PSP): a novel contextualization approach for cancer proteomics

Traditional proteomics analysis is plagued by the use of arbitrary thresholds resulting in large loss of information. We propose here a novel method in proteomics that utilizes all detected proteins. We demonstrate its efficacy in a proteomics screen of 5 and 7 liver cancer patients in the moderate and late stage, respectively. Utilizing biological complexes as a cluster vector, and augmenting it with submodules obtained from partitioning an integrated and cleaned protein-protein interaction network, we calculate a Proteomics Signature Profile (PSP) for each patient based on the hit rates of their reported proteins, in the absence of fold change thresholds, against the cluster vector. Using this, we demonstrated that moderate- and late-stage patients segregate with high confidence. We also discovered a moderate-stage patient who displayed a proteomics profile similar to other poor-stage patients. We identified significant clusters using a modified version of the SNet approach. Comparing our results against the Proteomics Expansion Pipeline (PEP) on which the same patient data was analyzed, we found good correlation. Building on this finding, we report significantly more clusters (176 clusters here compared to 70 in PEP), demonstrating the sensitivity of this approach. Gene Ontology (GO) terms analysis also reveals that the significant clusters are functionally congruent with the liver cancer phenotype. PSP is a powerful and sensitive method for analyzing proteomics profiles even when sample sizes are small. It does not rely on the ratio scores but, rather, whether a protein is detected or not. Although consistency of individual proteins between patients is low, we found the reported proteins tend to hit clusters in a meaningful and informative manner. By extracting this information in the form of a Proteomics Signature Profile, we confirm that this information is conserved and can be used for (1) clustering of patient samples, (2) identification of significant clusters based on real biological complexes, and (3) overcoming consistency and coverage issues prevalent in proteomics data sets.     

Genealogies: Pedigrees and Phylogenies are Reticulating Networks Not Just Divergent Trees

Pedigrees illustrate the genealogical relationships among individuals, and phylogenies do the same for groups of organisms (such as species, genera, etc.). Here, I provide a brief survey of current concepts and methods for calculating and displaying genealogical relationships. These relationships have long been recognized to be reticulating, rather than strictly divergent, and so both pedigrees and phylogenies are correctly treated as networks rather than trees. However, currently most pedigrees are instead presented as “family trees”, and most phylogenies are presented as phylogenetic trees. Nevertheless, the historical development of concepts shows that networks pre-dated trees in most fields of biology, including the study of pedigrees, biology theory, and biology practice, as well as in historical linguistics in the social sciences. Trees were actually introduced in order to provide a simpler conceptual model for historical relationships, since trees are a specific type of simple network. Computationally, trees and networks are a part of graph theory, consisting of nodes connected by edges. In this mathematical context they differ solely in the absence or presence of reticulation nodes, respectively. There are two types of graphs that can be called phylogenetic networks: (1) rooted evolutionary networks, and (2) unrooted affinity networks. There are quite a few computational methods for unrooted networks, which have two main roles in phylogenetics: (a) they act as a generic form of multivariate data display; and (b) they are used specifically to represent haplotype networks. Evolutionary networks are more difficult to infer and analyse, as there is no mathematical algorithm for reconstructing unique historical events. There is thus currently no coherent analytical framework for computing such networks.     

Targeted proteomics using immunoblotting technique for studying dysregulation of ion transporters in renal disorders

Renal salt and water transport physiology has benefited tremendously from the rapid advance of proteomics. Proteomics developed as a fast-throughput means of screening for global changes in proteins in a selected tissue, organ or cell type, as a logical offshoot of similar comprehensive, messenger RNA array-type technology. Targeted proteomics utilizes similar techniques but examines a predetermined set of proteins. One approach that has been rigorously employed over the last 10 years to evaluate differences in renal protein abundances due to a treatment or genotype has been parallel semiquantitative immunoblotting using antibody arrays. This approach, and newer ones on the horizon, provide a rapid global overview of regulation of the individual proteins whose integrated action determines overall renal sodium or water reabsorption.     

3rd Annual FinnProt Meeting: from cells to systems

The Finnish Proteomics Society, FinnProt ( www.finnprot.org ), was founded in November 2004 as the Proteomics Division of the Societas biochemica, biophysica et microbiologica Fenniae ( www.biobio.org ). The mission of FinnProt is to make proteomics research readily available for the large scientific community in Finland, promote research and education in proteomics and protein chemistry, and act as the official Finnish collaborative body to international proteomics organizations such as the European Proteomics Association.     

Targeted proteomics using immunoblotting technique for studying dysregulation of ion transporters in renal disorders

Renal salt and water transport physiology has benefited tremendously from the rapid advance of proteomics. Proteomics developed as a fast-throughput means of screening for global changes in proteins in a selected tissue, organ or cell type, as a logical offshoot of similar comprehensive, messenger RNA array-type technology. Targeted proteomics utilizes similar techniques but examines a predetermined set of proteins. One approach that has been rigorously employed over the last 10 years to evaluate differences in renal protein abundances due to a treatment or genotype has been parallel semiquantitative immunoblotting using antibody arrays. This approach, and newer ones on the horizon, provide a rapid global overview of regulation of the individual proteins whose integrated action determines overall renal sodium or water reabsorption.     

Proteomic analysis and identification of aqueous humor proteins with a pathophysiological role in diabetic retinopathy

Diabetic retinopathy (DR) can cause irreversible blindness and is the severest microvascular complication in the eyes of patients with diabetic mellitus (DM). The identification of susceptibility factors contributing to development of DR is helpful for identifying predisposed patients and improving treatment efficacy. Although proteomics analysis is useful for identifying protein markers related to diseases, it has never been used to explore DR-associated susceptibility factors in the aqueous humor (AH). To better understand the pathophysiology of DR and to identify DR-associated risk factors, a gel-based proteomics analysis was performed to compare AH protein profiles of DM patients with and without development of DR. MALDI-TOF MS was then performed to identify protein spots that were differentially expressed between the two groups and western blot analysis was used to validate the expressional change of protein demonstrated by proteomics. Our proteomics and bioinformatics analysis identified 11 proteins differentially expressed between DR and control groups. These proteins are linked to biological networks associated with nutrition transport, microstructure reorganization, angiogenesis, anti-oxidation, and neuroprotection. The data may provide potential AH biomarkers and susceptibility factors for predicting DR development, and provide an insight into the underlying pathophysiological mechanisms of DR. This article is part of a Special Issue entitled: Proteomics: The clinical link.     

Novel insights through the integration of structural and functional genomics data with protein networks

In recent years, major advances in genomics, proteomics, macromolecular structure determination, and the computational resources capable of processing and disseminating the large volumes of data generated by each have played major roles in advancing a more systems-oriented appreciation of biological organization. One product of systems biology has been the delineation of graph models for describing genome-wide protein-protein interaction networks. The network organization and topology which emerges in such models may be used to address fundamental questions in an array of cellular processes, as well as biological features intrinsic to the constituent proteins (or "nodes") themselves. However, graph models alone constitute an abstraction which neglects the underlying biological and physical reality that the network's nodes and edges are highly heterogeneous entities. Here, we explore some of the advantages of introducing a protein structural dimension to such models, as the marriage of conventional network representations with macromolecular structural data helps to place static node and edge constructs in a biologically more meaningful context. We emphasize that 3D protein structures constitute a valuable conceptual and predictive framework by discussing examples of the insights provided, such as enabling in silico predictions of protein-protein interactions, providing rational and compelling classification schemes for network elements, as well as revealing interesting intrinsic differences between distinct node types, such as disorder and evolutionary features, which may then be rationalized in light of their respective functions within networks.     

Failure tolerance of motif structure in biological networks

Complex networks serve as generic models for many biological systems that have been shown to share a number of common structural properties such as power-law degree distribution and small-worldness. Real-world networks are composed of building blocks called motifs that are indeed specific subgraphs of (usually) small number of nodes. Network motifs are important in the functionality of complex networks, and the role of some motifs such as feed-forward loop in many biological networks has been heavily studied. On the other hand, many biological networks have shown some degrees of robustness in terms of their efficiency and connectedness against failures in their components. In this paper we investigated how random and systematic failures in the edges of biological networks influenced their motif structure. We considered two biological networks, namely, protein structure network and human brain functional network. Furthermore, we considered random failures as well as systematic failures based on different strategies for choosing candidate edges for removal. Failure in the edges tipping to high degree nodes had the most destructive role in the motif structure of the networks by decreasing their significance level, while removing edges that were connected to nodes with high values of betweenness centrality had the least effect on the significance profiles. In some cases, the latter caused increase in the significance levels of the motifs.     

The 8th International Conference of the Canadian Proteomics Initiative

The 8th International Conference of the Canadian Proteomics Initiative (CPI) was held at the Hilton Vancouver Metrotown Hotel in Burnaby, British Columbia from 3–5 May, 2008. With nearly 200 delegates, 32 speakers and more than 50 poster presentations, CPI 2008 covered a wide range of topics, including novel technologies, human and clinical proteomics, structural proteomics, bioinformatics, post-translational modifications, membrane and receptor proteomics, and plant and animal proteomics. This year’s conference was also highlighted by hands-on proteomics workshops at the University of Victoria Genome BC Proteomics Centre and the University of British Columbia, an inaugural meeting of the British Columbia Proteomics Network and focus meetings exploring opportunities for the formation of a nationwide association for Canadian proteomics and Genome British Columbia’s efforts towards large-scale global genomics and proteomics projects.     

Optimized Null Model for Protein Structure Networks

Much attention has recently been given to the statistical significance of topological features observed in biological networks. Here, we consider residue interaction graphs (RIGs) as network representations of protein structures with residues as nodes and inter-residue interactions as edges. Degree-preserving randomized models have been widely used for this purpose in biomolecular networks. However, such a single summary statistic of a network may not be detailed enough to capture the complex topological characteristics of protein structures and their network counterparts. Here, we investigate a variety of topological properties of RIGs to find a well fitting network null model for them. The RIGs are derived from a structurally diverse protein data set at various distance cut-offs and for different groups of interacting atoms. We compare the network structure of RIGs to several random graph models. We show that 3-dimensional geometric random graphs, that model spatial relationships between objects, provide the best fit to RIGs. We investigate the relationship between the strength of the fit and various protein structural features. We show that the fit depends on protein size, structural class, and thermostability, but not on quaternary structure. We apply our model to the identification of significantly over-represented structural building blocks, i.e., network motifs, in protein structure networks. As expected, choosing geometric graphs as a null model results in the most specific identification of motifs. Our geometric random graph model may facilitate further graph-based studies of protein conformation space and have important implications for protein structure comparison and prediction. The choice of a well-fitting null model is crucial for finding structural motifs that play an important role in protein folding, stability and function. To our knowledge, this is the first study that addresses the challenge of finding an optimized null model for RIGs, by comparing various RIG definitions against a series of network models.