Bioinformatical Analysis of Organ-Related (Heart, Brain, Liver, and Kidney) and Serum Proteomic Data to Identify Protein Regulation Patterns and Potential Sepsis Biomarkers

Abstract

During the last years, proteomic studies have revealed several interesting findings in experimental sepsis models and septic patients. However, most studies investigated protein alterations only in single organs or in whole blood. To identify possible sepsis biomarkers and to evaluate the relationship between protein alteration in sepsis affected organs and blood, proteomics data from the heart, brain, liver, kidney, and serum were analysed. Using functional network analyses in combination with hierarchical cluster analysis, we found that protein regulation patterns in organ tissues as well as in serum are highly dynamic. In the tissue proteome, the main functions and pathways affected were the oxidoreductive activity, cell energy generation, or metabolism, whereas in the serum proteome, functions were associated with lipoproteins metabolism and, to a minor extent, with coagulation, inflammatory response, and organ regeneration. Proteins from network analyses of organ tissue did not correlate with statistically significantly regulated serum proteins or with predicted proteins of serum functions. In this study, the combination of proteomic network analyses with cluster analyses is introduced as an approach to deal with high-throughput proteomics data to evaluate the dynamics of protein regulation during sepsis.

Evaluation of the Molecular Mechanisms of Sepsis Using Proteomics

“…This conclusion is consistent with that of Hohn et al. ( 118 ). In their study, differentially expressed proteins were obtained via dynamic studies (12, 24, and 48 h) of the organ proteomes and serum proteomes.…”

Proteomic Analysis of Morphologically Changed Tissues after Prolonged Dexamethasone Treatment

“…Single protein datasets from individual organs provide only lists of proteins. On the other hand, integration of the proteins from the different organs, at the same time, will provide a better picture of complete biochemical changes and protein dynamics taking place within the animals [11].…”

Influence of 30 and 60 Min of Hypobaric Hypoxia in Simulated Altitude of 15,000 ft on Human Proteome Profile

“…It provides the possibility to customize the network and allows for the choosing of data sources or highlighting of specific functions with a more comfortable graphic experience. It is developed and continually updated by the University of Toronto and is funded by the Ontario Ministry of Research and Innovation [ 13 ]. GeneMania knowledge is based on data from large databases, which comprise Gene Expression Omnibus, BioGRID, EMBL-EBI, Pfam, Ensembl, Mouse Genome Informatics, the National Center for Biotechnology Information, InParanoid and Pathway Commons [ 14 ].…”

“…It was developed for making predictions about a gene or protein function based on a query of a list of proteins that share a function of interest. The software allows for taking advantage of the persistent improvement and proliferation of high-throughput genomics and proteomics datasets by making up-to-date predictions of their interaction with other genes or proteins [ 13 , 14 ].…”

“…Currently, only very few investigators have applied these technologies to the study of humans at (simulated) altitude and only under a limited number of hypoxic conditions and durations, and with usually small sample sizes [ 1 ]. Hence, previous data suggested that hypobaric hypoxia during airline travel (2300 m or 8000 ft) induces several (patho-) physiological reactions in the human body [ 12 , 13 , 14 , 15 ] as well as an association in signaling pathways, for example, in immune response, protein metabolism and hemostasis [ 6 ].…”