Alterations in rat serum proteome and metabolome as putative disease markers in sepsis

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

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.

Decreased Tissue COX5B Expression and Mitochondrial Dysfunction during Sepsis-Induced Kidney Injury in Rats

Background. Sepsis is defined as a life-threatening organ dysfunction due to a dysregulated host response to infection. Sepsis is the dominant cause of acute kidney injury (AKI), accounting for nearly 50% of episodes of acute renal failure. Signaling cascades and pathways within the kidney are largely unknown and analysis of these molecular mechanisms may enhance knowledge on pathophysiology and possible therapeutic options. Material and Methods. 26 male Wistar rats were assigned to either a sham group (control, N = 6) or sepsis group (N = 20; cecal ligature and puncture model, 24 and 48 hours after CLP). Surviving rats (n = 12) were decapitated at 24 hours (early phase; n = 6) or 48 hours (late phase; n = 6) after CLP and kidneys removed for proteomic analysis. 2D-DIGE and DeCyder 2D software (t-test, P < 0.01) were used for analysis of significantly regulated protein spots. MALDI-TOF in combination with peptide mass fingerprinting (PMF) as well as Western Blot analysis was used for protein identification. Bioinformatic network analyses (STRING, GeneMania, and PCViz) were used to describe protein-protein interactions. Results. 12 spots were identified with significantly altered proteins (P < 0.01) in the three analyzed groups. Two spots could not be identified. Four different proteins were found significantly changed among the groups: major urinary protein (MUP5), cytochrome c oxidase subunit B (COX5b), myosin-6 (MYH6), and myosin-7 (MYH7). A significant correlation with the proteins was found for mitochondrial energy production and electron transport. Conclusions. COX5B could be a promising biomarker candidate since a significant association was found during experimental sepsis in the present study. For future research, COX5B should be evaluated as a biomarker in both human urine and serum to identify sepsis.

Hyperoxia-Induced Protein Alterations in Renal Rat Tissue: A Quantitative Proteomic Approach to Identify Hyperoxia-Induced Effects in Cellular Signaling Pathways

Introduction. In renal tissue as well as in other organs, supranormal oxygen pressure may lead to deleterious consequences on a cellular level. Additionally, hyperoxia-induced effect in cells and related free radicals may potentially contribute to renal failure. The aim of this study was to analyze time-dependent alterations of rat kidney protein expression after short-term normobaric hyperoxia using proteomics and bioinformatic approaches. Material and Methods. N = 36 Wistar rats were randomized into six different groups: three groups with normobaric hyperoxia (exposure to 100% oxygen for 3 h) and three groups with normobaric normoxia (NN; room air). After hyperoxia exposure, kidneys were removed immediately, after 3 days and after 7 days. Kidney lysates were analyzed by two-dimensional gel electrophoresis followed by peptide mass fingerprinting using tandem mass spectrometry. Statistical analysis was performed with DeCyder 2D software (p < 0.01). Biological functions of differential regulated proteins were studied using functional network analysis (Ingenuity Pathways Analysis and PathwayStudio). Results. Expression of 14 proteins was significantly altered (p < 0.01): eight proteins (MEP1A_RAT, RSSA_RAT, F16P1_RAT, STML2_RAT, BPNT1_RAT, LGMN_RAT, ATPA_RAT, and VDAC1_RAT) were downregulated and six proteins (MTUS1_RAT, F16P1_RAT, ACTG_RAT, ACTB_RAT, 2ABA_RAT, and RAB1A_RAT) were upregulated. Bioinformatic analyses revealed an association of regulated proteins with inflammation. Conclusions. Significant alterations in renal protein expression could be demonstrated for up to 7 days even after short-term hyperoxia. The identified proteins indicate an association with inflammation signaling cascades. MEP1A and VDAC1 could be promising candidates to identify hyperoxic injury in kidney cells.

The role of proteomics in understanding biological mechanisms of sepsis

Sepsis is a systemic inflammatory state caused by infection. Complications of this infection with multiple organ failure lead to more lethal conditions, such as severe sepsis and septic shock. Sepsis is one of the leading causes of US deaths. Novel biomarkers with high sensitivity and specificity may be helpful for early diagnosis of sepsis and for improvement of patient outcomes through the development of new therapies. Mass spectrometry-based proteomics offers powerful tools to identify such biomarkers and furthermore to give insight to fundamental mechanisms of this clinical condition. In this review, we summarize findings from proteomics studies of sepsis and how their applications have provided more understanding into the pathogenesis of septic infection. Literatures related to "proteomics", "sepsis", "systemic inflammatory response syndrome", "severe sepsis", "septic infection", and "multiple organ dysfunction syndrome" were searched using PubMed. Findings about neonatal and adult sepsis are discussed separately. Within the adult sepsis studies, results are grouped based on the models (e.g., human or animal). Across investigations in clinical populations and in rodent and mammalian animal models, biological pathways, such as inflammatory and acute phase response, coagulation, complement, mitochondrial energy metabolism, chaperones, and oxidative stress, are altered at the protein level. These proteomics studies have discovered many novel biomarker candidates of septic infection. Validation the clinical use of these biomarker candidates may significantly impact the diagnosis and prognosis of sepsis. In addition, the molecular mechanisms revealed by these studies may also guide the development of more effective treatments.

A metabolomic approach for diagnosis of experimental sepsis

BACKGROUND

The search for reliable diagnostic biomarkers of sepsis remains necessary. Assessment of global metabolic profiling using quantitative nuclear magnetic resonance (NMR)-based metabolomics offers an attractive modern methodology for fast and comprehensive determination of multiple circulating metabolites and for defining the metabolic phenotype of sepsis.

OBJECTIVE

To develop a novel NMR-based metabolomic approach for diagnostic evaluation of sepsis.

METHODS

Male Sprague-Dawley rats (weight 325-375 g) underwent cecal ligation and puncture (n = 14, septic group) or sham procedure (n = 14, control group) and 24 h later were euthanized. Lung tissue, bronchoalveolar lavage (BAL) fluid, and serum samples were obtained for (1)H NMR and high-resolution magic-angle spinning analysis. Unsupervised principal components analysis was performed on the processed spectra, and a predictive model for diagnosis of sepsis was constructed using partial least-squares discriminant analysis.

RESULTS

NMR-based metabolic profiling discriminated characteristics between control and septic rats. Characteristic metabolites changed markedly in septic rats as compared with control rats: alanine, creatine, phosphoethanolamine, and myoinositol concentrations increased in lung tissue; creatine increased and myoinositol decreased in BAL fluid; and alanine, creatine, phosphoethanolamine, and acetoacetate increased whereas formate decreased in serum. A predictive model for diagnosis of sepsis using these metabolites classified cases with sensitivity and specificity of 100%.

CONCLUSIONS

NMR metabolomic analysis is a potentially useful technique for diagnosis of sepsis. The concentrations of metabolites involved in energy metabolism and in the inflammatory response change in this model of sepsis.

The emerging field of quantitative blood metabolomics for biomarker discovery in critical illnesses

Metabolomics, a science of systems biology, is the global assessment of endogenous metabolites within a biologic system and represents a "snapshot" reading of gene function, enzyme activity, and the physiological landscape. Metabolite detection, either individual or grouped as a metabolomic profile, is usually performed in cells, tissues, or biofluids by either nuclear magnetic resonance spectroscopy or mass spectrometry followed by sophisticated multivariate data analysis. Because loss of metabolic homeostasis is common in critical illness, the metabolome could have many applications, including biomarker and drug target identification. Metabolomics could also significantly advance our understanding of the complex pathophysiology of acute illnesses, such as sepsis and acute lung injury/acute respiratory distress syndrome. Despite this potential, the clinical community is largely unfamiliar with the field of metabolomics, including the methodologies involved, technical challenges, and, most importantly, clinical uses. Although there is evidence of successful preclinical applications, the clinical usefulness and application of metabolomics in critical illness is just beginning to emerge, the advancement of which hinges on linking metabolite data to known and validated clinically relevant indices. In addition, other important aspects, such as patient selection, sample collection, and processing, as well as the needed multivariate data analysis, have to be taken into consideration before this innovative approach to biomarker discovery can become a reliable tool in the intensive care unit. The purpose of this review is to begin to familiarize clinicians with the field of metabolomics and its application for biomarker discovery in critical illnesses such as sepsis.

Proteomic profiling of renal allograft rejection in serum using magnetic bead-based sample fractionation and MALDI-TOF MS

Proteomics is one of the emerging techniques for biomarker discovery. Biomarkers can be used for early noninvasive diagnosis and prognosis of diseases and treatment efficacy evaluation. In the present study, the well-established research systems of ClinProt Micro solution incorporated unique magnetic bead sample preparation technology, which, based on matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS), have become very successful in bioinformatics due to its outstanding performance and reproducibility for discovery disease-related biomarker. We collected fasting blood samples from patients with biopsy-confirmed acute renal allograft rejection (n = 12), chronic rejection (n = 12), stable graft function (n = 12) and also from healthy volunteers (n = 13) to study serum peptidome patterns. Specimens were purified with magnetic bead-based weak cation exchange chromatography and analyzed with a MALDI-TOF mass spectrometer. The results indicated that 18 differential peptide peaks were selected as potential biomarkers of acute renal allograft rejection, and 6 differential peptide peaks were selected as potential biomarkers of chronic rejection. A Quick Classifier Algorithm was used to set up the classification models for acute and chronic renal allograft rejection. The algorithm models recognize 82.64% of acute rejection and 98.96% of chronic rejection episodes, respectively. We were able to identify serum protein fingerprints in small sample sizes of recipients with renal allograft rejection and establish the models for diagnosis of renal allograft rejection. This preliminary study demonstrated that proteomics is an emerging tool for early diagnosis of renal allograft rejection and helps us to better understand the pathogenesis of disease process.