Identification of serum prognostic biomarkers of severe COVID-19 using a quantitative proteomic approach

Dynamics of scFv-targeted VAP2 correlating with IL-16, MIF and IL-1Ra in ANCA-associated vasculitis

BACKGROUND AND HYPOTHESIS

Using a mouse model of MPA with microvascular lesion with a clone (VasSF) of recombinant single chain fragments of the variable region of human IgG, we previously showed that vasculitis-associated apolipoprotein A2 (VAP2) may be a therapeutic target for vasculitis. The present study estimated the target molecules for VasSF and the association between VAP2 and cytokine levels in patient sera in terms of microvascular lesion severity.

METHODS

Sera and clinical information were collected from patients with microscopic polyangiitis and granulomatosis with polyangiitis (MPA/GPA) and infectious disease. Neutrophil counts, levels of C-reactive protein (CRP), creatinine, total cholesterol associated with microvascular lesion, HDL cholesterol, low-density lipoprotein cholesterol, triglycerides, glomerular filtration rate (eGFR), and cytokines were estimated. Serum VAP2 signals were determined with Western blotting.

RESULTS

VasSF bound to a 24 kDa molecule in the serum of active MPA/GPA patients. Anti-AP2 antibody also bound with the same 24 kDa molecule, named VAP2, because of size difference from normal APOA2. The VAP2 signal was significantly stronger in the active-disease group but significantly weakened in remission. The signal correlated positively with eGFR but not with the Birmingham Vasculitis Activity Score, CRP, MPO-ANCA, or PR3-ANCA levels. It correlated negatively with MPO activity, IL-16, MIF, and IL-1Ra. Moreover, VasSF bound to a 17 kDa molecule in the remission phase.

CONCLUSION

The 24 kDa VAP2 molecule may be associated with neutrophil functions because of its inverse correlation with MPO activity, IL-16, MIF, and IL-1Ra, suggesting that VAP2-APOA1 formation in HDL triggers microvascular injury. VasSF may reverse the injury by removing APOA1-VAP2 heterodimers from peripheral blood vessels.

Endocrine and cellular physiology and pathology of the insulin-like growth factor acid-labile subunit

The acid-labile subunit (ALS) of the insulin-like growth factor (IGF) binding protein (IGFBP) complex, encoded in humans by IGFALS, has a vital role in regulating the endocrine transport and bioavailability of IGF-1 and IGF-2. Accordingly, ALS has a considerable influence on postnatal growth and metabolism. ALS is a leucine-rich glycoprotein that forms high-affinity ternary complexes with IGFBP-3 or IGFBP-5 when they are occupied by either IGF-1 or IGF-2. These complexes constitute a stable reservoir of circulating IGFs, blocking the potentially hypoglycaemic activity of unbound IGFs. ALS is primarily synthesized by hepatocytes and its expression is lower in non-hepatic tissues. ALS synthesis is strongly induced by growth hormone and suppressed by IL-1β, thus potentially serving as a marker of growth hormone secretion and/or activity and of inflammation. IGFALS mutations in humans and Igfals deletion in mice cause modest growth retardation and pubertal delay, accompanied by decreased osteogenesis and enhanced adipogenesis. In hepatocellular carcinoma, IGFALS is described as a tumour suppressor; however, its contribution to other cancers is not well delineated. This Review addresses the endocrine physiology and pathology of ALS, discusses the latest cell and proteomic studies that suggest emerging cellular roles for ALS and outlines its involvement in other disease states.

Use of data-independent acquisition mass spectrometry to identify an objective serum indicator of the need for osteoporotic therapeutic intervention

Osteoporosis is characterized by weakened bone microstructure and loss of bone mass. Current diagnostic criteria for osteoporosis are based on the T-score, which is a measure of bone mineral density. However, osteoporotic fragility fractures can occur regardless of the T-score, underscoring the need for additional criteria for the early detection of patients at fracture risk. To identify indicators of reduced bone strength, we performed serum proteomic analysis using data-independent acquisition mass spectrometry with serum samples from two patient groups, one with osteoporosis but no fractures and the other with osteopenia and fragility fractures. Collective evaluation of the results identified six serum proteins that changed to a similar extent in both patient groups compared with controls. Of these, extracellular matrix protein 1 (ECM1), which contributes to bone formation, showed the most significant increase in serum levels in both patient groups. An ELISA-based assay suggested that ECM1 could serve as a serum indicator of the need for therapeutic intervention; however, further prospective studies with a larger sample size are necessary to confirm these results. The present findings may contribute to the provision of early and appropriate therapeutic strategies for patients at risk of osteoporotic fractures. SIGNIFICANCE: This study aimed to identify objective serum indicators of the need for therapeutic intervention in individuals at risk of osteoporotic fracture. Comprehensive proteome analyses of serum collected from patients with osteoporosis but no fractures, patients with osteopenia and fragility fractures, and controls were performed by data-independent acquisition mass spectrometry. Collective evaluation of the proteome analysis data and ELISA-based assays identified serum ECM1 as a potential objective marker of the risk of fragility fractures in patients with osteoporosis or osteopenia. The findings are an important step toward the development of appropriate bone health management methods to improve well-being and maintain quality of life.

Plasma Proteins Associated with COVID-19 Severity in Puerto Rico

Viral strains, age, and host factors are associated with variable immune responses against SARS-CoV-2 and disease severity. Puerto Ricans have a genetic mixture of races: European, African, and Native American. We hypothesized that unique host proteins/pathways are associated with COVID-19 disease severity in Puerto Rico. Following IRB approval, a total of 95 unvaccinated men and women aged 21-71 years old were recruited in Puerto Rico from 2020-2021. Plasma samples were collected from COVID-19-positive subjects (n = 39) and COVID-19-negative individuals (n = 56) during acute disease. COVID-19-positive individuals were stratified based on symptomatology as follows: mild (n = 18), moderate (n = 13), and severe (n = 8). Quantitative proteomics was performed in plasma samples using tandem mass tag (TMT) labeling. Labeled peptides were subjected to LC/MS/MS and analyzed by Proteome Discoverer (version 2.5), Limma software (version 3.41.15), and Ingenuity Pathways Analysis (IPA, version 22.0.2). Cytokines were quantified using a human cytokine array. Proteomics analyses of severely affected COVID-19-positive individuals revealed 58 differentially expressed proteins. Cadherin-13, which participates in synaptogenesis, was downregulated in severe patients and validated by ELISA. Cytokine immunoassay showed that TNF-α levels decreased with disease severity. This study uncovers potential host predictors of COVID-19 severity and new avenues for treatment in Puerto Ricans.

SARS-CoV-2 Infection-Dependent Modulation in Vital Components of the Serum Profile of Severely SARS-CoV-2 Infected Patients

Background

COVID-19 modulates many serological biomarkers during the progress of disease severity. The study aimed to determine COVID-19 severity-associated perturbance in the serum profile.

Methods

A retrospective study including COVID-19-positive individuals (n = 405) was accomplished. The serum profile of COVID-19 participants was mined from laboratory records. Severity-associated alteration in the serum profile was evaluated using Pearson correlation, regression, VCramer, Bayesian posterior VCramer, and bias factor using R-base-RStudio-version-3.3.0 with a significant cut-off of p < 0.05.

Results

Significantly different mean ± standard deviation (SD) (highly versus moderately severe) of C-reactive protein (CRP), ferritin, neutrophil-lymphocyte ratio (NLR), D-dimer, platelets, prothrombin time (PT), partial prothrombin time (PTT), troponin 1, lactate dehydrogenase (LDH), aspartate-aminotransferase (AST), alanine aminotransferase (ALT), and AST/ALT ratio was observed (p < 0.001). Highly severe COVID-19 associated with CRP, ferritin, NLR, in D-dimer, PT, PTT, troponin 1, AST/ALT ratio, AST and ALT (adjusted odds ratio (AOR): 1.346, 1.05, 1.46, 1.33, 1.42, 1.23, 4.07, 3.9, 1.24, 1.45, p < 0.001). CRP with ferritin (r = 0.743), NLR (r = 0.77), white blood cells (WBC) (r = 0.8), troponin1 with LDH (r = 0.757), and D-dimer with platelets (r = -0.81) were highly correlated. X2pearson (p < 0.001), VCramer (0.71), Bayesian-VCramer (0.7), and bias-factor (-125) for troponin 1 indicate the strong association of troponin 1 level and with COVID-19 severity. X2pearson (p < 0.001), VCramer (1), Bayesian-VCramer (0.98), and bias-factor (-266.3) for NLR exhibited a very strong association of pathologic conditions with the high severity of the disease.

Conclusion

These biomarkers of inflammation (CRP, Ferritin, NLR), coagulation disorders (D-dimer, PT, and PTT) cardiac abnormality (troponin 1), and liver injury (AST/ALT) could be crucial in low-medical resource settings as potential prognosticator/predictors of the COVID-19 severity and clinical outcomes. Moreover, the outcome of this study could be leveraged for the early prediction of disease severity during SARS-CoV or Middle East Respiratory Coronavirus (MERS-CoV) infection.

Recently Updated Role of Chitinase 3-like 1 on Various Cell Types as a Major Influencer of Chronic Inflammation

Chitinase 3-like 1 (also known as CHI3L1 or YKL-40) is a mammalian chitinase that has no enzymatic activity, but has the ability to bind to chitin, the polymer of N-acetylglucosamine (GlcNAc). Chitin is a component of fungi, crustaceans, arthropods including insects and mites, and parasites, but it is completely absent from mammals, including humans and mice. In general, chitin-containing organisms produce mammalian chitinases, such as CHI3L1, to protect the body from exogenous pathogens as well as hostile environments, and it was thought that it had a similar effect in mammals. However, recent studies have revealed that CHI3L1 plays a pathophysiological role by inducing anti-apoptotic activity in epithelial cells and macrophages. Under chronic inflammatory conditions such as inflammatory bowel disease and chronic obstructive pulmonary disease, many groups already confirmed that the expression of CHI3L1 is significantly induced on the apical side of epithelial cells, and activates many downstream pathways involved in inflammation and carcinogenesis. In this review article, we summarize the expression of CHI3L1 under chronic inflammatory conditions in various disorders and discuss the potential roles of CHI3L1 in those disorders on various cell types.

CCL18, CHI3L1, ANG2, IL-6 systemic levels are associated with the extent of lung damage and radiomic features in SARS-CoV-2 infection

OBJECTIVE AND DESIGN

We aimed to identify cytokines whose concentrations are related to lung damage, radiomic features, and clinical outcomes in COVID-19 patients.

MATERIAL OR SUBJECTS

Two hundred twenty-six patients with SARS-CoV-2 infection and chest computed tomography (CT) images were enrolled.

METHODS

CCL18, CHI3L1/YKL-40, GAL3, ANG2, IP-10, IL-10, TNFα, IL-6, soluble gp130, soluble IL-6R were quantified in plasma samples using Luminex assays. The Mann-Whitney U test, the Kruskal-Wallis test, correlation and regression analyses were performed. Mediation analyses were used to investigate the possible causal relationships between cytokines, lung damage, and outcomes. AVIEW lung cancer screening software, pyradiomics, and XGBoost classifier were used for radiomic feature analyses.

RESULTS

CCL18, CHI3L1, and ANG2 systemic levels mainly reflected the extent of lung injury. Increased levels of every cytokine, but particularly of IL-6, were associated with the three outcomes: hospitalization, mechanical ventilation, and death. Soluble IL-6R showed a slight protective effect on death. The effect of age on COVID-19 outcomes was partially mediated by cytokine levels, while CT scores considerably mediated the effect of cytokine levels on outcomes. Radiomic-feature-based models confirmed the association between lung imaging characteristics and CCL18 and CHI3L1.

CONCLUSION

Data suggest a causal link between cytokines (risk factor), lung damage (mediator), and COVID-19 outcomes.

Meta-analysis of the serum/plasma proteome identifies significant associations between COVID-19 with Alzheimer's/Parkinson's diseases

In recent years, we have seen the widespread devastations and serious health complications manifested by COVID-19 globally. Although we have effectively controlled the pandemic, uncertainties persist regarding its potential long-term effects, including prolonged neurological issues. To gain comprehensive insights, we conducted a meta-analysis of mass spectrometry-based proteomics data retrieved from different studies with a total of 538 COVID-19 patients and 523 healthy controls. The meta-analysis revealed that top-enriched pathways were associated with neurological disorders, including Alzheimer's (AD) and Parkinson's disease (PD). Further analysis confirmed a direct correlation in the expression patterns of 24 proteins involved in Alzheimer's and 23 proteins in Parkinson's disease with COVID-19. Protein-protein interaction network and cluster analysis identified SNCA as a hub protein, a known biomarker for Parkinson's disease, in both AD and PD. To the best of our knowledge, this is the first meta-analysis study providing proteomic profiling evidence linking COVID-19 to neurological complications.

Longitudinal plasma proteomics reveals biomarkers of alveolar-capillary barrier disruption in critically ill COVID-19 patients

The pathobiology of respiratory failure in COVID-19 consists of a complex interplay between viral cytopathic effects and a dysregulated host immune response. In critically ill patients, imatinib treatment demonstrated potential for reducing invasive ventilation duration and mortality. Here, we perform longitudinal profiling of 6385 plasma proteins in 318 hospitalised patients to investigate the biological processes involved in critical COVID-19, and assess the effects of imatinib treatment. Nine proteins measured at hospital admission accurately predict critical illness development. Next to dysregulation of inflammation, critical illness is characterised by pathways involving cellular adhesion, extracellular matrix turnover and tissue remodelling. Imatinib treatment attenuates protein perturbations associated with inflammation and extracellular matrix turnover. These proteomic alterations are contextualised using external pulmonary RNA-sequencing data of deceased COVID-19 patients and imatinib-treated Syrian hamsters. Together, we show that alveolar capillary barrier disruption in critical COVID-19 is reflected in the plasma proteome, and is attenuated with imatinib treatment. This study comprises a secondary analysis of both clinical data and plasma samples derived from a clinical trial that was registered with the EU Clinical Trials Register (EudraCT 2020-001236-10, https://www.clinicaltrialsregister.eu/ctr-search/trial/2020-001236-10/NL ) and Netherlands Trial Register (NL8491, https://www.trialregister.nl/trial/8491 ).

Early cellular and molecular signatures correlate with severity of West Nile virus infection

Infection with West Nile virus (WNV) drives a wide range of responses, from asymptomatic to flu-like symptoms/fever or severe cases of encephalitis and death. To identify cellular and molecular signatures distinguishing WNV severity, we employed systems profiling of peripheral blood from asymptomatic and severely ill individuals infected with WNV. We interrogated immune responses longitudinally from acute infection through convalescence employing single-cell protein and transcriptional profiling complemented with matched serum proteomics and metabolomics as well as multi-omics analysis. At the acute time point, we detected both elevation of pro-inflammatory markers in innate immune cell types and reduction of regulatory T cell activity in participants with severe infection, whereas asymptomatic donors had higher expression of genes associated with anti-inflammatory CD16+ monocytes. Therefore, we demonstrated the potential of systems immunology using multiple cell-type and cell-state-specific analyses to identify correlates of infection severity and host cellular activity contributing to an effective anti-viral response.

Variation in presepsin and thrombomodulin levels for predicting COVID-19 mortality

Coronavirus disease (COVID-19) has caused extensive mortality globally; therefore, biomarkers predicting the severity and prognosis of COVID-19 are essential. This study aimed to evaluate the application of presepsin (P-SEP) and thrombomodulin (TM), which are biomarkers of sepsis and endothelial dysfunction, respectively, in the prognosis of COVID-19. Serum P-SEP and TM levels from COVID-19 patients (n = 183) were measured. Disease severity was classified as mild, moderate I, moderate II, or severe based on hemoglobin oxygen saturation and the history of intensive care unit transfer or use of ventilation at admission. Patients in the severe group were further divided into survivors and non-survivors. P-SEP and TM levels were significantly higher in the severe group than those in the mild group, even after adjusting for creatinine values. In addition, TM levels were significantly higher in non-survivors than in survivors. Changes in the P-SEP levels at two time points with an interval of 4.1 ± 2.2 days were significantly different between the survivors and non-survivors. In conclusion, TM and continuous P-SEP measurements may be useful for predicting mortality in patients with COVID-19. Moreover, our data indicate that P-SEP and TM values after creatinine adjustment could be independent predictive markers, apart from renal function.

Circulating markers of extracellular matrix remodelling in severe COVID-19 patients

BACKGROUND

Abnormal remodelling of the extracellular matrix (ECM) has generally been linked to pulmonary inflammation and fibrosis and may also play a role in the pathogenesis of severe COVID-19. To further elucidate the role of ECM remodelling and excessive fibrogenesis in severe COVID-19, we examined circulating levels of mediators involved in various aspects of these processes in COVID-19 patients.

METHODS

Serial blood samples were obtained from two cohorts of hospitalised COVID-19 patients (n = 414). Circulating levels of ECM remodelling mediators were quantified by enzyme immunoassays in samples collected during hospitalisation and at 3-month follow-up. Samples were related to disease severity (respiratory failure and/or treatment at the intensive care unit), 60-day total mortality and pulmonary pathology after 3-months. We also evaluated the direct effect of inactivated SARS-CoV-2 on the release of the different ECM mediators in relevant cell lines.

RESULTS

Several of the measured markers were associated with adverse outcomes, notably osteopontin (OPN), S100 calcium-binding protein A12 and YKL-40 were associated with disease severity and mortality. High levels of ECM mediators during hospitalisation were associated with computed tomography thorax pathology after 3-months. Some markers (i.e. growth differential factor 15, galectin 3 and matrix metalloproteinase 9) were released from various relevant cell lines (i.e. macrophages and lung cell lines) in vitro after exposure to inactivated SARS-CoV-2 suggesting a direct link between these mediators and the causal agent of COVID-19.

CONCLUSION

Our findings highlight changes to ECM remodelling and particularly a possible role of OPN, S100A12 and YKL-40 in the pathogenesis of severe COVID-19.

Identification of gravity-responsive proteins in the femur of spaceflight mice using a quantitative proteomic approach

Although the microgravity (μ-g) environment that astronauts encounter during spaceflight can cause severe acute bone loss, the molecular mechanism of this bone loss remains unclear. To investigate the gravity-response proteins involved in bone metabolism, it is important to comprehensively determine which proteins exhibit differential abundance associated with mechanical stimuli. However, comprehensive proteomic analysis using small bone samples is difficult because protein extraction in mineralized bone tissue is inefficient. Here, we established a high-sensitivity analysis system for mouse bone proteins using data-independent acquisition mass spectrometry. This system successfully detected 40 proteins in the femoral diaphysis showing differential abundance between mice raised in a μ-g environment, where the bone mass was reduced by gravity unloading, and mice raised in an artificial 1-gravity environment on the International Space Station. Additionally, 22 proteins, including noncollagenous bone matrix proteins, showed similar abundance between the two groups in the mandible, where bone mass was unaltered due to mastication stimuli, suggesting that these proteins are responsive to mechanical stimuli. One of these proteins, SPARCL1, is suggested to promote osteoclastogenesis induced by receptor activator of nuclear factor-κB ligand. We expect these findings to lead to new insights into the mechanisms of bone metabolism induced by mechanical stimuli. SIGNIFICANCE: We aimed to investigate the gravity-response proteins involved in bone metabolism. To this end, we established a comprehensive analysis system for mouse bone proteins using data-independent acquisition mass spectrometry, which is particularly useful in comprehensively analyzing the bone proteome using small sample volumes. In addition, a comprehensive proteomic analysis of the femoral diaphysis and mandible, which exhibit different degrees of bone loss in mice raised on the International Space Station, identified proteins that respond to mechanical stimuli. SPARCL1, a mechanical stimulus-responsive protein, was consequently suggested to be involved in osteoclast differentiation associated with bone remodeling. Our findings represent an important step toward elucidating the molecular mechanism of bone metabolism induced by mechanical stimuli.

Proteomic profiling of sudden cardiac death with acquired cardiac hypertrophy

BACKGROUND

Cardiac hypertrophy, which develops in middle-aged and older individuals as a consequence of hypertension and obesity, is an established risk factor for sudden cardiac death (SCD). However, it is sometimes difficult to differentiate SCD with acquired cardiac hypertrophy (SCH) from compensated cardiac hypertrophy (CCH), at autopsy. We aimed to elucidate the proteomic alteration in SCH, which can be a guideline for future postmortem diagnosis.

METHODS

Cardiac tissues were sampled at autopsy. SCH group consisted of ischemic heart failure, hypertensive heart failure, and aortic stenosis. CCH group included cases of non-cardiac death with cardiac hypertrophy. The control group comprised cases of non-cardiac death without cardiac hypertrophy. All patients were aged > 40 years, and hypertrophic cardiomyopathy was not included in this study. We performed histological examination and shotgun proteomic analysis, followed by quantitative polymerase chain reaction analysis.

RESULTS

Significant obesity and myocardial hypertrophy, and mild myocardial fibrosis were comparable in SCH and CCH cases compared to control cases. The proteomic profile of SCH cases was distinguishable from those of CCH and control cases, and many sarcomere proteins were increased in SCH cases. Especially, the protein and mRNA levels of MYH7 and MYL3 were significantly increased in SCH cases.

CONCLUSION

This is the first report of cardiac proteomic analysis in SCH and CCH cases. The stepwise upregulation of sarcomere proteins may increase the risk for SCD in acquired cardiac hypertrophy before cardiac fibrosis progresses significantly. These findings can possibly aid in the postmortem diagnosis of SCH in middle-aged and older individuals.

VOC-Based Probes, a New Set of Analytical Tools to Monitor Patient Health from Blood Sample. Proof of Concept on Tracking COVID-19 Infection

Induced volatolomics is an emerging field that holds promise for many biomedical applications including disease detection and prognosis. In this pilot study, we report the first use of a cocktail of volatile organic compounds (VOCs)-based probes to highlight new metabolic markers allowing disease prognosis. In this pilot study, we specifically targeted a set of circulating glycosidases whose activities could be associated with critical COVID-19 illness. Starting from blood sample collection, our approach relies on the incubation of VOC-based probes in plasma samples. Once activated, the probes released a set of VOCs in the sample headspace. The dynamic monitoring of the signals of VOC tracers enabled the identification of three dysregulated glycosidases in the initial phase after infection, for which preliminary machine learning analyses suggested an ability to anticipate critical disease development. This study demonstrates that our VOC-based probes are a new set of analytical tools that can provide access to biological signals until now unavailable to biologists and clinicians and which could be included in biomedical research to properly construct multifactorial therapy algorithms, necessary for personalized medicine.

A novel device for swift and efficient CD44 protein digestion of pipette tips in human serum

For molecular diagnostics in modern biomedical research, electrospray ionisation mass spectrometry (ESI-MS) based on proteome profiling is important. Now a days, sample preparation such as proteolysis and protein extraction remain incredibly challenging and inefficient. Recent sample-preparation methods based on micro tips show promising results toward the aim "a proteome in an hour". Proteolysis at the tip, is still infrequently observed and does not represent the processing of complex bio-samples. In this study, we outline a unique technique for detecting and extracting human serum CD44 biomarkers by ligand-protein interactions. This method employs macropores silica particles (MPSP) or (MOSF) modified with hyaluronic acid (HA). In order to assist in the profile of the human serum proteome, we limitations of immunoassays for rapid and multimodal proteolysis. For effective in situ proteolysis, in micropipette tips, MPSP were designed as nanoreactors with variable pore size and surface chemistry. In MS-based bottom-up proteome analysis, the device as-built demonstrated favourable sensitivity (LOD of 0.304 ± 0.007 ng/mL and LOQ of 0.973 ± 0.054 ng/mL), selectivity, durability (at -20 °C for 2 months), reuse (at least 10 times), and minimal memory impact. In addition, we examined into specific surface chemistries of nanoparticles for the absorption of proteins in serum and profiled the HA-binding serum proteome, setting a new preliminary benchmark for future databases. Our study not only helped establish a new platform for extracting/detection of CD44 and identifying the HA-binding proteome, but it also offered design recommendations for ligand affinity-based techniques for the antibody-free study of serum biomarkers with a view towards diagnostic applications.

Low-Abundance Protein Enrichment for Medical Applications: The Involvement of Combinatorial Peptide Library Technique

The discovery of low- and very low-abundance proteins in medical applications is considered a key success factor in various important domains. To reach this category of proteins, it is essential to adopt procedures consisting of the selective enrichment of species that are present at extremely low concentrations. In the past few years pathways towards this objective have been proposed. In this review, a general landscape of the enrichment technology situation is made first with the presentation and the use of combinatorial peptide libraries. Then, a description of this peculiar technology for the identification of early-stage biomarkers for well-known pathologies with concrete examples is given. In another field of medical applications, the determination of host cell protein traces potentially present in recombinant therapeutic proteins, such as antibodies, is discussed along with their potentially deleterious effects on the health of patients on the one hand, and on the stability of these biodrugs on the other hand. Various additional applications of medical interest are disclosed for biological fluids investigations where the target proteins are present at very low concentrations (e.g., protein allergens).

Identification of two early blood biomarkers ACHE and CLEC12A for improved risk stratification of critically ill COVID-19 patients

In order to identify biomarkers for earlier prediction of COVID-19 outcome, we collected blood samples from patients with fatal outcomes (non-survivors) and with positive clinical outcomes (survivors) at ICU admission and after seven days. COVID-19 survivors and non-survivors showed significantly different transcript levels for 93 genes in whole blood already at ICU admission as revealed by RNA-Seq. These differences became even more pronounced at day 7, resulting in 290 differentially expressed genes. Many identified genes play a role in the differentiation of hematopoietic cells. For validation, we designed an RT-qPCR assay for C-type lectin domain family 12 member A (CLEC12A) and acetylcholinesterase (ACHE), two transcripts that showed highest potential to discriminate between survivors and non-survivors at both time points. Using our combined RT-qPCR assay we examined 33 samples to accurately predict patient survival with an AUROC curve of 0.931 (95% CI = 0.814-1.000) already at ICU admission. CLEC12A and ACHE showed improved prediction of patient outcomes compared to standard clinical biomarkers including CRP and PCT in combination (AUROC = 0.403, 95% CI = 0.108-0.697) or SOFA score (AUROC = 0.701 95% CI = 0.451-0.951) at day 0. Therefore, analyzing CLEC12A and ACHE gene expression from blood may provide a promising approach for early risk stratification of severely ill COVID-19 patients.

An Optimized Data-Independent Acquisition Strategy for Comprehensive Analysis of Human Plasma Proteome

Cartography of the plasma proteome remains technically challenging, primarily due to the abundance and dynamic range of plasma proteins and their concentrations, exceeding ten orders of magnitude, including low-abundant tissue-derived proteins in the pg/mL range. Data-independent acquisition mass spectrometry (DIA-MS) has seen advances in unbiased mass spectrometry-based proteomic analysis of the plasma proteome. Here, we describe a comprehensive proteomic workflow of human plasma from clinically relevant sample (10 μL) that includes anti-protein immunodepletion and highly sensitive sample preparation workflow, with optimized scheduled isolation DIA-MS and deep learning analysis. This approach results in over 960 proteins quantified from a single-shot analysis of broad dynamic range, across 8 orders of magnitude (8.2 ng/L to 0.67 g/L). We further compare data-dependent acquisition (DDA) MS to highlight the advantage in protein quantification and inter-sample variation. These developments have provided streamlined identification of the human plasma proteome, including low-abundant tissue-enriched proteins, and applications toward understanding the plasma proteome.

A two-gene marker for the two-tiered innate immune response in COVID-19 patients

For coronavirus disease 2019 (COVID-19), a pandemic disease characterized by strong immune dysregulation in severe patients, convenient and efficient monitoring of the host immune response is critical. Human hosts respond to viral and bacterial infections in different ways, the former is characterized by the activation of interferon stimulated genes (ISGs) such as IFI27, while the latter is characterized by the activation of anti-bacterial associated genes (ABGs) such as S100A12. This two-tiered innate immune response has not been examined in COVID-19. In this study, the activation patterns of this two-tiered innate immune response represented by IFI27 and S100A12 were explored based on 1421 samples from 17 transcriptome datasets derived from the blood of COVID-19 patients and relevant controls. It was found that IFI27 activation occurred in most of the symptomatic patients and displayed no correlation with disease severity, while S100A12 activation was more restricted to patients under severe and critical conditions with a stepwise activation pattern. In addition, most of the S100A12 activation was accompanied by IFI27 activation. Furthermore, the activation of IFI27 was most pronounced within the first week of symptom onset, but generally waned after 2-3 weeks. On the other hand, the activation of S100A12 displayed no apparent correlation with disease duration and could last for several months in certain patients. These features of the two-tiered innate immune response can further our understanding on the disease mechanism of COVID-19 and may have implications to the clinical triage. Development of a convenient two-gene protocol for the routine serial monitoring of this two-tiered immune response will be a valuable addition to the existing laboratory tests.

Molecular Pathogenesis of Fibrosis, Thrombosis and Surfactant Dysfunction in the Lungs of Severe COVID-19 Patients

The global scope and scale of the SARS-CoV-2 pandemic led to huge amounts of important data from clinical observations and experimental analyses being collected, in particular, regarding the long-term impact of COVID-19 on lung tissue. Visible changes in lung tissue mainly relate to the destruction of the alveolar architecture, dense cellularity, and pulmonary fibrosis with myofibroblast proliferation and collagen deposition. These changes are the result of infection, mainly with virus variants from the first pandemic waves (Alpha to Delta). In addition, proper regulation of immune responses to pathogenic viral stimuli is critical for the control of and recovery from tissue/organ damage, including in the lungs. We can distinguish three main processes in the lungs during SARS-CoV-2 infection: damage or deficiency of the pulmonary surfactant, coagulation processes, and fibrosis. Understanding the molecular basis of these processes is extremely important in the context of elucidating all pathologies occurring after virus entry. In the present review, data on the abovementioned three biochemical processes that lead to pathological changes are gathered together and discussed. Systematization of the knowledge is necessary to explore the three key pathways in lung tissue after SARS-CoV-2 virus infection as a result of a prolonged and intense inflammatory process in the context of pulmonary fibrosis, hemostatic disorders, and disturbances in the structure and/or metabolism of the surfactant. Despite the fact that the new Omicron variant does not affect the lungs as much as the previous variants, we cannot ignore the fact that other new mutations and emerging variants will not cause serious damage to the lung tissue. In the future, this review will be helpful to stratify the risk of serious complications in patients, to improve COVID-19 treatment outcomes, and to select those who may develop complications before clinical manifestation.

Longitudinal serum proteomics analyses identify unique and overlapping host response pathways in Lyme disease and West Nile virus infection

Advancement in proteomics methods for interrogating biological samples has helped identify disease biomarkers for early diagnostics and unravel underlying molecular mechanisms of disease. Herein, we examined the serum proteomes of 23 study participants presenting with one of two common arthropod-borne infections: Lyme disease (LD), an extracellular bacterial infection or West Nile virus infection (WNV), an intracellular viral infection. The LC/MS based serum proteomes of samples collected at the time of diagnosis and during convalescence were assessed using a depletion-based high-throughput shotgun proteomics (dHSP) pipeline as well as a non-depleting blotting-based low-throughput platform (MStern). The LC/MS integrated analyses identified host proteome responses in the acute and recovery phases shared by LD and WNV infections, as well as differentially abundant proteins that were unique to each infection. Notably, we also detected proteins that distinguished localized from disseminated LD and asymptomatic from symptomatic WNV infection. The proteins detected in both diseases with the dHSP pipeline identified unique and overlapping proteins detected with the non-depleting MStern platform, supporting the utility of both detection methods. Machine learning confirmed the use of the serum proteome to distinguish the infection from healthy control sera but could not develop discriminatory models between LD and WNV at current sample numbers. Our study is the first to compare the serum proteomes in two arthropod-borne infections and highlights the similarities in host responses even though the pathogens and the vectors themselves are different.

Longitudinal characterization of circulating neutrophils uncovers phenotypes associated with severity in hospitalized COVID-19 patients

Mechanisms of neutrophil involvement in severe coronavirus disease 2019 (COVID-19) remain incompletely understood. Here, we collect longitudinal blood samples from 306 hospitalized COVID-19+ patients and 86 controls and perform bulk RNA sequencing of enriched neutrophils, plasma proteomics, and high-throughput antibody profiling to investigate relationships between neutrophil states and disease severity. We identify dynamic switches between six distinct neutrophil subtypes. At days 3 and 7 post-hospitalization, patients with severe disease display a granulocytic myeloid-derived suppressor cell-like gene expression signature, while patients with resolving disease show a neutrophil progenitor-like signature. Humoral responses are identified as potential drivers of neutrophil effector functions, with elevated severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-specific immunoglobulin G1 (IgG1)-to-IgA1 ratios in plasma of severe patients who survived. In vitro experiments confirm that while patient-derived IgG antibodies induce phagocytosis in healthy donor neutrophils, IgA antibodies predominantly induce neutrophil cell death. Overall, our study demonstrates a dysregulated myelopoietic response in severe COVID-19 and a potential role for IgA-dominant responses contributing to mortality.

Serum biomarkers associated with SARS-CoV-2 severity

Immunity with SARS-CoV-2 infection during the acute phase is not sufficiently well understood to differentiate mild from severe cases and identify prognostic markers. We evaluated the immune response profile using a total of 71 biomarkers in sera from patients with SARS-CoV-2 infection, confirmed by RT-PCR and controls. We correlated biological marker levels with negative control (C) asymptomatic (A), nonhospitalized (mild cases-M), and hospitalized (severe cases-S) groups. Among angiogenesis markers, we identified biomarkers that were more frequently elevated in severe cases when compared to the other groups (C, A, and M). Among cardiovascular diseases, there were biomarkers with differences between the groups, with D-dimer, GDF-15, and sICAM-1 higher in the S group. The levels of the biomarkers Myoglobin and P-Selectin were lower among patients in group M compared to those in groups S and A. Important differences in cytokines and chemokines according to the clinical course were identified. Severe cases presented altered levels when compared to group C. This study helps to characterize biological markers related to angiogenesis, growth factors, heart disease, and cytokine/chemokine production in individuals infected with SARS-CoV-2, offering prognostic signatures and a basis for understanding the biological factors in disease severity.

Opportunities for pharmacoproteomics in biomarker discovery

Proteomic data are a uniquely valuable resource for drug response prediction and biomarker discovery because most drugs interact directly with proteins in target cells rather than with DNA or RNA. Recent advances in mass spectrometry and associated processing methods have enabled the generation of large-scale proteomic datasets. Here we review the significant opportunities that currently exist to combine large-scale proteomic data with drug-related research, a field termed pharmacoproteomics. We describe successful applications of drug response prediction using molecular data, with an emphasis on oncology. We focus on technical advances in data-independent acquisition mass spectrometry (DIA-MS) that can facilitate the discovery of protein biomarkers for drug responses, alongside the increased availability of big biomedical data. We spotlight new opportunities for machine learning in pharmacoproteomics, driven by the combination of these large datasets and improved high-performance computing. Finally, we explore the value of pre-clinical models for pharmacoproteomic studies and the accompanying challenges of clinical validation. We propose that pharmacoproteomics offers the potential for novel discovery and innovation within the cancer landscape. This article is protected by copyright. All rights reserved.

Plasma biomarkers for systemic inflammation in COVID-19 survivors

BACKGROUND

While the majority of COVID-19 patients fully recover from the infection and become asymptomatic, a significant proportion of COVID-19 survivors experience a broad spectrum of symptoms lasting weeks to months post-infection, a phenomenon termed "post-acute sequelae of COVID-19 (PASC)." The aim of this study is to determine whether inflammatory proteins are dysregulated and can serve as potential biomarkers for systemic inflammation in COVID-19 survivors.

METHODS

We determined the levels of inflammatory proteins in plasma from 22 coronavirus disease 2019 (COVID-19) long haulers (COV-LH), 22 COVID-19 asymptomatic survivors (COV-AS), and 22 healthy subjects (HS) using an Olink proteomics assay and assessed the results by a beads-based multiplex immunoassay.

RESULTS

Compared to HS, we found that COVID-19 survivors still exhibited systemic inflammation, as evidenced by significant changes in the levels of multiple inflammatory proteins in plasma from both COV-LH and COV-AS. CXCL10 was the only protein that significantly upregulated in COV-LH compared with COV-AS and HS.

CONCLUSIONS

Our results indicate that several inflammatory proteins remain aberrantly dysregulated in COVID-19 survivors and CXCL10 might serve as a potential biomarker to typify COV-LH. Further characterization of these signature inflammatory molecules might improve the understanding of the long-term impacts of COVID-19 and provide new targets for the diagnosis and treatment of COVID-19 survivors with PASC.

Characterization of aging cancer-associated fibroblasts draws implications in prognosis and immunotherapy response in low-grade gliomas

Background: Due to the highly variable prognosis of low-grade gliomas (LGGs), it is important to find robust biomarkers for predicting clinical outcomes. Aging cancer-associated fibroblasts (CAFs) within the senescent stroma of a tumor microenvironment (TME) have been recently reported to play a key role in tumor development. However, there are few studies focusing on this topic in gliomas.

Methods and Results: Based on the transcriptome data from TCGA and CGGA databases, we identified aging CAF-related genes (ACAFRGs) in LGGs by the weighted gene co-expression network analysis (WGCNA) method, followed by which LGG samples were classified into two aging CAF-related gene clusters with distinct prognosis and characteristics of the TME. Machine learning algorithms were used to screen out eight featured ACAFRGs to characterize two aging CAF-related gene clusters, and a nomogram model was constructed to predict the probability of gene cluster A for each LGG sample. Then, a powerful aging CAF scoring system was developed to predict the prognosis and response to immune checkpoint blockage therapy. Finally, the ACAFRGs were verified in two glioma-related external datasets. The performance of the aging CAF score in predicting the immunotherapy response was further validated in two independent cohorts. We also confirmed the expression of ACAFRGs at the protein level in glioma tissues through the Human Protein Atlas website and Western blotting analysis.

Conclusion: We developed a robust aging CAF scoring system to predict the prognosis and immunotherapy response in LGGs. Our findings may provide new targets for therapeutics and contribute to the exploration focusing on aging CAFs.

Combining Deep Phenotyping of Serum Proteomics and Clinical Data via Machine Learning for COVID-19 Biomarker Discovery

The persistence of long-term coronavirus-induced disease 2019 (COVID-19) sequelae demands better insights into its natural history. Therefore, it is crucial to discover the biomarkers of disease outcome to improve clinical practice. In this study, 160 COVID-19 patients were enrolled, of whom 80 had a “non-severe” and 80 had a “severe” outcome. Sera were analyzed by proximity extension assay (PEA) to assess 274 unique proteins associated with inflammation, cardiometabolic, and neurologic diseases. The main clinical and hematochemical data associated with disease outcome were grouped with serological data to form a dataset for the supervised machine learning techniques. We identified nine proteins (i.e., CD200R1, MCP1, MCP3, IL6, LTBP2, MATN3, TRANCE, α2-MRAP, and KIT) that contributed to the correct classification of COVID-19 disease severity when combined with relative neutrophil and lymphocyte counts. By analyzing PEA, clinical and hematochemical data with statistical methods that were able to handle many variables in the presence of a relatively small sample size, we identified nine potential serum biomarkers of a “severe” outcome. Most of these were confirmed by literature data. Importantly, we found three biomarkers associated with central nervous system pathologies and protective factors, which were downregulated in the most severe cases.

A Meta-summary and Bioinformatic Analysis Identified Interleukin 6 as a Master Regulator of COVID-19 Severity Biomarkers

With the rising demand for improved COVID-19 disease monitoring and prognostic markers, studies have aimed to identify biomarkers using a range of screening methods. However, the selection of biomarkers for validation from large datasets may result in potentially important biomarkers being overlooked when datasets are considered in isolation. Here, we have utilized a meta-summary approach to investigate COVID-19 biomarker datasets to identify conserved biomarkers of COVID-19 severity. This approach identified a panel of 17 proteins that showed a consistent direction of change across two or more datasets. Furthermore, bioinformatics analysis of these proteins highlighted a range of enriched biological processes that include inflammatory responses and compromised integrity of physiological systems including cardiovascular, neurological, and metabolic. A panel of upstream regulators of the COVID-19 severity biomarkers were identified, including chemical compounds currently under investigation for COVID-19 treatment. One of the upstream regulators, interleukin 6 (IL6), was identified as a “master regulator” of the severity biomarkers. COVID-19 disease severity is intensified due to the extreme viral immunological reaction that results in increased inflammatory biomarkers and cytokine storm. Since IL6 is the primary stimulator of cytokines, it could be used independently as a biomarker in determining COVID-19 disease progression, in addition to a potential therapeutic approach targeting IL6. The array of upstream regulators of the severity biomarkers identified here serve as attractive candidates for the development of new therapeutic approaches to treating COVID-19. In addition, the findings from this study highlight COVID-19 severity biomarkers which represent promising, robust biomarkers for future validation studies for their use in defining and monitoring disease severity and patient prognosis.

Longitudinal Serum Proteome Characterization of COVID-19 Patients With Different Severities Revealed Potential Therapeutic Strategies

The COVID-19 pandemic caused by SARS-CoV-2 is exerting huge pressure on global healthcare. Understanding of the molecular pathophysiological alterations in COVID-19 patients with different severities during disease is important for effective treatment. In this study, we performed proteomic profiling of 181 serum samples collected at multiple time points from 79 COVID-19 patients with different severity levels (asymptomatic, mild, moderate, and severe/critical) and 27 serum samples from non-COVID-19 control individuals. Dysregulation of immune response and metabolic reprogramming was found in severe/critical COVID-19 patients compared with non-severe/critical patients, whereas asymptomatic patients presented an effective immune response compared with symptomatic COVID-19 patients. Interestingly, the moderate COVID-19 patients were mainly grouped into two distinct clusters using hierarchical cluster analysis, which demonstrates the molecular pathophysiological heterogeneity in COVID-19 patients. Analysis of protein-level alterations during disease progression revealed that proteins involved in complement activation, the coagulation cascade and cholesterol metabolism were restored at the convalescence stage, but the levels of some proteins, such as anti-angiogenesis protein PLGLB1, would not recovered. The higher serum level of PLGLB1 in COVID-19 patients than in control groups was further confirmed by parallel reaction monitoring (PRM). These findings expand our understanding of the pathogenesis and progression of COVID-19 and provide insight into the discovery of potential therapeutic targets and serum biomarkers worth further validation.

Lectin Pathway Enzyme MASP-2 and Downstream Complement Activation in COVID-19

Mannose-binding lectin-associated serine protease 2 (MASP-2) is the main activator of the lectin complement pathway and has been suggested to be involved in the pathophysiology of coronavirus disease 2019 (COVID-19). To study a possible association between MASP-2 and COVID-19, we aimed at developing a sensitive and reliable MASP-2 ELISA. From an array of novel mouse-monoclonal antibodies using recombinant MASP-2 as antigen, two clones were selected to create a sandwich ELISA. Plasma samples were obtained from 216 healthy controls, 347 convalescent COVID-19 patients, and 147 prospectively followed COVID-19 patients. The assay was specific towards MASP-2 and did not recognize the truncated MASP2 splice variant MAP-2 (MAp19). The limit of quantification was shown to be 0.1 ng/mL. MASP-2 concentration was found to be stable after multiple freeze-thaw cycles. In healthy controls, the mean MASP-2 concentration was 524 ng/mL (95% CI: 496.5-551.6). No significant difference was found in the MASP-2 concentrations between COVID-19 convalescent samples and controls. However, a significant increase was observed in prospectively followed COVID-19 patients (mean: 834 ng/mL [95% CI: 765.3-902.7, p < 0.0001]). In these patients, MASP-2 concentration correlated significantly with the concentrations of the terminal complement complex (ρ = 0.3596, p < 0.0001), with the lectin pathway pattern recognition molecules ficolin-2 (ρ = 0.2906, p = 0.0004) and ficolin-3 (ρ = 0.3952, p < 0.0001) and with C-reactive protein (ρ = 0.3292, p = 0.0002). Overall, we developed a specific quantitative MASP-2 sandwich ELISA. MASP-2 correlated with complement activation and inflammatory markers in COVID-19 patients, underscoring a possible role of MASP-2 in COVID-19 pathophysiology.

Prognostic Value of Serum MICA Levels as a Marker of Severity in COVID-19 Patients

The COVID-19 global pandemic and high mortality rates necessitate the development of diagnostic and prognostic tools, as well as expanding testing capacity. Existing methods for detecting and characterizing SARS-CoV-2 infection are typically based on viral genome detection or measuring COVID-19-specific antibody levels. Despite their value, these methods are unable to predict disease outcomes in patients. Given the critical role of innate immune cells, particularly natural killer (NK) cells, in antiviral defense, this study sought to determine the prognostic value of serum secretory MHC class I polypeptide-related sequence A (sMICA) levels as an essential ligand for the NKG2D receptor, the master regulator of NK cell development and responsiveness. Serum MICA levels were measured by ELISA assay. Sera (n = 60) from SARS-CoV-2 positive patients were collected, and disease severity was determined using clinical criteria. The patient group included 30 patients with mild disease and 30 severely ill patients, as well as 30 healthy controls. Our findings revealed that serum MICA levels were significantly higher in patients than in controls, especially in cases with severe complications (P < .0001). Higher serum MICA levels may be associated with respiratory failure in COVID-19 and may serve as a marker of clinical severity in patients infected with SARS-CoV-2, particularly when clinical manifestations are insufficient to make a confident prediction.

In-Depth Serum Proteomics by DIA-MS with In Silico Spectral Libraries Reveals Dynamics during the Active Phase of Systemic Juvenile Idiopathic Arthritis

In serum proteomics using mass spectrometry, the number of detectable proteins is reduced due to high-abundance proteins, such as albumin. However, recently developed data-independent acquisition mass spectrometry (DIA-MS) proteomics technology has made it possible to remarkably improve the number of proteins in a serum analysis by removing high-abundance proteins. Using this technology, we analyzed sera from patients with systemic juvenile idiopathic arthritis (sJIA), a rare pediatric disease. As a result, we identified 2727 proteins with a wide dynamic range derived from various tissue leakages. We also selected 591 proteins that differed significantly in their active phases. These proteins were involved in many inflammatory processes, and we also identified immunoproteasomes, which were not previously found in serum, suggesting that they may be involved in the pathogenesis of sJIA. A detailed high-depth DIA-MS proteomic analysis of serum may be useful for understanding the pathogenesis of sJIA and may provide clues for the development of new biomarkers.

COVIDomics: The Proteomic and Metabolomic Signatures of COVID-19

Omics-based technologies have been largely adopted during this unprecedented global COVID-19 pandemic, allowing the scientific community to perform research on a large scale to understand the pathobiology of the SARS-CoV-2 infection and its replication into human cells. The application of omics techniques has been addressed to every level of application, from the detection of mutations, methods of diagnosis or monitoring, drug target discovery, and vaccine generation, to the basic definition of the pathophysiological processes and the biochemical mechanisms behind the infection and spread of SARS-CoV-2. Thus, the term COVIDomics wants to include those efforts provided by omics-scale investigations with application to the current COVID-19 research. This review summarizes the diverse pieces of knowledge acquired with the application of COVIDomics techniques, with the main focus on proteomics and metabolomics studies, in order to capture a common signature in terms of proteins, metabolites, and pathways dysregulated in COVID-19 disease. Exploring the multiomics perspective and the concurrent data integration may provide new suitable therapeutic solutions to combat the COVID-19 pandemic.