BIOLOGICAL SYSTEMS ANALYSIS BY A NETWORK PROTEOMICS APPROACH AND SUBCELLULAR PROTEIN PROFILING

2006 ◽  
Vol 09 (04) ◽  
pp. 299-314
Author(s):  
CHRISTIAN V. FORST ◽  
LAWRENCE CABUSORA ◽  
KWASI G. MAWUENYEGA ◽  
XIAN CHEN
Keyword(s):  
Cell Wall ◽  
Large Scale ◽  
Organizational Structures ◽  
Protein Profiling ◽  
Proteomics Data ◽  
Systematic Analysis ◽  
Network Analyses ◽  
Its Gene ◽  
Proteomics Approach ◽  
Related Proteins

We provide a systematic analysis of a biological system, the microbial pathogen Mycobacterium tuberculosis (Mtb) by directly profiling its gene products. This analysis combines high-throughput proteomics and biocomputational approaches to elucidate the globally expressed complements of the three subcellular compartments (the cell wall, membrane and cytosol) of Mtb. We report the compartmentalization of 1,044 identified proteins using proteomics methods. Genome-based biological network analyses were performed and integrated with proteomics data to reconstruct response networks. From the reconstructed response networks for fatty acid degradation and lipid biosynthesis pathways in Mtb, we identified proteins whose involvements in these pathways were not previously suspected. Furthermore, the subcellular localizations of these expressed proteins provide interesting insights into the compartmentalization of these pathways, which appear to traverse from cell wall to cytoplasm. Results of this large-scale subcellular proteome profile of Mtb have confirmed and validated the computational network hypothesis that functionally related proteins work together in larger organizational structures.

scholarly journals Mycobacterium tuberculosisFunctional Network Analysis by Global Subcellular Protein Profiling

2005 ◽  
Vol 16 (1) ◽  
pp. 396-404 ◽  
Author(s):  
Kwasi G. Mawuenyega ◽  
Christian V. Forst ◽  
Karen M. Dobos ◽  
John T. Belisle ◽  
Jin Chen ◽  
...  
Keyword(s):  
Cell Wall ◽  
Large Scale ◽  
Organizational Structures ◽  
Protein Profiling ◽  
Gene Products ◽  
Proteomics Data ◽  
Systematic Analysis ◽  
Its Gene ◽  
Proteome Profile ◽  
Related Proteins

Trends in increased tuberculosis infection and a fatality rate of ∼23% have necessitated the search for alternative biomarkers using newly developed postgenomic approaches. Here we provide a systematic analysis of Mycobacterium tuberculosis (Mtb) by directly profiling its gene products. This analysis combines high-throughput proteomics and computational approaches to elucidate the globally expressed complements of the three subcellular compartments (the cell wall, membrane, and cytosol) of Mtb. We report the identifications of 1044 proteins and their corresponding localizations in these compartments. Genome-based computational and metabolic pathways analyses were performed and integrated with proteomics data to reconstruct response networks. From the reconstructed response networks for fatty acid degradation and lipid biosynthesis pathways in Mtb, we identified proteins whose involvements in these pathways were not previously suspected. Furthermore, the subcellular localizations of these expressed proteins provide interesting insights into the compartmentalization of these pathways, which appear to traverse from cell wall to cytoplasm. Results of this large-scale subcellular proteome profile of Mtb have confirmed and validated the computational network hypothesis that functionally related proteins work together in larger organizational structures.

scholarly journals Large-scale proteomic analysis of the grapevine leaf apoplastic fluid reveals mainly stress-related proteins and cell wall modifying enzymes

2013 ◽  
Vol 13 (1) ◽  
pp. 24 ◽  
Author(s):  
Bertrand Delaunois ◽  
Thomas Colby ◽  
Nicolas Belloy ◽  
Alexandra Conreux ◽  
Anne Harzen ◽  
...  
Keyword(s):  
Cell Wall ◽  
Proteomic Analysis ◽  
Large Scale ◽  
Apoplastic Fluid ◽  
Related Proteins

scholarly journals piNET: a versatile web platform for downstream analysis and visualization of proteomics data

2020 ◽  
Vol 48 (W1) ◽  
pp. W85-W93 ◽  
Author(s):  
Behrouz Shamsaei ◽  
Szymon Chojnacki ◽  
Marcin Pilarczyk ◽  
Mehdi Najafabadi ◽  
Wen Niu ◽  
...  
Keyword(s):  
Quantitative Proteomics ◽  
Large Scale ◽  
Model Organism ◽  
Proteomics Data ◽  
Primary Input ◽  
Network Analyses ◽  
Enzyme Substrate ◽  
Chemical Inhibitors ◽  
Downstream Analysis ◽  
Web Platform

Abstract Rapid progress in proteomics and large-scale profiling of biological systems at the protein level necessitates the continued development of efficient computational tools for the analysis and interpretation of proteomics data. Here, we present the piNET server that facilitates integrated annotation, analysis and visualization of quantitative proteomics data, with emphasis on PTM networks and integration with the LINCS library of chemical and genetic perturbation signatures in order to provide further mechanistic and functional insights. The primary input for the server consists of a set of peptides or proteins, optionally with PTM sites, and their corresponding abundance values. Several interconnected workflows can be used to generate: (i) interactive graphs and tables providing comprehensive annotation and mapping between peptides and proteins with PTM sites; (ii) high resolution and interactive visualization for enzyme-substrate networks, including kinases and their phospho-peptide targets; (iii) mapping and visualization of LINCS signature connectivity for chemical inhibitors or genetic knockdown of enzymes upstream of their target PTM sites. piNET has been built using a modular Spring-Boot JAVA platform as a fast, versatile and easy to use tool. The Apache Lucene indexing is used for fast mapping of peptides into UniProt entries for the human, mouse and other commonly used model organism proteomes. PTM-centric network analyses combine PhosphoSitePlus, iPTMnet and SIGNOR databases of validated enzyme-substrate relationships, for kinase networks augmented by DeepPhos predictions and sequence-based mapping of PhosphoSitePlus consensus motifs. Concordant LINCS signatures are mapped using iLINCS. For each workflow, a RESTful API counterpart can be used to generate the results programmatically in the json format. The server is available at http://pinet-server.org, and it is free and open to all users without login requirement.

scholarly journals Prediction of Gastric Cancer-Related Proteins Based on Graph Fusion Method

2021 ◽  
Vol 9 ◽  
Author(s):  
Hao Zhang ◽  
Ruisi Xu ◽  
Meng Ding ◽  
Ying Zhang
Keyword(s):  
Gastric Cancer ◽  
Early Diagnosis ◽  
High Speed ◽  
Large Scale ◽  
Interaction Network ◽  
Proteomics Data ◽  
Speed Increase ◽  
Topological Features ◽  
Potential Biomarker ◽  
Related Proteins

Gastric cancer is a common malignant tumor of the digestive system with no specific symptoms. Due to the limited knowledge of pathogenesis, patients are usually diagnosed in advanced stage and do not have effective treatment methods. Proteome has unique tissue and time specificity and can reflect the influence of external factors that has become a potential biomarker for early diagnosis. Therefore, discovering gastric cancer-related proteins could greatly help researchers design drugs and develop an early diagnosis kit. However, identifying gastric cancer-related proteins by biological experiments is time- and money-consuming. With the high speed increase of data, it has become a hot issue to mine the knowledge of proteomics data on a large scale through computational methods. Based on the hypothesis that the stronger the association between the two proteins, the more likely they are to be associated with the same disease, in this paper, we constructed both disease similarity network and protein interaction network. Then, Graph Convolutional Networks (GCN) was applied to extract topological features of these networks. Finally, Xgboost was used to identify the relationship between proteins and gastric cancer. Results of 10-cross validation experiments show high area under the curve (AUC) (0.85) and area under the precision recall (AUPR) curve (0.76) of our method, which proves the effectiveness of our method.

scholarly journals Ranking Cancer Proteins by Integrating PPI Network and Protein Expression Profiles

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Jie Ren ◽  
Lulu Shang ◽  
Qing Wang ◽  
Jing Li
Keyword(s):  
Differential Expression ◽  
Large Scale ◽  
Expression Profiles ◽  
A Priori ◽  
Cancer Genes ◽  
Ppi Network ◽  
Proteomics Data ◽  
Global Ranking ◽  
Colon And Rectal Cancer ◽  
Related Proteins

Proteomics, the large-scale analysis of proteins, is contributing greatly to understanding gene function in the postgenomic era. However, disease protein ranking using shotgun proteomics data has not been fully evaluated. In this study, we prioritized disease-related proteins by integrating the protein-protein interaction (PPI) network and protein differential expression profiles from colon and rectal cancer (CRC) or breast cancer (BC) proteomics. We applied Local Ranking (LR) and Global Ranking (GR) methods in network with three kinds of protein sets as a priori knowledge, which were known disease proteins (KDPs) that were collected from the Online Mendelian Inheritance in Man (OMIM) database, differentially expressed proteins (DEPs), and the collection of KDPs and their direct neighborhood with differential expression (eKDPs). The cross-validations showed that GR method outperformed LR method while using eKDPs as the initial training showed significantly higher accuracy compared to using the other two a priori sets. And then we validated the top ranked proteins using RNAi-based loss-of-function screens in the DepMap database. The results showed that 75% of top 20 proteins in CRC are necessary for tumor survival. In summary, the network-based Global Ranking with protein differential expression can efficiently prioritize cancer-related proteins and discover new candidate cancer genes or proteins.

Recent Developments in the Downstream Processing of Phycobiliproteins from Algae: A Review

2021 ◽  
Vol 06 ◽  
Author(s):  
Ayekpam Chandralekha Devi ◽  
G. K. Hamsavi ◽  
Simran Sahota ◽  
Rochak Mittal ◽  
Hrishikesh A. Tavanandi ◽  
...  
Keyword(s):  
Cell Wall ◽  
Large Scale ◽  
Downstream Processing ◽  
Review Article ◽  
Current Status ◽  
Wall Structure ◽  
Scale Production ◽  
Cell Wall Disruption ◽  
Recent Developments ◽  
Macro Algae

Abstract: Algae (both micro and macro) have gained huge attention in the recent past for their high commercial value products. They are the source of various biomolecules of commercial applications ranging from nutraceuticals to fuels. Phycobiliproteins are one such high value low volume compounds which are mainly obtained from micro and macro algae. In order to tap the bioresource, a significant amount of work has been carried out for large scale production of algal biomass. However, work on downstream processing aspects of phycobiliproteins (PBPs) from algae is scarce, especially in case of macroalgae. There are several difficulties in cell wall disruption of both micro and macro algae because of their cell wall structure and compositions. At the same time, there are several challenges in the purification of phycobiliproteins. The current review article focuses on the recent developments in downstream processing of phycobiliproteins (mainly phycocyanins and phycoerythrins) from micro and macroalgae. The current status, the recent advancements and potential technologies (that are under development) are summarised in this review article besides providing future directions for the present research area.

scholarly journals Microtubule Dynamics Plays a Vital Role in Plant Adaptation and Tolerance to Salt Stress

2021 ◽  
Vol 22 (11) ◽  
pp. 5957
Author(s):  
Hyun Jin Chun ◽  
Dongwon Baek ◽  
Byung Jun Jin ◽  
Hyun Min Cho ◽  
Mi Suk Park ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Wall ◽  
Salt Stress ◽  
Differentially Expressed ◽  
Differentially Expressed Proteins ◽  
Related Gene ◽  
Plant Adaptation ◽  
Salt Stress Response ◽  
Network Analyses ◽  
Cell Wall Biogenesis

Although recent studies suggest that the plant cytoskeleton is associated with plant stress responses, such as salt, cold, and drought, the molecular mechanism underlying microtubule function in plant salt stress response remains unclear. We performed a comparative proteomic analysis between control suspension-cultured cells (A0) and salt-adapted cells (A120) established from Arabidopsis root callus to investigate plant adaptation mechanisms to long-term salt stress. We identified 50 differentially expressed proteins (45 up- and 5 down-regulated proteins) in A120 cells compared with A0 cells. Gene ontology enrichment and protein network analyses indicated that differentially expressed proteins in A120 cells were strongly associated with cell structure-associated clusters, including cytoskeleton and cell wall biogenesis. Gene expression analysis revealed that expressions of cytoskeleton-related genes, such as FBA8, TUB3, TUB4, TUB7, TUB9, and ACT7, and a cell wall biogenesis-related gene, CCoAOMT1, were induced in salt-adapted A120 cells. Moreover, the loss-of-function mutant of Arabidopsis TUB9 gene, tub9, showed a hypersensitive phenotype to salt stress. Consistent overexpression of Arabidopsis TUB9 gene in rice transgenic plants enhanced tolerance to salt stress. Our results suggest that microtubules play crucial roles in plant adaptation and tolerance to salt stress. The modulation of microtubule-related gene expression can be an effective strategy for developing salt-tolerant crops.

scholarly journals A primary human T-cell spectral library to facilitate large scale quantitative T-cell proteomics

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Harshi Weerakoon ◽  
Jeremy Potriquet ◽  
Alok K. Shah ◽  
Sarah Reed ◽  
Buddhika Jayakody ◽  
...  
Keyword(s):  
T Cell ◽  
Large Scale ◽  
Large Data ◽  
Spectral Library ◽  
Proteomics Data ◽  
Cell Library ◽  
Public Resource ◽  
Human T Cell ◽  
Theoretical Mass ◽  
Current Resource

AbstractData independent analysis (DIA) exemplified by sequential window acquisition of all theoretical mass spectra (SWATH-MS) provides robust quantitative proteomics data, but the lack of a public primary human T-cell spectral library is a current resource gap. Here, we report the generation of a high-quality spectral library containing data for 4,833 distinct proteins from human T-cells across genetically unrelated donors, covering ~24% proteins of the UniProt/SwissProt reviewed human proteome. SWATH-MS analysis of 18 primary T-cell samples using the new human T-cell spectral library reliably identified and quantified 2,850 proteins at 1% false discovery rate (FDR). In comparison, the larger Pan-human spectral library identified and quantified 2,794 T-cell proteins in the same dataset. As the libraries identified an overlapping set of proteins, combining the two libraries resulted in quantification of 4,078 human T-cell proteins. Collectively, this large data archive will be a useful public resource for human T-cell proteomic studies. The human T-cell library is available at SWATHAtlas and the data are available via ProteomeXchange (PXD019446 and PXD019542) and PeptideAtlas (PASS01587).

DISAST-CIR: Disastrous Incidents Systematic Analysis Through Components, Interactions and Results: Application to a Large-Scale Train Accident

2009 ◽  
Vol 37 (1) ◽  
pp. 46-50 ◽  
Author(s):  
Adi Leiba ◽  
Dagan Schwartz ◽  
Talor Eran ◽  
Amir Blumenfeld ◽  
Daniel Laor ◽  
...  
Keyword(s):  
Large Scale ◽  
Systematic Analysis
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