Urine proteome analysis as a discovery tool in patients with deep vein thrombosis and pulmonary embolism

A clinical proteomics study of exhaled breath condensate and biomarkers for pulmonary embolism

Pulmonary embolism (PE) can be a diagnostic challenge. Current diagnostic markers for PE are unspecific and new diagnostic tools are needed. The air we exhale is a possible new source for biomarkers which can be tapped into by analysing the exhaled breath condensate (EBC). We analysed the EBC from patients with PE and controls to investigate if the EBC is a useful source for new diagnostic biomarkers of PE. We collected and analysed EBC samples from patients with suspected PE and controls matched on age and sex. Patients in whom PE was ruled out after diagnostic work-up were included in the control group to increase the sensitivity and generalizability of the identified markers. EBC samples were collected using an RTube™. The protein composition of the EBCs were analysed using data dependent label-free quantitative nano liquid chromatography-tandem mass spectrometry. EBC samples from 28 patients with confirmed PE, and 49 controls were analysed. A total of 928 EBC proteins were identified in the 77 EBC samples. As expected, a low protein concentration was determined which resulted in many proteins with unmeasurable levels in several samples. The levels of HSPA5, PEBP1 and SFTPA2 were higher and levels of POF1B, EPPK1, PSMA4, ALDOA, and CFL1 were lower in PE compared with controls. In conclusion, the human EBC contained a variety of endogenous proteins and may be a source for new diagnostic markers of PE and other diseases.

Proteomic research on new urinary biomarkers of renal disease in canine leishmaniosis: Survival and monitoring response to treatment

The objective of our study was to search for survival biomarkers (SB) and treatment response monitoring biomarkers (TRMB) in the urinary proteome of dogs with renal disease secondary to canine leishmaniosis (CanL), using UHPLC-MS/MS. The proteomic data are available via ProteomeXchange with identifier PXD042578. Initially, a group of 12 dogs was evaluated and divided into survivors (SG; n = 6) and nonsurvivors (NSG; n = 6). A total of 972 proteins were obtained from the evaluated samples. Then, bioinformatic analysis reduced them to 6 proteins like potential SB increased in the NSG, specifically, Haemoglobin subunit Alpha 1, Complement Factor I, Complement C5, Fibrinogen beta chain (fragment), Peptidase S1 domain-containing protein, and Fibrinogen gamma chain. Afterwards, SG was used to search for TRMB, studying their urine at 0, 30, and 90 days, and 9 proteins that decreased after treatment were obtained: Apolipoprotein E, Cathepsin B, Cystatin B, Cystatin-C-like, Lysozyme, Monocyte differentiation CD14, Pancreatitis-associated precursor protein, Profilin, and Protein FAM3C. Finally, enrichment analysis provided information about the biological mechanisms in which these proteins are involved. In conclusion, this study provides 15 new candidate urinary biomarkers and an improved understanding of the pathogenesis of kidney disease in CanL.

Screening of potential hub genes in pulmonary thromboembolism

Pulmonary thromboembolism (PTE) is a fatal clinical syndrome that usually occurs in elderly individuals. The present study aimed to identify functional and key genes involved in the early diagnosis of PTE using bioinformatics analysis. The GSE84738 dataset was retrieved from the Gene Expression Omnibus database. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were subsequently performed. In addition, Cytoscape software v.3.7.2 was used to construct a protein-protein interaction (PPI) network. Serum samples from patients with PTE and healthy individuals were collected and the expression levels of Toll-like receptor (TLR)4, TLR2, IL-1β, JUN, prostaglandin-endoperoxide synthase 2 (PTGS2), osteopontin (SPP1) and endothelin-1 (ET-1) were analyzed by reverse transcription-quantitative PCR. A total of 160 upregulated and 159 downregulated differentially expressed genes were identified between patients with PTE and healthy individuals. TNF, IL-1β, JUN, TLR4, PTGS2, vascular cell adhesion molecule 1, SPP1, ryanodine receptor 2, TLR2 and ET-1 were considered as hub genes, which are defined as the genes with the highest degree of interaction in the enrichment and PPI network analyses. The top 10 common genes with the highest degree in the PPI network and the top 10 genes in modules 1 and 2 were TLR4, TLR2, IL-1β, JUN, PTGS2, SPP1 and ET-1. Taken together, the present study suggested that TLR4, TLR2, IL-1β and SPP1 were enriched in patients with PTE, thus providing novel potential biomarkers for the diagnosis of PTE.

Putative Biomarkers for Acute Pulmonary Embolism in Exhaled Breath Condensate

Current diagnostic markers for pulmonary embolism (PE) are unspecific. We investigated the proteome of the exhaled breath condensate (EBC) in a porcine model of acute PE in order to identify putative diagnostic markers for PE. EBC was collected at baseline and after the induction of autologous intermediate-risk PE in 14 pigs, plus four negative control pigs. The protein profiles of the EBC were analyzed using label-free quantitative nano liquid chromatography-tandem mass spectrometry. A total of 897 proteins were identified in the EBCs from the pigs. Alterations were found in the levels of 145 different proteins after PE compared with the baseline and negative controls: albumin was among the most upregulated proteins, with 14-fold higher levels 2.5 h after PE (p-value: 0.02). The levels of 49 other proteins were between 1.3- and 17.1-fold higher after PE. The levels of 95 proteins were lower after PE. Neutrophil gelatinase-associated lipocalin (fold change 0.3, p-value < 0.01) was among the most reduced proteins 2.5 h after PE. A prediction model based on penalized regression identified five proteins including albumin and neutrophil gelatinase-associated lipocalin. The model was capable of discriminating baseline samples from EBC samples collected 2.5 h after PE correctly in 22 out of 27 samples. In conclusion, the EBC from pigs with acute PE contained several putative diagnostic markers of PE.

Current applications of artificial intelligence combined with urine detection in disease diagnosis and treatment

In recent years, the advantages of artificial intelligence (AI) in data processing and model analysis have emerged in the medical field, enabled by computer technology developments and the integration of multiple disciplines. The application of AI in the medical field has gradually deepened and broadened. Among them, the development of clinical medicine intelligent decision-making is the fastest. The advantage of clinical medicine intelligent decision-making is to make the diagnosis faster and more accurate on the basis of certain information. Urine detection technologies, such as urine proteomics, urine metabolomics, and urine RNomics, have developed rapidly with the advancements in omics and medical tests. Advances in urine testing have made it possible to obtain a wealth of information from easily accessible urine. However, it has always been a problem to extract effective information from this information and use it. AI technology provides the possibility to process and use the information in urine. AI, combined with urine detection, not only provides new possibilities for precise and individual diagnosis and disease treatment, but also helps promote non-invasive diagnosis and treatment. This article reviews the research and applications of AI combined with urine detection for disease diagnosis and treatment and discusses its existing problems and future development.

Predicting the Risk of Recurrent Venous Thromboembolism: Current Challenges and Future Opportunities

Acute venous thromboembolism (VTE) is a commonly diagnosed condition and requires treatment with anticoagulation to reduce the risk of embolisation as well as recurrent venous thrombotic events. In many cases, cessation of anticoagulation is associated with an unacceptably high risk of recurrent VTE, precipitating the use of indefinite anticoagulation. In contrast, however, continuing anticoagulation is associated with increased major bleeding events. As a consequence, it is essential to accurately predict the subgroup of patients who have the highest probability of experiencing recurrent VTE, so that treatment can be appropriately tailored to each individual. To this end, the development of clinical prediction models has aided in calculating the risk of recurrent thrombotic events; however, there are several limitations with regards to routine use for all patients with acute VTE. More recently, focus has shifted towards the utility of novel biomarkers in the understanding of disease pathogenesis as well as their application in predicting recurrent VTE. Below, we review the current strategies used to predict the development of recurrent VTE, with emphasis on the application of several promising novel biomarkers in this field.

Elucidation of a Human Urine Metabolite as a Seryl-Leucine Glycopeptide and as a Biomarker of Effective Anti-Tuberculosis Therapy

The evaluation of new tuberculosis (TB) therapies is limited by the paucity of biomarkers to monitor treatment response. Previous work detected an uncharacterized urine metabolite with a molecular mass of 874.3547 Da that showed promise as a biomarker for successful TB treatment. Using mass spectrometry combined with enzymatic digestions, the metabolite was structurally characterized as a seryl-leucine core 1 O-glycosylated peptide (SLC1G) of human origin. Examination of SLC1G in urine revealed a significant abundance increase in individuals with active TB versus their household contacts and healthy controls. Moreover, differential decreases in SLC1G levels were observed by week one in TB patients during successful treatment versus those that failed treatment. The SLC1G levels were also associated with clinical parameters used to measure bacterial burden (GeneXpert) and inflammation (positron emission tomography-computed tomography (PET-CT)). These results demonstrate the importance of metabolite identification and provide strong evidence for applying SLC1G as a biomarker of TB treatment response.

iTRAQ-based proteomic analysis of human umbilical vein endothelial cells with platelet endothelial aggregation receptor-1 knockdown

The disorders of hemostasis and coagulation were believed to be the main contributors to the pathogenesis of pulmonary thromboembolism (PTE), and platelets are the basic factors regulating hemostasis and coagulation and play important roles in the process of thrombosis. This study investigated the proteome of human umbilical vein endothelial cells (HUVECs) with platelet endothelial aggregation receptor-1 (PEAR1) knockdown using the isobaric tags for relative and absolute quantitation (iTRAQ) method and analyzed the role of differential abundance proteins (DAPs) in the regulation of platelets aggregation. Our results showed that the conditioned media-culturing HUVECs with PEAR1 knockdown partially suppressed the adenosine diphosphate (ADP)-induced platelet aggregation. The proteomics analysis was performed by using the iTRAQ technique, and a total of 215 DAPs (124 protein was upregulated and 91 protein were downregulated) were identified. The Gene Ontology (GO) enrichment analysis showed that proteins related to platelet α granule, adenosine triphosphate metabolic process, and endocytosis were significantly enriched. Further, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis also identified the significant enrichment of endocytosis-related pathways. The real-time polymerase chain reaction assay confirmed that the expression of P2Y₁₂ , mitochondrial carrier 2, NADH dehydrogenase (ubiquinone) iron-sulfur protein 3, and ubiquinol-cytochrome c reductase hinge protein are significantly downregulated in the HUVECs with PEAR1 knockdown. In conclusion, our in vitro results implicated that DAPs induced by PEAR1 knockdown might contribute to the platelet aggregation. Proteomic studies by employing GO enrichment and KEGG pathway analysis suggested that the potential effects of DAPs on platelet aggregation may be linked to the balance of ADP synthesis or degradation in mitochondria.

New biomarkers of Kawasaki disease identified by urine proteomic analysis

Kawasaki disease (KD) is an acute systemic vasculitis that mainly afflicts infants and young children. The symptoms of KD are similar to those of various febrile diseases. Here, we attempted to develop accurate diagnostic biomarkers of KD by performing urine proteomic analysis of samples from healthy controls, patients with KD, and patients with another febrile disease, pneumonia (two patients). We identified differentially expressed proteins (DEPs) in KD as compared to normal controls. We also constructed functional annotation and protein-protein interaction (PPI) networks of DEPs in KD and pneumonia. DEPs common to both KD and pneumonia were identified, as well as DEPs specific to KD. Compared to normal control, 43 and 62 DEPs were identified in KD and pneumonia, respectively. Serine hydroxymethyltransferase 1 is a hub protein of the KD-specific PPI network. Thirteen DEPs common to both KD and pneumonia and 30 DEPs specific to KD were identified. Of these, the expression of eight DEPs could cluster normal and pneumonia samples into one group and cluster KD samples into another group based on hierarchical clustering. Our study identified several DEPs that may play a role in KD and that may serve as diagnostic biomarkers to distinguish patients with KD from both normal control and other febrile diseases.

Identification of novel diagnostic biomarkers for deep venous thrombosis

The combination of a negative D-dimer and a Wells score can rule out, but not confirm, a diagnosis of deep venous thrombosis (DVT). We aimed to identify new diagnostic biomarkers for DVT and to investigate their relationship with hypercoagulability markers [D-dimer and activated protein C-protein C inhibitor (APC-PCI) complex]. We screened 92 cardiovascular-specific proteins in plasma samples from 45 confirmed DVT patients and 45 age- and sex-matched non-DVT patients selected from a prospective multicentre diagnostic management study (SCORE) by Proseek Multiplex CVDIII^96×96 . Plasma levels of 30 proteins were significantly different between DVT and non-DVT patients. After Bonferroni correction, plasma levels of seven proteins: P-selectin, transferrin receptor protein 1, von Willebrand factor, tissue factor pathway inhibitor, osteopontin (OPN), bleomycin hydrolase and ST2 protein remained significantly different. The area under curve (AUC) for these proteins ranged from 0·70 to 0·84. Furthermore, all seven identified proteins were significantly associated with markers of hypercoagulability. A combination of OPN and APC-PCI had the best ability to discriminate DVT from non-DVT patients (AUC = 0·94; sensitivity = 89% and specificity = s84%). In conclusion, we identified multiple proteins associated with markers of hypercoagulability and with a potential to become novel diagnostic biomarkers for DVT.