Serum amyloid A4 is a procoagulant apolipoprotein that it is elevated in venous thrombosis patients

Effect of N-glycosylation on secretion, degradation and lipoprotein distribution of human serum amyloid A4

Serum amyloid A (SAA) is a family of apolipoproteins predominantly synthesized and secreted by the liver. Human SAA4 is constitutively expressed and contains an N-glycosylation site that is not present in other SAA subtypes. SAA4 proteins are not fully glycosylated, resulting in the presence of both glycosylated and non-glycosylated forms in human plasma. The efficiency of N-glycosylation in SAA4 is known to be influenced by some reasons such as genetic polymorphism and metabolic disorders. However, the specific role of N-glycosylation in SAA4 remains largely unexplored. This study aimed to investigate how N-glycosylation affects the secretion, degradation, and lipoprotein distribution of SAA4. Initially, we designed and constructed an SAA4 plasmid vector to compare with the expression pattern of endogenous SAA4. The exogenous SAA4 was partially N-glycosylated, analogous to endogenous SAA4 in human hepatocellular carcinoma cells. Subsequently, we created a non-glycosylated mutant by replacing asparagine 76 with glutamine. Immunoblotting assays showed that the disruption of N-glycans did not affect the secretion and degradation of SAA4. Furthermore, we analyzed the lipoprotein profiles of SAA4 in the conditioned medium derived from transfected cells. The results revealed that non-glycosylated mutant SAA4 exhibited a distinct lipoprotein distribution compared to wild-type SAA4. Our findings suggest that N-glycosylation may be a key regulator of the distribution of SAA4 in lipoproteins, shedding light on the previously unknown physiological activities of human SAA4.

Post-acute sequelae of SARS-CoV-2 cardiovascular symptoms are associated with trace-level cytokines that affect cardiomyocyte function

An estimated 65 million people globally suffer from post-acute sequelae of COVID-19 (PASC), with many experiencing cardiovascular symptoms (PASC-CVS) like chest pain and heart palpitations. This study examines the role of chronic inflammation in PASC-CVS, particularly in individuals with symptoms persisting over a year after infection. Blood samples from three groups-recovered individuals, those with prolonged PASC-CVS and SARS-CoV-2-negative individuals-revealed that those with PASC-CVS had a blood signature linked to inflammation. Trace-level pro-inflammatory cytokines were detected in the plasma from donors with PASC-CVS 18 months post infection using nanotechnology. Importantly, these trace-level cytokines affected the function of primary human cardiomyocytes. Plasma proteomics also demonstrated higher levels of complement and coagulation proteins in the plasma from patients with PASC-CVS. This study highlights chronic inflammation's role in the symptoms of PASC-CVS.

Proteomics in Patients with Fibromyalgia Syndrome: A Systematic Review of Observational Studies

PURPOSE OF REVIEW

Fibromyalgia syndrome (FMS) is a disease of unknown pathophysiology, with the diagnosis being based on a set of clinical criteria. Proteomic analysis can provide significant biological information for the pathophysiology of the disease but may also reveal biomarkers for diagnosis or therapeutic targets. The present systematic review aims to synthesize the evidence regarding the proteome of adult patients with FMS using data from observational studies.

RECENT FINDINGS

An extensive literature search was conducted in MEDLINE/PubMed, CENTRAL, and clinicaltrials.gov from inception until November 2022. The study protocol was published in OSF. Two independent reviewers evaluated the studies and extracted data. The quality of studies was assessed using the modified Newcastle-Ottawa scale adjusted for proteomic research. Ten studies fulfilled the protocol criteria, identifying 3328 proteins, 145 of which were differentially expressed among patients with FMS against controls. The proteins were identified in plasma, serum, cerebrospinal fluid, and saliva samples. The control groups included healthy individuals and patients with pain (inflammatory and non-inflammatory). The most important proteins identified involved transferrin, α-, β-, and γ-fibrinogen chains, profilin-1, transaldolase, PGAM1, apolipoprotein-C3, complement C4A and C1QC, immunoglobin parts, and acute phase reactants. Weak correlations were observed between proteins and pain sensation, or quality of life scales, apart from the association of transferrin and a2-macroglobulin with moderate-to-severe pain sensation. The quality of included studies was moderate-to-good. FMS appears to be related to protein dysregulation in the complement and coagulation cascades and the metabolism of iron. Several proteins may be dysregulated due to the excessive oxidative stress response.

Interactions of Serum Amyloid A Proteins with the Blood-Brain Barrier: Implications for Central Nervous System Disease

Serum amyloid A (SAA) proteins are highly conserved lipoproteins that are notoriously involved in the acute phase response and systemic amyloidosis, but their biological functions are incompletely understood. Recent work has shown that SAA proteins can enter the brain by crossing the intact blood-brain barrier (BBB), and that they can impair BBB functions. Once in the central nervous system (CNS), SAA proteins can have both protective and harmful effects, which have important implications for CNS disease. In this review of the thematic series on SAA, we discuss the existing literature that relates SAA to neuroinflammation and CNS disease, and the possible roles of the BBB in these relations.

Serum Amyloid A 4 as a Common Marker of Persistent Inflammation in Patients with Neovascular Age-Related Macular Degeneration and Polypoidal Choroidal Vasculopathy

Background

Neovascular age-related macular degeneration (nAMD) and its subtype, polypoidal choroidal vasculopathy (PCV), are common choroidal vasculopathies. Although they share many common clinical manifestations and treatment strategies, a lack of comprehensive analysis of these conditions means that it is difficult for researchers to further explore the common pathomechanisms of nAMD and PCV. The aim of this study was to characterize aqueous humor (AH) proteome alterations and identify a novel biomarker related to both nAMD and PCV.

Methods

Liquid Chromatography with tandem mass spectrometry (LC-MS/MS) was adopted to analyze the AH proteomes of nAMD, PCV and controls. The target protein was validated using the enzyme-linked immunosorbent assay (ELISA) and subjected to receiver operating characteristic (ROC) curve analysis.

Results

A total of 737 different proteins were identified in all the groups, of which 544 were quantifiable. The bioinformatics analysis suggested that immune response activation is the essential event in both nAMD and PCV. Serum amyloid A (SAA) 4 is closely associated with a number of chronic inflammatory diseases, and it was enriched as the hub protein. ROC analysis showed that SAA4 could distinguish both nAMD and PCV from the controls.

Conclusion

This comprehensive study provides insights into, and furthers our understanding of, the pathological mechanism of nAMD and PCV. Additionally, the SAA4 level alteration may serve as a common biomarker of nAMD and PCV.

A diagnostic model for COVID-19 based on proteomics analysis

BACKGROUND AND OBJECTIVE

Early diagnosis of Coronavirus Disease 2019 (COVID-19) can help save patients' lives before the disease turns severe. This can be achieved through an effective and correct treatment protocol. In this paper, a prediction model is proposed to detect infected cases and determine the severity level of the disease.

METHODS

The proposed model is based on utilizing proteins and metabolites as features for each patient, which are then analyzed using feature selection methods such as Principal Component Analysis (PCA), Information Gain (IG), and analysis of Variance (ANOVA) to select the most significant features. The model employs three classifiers, namely K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Random Forest (RF), to predict and classify the severity level of the COVID-19 infection. The proposed model is evaluated using four performance measures: accuracy, sensitivity, specificity, and precision.

RESULTS

The experiment results show that the proposed model accuracy can reach 80% using RF classifier with PCA. The PCA selects 22 proteins and 10 metabolites. While ANOVA selects 9 proteins and 5 metabolites. The accuracy reaches 92% after applying RF classifier with the ANOVA. Finally, the accuracy reaches 93% using the RF classifier with only ten features. The selected features are 7 proteins and 3 metabolites. Moreover, it shows that the selected features have a relation to the immune system and respiratory systems.

CONCLUSION

The proposed model uses three classifiers and shows promising results by selecting the important features and maximizing the prediction accuracy.