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Neutrophils are the innate immune system’s first line of defense. Neutrophils play a critical role in protecting the host against infectious pathogens, resolving sterile injuries, and mediating inflammatory responses. The granules of neutrophils and their constituent proteins are central to these functions. Although neutrophils may exert a protective role upon acute inflammatory conditions or insults, continued activity of neutrophils in chronic inflammatory diseases can contribute to tissue damage. Neutrophil granule proteins are involved in a number of chronic inflammatory conditions and diseases. However, the functions of these proteins in neuroinflammation and chronic neuroinflammatory diseases, including Alzheimer’s disease (AD), remain to be elucidated. In this review, we discuss recent findings from our lab and others that suggest possible functions for neutrophils and the neutrophil granule proteins, CAP37, neutrophil elastase, and cathepsin G, in neuroinflammation, with an emphasis on AD. These findings reveal that neutrophil granule proteins may exert both neuroprotective and neurotoxic effects. Further research should determine whether neutrophil granule proteins are valid targets for therapeutic interventions in chronic neuroinflammatory diseases.

Neutrophil Extracellular Traps (NETs) are implicated in the development of auto-immunity in diseases such as rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE) through the externalization of intracellular neoepitopes e.g., dsDNA and nuclear proteins in SLE and citrullinated peptides in RA. The aim of this work was to use quantitative proteomics to identify and measure NET proteins produced by neutrophils from healthy controls, and from patients with RA and SLE to determine if NETs can be differentially-generated to expose different sets of neoepitopes. Ultra-pure neutrophils (>99%) from healthy individuals ( = 3) and patients with RA or SLE ( = 6 each) were incubated ± PMA (50 nM, PKC super-activator) or A23187 (3.8 μM, calcium ionophore) for 4 h. NETs were liberated by nuclease digestion and concentrated onto Strataclean beads prior to on-bead digestion with trypsin. Data-dependent LC-MS/MS analyses were conducted on a QExactive HF quadrupole-Orbitrap mass spectrometer, and label-free protein quantification was carried out using Progenesis QI. PMA-induced NETs were decorated with annexins, azurocidin and histone H3, whereas A23187-induced NETs were decorated with granule proteins including CAMP/LL37, CRISP3, lipocalin and MMP8, histones H1.0, H1.4, and H1.5, interleukin-8, protein-arginine deiminase-4 (PADI4), and α-enolase. Four proteins were significantly different between PMA-NETs from RA and SLE neutrophils ( < 0.05): RNASE2 was higher in RA, whereas MPO, leukocyte elastase inhibitor and thymidine phosphorylase were higher in SLE. For A23187-NETs, six NET proteins were higher in RA ( < 0.05), including CAMP/LL37, CRISP3, interleukin-8, MMP8; Thirteen proteins were higher in SLE, including histones H1.0, H2B, and H4. This work provides the first, direct comparison of NOX2-dependent (PMA) and NOX2-independent (A23187) NETs using quantitative proteomics, and the first direct comparison of RA and SLE NETs using quantitative proteomics. We show that it is the nature of the stimulant rather than neutrophil physiology that determines NET protein profiles in disease, since stimulation of NETosis in either a NOX2-dependent or a NOX2-independent manner generates broadly similar NET proteins irrespective of the disease background. We also use our proteomics pipeline to identify an extensive range of post-translationally modified proteins in RA and SLE, including histones and granule proteins, many of which are known targets of auto-antibodies in each disease.

COVID-19 or severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) appeared throughout the World and currently affected more than 9 million people and caused the death of around 470,000 patients. The novel strain of the coronavirus disease is transmittable at a devastating rate with a high rate of severe hospitalization even more so for the elderly population. Naso-oro-pharyngeal swab samples as the first step towards detecting suspected infection of SARS-CoV-2 provides a non-invasive method for PCR testing at a high confidence rate. Furthermore, proteomics analysis of PCR positive and negative naso-oropharyngeal samples provides information on the molecular level which highlights disease pathology. Samples from 15 PCR positive cases and 15 PCR negative cases were analyzed with nanoLC-MS/MS to identify the differentially expressed proteins. Proteomic analyses identified 207 proteins across the sample set and 17 of them were statistically significant. Protein-protein interaction analyses emphasized pathways like Neutrophil degranulation, Innate Immune System, Antimicrobial Peptides. Neutrophil Elastase (ELANE), Azurocidin (AZU1), Myeloperoxidase (MPO), Myeloblastin (PRTN3), Cathepsin G (CTSG) and Transcobalamine-1 (TCN1) were found to be significantly altered in naso-oropharyngeal samples of SARS-CoV-2 patients. The identified proteins are linked to alteration in the innate immune system specifically via neutrophil degranulation and NETosis.

Severe acute pancreatitis (SAP) presents with an aggressive clinical presentation and high lethality rate. Early prediction of the severity of acute pancreatitis will help physicians to further precise treatment and improve intervention. This study aims to construct a composite model that can predict SAP using inflammatory markers. 212 patients with acute pancreatitis enrolled from January 2018 to June 2020 were included in this study, basic parameters at admission and 24 h after hospitalization, and laboratory results such as inflammatory markers were collected. Pearson's test was used to analyze the correlation between heparin-binding protein (HBP), procalcitonin (PCT), and C-reactive protein (CRP). Risk factors affecting SAP were analyzed using multivariate logistic regression, inflammatory marker models were constructed, and subject operating curves were used to verify the discrimination of individual as well as inflammatory marker models and to find the optimal cut-off value based on the maximum Youden index. In the SAP group, the plasma levels of HBP, CRP, and PCT were 139.1 ± 74.8 ng/mL, 190.7 ± 106.3 mg/L and 46.3 ± 22.3 ng/mL, and 25.3 ± 16.0 ng/mL, 145.4 ± 67.9 mg/L and 27.9 ± 22.4 ng/mL in non-SAP patients, with a statistically significant difference between the two groups ( P  < 0.001), The Pearson correlation analysis showed a positive correlation between the three values of HBP, CRP, and PCT. The results of the multivariate logistic regression analysis showed that HBP (OR = 1.070 [1.044–1.098], P  < 0.001), CRP (OR = 1.010 [1.004–1.016], P  = 0.001), and PCT (OR = 1.030[1.007–1.053], P  < 0.001) were risk factors for SAP, and the area under the curve of the HBP-CRP-PCT model was 0.963 (0.936–0.990). The HCP model, consisting of HBP, CRP, and PCT; is well differentiated and easy to use and can predict the risk of SAP in advance.

Germinal matrix hemorrhage (GMH) is a devastating neurodevelopmental condition affecting preterm infants, but why blood vessels in this brain region are vulnerable to rupture remains unknown. Here we show that microglia in prenatal mouse and human brain interact with nascent vasculature in an age-dependent manner and that ablation of these cells in mice reduces angiogenesis in the ganglionic eminences, which correspond to the human germinal matrix. Consistent with these findings, single-cell transcriptomics and flow cytometry show that distinct subsets of CD45 + cells from control preterm infants employ diverse signaling mechanisms to promote vascular network formation. In contrast, CD45 + cells from infants with GMH harbor activated neutrophils and monocytes that produce proinflammatory factors, including azurocidin 1, elastase and CXCL16, to disrupt vascular integrity and cause hemorrhage in ganglionic eminences. These results underscore the brain’s innate immune cells in region-specific angiogenesis and how aberrant activation of these immune cells promotes GMH in preterm infants. Chen et al. show that subtypes of immune cells in prenatal human brain promote angiogenesis in the germinal matrix. Conversely, in preterm infants, proinflammatory immune cells disrupt angiogenesis and promote germinal matrix hemorrhage.

The 2019 novel coronavirus disease (COVID-19) is an infectious disease that began to spread globally since 2019. Some COVID-19 patients have neurological complications, such as olfactory disorders and movement disorders, which coincide with the symptoms of Parkinson’s disease (PD). Increasing imaging and autopsy evidence supports that the density of dopaminergic neurons in the nigrostriatal pathway is damaged in some COVID-19 patients. However, the underlying mechanism that causes PD-like symptoms remains unclear. PD is an age-related neurodegenerative disease with Lewy bodies (LBs) as its histopathologic feature. The main component of LBs is abnormally aggregated α-synuclein (α-syn). The prion-like propagation of α-syn aggregates plays a key role in the onset and progression of PD. The spike protein (S protein) of SARS-CoV-2 is a heparin-binding protein that mediates the entry of the virus into host cells. Here we found that the S1 domain interacts with α-syn and promotes α-syn aggregation. The S1 domain induces mitochondrial dysfunction, oxidative stress, and cytotoxicity. The S1-seeded α-syn fibrils show enhanced seeding activity and induce synaptic damage and cytotoxicity. Thus, the S1 domain of SARS-CoV-2 promotes the aggregation of α-syn in the cellular model of synucleinopathy and may contribute to the pathogenesis of PD.

Recent publications on the probable role of heparin-binding protein (HBP) as a biomarker in sepsis prompted us to investigate its diagnostic and prognostic performance in severe COVID-19. HBP and IL-6 were measured by immunoassays at admission and on day 7 in 178 patients with pneumonia by SARS-CoV-2. Patients were classified into non-sepsis and sepsis as per the Sepsis-3 definitions and were followed up for the development of severe respiratory failure (SRF) and for outcome. Results were confirmed by multivariate analyses. HBP was significantly higher in patients classified as having sepsis and was negatively associated with the oxygenation ratio and positively associated with creatinine and lactate. Logistic regression analysis evidenced admission HBP more than 18 ng/ml and IL-6 more than 30 pg/ml as independent risk factors for the development of SRP. Their integration prognosticated SRF with respective sensitivity, specificity, positive predictive value, and negative predictive 59.1%, 96.3%, 83.9%, and 87.8%. Cox regression analysis evidenced admission HBP more than 35 ng/ml and IL-6 more than 30 pg/ml as independent risk factors for 28-day mortality. Their integration prognosticated 28-day mortality with respective sensitivity, specificity, positive predictive value, and negative predictive value 69.2%, 92.7%, 42.9%, and 97.5%. HBP remained unchanged over-time course. A prediction score of the disposition of patients with COVID-19 is proposed taking into consideration admission levels of IL-6 and HBP. Using different cut-offs, the score may predict the likelihood for SRF and for 28-day outcome.

Blood heparin-binding protein (HBP) for diagnosing bacteremia in patients with sepsis has not been fully investigated. This study aims to explore the diagnostic value of blood HBP in predicting bacteremia in patients with sepsis compared with procalcitonin (PCT), Interleukin 6 (IL-6), C-reactive protein (CRP), white blood count (WBC), and neutrophil. In this observational study, we enrolled consecutive 146 patients, who were divided into two groups as the bacteremia group ( n  = 57) and the control group ( n  = 89). HBP, PCT, IL-6, CRP, WBC, and neutrophil were measured. The Chi-squared test and Student’s t-test were used to compare the baseline characteristics. The area under the receiver operating characteristic curve (AUC) was calculated to describe the diagnostic accuracy of the biomarkers for predicting bacteremia in patients with sepsis. Spearman’s correlation was used to analyze associations between the biomarkers. The concentration of HBP (204.13 ± 87.30 ng/mL ) in the bacteremia group was significantly higher than that in the control group (81.43 ± 61.53). HBP achieved the largest AUC for predicting bacteremia, with a value of 0.88 (95% confidence interval [CI], 0.82–0.94 ). This value was higher than those of the other biomarkers: 0.78 (95% CI 0.69–0.86) for PCT, 0.59 (95% CI 0.48–0.70) for IL-6, 0.56 (95% CI 0.45–0.67) for WBC, 0.73 (95% CI 0.64–0.83) for CRP, and 0.64 (95% CI 0.53–0.74) for neutrophil. The best cut-off value of blood HBP for identifying bacteremia was 95.69 ng/mL, with a sensitivity of 88.64%, a specificity of 68.06%, a positive predictive value of 64.1%, and a negative predictive value of 89.71%. A significant association was found between HBP and CRP (Spearman’s rho = 0.528, p  < 0.01). However, the correlations among PCT, IL-6, WBC, and neutrophil (Spearman’s rho < 0.5, p  < 0.01) were relatively weak. Blood HBP may be a useful auxiliary diagnostic marker that is preferable over PCT, IL-6, CRP, WBC, and neutrophil in identifying bacteremia in patients with sepsis.

Heparin-binding protein is an inflammatory factor with predictive value for sepsis and participates in the inflammatory response through antibacterial effects, chemotaxis, and increased vascular permeability. The role of heparin-binding protein in sepsis has been progressively demonstrated, but few studies have been conducted in the context of polytrauma combined with bacterial infections. This study aims to investigate the predictive value of heparin-binding protein for bacterial infections in patients with severe polytrauma. This is a prospective single-center study. Patients with polytrauma in the emergency intensive care unit were selected for the study, and plasma heparin-binding protein concentrations and other laboratory parameters were measured within 48 hours of admission to the hospital. A two-sample comparison and univariate logistic regression analysis investigated the relationship between heparin-binding protein and bacterial infection in polytrauma patients. A multifactor logistic regression model was constructed, and the ROC curve was plotted. Ninety-seven patients with polytrauma were included in the study, 43 with bacterial infection and 54 without infection. Heparin-binding protein was higher in the infected group than in the control group [(32.00±3.20) ng/mL vs. (18.52±1.33) ng/mL, P = 0.001]. Univariate logistic regression analysis shows that heparin-binding protein is related to bacterial infection (OR = 1.10, Z = 3.91, 95%CI:1.05~1.15, P = 0.001). Multivariate logistic regression equations showed that patients were 1.12 times more likely to have bacterial infections for each value of heparin-binding protein increase, holding neutrophils and Procalcitonin (PCT) constant. ROC analysis shows that heparin-binding protein combined with neutrophils and PCT has better predictive value for bacterial infection [AUC = 0.935, 95%CI:0.870~0.977]. Heparin-binding protein may predict bacterial infection in patients with severe polytrauma. Combining heparin-binding protein, PCT, and neutrophils may improve bacterial infection prediction.

Heparin-binding protein (HBP) has been shown to be a robust predictor of the progression to organ dysfunction from sepsis, and we hypothesized that dynamic changes in HBP may reflect the severity of sepsis. We therefore aim to investigate the predictive value of baseline HBP, 24-h, and 48-h HBP change for prediction of 30-day mortality in adult patients with sepsis. This is a prospective observational study in an intensive care unit of a tertiary center. Patients aged 20 years or older who met SEPSIS-3 criteria were prospectively enrolled from August 2019 to January 2020. Plasma levels of HBP were measured at admission, 24 h, and 48 h and dynamic changes in HBP were calculated. The Primary endpoint was 30-day mortality. We tested whether the biomarkers could enhance the predictive accuracy of a multivariable predictive model. A total of 206 patients were included in the final analysis. 48-h HBP change (HBPc-48 h) had greater predictive accuracy of area under the curve (AUC: 0.82), followed by baseline HBP (0.79), PCT (0.72), lactate (0.71), and CRP (0.65), and HBPc-24 h (0.62). Incorporation of HBPc-48 h into a clinical prediction model significantly improved the AUC from 0.85 to 0.93. HBPc-48 h may assist clinicians with clinical outcome prediction in critically ill patients with sepsis and can improve the performance of a prediction model including age, SOFA score and Charlson comorbidity index.