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PubMed2006

GenePro: a Cytoscape plug-in for advanced visualization and analysis of interaction networks.

MOTIVATION: Analyzing the networks of interactions between genes and proteins has become a central theme in systems biology. Versatile software tools for interactively displaying and analyzing these networks are therefore very much in demand. The public-domain open software environment Cytoscape has been developed with the goal of facilitating the design and development of such software tools by the scientific community. RESULTS: We present GenePro, a plugin to Cytoscape featuring a set of versatile tools that greatly facilitates the visualization and analysis of protein networks derived from high-throughput interactions data and the validation of various methods for parsing these networks into meaningful functional modules. AVAILABILITY: The GenePro plugin is available at the website http://genepro.ccb.sickkids.ca.

Algorithms↗
PubMed2005

Structural comparison of metabolic networks in selected single cell organisms.

BACKGROUND: There has been tremendous interest in the study of biological network structure. An array of measurements has been conceived to assess the topological properties of these networks. In this study, we compared the metabolic network structures of eleven single cell organisms representing the three domains of life using these measurements, hoping to find out whether the intrinsic network design principle(s), reflected by these measurements, are different among species in the three domains of life. RESULTS: Three groups of topological properties were used in this study: network indices, degree distribution measures and motif profile measure. All of which are higher-level topological properties except for the marginal degree distribution. Metabolic networks in Archaeal species are found to be different from those in S. cerevisiae and the six Bacterial species in almost all measured higher-level topological properties. Our findings also indicate that the metabolic network in Archaeal species is similar to the exponential random network. CONCLUSION: If these metabolic network properties of the organisms studied can be extended to other species in their respective domains (which is likely), then the design principle(s) of Archaea are fundamentally different from those of Bacteria and Eukaryote. Furthermore, the functional mechanisms of Archaeal metabolic networks revealed in this study differentiate significantly from those of Bacterial and Eukaryotic organisms, which warrant further investigation.

Algorithms↗
PubMed2005

Bioinformatic methods to exploit mass spectrometric data for proteomic applications.

The new technologies in mass spectrometric analysis of peptides and proteins necessary to accommodate proteomics-scale analyses require, in turn, concomitant development of informatics technologies suitable for very large-scale data handling and analysis. This chapter focuses on the data analysis tools available to the community for analysis of mass spectrometric proteomics data. Different database searching strategies are discussed for peptide and protein identification, and approaches and tools available for comparative quantitative analysis of samples are outlined.

Animals↗
PubMed2001

Towards the Toxoplasma gondii proteome: position of 13 parasite excretory antigens on a standardized map of two-dimensionally separated tachyzoite proteins.

High resolution two-dimensional separation of Toxoplasma gondii tachyzoite lysate revealed up to 224 distinct protein spots in Coomassie-stained gel. Computional matching of 14 digitized gels yielded a standard two-dimensional proteome map. The excretory T. gondii dense granule proteins GRA1-GRA8, S16/acid phosphatase, nucleoside triphosphate hydrolase, and H4 were identified by Western blotting of both total gel and isolated protein spots. In addition, two excretory antigens defined by parasite-specific monoclonal T cells, p36 and p40, were mapped by a novel T-cell blotting technique based on electroeluting single protein spots and testing the eluates for antigenic activity against the T-cell clones. In summary, these results represent a first step in Toxoplasma proteome analysis.

Animals↗
PubMed2025

Identifying potential biomarkers in the hippocampus of chronic fatigue syndrome rats treated with moxibustion at Zusanli (ST36): a proteomics study.

OBJECTIVE: To observe the effects of moxibustion at Zusanli (ST36) on rats with chronic fatigue syndrome (CFS) and to analyze the mechanisms of moxibustion through hippocampal Proteomics. METHODS: Male Sprague-Dawley (SD) rats were randomly divided into three groups: control group (CON), model group (MOD), and moxibustion group (MOX), with 12 rats in each group. The MOD and MOX groups underwent chronic multi-factor stress stimulation for 35 d to establish the CFS model. After modeling, the rats in the MOX group received mild moxibustion at Zusanli (ST36) (bilateral) for 10 minutes daily for 28 d. During the treatment period, rats in both the MOD and MOX groups continued modeling, while the CON group was kept under normal breeding conditions. The general condition of the rats was monitored, and behaviors were assessed using the Open Field Test (OFT), Exhaustion Treadmill Test, and Morris Water Maze (MWM). Hematoxylin and eosin (HE) staining and transmission electron microscopy (TEM) were employed to observe morphological changes in the hippocampus. Label-free Proteomics were utilized to identify differentially expressed proteins (DEPs) in the hippocampus, followed by bioinformatics analysis. The reliability of the Proteomics results was verified using Parallel Reaction Monitoring. RESULTS: A: Moxibustion at Zusanli (ST36) significantly reduced the general condition score of CFS rats, improved their behavioral performance in OFT, treadmill and MWM, and repaired the pathological and synaptic structural damage in the hippocampus.B: We identified DEPs by applying a fold change threshold of 1.2 and a significance level of P < 0.05. In the comparison between the CON and the MOD, we identified a total of 72 DEPs (31 up-regulated and 41 down-regulated) associated with the development of CFS. In the comparison between the MOX and the MOD group, we identified a total of 103 DEPs (40 up-regulated and 63 down-regulated) related to the therapeutic effects of moxibustion. Gene Ontology (GO) enrichment analysis showed that CFS and moxibustion treatment were related to multiple biological processes, molecular functions, and cellular components. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis revealed that CFS pathogenesis was linked to base excision repair, steroid biosynthesis, and systemic lupus erythematosus, Furthermore, the treatment of CFS with moxibustion was relevant to terpenoid skeleton biosynthesis.C: Compared with the two comparison groups, we identified 16 potential biomarkers, noting that moxibustion reversed the up-regulation of 14 DEPs and the down-regulation of 2 DEPs in CFS. These proteins are mainly associated with synaptic plasticity, ribosomal function, neurotransmitter secretion, glycine metabolism, and mitochondrial function. CONCLUSION: Moxibustion at Zusanli (ST36) is effective in treating CFS, the potential biomarkers identified by Proteomics confirm that the mechanisms of moxibustion involve multiple targets and pathways, which may be key to regulating the structural and functional damage in the hippocampus associated with CFS, highlighting their significant value for future research.

Animals↗
PubMed2004

Proteomics/genomics and signaling in lymphocytes.

Recent technological advances in genomics, proteomics and bioinformatics have offered new insights into the molecular mechanisms that underlie lymphocyte signaling and function, and the development of new tools in these areas has opened up new avenues for biological investigation. By adding a quantitative dimension to lymphocyte proteome profiling, molecular machines and spatiotemporal regulatory processes can now be analyzed using such discovery-driven approaches. Biologists employing genomic and proteomic tools are gathering data at increasing speed and their struggle to extract maximal biological information is helped by new software tools that enable the detailed comparison of multiple datasets.

Animals↗
PubMed2001

Predicting protein--protein interactions from primary structure.

MOTIVATION: An ambitious goal of proteomics is to elucidate the structure, interactions and functions of all proteins within cells and organisms. The expectation is that this will provide a fuller appreciation of cellular processes and networks at the protein level, ultimately leading to a better understanding of disease mechanisms and suggesting new means for intervention. This paper addresses the question: can protein-protein interactions be predicted directly from primary structure and associated data? Using a diverse database of known protein interactions, a Support Vector Machine (SVM) learning system was trained to recognize and predict interactions based solely on primary structure and associated physicochemical properties. RESULTS: Inductive accuracy of the trained system, defined here as the percentage of correct protein interaction predictions for previously unseen test sets, averaged 80% for the ensemble of statistical experiments. Future proteomics studies may benefit from this research by proceeding directly from the automated identification of a cell's gene products to prediction of protein interaction pairs.

Artificial Intelligence↗
PubMed2006

LMPD: LIPID MAPS proteome database.

The LIPID MAPS Proteome Database (LMPD) is an object-relational database of lipid-associated protein sequences and annotations. The initial release contains 2959 records, representing human and mouse proteins involved in lipid metabolism. UniProt IDs were obtained based on keyword search of KEGG and GO databases, and this LMPD protein list was then enhanced with annotations from UniProt, EntrezGene, ENZYME, GO, KEGG and other public resources. We also assigned associations with general lipid categories, based on GO and KEGG annotations. Users may search LMPD by database ID or keyword, and filter by species and/or lipid class associations; from the search results, one can then access a compilation of data relevant to each protein of interest, cross-linked to external databases. The LIPID MAPS Proteome Database (LMPD) is publicly available from the LIPID MAPS Consortium website (http://www.lipidmaps.org/). The direct URL is http://www.lipidmaps.org/data/proteome/index.cgi.

Animals↗
PubMed2003

Expanding the organismal scope of proteomics: cross-species protein identification by mass spectrometry and its implications.

Due to the limited applicability of conventional protein identification methods to the proteomes of organisms with unsequenced genomes, researchers have developed approaches to identify proteins using mass spectrometry and sequence similarity database searches. Both the integration of mass spectrometry with bioinformatics and genomic sequencing drive the expanding organismal scope of proteomics.

Amino Acid Sequence↗
PubMed2005

Statistical signals in bioinformatics.

The Arthur M. Sackler Colloquium of the National Academy of Sciences, "Frontiers in Bioinformatics: Unsolved Problems and Challenges," organized by David Eisenberg, Russ Altman, and myself, was held October 15-17, 2004, to provide a forum for discussing concepts and methods in bioinformatics serving the biological and medical sciences. The deluge of genomic and proteomic data in the last two decades has driven the creation of tools that search and analyze biomolecular sequences and structures. Bioinformatics is highly interdisciplinary, using knowledge from mathematics, statistics, computer science, biology, medicine, physics, chemistry, and engineering.

Animals↗
PubMed2006

WebCell: a web-based environment for kinetic modeling and dynamic simulation of cellular networks.

SUMMARY: WebCell is a web-based environment for managing quantitative and qualitative information on cellular networks and for interactively exploring their steady-state and dynamic behaviors in response to systemic perturbations. It is designed as a user-friendly web interface, allowing users to efficiently construct, visualize, analyze and store reaction network models, thereby facilitating kinetic modeling and in silico simulation of biological systems of interest. A collected model library is also available to provide comprehensive implications for cellular dynamics of the published models.

Cell Physiological Phenomena↗
PubMed2026

Proteomic profiling of bone for the estimation of post-mortem interval and post-mortem submersion interval: a systematic review.

Accurate estimation of the Post-Mortem Interval (PMI) and Post-Mortem Submersion Interval (PMSI) remains a persistent challenge in forensic science, especially when traditional morphological and entomological methods fail due to advanced decomposition or in aquatic environments. Proteomic profiling of bone tissues has recently emerged as a promising approach, leveraging the predictable degradation patterns of bone proteins to estimate time since death more reliably. This systematic review, conducted in accordance with PRISMA guidelines, analyzed 24 peer-reviewed studies focusing on the application of proteomic techniques to bone tissue for PMI and PMSI estimation. The included studies were evaluated based on sample type, analytical techniques used, identified biomarkers, environmental conditions assessed, and the overall reliability and reproducibility of the findings. The review found that specific bone proteins, particularly collagen, osteocalcin, fetuin-A, etc. exhibited consistent degradation patterns that correlated strongly with elapsed post-mortem time. Cortical bone was identified as a more stable and informative matrix compared to trabecular bone. Mass spectrometry, especially LC-MS/MS, emerged as the predominant analytical technique due to its high sensitivity and accuracy in detecting low-abundance proteins over extended PMIs and PMSIs. However, protein degradation rates were significantly influenced by environmental variables such as temperature, humidity, soil pH, and microbial activity. This review also emphasizes the transformative role of bone proteomics in advancing forensic science while identifying key gaps that must be addressed to achieve global standardization and practical implementation in diverse forensic contexts. The integration of proteomics with other emerging technologies, such as machine learning algorithms and computational modeling, may further enhance the precision of PMI and PMSI estimation in future applications.

Postmortem Changes↗
PubMed2004

The International Protein Index: an integrated database for proteomics experiments.

Despite the complete determination of the genome sequence of several higher eukaryotes, their proteomes remain relatively poorly defined. Information about proteins identified by different experimental and computational methods is stored in different databases, meaning that no single resource offers full coverage of known and predicted proteins. IPI (the International Protein Index) has been developed to address these issues and offers complete nonredundant data sets representing the human, mouse and rat proteomes, built from the Swiss-Prot, TrEMBL, Ensembl and RefSeq databases.

Animals↗
PubMed2003

Improved proteome analysis of Saccharomyces cerevisiae mitochondria by free-flow electrophoresis.

The analysis of complex cellular proteomes by means of two-dimensional gel electrophoresis (2-DE) is significantly limited by the power of resolution of this technique. Although subcellular fractionation can be a fundamental first step to increase resolution, it frequently leads to preparations contaminated with other cellular structures. Here, we chose mitochondria of Saccharomyces cerevisiae to demonstrate that an integrated zone-electrophoretic purification step (ZE), with a free-flow electrophoresis device (FFE), can assist in overcoming this problem, while significantly improving their degree of purity. Whereas mitochondrial preparations isolated by means of differential centrifugation include a considerable degree of non-mitochondrial proteins (16%), this contamination could be effectually removed by the inclusion of a ZE-FFE purification step (2%). This higher degree of purity led to the identification of many more proteins from ZE-FFE purified mitochondrial protein extracts (n = 129), compared to mitochondrial protein extracts isolated by differential centrifugation (n = 80). Moreover, a marked decrease of degraded proteins was found in the ZE-FFE purified mitochondrial protein extracts. It is noteworthy that even at a low 2-DE resolution level, a four-fold higher number (17 versus 4) of presumably low abundance proteins could be identified in the ZE-FFE purified mitochondrial protein extracts. Therefore these results represent a feasible approach for an in-depth proteome analysis of mitochondria and possibly other organelles.

Electrophoresis↗
PubMed2003

Proteomics in biomarker discovery and drug development.

Proteomics is a research field aiming to characterize molecular and cellular dynamics in protein expression and function on a global level. The introduction of proteomics has been greatly broadening our view and accelerating our path in various medical researches. The most significant advantage of proteomics is its ability to examine a whole proteome or sub-proteome in a single experiment so that the protein alterations corresponding to a pathological or biochemical condition at a given time can be considered in an integrated way. Proteomic technology has been extensively used to tackle a wide variety of medical subjects including biomarker discovery and drug development. By complement with other new technique advances in genomics and bioinformatics, proteomics has a great potential to make considerable contribution to biomarker identification and to revolutionize drug development process. This article provides a brief overview of the proteomic technologies and their application in biomarker discovery and drug development.

Animals↗
PubMed2004

Enhanced functional information from predicted protein networks.

Experimentally derived genome-wide protein interaction networks have been useful in the elucidation of functional information that is not evident from examining individual proteins but determination of these networks is complex and time consuming. To address this problem, several computational methods for predicting protein networks in novel genomes have been developed. A recent publication by Date and Marcotte describes the use of phylogenetic profiling for elucidating novel pathways in proteomes that have not been experimentally characterized. This method, in combination with other computational methods for generating protein-interaction networks, might help identify novel functional pathways and enhance functional annotation of individual proteins.

Energy Metabolism↗