Identification of differentially expressed serum proteins in infectious purpura fulminans

Combination of HBA1, TTR, and SERPINF2 in plasma defines phenotype correlated with severe burn outcome

Recent advancements in proteomics allow for the concurrent identification and quantification of multiple proteins. This study aimed to identify proteins associated with severe burn pathology and establish a clinically useful molecular pathology classification. In a retrospective observational study, blood samples were collected from severe burn patients. Proteins were measured using mass spectrometry, and prognosis-related proteins were extracted by comparing survivors and non-survivors. Enrichment and ROC analyses evaluated the extracted proteins, followed by latent class analysis. Measurements were performed on 83 burn patients. In the non-survivor group, ten proteins significantly changing on the day of injury were associated with metabolic processes and toxin responses. ROC analysis identified HBA1, TTR, and SERPINF2 with AUCs > 0.8 as predictors of 28-day mortality. Latent class analysis classified three molecular pathotypes, and plasma mass spectrometry revealed ten proteins associated with severe burn prognosis. Molecular pathotypes based on HBA1, TTR, and SERPINF2 significantly correlated with outcomes.

Following up of Surgical Treated Human Liver Cystic Echinococcosis: A Proteomics Approach

Background:

Cystic echinococcosis (CE) is one of the most important parasitic zoonosis in the world. Post-surgery follow up in CE patients is an important non-solved problem up to now. Therefore, the investigations on this problematic issue would be very applicable in the view of CE clinical treatment.

Methods:

A total of 24 confirmed liver CE patients sera including eight sera before surgery (BS), eight sera three months post-surgery (3MPS), and eight sera six months post-surgery (6MPS) were used in the present study. Proteomics methods including 2DE and LC-MS/MS were performed on the specimens followed by bioinformatics analysis such as Gene Ontology (GO) and Protein-Protein Interaction (PPI) network analysis.

Results:

A total of 235 proteins were detected of which 12 differentially expressed proteins (DEP) were identified by LC-MS/MS in all sera. The proteins were presented in BS and suppressed after surgery as follows: HPX, SERPINA1, SERPINC1, CP, HBD, and HBA2. Comparisons of the protein expression in sera of patients BS, 3MPS, and 6MPS revealed that GC, IGJ, AHSG, CD5L, FGG, and APOC3 have been overexpressed in 3MPS and 6MPS. PPI network analysis demonstrated that SERPINC1 and AHSG with more connection in the network could be considered as hub proteins and potential prognostic biomarkers in response to surgical treatment of liver CE.

Conclusion:

Application of proteomics methods on patient’s sera could be used as a novel biomarker tool for following-up liver CE patients. In this regards, proteomics and, application of bioinformatics analysis including GO and PPI showed that SERPINC1, AHSG and HPX are of more value as a potential follow up biomarkers in response to surgical treatment.

Human body-fluid proteome: quantitative profiling and computational prediction

Empowered by the advancement of high-throughput bio technologies, recent research on body-fluid proteomes has led to the discoveries of numerous novel disease biomarkers and therapeutic drugs. In the meantime, a tremendous progress in disclosing the body-fluid proteomes was made, resulting in a collection of over 15 000 different proteins detected in major human body fluids. However, common challenges remain with current proteomics technologies about how to effectively handle the large variety of protein modifications in those fluids. To this end, computational effort utilizing statistical and machine-learning approaches has shown early successes in identifying biomarker proteins in specific human diseases. In this article, we first summarized the experimental progresses using a combination of conventional and high-throughput technologies, along with the major discoveries, and focused on current research status of 16 types of body-fluid proteins. Next, the emerging computational work on protein prediction based on support vector machine, ranking algorithm, and protein-protein interaction network were also surveyed, followed by algorithm and application discussion. At last, we discuss additional critical concerns about these topics and close the review by providing future perspectives especially toward the realization of clinical disease biomarker discovery.

Protective effects of Cinnamomum zeylanicum L. (Darchini) in acetaminophen-induced oxidative stress, hepatotoxicity and nephrotoxicity in mouse model

Cinnamomum zeylanicum, a widely used spice and flavor, is used in the treatment and prevention of many diseases. In the current study, Balb/c mice were pretreated with cinnamon bark aqueous extract (200 mg/kg/day i.g.) 14 days prior to intragastrically administer single toxic dose of acetaminophen (200 mg/kg). Blood samples were collected for analysis of biochemical and oxidative stress parameters and liver and kidney samples were collected for histopathological analysis. The results indicate that cinnamon aqueous extract exhibit a highly significant preventive potential by ameliorating APAP-induced elevated levels of serum alanine aminotransferase, aspartate aminotransferase, creatinine, urea and macroscopic and histological alterations in liver and kidney. Moreover, significant increase in total oxidant status and decrease in total antioxidant capacity accompanied by APAP exposure, were restored by cinnamon pretreatment. We found that prior administration of cinnamon prevented the toxic changes induced by acetaminophen as confirmed by histopathological examination, more possibly owing to its antioxidant potential. In conclusion, the pretreatment with cinnamon provide potential therapeutic applications in acute liver and kidney injury induced by APAP in experimental animal model and it could have therapeutic potential in oxidative stress associated diseases.

Proteomic analysis associated with coronary artery dilatation caused by Kawasaki disease using serum exosomes

INTRODUCTION

The aim of this study was to investigate the serum exosome proteome profile of coronary artery dilatation (CAD) caused by Kawasaki disease (KD).

METHODS

Two-dimensional electrophoresis was implemented on proteins of serum exosomes obtained from children with CAD caused by KD and from healthy controls. Differentially expressed proteins were identified by matrix-assisted laser desorption/ionization time-of-flight/time-of-flight mass spectrometry analysis.

RESULTS

We identified 38 differentially expressed proteins (13 up-regulated and 25 down-regulated) from serum exosomes of patients with CAD caused by KD compared with healthy controls. Expression levels of three differentially expressed proteins (leucine-rich alpha-2-glycoprotein, sex hormone-binding globulin, and serotransferrin) were validated using western blot analysis. Classification and protein-protein network analysis showed that they are associated with multiple functional groups involved in the acute inflammatory response, defense response, complement activation, humoral immune response, and response to wounding. The majority of the proteins are involved in the inflammation and coagulation cascades.

CONCLUSIONS

These findings establish a comprehensive proteome profile of CAD caused by KD and increase our knowledge of scientific insight into its mechanisms.

Pigment epithelium-derived factor regulates microvascular permeability through adipose triglyceride lipase in sepsis

PEDF induces vascular hyperpermeability by targeting the ATGL receptor, causing actin rearrangement and intercellular junctions disruption through activating RhoA. This damage can be arrested by PEDF-mAb or ATGL-shRNA, which may provide new potential therapeutic strategies for hyperpermeability in sepsis.