Deciphering tissue-based proteome signatures revealed novel subtyping and prognostic markers for thymic epithelial tumors

Screening of novel biomarkers for acute kidney transplant rejection using DIA-MS based proteomics

BACKGROUND

Kidney transplantation is the preferred treatment for patients with end-stage renal disease. However, acute rejection poses a threat to the graft long-term survival. The aim of this study was to identify novel biomarkers to detect acute kidney transplant rejection.

METHODS

The serum proteomic profiling of kidney transplant patients with T cell-mediated acute rejection (TCMR) and stable allograft function (STA) was analyzed using data-independent acquisition mass spectrometry (DIA-MS). The differentially expressed proteins (DEPs) of interest were further verified by enzyme-linked immunosorbent assay (ELISA).

RESULTS

A total of 131 DEPs were identified between STA and TCMR patients, 114 DEPs were identified between mild and severe TCMR patients. The verification results showed that remarkable higher concentrations of serum amyloid A protein 1 (SAA1) and insulin like growth factor binding protein 2 (IGFBP2), and lower fetuin-A (AHSG) concentration were found in TCMR patients when compared with STA patients. We also found higher SAA1 concentration in severe TCMR group when compared with mild TCMR group. The receiver operating characteristics (ROC) analysis further confirmed that combination of SAA1, AHSG, and IGFBP2 had excellent performance in the acute rejection diagnosis.

CONCLUSIONS

Our data demonstrated that serum SAA1, AHSG, and IGFBP2 could be effective biomarkers for diagnosing acute rejection after kidney transplantation. DIA-MS has great potential in biomarker screening of kidney transplantation.

Upregulated Cytoskeletal Proteins Promote Pathological Angiogenesis in Moyamoya Disease

BACKGROUND

Moyamoya disease (MMD) is a rare progressive vascular disease that leads to intracranial internal carotid artery stenosis and eventual occlusion. However, its pathogenesis remains unclear. The purpose of this study is to explore the role of abnormally expressed proteins in the pathogenesis of MMD.

METHODS

Data-independent acquisition mass spectrometry identifies the differentially expressed proteins in MMD serum by detecting the serum from 60 patients with MMD and 20 health controls. The differentially expressed proteins were validated using enzyme linked immunosorbent assays. Immunofluorescence for superficial temporal artery and middle cerebral artery specimens was used to explore the morphological changes of vascular wall in MMD. In vitro experiments were used to explore the changes and mechanisms of differentially expressed proteins on endothelial cells.

RESULTS

Proteomic analysis showed that a total of 14 726 peptides and 1555 proteins were quantified by mass spectrometry data. FLNA (filamin A) and ZYX (zyxin) proteins were significantly higher in MMD serum compared with those in health controls (Log2FC >2.9 and >2.8, respectively). Immunofluorescence revealed an intimal hyperplasia in superficial temporal artery and middle cerebral artery specimens of MMD. FLNA and ZYX proteins increased the proportion of endothelial cells in S phase and promoted their proliferation, angiogenesis, and cytoskeleton enlargement. Mechanistic studies revealed that AKT (serine/threonine kinase)/GSK-3β (glycogen synthase kinase 3β)/β-catenin signaling pathway plays a major role in these FLNA- and ZYX-induced changes in endothelial cells.

CONCLUSIONS

This study provides proteomic data on a large sample size of MMD. The differential expression of FLNA and ZYX in patient with MMD and following in vitro experiments suggest that these upregulated proteins are related to the pathology of cerebrovascular intimal hyperplasia in MMD and are involved in MMD pathogenesis, with diagnostic and therapeutic ramifications.

HNRNPA2B1 as a potential therapeutic target for thymic epithelial tumor recurrence: An integrative network analysis

Thymic epithelial tumors (TETs) are rare malignant tumors, and the molecular mechanisms of both primary and recurrent TETs are poorly understood. Here we established comprehensive proteomic signatures of 15 tumors (5 recurrent and 10 non-recurrent) and 15 pair wised tumor adjacent normal tissues. We then proposed an integrative network approach for studying the proteomics data by constructing protein-protein interaction networks based on differentially expressed proteins and a machine learning-based score, followed by network modular analysis, functional enrichment annotation and shortest path inference analysis. Network modular analysis revealed that primary and recurrent TETs shared certain common molecular mechanisms, including a spliceosome module consisting of RNA splicing and RNA processing, but the recurrent TET was specifically related to the ribosome pathway. Applying the shortest path inference to the collected seed gene module identified that the ribonucleoprotein hnRNPA2B1 probably serves as a potential target for recurrent TET therapy. The drug repositioning combined molecular dynamics simulations suggested that the compound ergotamine could potentially act as a repurposing drug to treat recurrent TETs by targeting hnRNPA2B1. Our study demonstrates the value of integrative network analysis to understand proteotype robustness and its relationships with genotype, and provides hits for further research on cancer therapeutics.

Urine inter‐alpha‐trypsin inhibitor family‐related proteins may serve as biomarkers for disease activity of lupus

Background

Systemic lupus erythematosus (SLE) is a chronic inflammatory disease involving multiple tissues. Inter‐Alpha‐Trypsin Inhibitor (ITI) family proteins have a role in maintaining tissue homeostasis, but their possible clinical significance in the SLE patients has not been reported. The aim of this study was to analyze and verify the expression of ITI‐related proteins in the urine of SLE patients, further explore the features of these proteins in disease activity.

Methods

Based on label‐free proteomics technology and bioinformatics technology, we analyzed the expression of ITI family‐related proteins in the urine of lupus. Subsequently, Western‐blot and targeted proteomics were used to qualitatively and quantitatively verify the expression of these proteins, respectively.

Results

A total of seven ITI family‐related proteins were screened and identified; and six of these proteins were differentially expressed in the urine of SLE patients. Further quantitative analysis showed that the expressions of ITIH2, ECM1, and ITIH5 in urine between active SLE group and stable SLE group were consistent with the preliminary screening results. The expression of ITIH2 and ECM1 in the renal damage group were also consistent with the screening results. Moreover, ITIH2 and ECM1 have a good correlation with disease activity and have a certain correlation with renal damage.

Conclusions

In this exploratory study, we evaluated the expression of ITI family‐related proteins in the urine of SLE and found that urine ITIH2 and ECM1 were closely related to SLE activity, especially kidney damage, providing an experimental basis for further exploration of the potential roles in monitoring lupus and lupus nephritis activity.

Serum Proteomic Analysis Identifies SAA1, FGA, SAP, and CETP as New Biomarkers for Eosinophilic Granulomatosis With Polyangiitis

Background

Eosinophilic granulomatosis with polyangiitis (EGPA) is characterized by asthma-like attacks in its early stage, which is easily misdiagnosed as severe asthma. Therefore, new biomarkers for the early diagnosis of EGPA are needed, especially for differentiating the diagnosis of asthma.

Objectives

To identify serum biomarkers that can be used for early diagnosis of EGPA and to distinguish EGPA from severe asthma.

Method

Data-independent acquisition (DIA) analysis was performed to identify 45 healthy controls (HC), severe asthma (S-A), and EGPA patients in a cohort to screen biomarkers for early diagnosis of EGPA and to differentiate asthma diagnosis. Subsequently, parallel reaction monitoring (PRM) analysis was applied to a validation cohort of 71 HC, S-A, and EGPA patients.

Result

Four candidate biomarkers were identified from DIA and PRM analysis-i.e., serum amyloid A1 (SAA1), fibrinogen-α (FGA), and serum amyloid P component (SAP)-and were upregulated in the EGPA group, while cholesteryl ester transfer protein (CETP) was downregulated in the EGPA group compared with the S-A group. Receiver operating characteristics analysis shows that, as biomarkers for early diagnosis of EGPA, the combination of SAA1, FGA, and SAP has an area under the curve (AUC) of 0.947, a sensitivity of 82.35%, and a specificity of 100%. The combination of SAA1, FGA, SAP, and CETP as biomarkers for differential diagnosis of asthma had an AUC of 0.921, a sensitivity of 78.13%, and a specificity of 100%, which were all larger than single markers. Moreover, SAA1, FGA, and SAP were positively and CETP was negatively correlated with eosinophil count.

Conclusion

DIA-PRM combined analysis screened and validated four previously unexplored but potentially useful biomarkers for early diagnosis of EGPA and differential diagnosis of asthma.

MoSBi: Automated signature mining for molecular stratification and subtyping

Molecular patient stratification and disease subtyping are ongoing and high-impact problems that rely on the identification of characteristic molecular signatures. Current computational methods show high sensitivity to custom parameterization, which leads to inconsistent performance on different molecular data. Our new method, MoSBi (molecular signature identification using biclustering), 1) enables so far unmatched high performance for stratification and subtyping across datasets of various different biomolecules, 2) provides a scalable solution for visualizing the results and their correspondence to clinical factors, and 3) has immediate practical relevance through its automatic workflow where individual selection, parameterization, screening, and visualization of biclustering algorithms is not required. MoSBi is a major step forward with a high impact for clinical and wet-lab researchers.

The improving access to increasing amounts of biomedical data provides completely new chances for advanced patient stratification and disease subtyping strategies. This requires computational tools that produce uniformly robust results across highly heterogeneous molecular data. Unsupervised machine learning methodologies are able to discover de novo patterns in such data. Biclustering is especially suited by simultaneously identifying sample groups and corresponding feature sets across heterogeneous omics data. The performance of available biclustering algorithms heavily depends on individual parameterization and varies with their application. Here, we developed MoSBi (molecular signature identification using biclustering), an automated multialgorithm ensemble approach that integrates results utilizing an error model-supported similarity network. We systematically evaluated the performance of 11 available and established biclustering algorithms together with MoSBi. For this, we used transcriptomics, proteomics, and metabolomics data, as well as synthetic datasets covering various data properties. Profiting from multialgorithm integration, MoSBi identified robust group and disease-specific signatures across all scenarios, overcoming single algorithm specificities. Furthermore, we developed a scalable network-based visualization of bicluster communities that supports biological hypothesis generation. MoSBi is available as an R package and web service to make automated biclustering analysis accessible for application in molecular sample stratification.

Quantitative proteomics analysis of human vitreous in rhegmatogenous retinal detachment associated with choroidal detachment by data-independent acquisition mass spectrometry

The prognosis of rhegmatogenous retinal detachment (RRD) with choroidal detachment (RRDCD) is often poor and complicated. This study focused on the identification of the characteristic proteins and signal pathways associated with the etiology of RRDCD and to provide guidance for diagnosis and treatment of RRDCD. In this study, vitreous humor samples were obtained from 16 RRDCD patients, 14 with RRD, 12 with idiopathic epiretinal macular membrane (IEMM), and 5 healthy controls from donated corpse eyes. Data-independent acquisition mass spectrometry and bioinformatics analysis were employed to identify differentially expressed proteins (DEPs). In the vitreous humor, 14,842 peptides were identified. Patients with RRDCD had 249 DEPs (93 upregulated and 156 downregulated), with 89 in patients with RRD and 61 in patients with IEMM. Enrichment analysis of the GO and Kyoto Encyclopedia of Genes and Genomes DEP databases indicated functional clusters related to inflammation and immunity, protein degradation and absorption, cell adhesion molecules (CAMs), the hedgehog signaling pathway, and lipid metabolism. Weighted gene co-expression network analysis showed that DEPs with positive co-expression of RRDCD participated in immune-related pathways led by the complement and coagulation cascade, whereas DEPs with negative co-expression of RRDCD participated in protein degradation and absorption, CAMs, and the hedgehog signaling pathway. In summary, our study provides important clues and the theoretical basis for exploring the pathogenesis, progression, and prognosis of ocular fundus disease.

A Novel Diagnostic Biomarker, PZP, for Detecting Colorectal Cancer in Type 2 Diabetes Mellitus Patients Identified by Serum-Based Mass Spectrometry

Background: Growing evidence has confirmed that populations with type 2 diabetes mellitus (T2DM) have an increasing risk of developing colorectal cancer (CRC). Thus, convenient and effective screening strategies for CRC should be developed for the T2DM population to increase the detection rate of CRC.

Methods: Twenty serum samples extracted from five healthy participants, five T2DM patients, five CRC patients and five T2DM patients with CRC (T2DM + CRC) were submitted to data-independent acquisition mass spectrometry (DIA-MS) analysis to discover unique differentially altered proteins (DAPs) for CRC in patients with T2DM. Then, the diagnostic value of pregnancy zone protein (PZP) was validated by ELISA analysis in the validated cohort.

Results: Based on DIA-MS analysis, we found eight unique proteins specific to T2DM patients with CRC. Among these proteins, four proteins showed different expression between the T2DM + CRC and T2DM groups, and PZP exhibited the largest difference. Next, the diagnostic value of serum PZP was validated by ELISA analysis with an AUC of 0.713. Moreover, the combination of PZP, CA199 and CEA exhibited encouraging diagnostic value, and the AUC reached 0.916.

Conclusion: Overall, our current research implied that PZP could be regarded as a newfound serum biomarker for CRC medical diagnosis in T2DM patients.