Serum proteomics reveals the proteomic characteristics involved in the progressive feature of acute ischemic stroke (AIS)


 Background

To distinguish acute progressive ischemic stroke (APIS) from acute non-progressive ischemic stroke (ANPIS) is clinically critical for the precise treatment and prevention of acute ischemic stroke (AIS) deterioration. Serum proteins could reflect the unique pathophysiological processes of APIS. It is expected to find serum protein biomarkers to identify APIS at the early state, especially when typical symptoms have not been present in conventional clinical indices.
Methods

We recruited 178 subjects, including 133 AIS patients and 45 healthy controls. The discovery set, which included 10 age- and sex- matched cases from each of the APIS, ANPIS and control groups, was used for serum proteomic analysis with a mass spectrometry-based proteomics. The disrupted proteins of APIS were screened, of which, those common to ANPIS and specific to APIS were particularly concerned. Potential biomarkers of APIS were screened based on the theoretical knowledge to explain the mechanism and diagnostic value, and subsequently tested in the validated set (25 APIS patients, 88 ANPIS patients, and 35 controls).
Results

We found that 46 serum proteins were disturbed in the APIS patients; 23 differentially expressed proteins (DEPs) were common to these two groups, and 23 DEPs were associated with APIS alone. Enrichment analysis suggested that complement activation, cytolysis, proteolysis, positive regulation of fibrinolysis, blood coagulation were specifically associated with APIS; while inflammatory response, neutrophil chemotaxis, oxygen transport, and positive regulation of cell death process were common to AIPS and ANPIS. Serum amyloid A1 (SAA1) and S100 calcium binding protein A9 (S100-A9) jointly conferred a moderate value (AUC = 0.799) to diagnose APIS and to distinguish APIS from ANPIS (AUC = 0.699).
Conclusions

This study provides human serum proteomic evidence that inflammation, oxidative stress, and necrosis in APIS are common to ANPIS; the progressive feature of APIS may be mainly associated with complement activation, more serious inflammation, and lipid metabolic disorder; SAA1 and S100-A9 may be considered as potential biomarker panel of APIS.


Background
Worldwide, stroke is the second leading cause of death, responsible for approximately 10% total mortality; and it is also the major cause of disability, accounting for almost 5% of all disability-adjusted life-year [1,2]. Ischemic stroke is the most common subtype of stroke, and it often occurs urgently and appears as a sudden loss of brain function due to the reduction or even total blockage in blood ow to a speci c area of the brain [1,3]. Acute progressive ischemic stroke (APIS) is a common and serious clinical subtype of acute ischemic stroke (AIS), characterized as gradual progression and worsening in stroke symptoms [4]. Nowadays, substantial improvement has been achieved in AIS treatment since the application of intravenous thrombolysis and endovascular clot retrieval, which can remove the block and restore blood ow to the insulted brain tissues [5,6]. However, neurologists are still facing with huge challenges from high morbidity and mortality of AIS, partly owing to poor outcome in the treatment of APIS [7]. Nevertheless, the underlying mechanism of APIS remains unclear, which deserves further studies.
Proteomics is an effective strategy to comprehensively explore the pathophysiological mechanism of disease, and it has been widely applied in clinical and basic medicine researches [8]. In recent years, proteomics has been used as an effective way to identify proteins involved in the pathogenesis of stroke [9]. However, few studies have explored the mechanism of AIS at serum proteome level [10]. Serum is the predominant specimen for clinical studies as it can be easily accessed; it contains measurable potential protein biomarkers, uncovering pathological changes that are associated with the mechanism of disease. Therefore, serum proteomic analysis is helpful in clarifying the mechanism of APIS and nding its novel potential biomarkers.
Here, we carried out a serum proteome investigation on patients with acute progressive ischemic stroke (APIS) and acute non-progressive ischemic stroke (ANPIS), and age-and sex-matched controls (10 cases per group). Differentially expressed proteins (DEPs) among these three groups were screened; where the disturbed proteins of APIS common to ANPIS and those associated with APIS alone were particularly concerned. Based on the related DEPS, enrichments were analysed, attempting to explore the underlying pathophysiological processes of APIS. Potential biomarkers of APIS were identi ed based on the theoretical knowledge to explain the mechanism and diagnostic value; the latter was subsequently tested in the validated set.

Materials & Methods
The subjects This study was approved by the ethics committee of Shantou University Medical College, and written informed consent was obtained from all the subjects. Totally, 178 subjects were enrolled in this study, which included 45 controls and 133 patients with AIS. The AIS patients were further divided into acute progressive ischemic stroke (APIS) group and acute non-progressive ischemic stroke (ANPIS) group according to their clinical conditions. The enrolled criterion of the patients was the acute phase of ischemic stroke (recruited within seven days of the onset of ischemic stroke symptoms); those with progression of ≥ two points on the National Institute of Health Stroke Scale (NIHSS) in the initial ve days after stroke onset were considered as APIS [4]. The exclusion criteria included: (i) the presence of severe renal and liver diseases, malignancy, infection, trauma, autoimmune diseases, and hypothyroidism; and (ii) inability to comply with neuroimaging examination. The controls were relatively healthy subjects, which were enrolled with the same exclusion criteria as those of the patients. The NIHSS and mRS scores were calculated at admission and/or discharge to assess the severity of AIS and therapeutic outcome, as well as to assist us in distinguishing the APIS patients from ANPIS ones. The subjects' basic clinical information, including gender, age, complications and data of relative clinical examinations, was collected or evaluated during the hospitalization.
Thirty subjects, namely, ten sex-and age-matched cases from each group, were selected as the discovery set and used for serum proteomic analysis. Other subjects, which included 25 APIS patients, 88 ANPIS patients, and 35 controls were used as the validation set to assess the diagnostic value of the potential biomarkers. The detailed clinical characteristics of all subjects are described in Table 1 and Table 2.

Sample collections
The fasting blood samples of all subjects were obtained by venipuncture and collected in glass tubes containing coagulant. The serum was separated by centrifugation at 3000 rpm for ve min at four °C and stored at -80 °C until use.

Serum protein extraction and digestion
Serum samples in the discovery set were used for the proteomic analysis. Brie y, total protein concentration in the serum was measured by the bicinchoninic acid (BCA) method (Beyotime, China). Then, the High-Select TM HAS/Immunoglobulin Depletion Resin kit (Thermo Scienti c, USA) was used to remove human serum albumin (HSA) and immunoglobulin in the serum according to the protocol provided by the manufacturer. Thereafter, 50 μg of protein from each sample was treated with lter-aided sample preparation (FASP) methods, followed by reduction, alkylation with dithiothreitol (DTT, 10 mM) and iodoacetamide (IAA 50 mM), respectively. Then, the Sequencing Grade Modi ed Trypsin (Promega, USA), with a protease: protein ratio of 1:50 (w/w), was used to digest the protein at 37 °C overnight. The resultant peptides were subsequently desalted on Pierce™ C18 Tips (Thermo Scienti c, USA) and dried under vacuum centrifugation in succession and stored at -80 °C until detection.

LC-MS/MS analysis
An LTQ-Orbitrap mass spectrometry platform, coupled with EASY-nLC 1000 liquid chromatography (Thermo Scienti c™), was used for serum proteomic analysis. An analytical column (75 μm ID with 15 μm tip, 10.5 cm bed length, Reprosil-PUR C18 3 μm 120 Å, New Objective) was utilized for analytical separation with eluent A (0.1% formic acid in water) and eluent B (0.1% formic acid in acetonitrile). For each sample, we loaded 2 μg of desalted peptides onto the column. The ow rate was 300 nL min -1 . The gradient was 5-9% eluent B for 2 min, 9%-27% eluent B for 90 min, 27%-40% eluent B for 13 min, 40%-90% eluent B for 5 min, and 90%-100% eluent B for 10 min. First stage mass spectrum data (scan range 350-1800 m/z) were acquired from the Orbitrap at resolution of 60,000. The AGC target was set as 3e6 with a maximum injection time of 200 ms. Top 20 multiply charged precursor ions were selected for collision induced dissociation (CID) analysis in the linear ion trap with centroid data type. Activation times were 30 ms for CID fragmentation with normalized collision energy of 35.0 [11].
Label-free quanti cation Proteomic data were analyzed by the MaxQuant software (Version 1.6.6.0). Proteins were identi ed by searching MS and MS/MS data of peptides against a decoy version of the UniprotKB (June 2020). The search parameters were speci ed as follows: the mass tolerance was set to 20 ppm for precursor ions and 20 mmu for fragment ions. Trypsin was speci ed as the digesting enzyme, and two missed cleavages were allowed. A false positive detection rate (FDR) was calculated using a decoy database search. The results were ltered using the following settings: only highly con dent peptides, with a global FDR < 1% based on the target decoy approach, were included in the results. The most con dent centroid method was used with an integration window of 20 ppm. For protein quantitation, only unique peptides were used to quantify proteins.
Validation of potential biomarkers of APIS After screening, serum amyloid A1 (SAA1) and S100 Calcium-Binding Protein (S100-A9) were selected as potential biomarkers for APIS. Human Serum Amyloid A1 DuoSet ELISA kit (R&D: DY3019-05) and Human S100-A9 Heterodimer DuoSet ELISA kit (R&D: DY8226-05) were used to measure the absolute concentration of SAA1 and S100-A9 in the serum of the validation set, respectively, following the protocol provided by the manufacturer.

Data processing
Continuous variables are presented as mean ± SEM and categorical variables are presented as number or percentage. Differences between two groups were assessed by using the Student's t-test or χ2 test. One-way ANONA was performed for comparisons among three groups. Statistical analysis was carried out using SPSS version 17.0 (IBM Corp). A p-value of < 0.05 was considered statistically signi cant.
Binary logistic regression was performed to analyze the synergetic effect of several potential biomarkers. The receiver operating characteristic curve (ROC) analysis was performed by using the MedCalc statistical software for selecting the optimal cut-off point for each potential biomarker, where area under the curve (AUC), speci city, and sensitivity were used to evaluate their diagnostic value.

Clinical Features of the Subjects
A total of 178 subjects, including 133 AIS patients and 45 healthy controls, were enrolled in this study. The discovery set contained 10 ageand sex-matched cases from each of the APIS, ANPIS and control groups. Basic clinical information showed that the APIS patients had higher incidence of atrial brillation, history of arrhythmias, and intracranial artery stenosis than the ANPIS patients (30% vs. 0%, 70% vs. 20%, and 70% vs. 30%, respectively). In addition, compared with the ANPIS patients, the APIS patients also had higher NIHSS scores at admission and discharge (7.5 vs. 2.2, 11.9 vs. 1.6, respectively). Noticeably, even receiving conventional treatments, APIS patients' NHISS and mRS (modi ed Rankin Scale) scores have still remarkably increased (7.5→11.9), particularly during the worsening of stroke symptoms (NHISS=13.7). Consequently, the APIS patients required longer hospitalization than the ANPIS patients (19.2 vs. 9.3).
The APIS patients also exhibited elevated serum levels of (i) white blood cell, neutrophils ratio (%), erythrocyte sediment rate (ESR), C-reactive protein (CRP) and hs-CRP, indicative of more serious in ammation and stress condition during APIS; (ii) homocysteine, suggesting dysfunction or disturbance in coagulation, platelet aggregation, and blood lipid metabolism; and (iii) BUN and Cr, implying kidney dysfunction (Table 2). Therefore, clinically, the APIS patients had more critical conditions and worse prognosis than the ANPIS patients.
The validation set, which included 25 APIS patients, 88 ANPIS patients, and 35 controls, had similar clinical characters as their counterparts in the discovery set (Table1 and Table 2).

The basic results of proteomic analysis
Totally, 796 proteins were successfully identi ed in the serum samples of the subjects after removing the high-abundant proteins (Fig. 1). Of these, 161 proteins that had detailed quantitative information and annotation terms were used to screen DEPs (Table S1). As a result, 46 proteins were disturbed in AIPS, of these, 23 were common to ANPIS, and the other 23 were speci c to APIS (Table 3).
APIS proteomic pro le common to that of ANPIS To obtain comprehensive insight into the proteomic feature of APIS that common to ANPIS, we made GO and pathway enrichment analyses of the common DEPs (13 were upregulated and 10 were downregulated) between these two groups. The signi cant terms in the enrichments are given in Fig. 2 and Table S2.
The signi cant BPs mainly focused on platelet degranulation, innate immune response, acute-phase response, oxygen transport, hydrogen peroxide catabolic process, neutrophil chemotaxis, cellular oxidant detoxi cation, phosphatidylcholine metabolic process, leukocyte migration involved in in ammatory response, and positive regulation of cell death ( Fig. 2 and Table S2). Accordingly, the signi cant MFs mostly focused on oxygen transporter activity, oxygen binding, protease binding, aryl esterase activity, antioxidant activity, peroxidase activity, immunoglobulin receptor binding, and serine-type endopeptidase activity. The signi cant CCs were largely related to blood microparticle, exosome, platelet alpha granule lumen, extracellular matrix, and external side of plasma membrane. The network chart of enriched ontology cluster illustrates that hydrogen peroxide catabolic process, acute-phase response and humoral immune response are the most signi cant pathways (Fig. 3).
The proteomic feature speci cally associated with APIS The current study has found that 23 DEPs were speci c to APIS; of these, 13 were upregulated and 10 were downregulated ( Table 3). The crucial mechanistic factors leading to progressive quality of APIS were analyzed using the aforementioned strategy.
The signi cant terms included complement activation, proteolysis, positive regulation of brinolysis, negative regulation of very-low-density lipoprotein particle clearance, cholesterol transport, receptor-mediated endocytosis, plasminogen activation, phospholipid e ux, intrinsic pathway blood coagulation, serine-type endopeptidase activity, lipase inhibitor activity, complement binding, insulin-like growth factor binding, protein complex binding, and bronectin binding, extracellular region, extracellular space, extracellular exosome, blood microparticle, membrane attack complex, chylomicron, and lipoprotein particle ( Fig. 2 and Table S3). In addition, the network chart of enriched ontology clusters indicates complement and coagulation cascades, neutrophil degranulation, and blood coagulation response are the most signi cant functions involved in the worsening progress of AIS symptoms (Fig. 3).
Diagnostic potential of SAA1 and S100-A9 In this study, potential serum protein biomarkers were selected by the following strategies. Firstly, DEPs with high fold change between the APIS patients and controls were chosen. Then, those proteins that could reasonably explain the pathophysiological mechanism of APIS were given a priority consideration. As a result, SAA1 and S100-A9, both met the two requirements, were selected as potential biomarkers, and then their absolute concentrations were measured to analyze the diagnostic value. The mean concentrations of SAA1 in the APIS, ANPIS, and control groups were 373.59 ng/mL, 284.58 ng/mL, and 199.45 ng/mL, respectively, which were signi cantly different among these three groups (Fig. 4). The SAA1 contents in the AIS patients correlated with several clinical indicators ( Table 4). The concentrations of S100-A9 in these three groups were 734.44 pg/mL, 658.74 pg/mL, and 607.05 pg/mL, respectively. SAA1 demonstrated good potential to identify APIS (AUC = 0.729, p=0.002, cut-off value > 386.72 ng/mL) and to distinguish APIS from ANPIS (AUC =0.635, p = 0.036, cut-off value > 233.25 ng/mL). Although the contents of S100-A9 were not signi cantly different among these three groups; however, its content in the APIS group was the highest among them, and it illustrated moderate potential to diagnose APIS (AUC = 0.675, p = 0.013). Lastly, to determine whether these biomarkers had synergetic effect to diagnose APIS or AIS, we performed a binary logistic regression analysis and found that they jointly yielded AUC of 0.799, sensitivity of 63.6%, and speci city of 92.6% to diagnose APIS; moreover, they jointly yielded AUC of 0.699, sensitivity of 72.7%, and speci city of 69.0% to distinguish APIS from ANPIS (Fig. 4).

Discussion
The prognosis of AIS is still not so desirable, which could largely be attributed to critical conditions and worse consequence in the APIS patients [13]. A better understanding of the pathogenesis is a prerequisite for improving clinical outcome in APIS therapy. Proteomics enables comprehensive explanation of the pathophysiological mechanism of disease. Serum is more feasible in clinical practices; their combination, namely, serum proteomics introduces a promising strategy for mechanistic understanding of molecular events in APIS. However, serum proteome analysis faces the interference from high-abundant proteins [14]. Removing of high-abundant proteins is likely to destroy other small molecular proteins. Our study exempli es a good try using serum proteomics to explore the mechanism of APIS. After removing highabundant proteins, we have identi ed 796 proteins. Of these, 59 proteins were differentially expressed between the AIS (APIS and ANPIS) patients and controls (Table 3), which concealed a rich information associated with the pathophysiological processes of AIS, especially with those of APIS.

Potential Biomarkers Of Apis
Biomarkers of disease or pathophysiological process should meet two requirements. First, they are able to explain the mechanism of disease reasonably. Second, they have a good diagnostic potential to identify the onset of disease and (or) to evaluate the prognosis of disease. From the perspective of mechanism, we found that APIS has more serious in ammation than ANPIS. Consistently, SAA1 and S100-A9, both represented in ammation and stress [24], and had higher fold changes in APIS, were selected as potential biomarkers of APIS. SAA1 is the major component of amyloid. Inducible expression of SAA1 is a hallmark of the acute-phase response of vertebrates to environmental challenges such as tissue injury and surgery [25]. Following in ammatory response, the liver secretes a large amount of SAA1, leading to a marked increase in SAA1 level [24]. Accumulating evidence has revealed that SAA1 plays an important role in the regulation of in ammation, lipid metabolism, and propagation of the primordial acute phase response [26]. The current data showed that the APIS patients had higher SAA1 content than the ANPIS and controls, and thereby indicating more serious in ammation and ischemia-related injury in the APIS setting. Consistently, SAA1 content demonstrated a moderate potential to diagnose APIS and to distinguish APIS from ANPIS (Fig. 3). S100-A9 is a calcium-and zinc-binding protein, which is commonly combined with S100A8 as a complex. The complex has been found to be highly expressed in the neutrophil, monocyte, extracellular milieu under in ammatory conditions, and to be involved in multiple I/R-induced stress responses and suppression of mitochondrial function [27]. Here, we found that S100-A9 concentration in the APIS was the highest among the three groups. In support, the clinical data demonstrated that the APIS patients had higher levels of in ammatory indicators (ESR, WBC and neutrophils percentage) than the ANPIS patients, suggestive of much more serious in ammation in the setting of APIS. In terms of diagnostic value, S100-A9 showed moderate potential to diagnose APIS (Fig. 3).
In addition, we found that the combination of SAA1 and S100-A9 illustrated a higher diagnostic value than either of them (Fig. 3). Although SAA1 and S100-A9 had been found to play critical roles in other clinical conditions (e. g. myocardial infarction, infection, etc.) [27]; however, the current study provides the possibility to increase the diagnostic speci city when used these two proteins simultaneously.

Conclusions
In this study, we have described a common landscape of serum proteome between APIS and ANPIS, which are mainly associated with in ammation, oxidative stress, and necrosis. More importantly, we have further identi ed a serum proteomic feature may be involved in the progressive feature of APIS, which are largely relevant to complement activation, more serious in ammation, and lipid metabolism disturbance. SAA1 and S100-A9 may serve as potential serum biomarker panel to diagnose APIS and to distinguish APIS from ANPIS. These ndings confer potential novel therapeutic targets and diagnostic indicators for APIS.

Declarations
Ethics approval and consent to participate: The study was carried out in conformity with the ethical standards established in the 1964 Declaration of Helsinki and its later amendments and was approved by the Ethics Committee of Shantou University Medical College (No. SUMC-2021-76-K). Informed consent was obtained from all study subjects.
Consent for publication: All authors consent to the publication of this manuscript.
Availability of data and materials: The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.
Competing interests: The authors declare that they have no con ict of interest. Tables Table 1. Basic information of the subjects APIS, acute progressive ischemic stroke group (n=10); ANPIS, acute non-progressive ischemic stroke group (n=10); controls, the control group (n=10); *comparisons within the same cohort, between the APIS group and ANPIS group except sex and age. NHISS-Ad, NHISS score at admission; NIHSS-Dis, NIHSS score at discharge; NIHSS-progressive, NIHSS score when stroke symptoms deteriorate or progressive; mRS, modi ed Rankin Scale; d, day; Y/N, Yes/no; h, hour; NA, not available or not applicable. N, not signi cantly different. Table 2. Blood biochemical indices of the AIS patients AAIS, acute progressive ischemic stroke; ANAIS, acute non-progressive ischemic stroke; Com*, t-test. #, the subjects' information was the same as table1.

Indices
The discovery set #  SAA1, serum amyloid A1; S100-A9; S100 Calcium-Binding Protein; CRP, C-reactive proteins; ESR, erythrocyte sediment rats; Fib, brinogen; PLT, platelet; SP, patients' systolic pressure at admission. R, Spearman's correlation coe cient; p, signi cance in spearman correlation. Figure 1 serum samples of the APIS, ANPIS, and Control group, respectively; of these, 289 proteins were common to all groups. APIS, acute progressive ischemic stroke; ANPIS, acute non-progressive ischemic stroke, CN, controls.  Network charts of enriched ontology clusters. The charts were produced by the Metascape database. The relationships between each enriched GO terms have been selected and rendered as a network plot, where terms with a similarity > 0.3 were connected by edges. The terms with the best p-values from each of the 20 clusters were selected, with the constraint that there are no more than 15 terms per cluster and no more than 250 terms in total. The network is visualized using Cytoscape5, where each node represents an enriched term and is colored rst by its cluster ID. EMC, extracellular matrix.

Figure 4
Absolute Concentration of SAA1 and S100-A9 in the subjects of the validation set and their diagnostic potential. The receiver operating characteristic curves (ROCs) were produced by the MedCalc statistical software. APIS/C represents the value of SAA1 or S100-A9 alone, or their combination to diagnose APIS. APIS/ANPIS represents the diagnostic value to distinguish APIS from ANPIS.

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