Prognostic tools and candidate drugs based on plasma proteomics of patients with severe COVID-19 complications

Multi-omics combined to investigate potential druggable therapeutic targets for stroke: A systematic Mendelian randomization study and transcriptome verification

OBJECTIVE

Stroke is a highly prevalent and disabling disease whose disease mechanisms are not fully understood. The discovery of disease-associated proteins with genetic evidence of pathogenicity provides an opportunity to identify new therapeutic targets.

METHOD

We examined the observed and causal associations of thousands of plasma and inflammatory proteins that were measured using affinity-based proteomic assays. First, we pooled >3000 relevant proteins using a fixed-effects meta-analysis of 2 population-based studies involving 48,383 participants, then investigated the causal effects of stroke and its subtype-associated proteins by forward Mendelian randomization using cis-protein quantitative locus genetic tools identified from genome-wide association studies of these >48,000 individuals. To improve the accuracy of causal estimation, we implemented a systematic Mendelian randomization model that accounts for cascading imbalances between instruments and tested the robustness of causal estimation through multi-method analyses. To further validate the hypothesis that ginsenoside Rg1 monomer acts on the five protein targets screened for drug-targeted regulation, we conducted a comparative analysis of the mRNA (gene) expression levels of a limited number of genes in the brain tissues of different groups of SD rats. The druggability of the candidate proteins was investigated and the mechanism of action and potential targeting side effects were explored by Phenome-wide MR.

RESULTS

Six circulating proteins were identified to have a significant genetic association with stroke (PFDR < 0.05). For example, in patients with cardioembolic stroke, higher genetically predicted APRT was associated with a lower risk of cardioembolic stroke (ORivw [95 % CI] = 0.641 [0.517, 0.795]; P = 5.25 × 10-5, ORSMR [95 % CI] = 0.572, [0.397, 0.825], PSMR = 0.003). Mediation analyses suggested that atrial fibrillation, angina pectoris, and heart failure may mediate the association of CD40L, LIFR, and UPA with stroke. Molecular docking revealed promising interactions between the identified proteins and glycosides. Transcriptomic sequencing in animal models indicated that ginsenoside Rg1 may act through APRT, IL15RA, and VSIR pathways, with APRT showing significant variability in mRNA sequencing expression. Phenome-wide MR of the six target proteins showed an overwhelming predominance of PFDR > 0.05, indicating less toxicity.

CONCLUSIONS

The present study provides genetic evidence to support the potential efficacy of targeting the three druggable protein targets for the treatment of stroke. This is achieved by triangulating population genomic and proteomic data. Furthermore, the study validates the pathway mechanisms by which APRT, IL15RA, and VSIR dock ginsenoside Rg1 in animal models. This will help to prioritize stroke drug development.

Metabolite profiles of distinct obesity phenotypes integrating impacts of altitude and their association with diet and metabolic disorders in Tibetans

OBJECTIVE

Improved understanding of metabolic obesity phenotypes holds great promise for personalized strategies to combat obesity and its co-morbidities. Such investigation is however lacking in Tibetans with unique living environments and lifestyle in the highlands. Effects of altitude on heterogeneous metabolic obesity phenotypes remain unexplored.

METHODS

We defined metabolic obesity phenotypes i.e., metabolically healthy/unhealthy and obesity/normal weight in Tibetans (n = 1204) living at 2800 m in the suburb or over 4000 m in pastoral areas. 129 lipoprotein parameters and 25 low-molecular-weight metabolites were quantified and their associations with each phenotype were assessed using logistic regression models adjusting for potential confounders. The metabolic BMI (mBMI) was generated using a machine learning strategy and its relationship with prevalence of obesity co-morbidities and dietary exposures were investigated.

RESULTS

Ultrahigh altitude positively associated with the metabolically healthy and non-obese phenotype and had a tendency towards a negative association with metabolically unhealthy phenotype. Phenotype-specific associations were found for 107 metabolites (e.g., lipoprotein subclasses, N-acetyl-glycoproteins, amino acids, fatty acids and lactate, p < 0.05), among which 55 were manipulated by altitude. The mBMI showed consistent yet more pronounced associations with cardiometabolic outcomes than BMI. The ORs for diabetes, prediabetes and hypertriglyceridemia were reduced in individuals residing at ultrahigh altitude compared to those residing at high altitude. The mBMI mediated the negative association between pastoral diet and prevalence of prediabetes, hypertension and hypertriglyceridemia, respectively.

CONCLUSIONS

We found metabolite markers representing distinct obesity phenotypes associated with obesity co-morbidities and the modification effect of altitude, deciphering mechanisms underlying protective effect of ultrahigh altitude and the pastoral diet on metabolic health.

Proteomic profiling of cerebrospinal fluid reveals TKT as a potential biomarker for medulloblastoma

Cerebrospinal fluid (CSF) plays an important role in brain tumors, including medulloblastoma (MBL). Recent advancements in mass spectrometry systems and 'Omics' data analysis methods enable unbiased, high proteome depth research. We conducted proteomic profiling of the total CSF in MBL patients with the purpose of finding a potential diagnostic biomarker for MBL. We quantified 1112 proteins per CSF sample. Feature selection identified four elevated soluble proteins (SPTBN1, HSP90AA1, TKT, and NME1-NME2) in MBL CSF. Validation with ELISA confirmed that TKT was significantly elevated in MBL. Additionally, TKT-positive extracellular vesicles were significantly enriched in MBL CSF and correlated with the burden of leptomeningeal seeding. Our results provide insights into the proteomics data of the total CSF of MBL patients. Furthermore, we identified the significance of TKT within the total CSF and its presence within circulating EVs in the CSF. We suggest that TKT may serve as a biomarker for MBL.

The Impact of Serum/Plasma Proteomics on SARS-CoV-2 Diagnosis and Prognosis

While COVID-19's urgency has diminished since its emergence in late 2019, it remains a significant public health challenge. Recent research reveals that the molecular intricacies of this virus are far more complex than initially understood, with numerous post-translational modifications leading to diverse proteoforms and viral particle heterogeneity. Mass spectrometry-based proteomics of patient serum/plasma emerges as a promising complementary approach to traditional diagnostic methods, offering insights into SARS-CoV-2 protein dynamics and enhancing understanding of the disease and its long-term consequences. This article highlights key findings from three years of pandemic-era proteomics research. It delves into biomarker discovery, diagnostic advancements, and drug development efforts aimed at monitoring COVID-19 onset and progression and exploring treatment options. Additionally, it examines global protein abundance and post-translational modification profiling to elucidate signaling pathway alterations and protein-protein interactions during infection. Finally, it explores the potential of emerging multi-omics analytic strategies in combatting SARS-CoV-2.

Dupilumab Therapy Modulates Circulating Inflammatory Mediators in Patients with Prurigo Nodularis

Prurigo nodularis (PN) is a chronic inflammatory skin disease characterized by intense pruritus and skin nodules. Beyond the skin, PN involves circulating blood inflammation that may contribute to systemic disease comorbidities. Dupilumab was recently approved for treatment of PN, but its effects on systemic inflammation are unknown. Thus, we aimed to characterize changes in plasma concentrations of inflammatory proteins after dupilumab treatment. In this exploratory study, plasma samples were collected from 3 patients with moderate-to-severe PN before and after ≥6 months of dupilumab treatment. All patients exhibited clinically significant improvements after treatment. Of the 2569 proteins tested, 186 were differentially expressed after treatment (q < 0.1, fold change > 1.3). Downregulated proteins included cytokines associated with T helper (Th) 1 (IFN-γ, TNF-α), Th2 (IL-4, IL-13), and Th17/Th22 (IL-6, IL-22) signaling. Markers of innate immunity (IL-19, toll-like receptor 1, nitric oxide synthase 2), immune cell migration (CCL20, CD177), and fibrosis (IL-11, IL-22) were also decreased (q < 0.1). Gene set variation analysis of Th2, Th17, and epithelial-mesenchymal transition gene sets showed reduced pathway expression in the post-treatment cohort (P < .05). Plasma cytokine levels of IL-11, nitric oxide synthase 2, IL-13, IL-4, and IFNG (R2 > 0.75, q < 0.10) showed the strongest correlations with pruritus severity. Dupilumab may reduce systemic inflammatory proteins associated with multiple immune and fibrosis pathways in patients with PN, potentially modulating the development of systemic disease comorbidities.

Microplastics dysregulate innate immunity in the SARS-CoV-2 infected lung

Introduction

Global microplastic (MP) pollution is now well recognized, with humans and animals consuming and inhaling MPs on a daily basis, with a growing body of concern surrounding the potential impacts on human health.

Methods

Using a mouse model of mild COVID-19, we describe herein the effects of azide-free 1 μm polystyrene MP beads, co-delivered into lungs with a SARS-CoV-2 omicron BA.5 inoculum. The effect of MPs on the host response to SARS-CoV-2 infection was analysed using histopathology and RNA-Seq at 2 and 6 days post-infection (dpi).

Results

Although infection reduced clearance of MPs from the lung, virus titres and viral RNA levels were not significantly affected by MPs, and overt MP-associated clinical or histopathological changes were not observed. However, RNA-Seq of infected lungs revealed that MP exposure suppressed innate immune responses at 2 dpi and increased pro-inflammatory signatures at 6 dpi. The cytokine profile at 6 dpi showed a significant correlation with the 'cytokine release syndrome' signature observed in some COVID-19 patients.

Discussion

The findings are consistent with the recent finding that MPs can inhibit phagocytosis of apoptotic cells via binding of Tim4. They also add to a growing body of literature suggesting that MPs can dysregulate inflammatory processes in specific disease settings.

[Prevention and treatment of COVID-19 based on post-genomic pharmacological analysis: Systematic computer analysis of 290,000 scientific articles on COVID-19]

The COVID-19 pandemic has highlighted pressing challenges in biomedical research methodology. It has become obvious that the rapid and effective development of treatments for "new" viral infections is impossible without the coordination of interdisciplinary research and in-depth analysis of data obtained within the framework of the post-genomic paradigm. Presents the results of a systematic computer analysis of 290,000 scientific articles on COVID-19, with an emphasis on the results of post-genomic studies of SARS-CoV-2. The futility of the overly simplified approach, which considers only one "most important receptor protein", only one "key virus gene", etc., is shown. It is shown how post-genomic technologies will make it possible to find informative biomarkers of severe coronavirus infection, including those based on complex immune disorders associated with COVID-19.

Safety and efficacy of sirolimus in hospitalised patients with COVID-19 pneumonia

BACKGROUND

There is a critical need to develop novel therapies for COVID-19.

METHODS

We conducted a phase 2, multicentre, placebo-controlled, double-blind, randomised trial; hospitalised patients with hypoxemic respiratory failure due to COVID-19 and at least one poor prognostic biomarker, were given sirolimus (6 mg on Day 1 followed by 2 mg daily for 14 days or hospital discharge, whichever happens first) or placebo, in a 2:1 randomization scheme favouring sirolimus. Primary outcome was the proportion of patients alive and free from advanced respiratory support measures at Day 28.

RESULTS

Between April 2020 and April 2021, 32 patients underwent randomization and 28 received either sirolimus (n = 18) or placebo (n = 10). Mean age was 57 years and 75 % of the subjects were men. Twenty-two subjects had at least one co-existing condition (Diabetes, hypertension, obesity, CHF, or asthma/COPD) associated with worse prognosis. Mean FiO2 requirement was 0.35. There was no difference in the proportion of patients who were alive and free from advanced respiratory support measures in the sirolimus group (n = 15, 83 %) compared with the placebo group (n = 8, 80 %). Although patients in the sirolimus group demonstrated faster improvement in oxygenation and spent less time in the hospital, these differences were not statistically significant. There was no between-group difference in the rate of change in serum biomarkers such as LDH, ferritin, d-dimer or lymphocyte count. There was a decreased risk of thromboembolic complications in patients on sirolimus compared with placebo.

CONCLUSIONS

Larger studies are warranted to evaluate the role sirolimus in COVID-19 infection.

Complex changes in serum protein levels in COVID-19 convalescents

The COVID-19 pandemic, triggered by severe acute respiratory syndrome coronavirus 2, has affected millions of people worldwide. Much research has been dedicated to our understanding of COVID-19 disease heterogeneity and severity, but less is known about recovery associated changes. To address this gap in knowledge, we quantified the proteome from serum samples from 29 COVID-19 convalescents and 29 age-, race-, and sex-matched healthy controls. Samples were acquired within the first months of the pandemic. Many proteins from pathways known to change during acute COVID-19 illness, such as from the complement cascade, coagulation system, inflammation and adaptive immune system, had returned to levels seen in healthy controls. In comparison, we identified 22 and 15 proteins with significantly elevated and lowered levels, respectively, amongst COVID-19 convalescents compared to healthy controls. Some of the changes were similar to those observed for the acute phase of the disease, i.e. elevated levels of proteins from hemolysis, the adaptive immune systems, and inflammation. In contrast, some alterations opposed those in the acute phase, e.g. elevated levels of CETP and APOA1 which function in lipid/cholesterol metabolism, and decreased levels of proteins from the complement cascade (e.g. C1R, C1S, and VWF), the coagulation system (e.g. THBS1 and VWF), and the regulation of the actin cytoskeleton (e.g. PFN1 and CFL1) amongst COVID-19 convalescents. We speculate that some of these shifts might originate from a transient decrease in platelet counts upon recovery from the disease. Finally, we observed race-specific changes, e.g. with respect to immunoglobulins and proteins related to cholesterol metabolism.

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 ).

Proteomic profiling identifies biomarkers of COVID-19 severity

SARS-CoV-2 infection remains a major public health concern, particularly for the aged and those individuals with co-morbidities at risk for developing severe COVID-19. Understanding the pathogenesis and biomarkers associated with responses to SARS-CoV-2 infection remain critical components in developing effective therapeutic approaches, especially in cases of severe and long-COVID-19. In this study blood plasma protein expression was compared in subjects with mild, moderate, and severe COVID-19 disease. Evaluation of an inflammatory protein panel confirms upregulation of proteins including TNFβ, IL-6, IL-8, IL-12, already associated with severe cytokine storm and progression to severe COVID-19. Importantly, we identify several proteins not yet associated with COVID-19 disease, including mesothelin (MSLN), that are expressed at significantly higher levels in severe COVID-19 subjects. In addition, we find a subset of markers associated with T-cell and dendritic cell responses to viral infection that are significantly higher in mild cases and decrease in expression as severity of COVID-19 increases, suggesting that an immediate and effective activation of T-cells is critical in modulating disease progression. Together, our findings identify new targets for further investigation as therapeutic approaches for the treatment of SARS-CoV-2 infection and prevention of complications of severe COVID-19.

Development of a proteomic signature associated with severe disease for patients with COVID-19 using data from 5 multicenter, randomized, controlled, and prospective studies

Significant progress has been made in preventing severe COVID-19 disease through the development of vaccines. However, we still lack a validated baseline predictive biologic signature for the development of more severe disease in both outpatients and inpatients infected with SARS-CoV-2. The objective of this study was to develop and externally validate, via 5 international outpatient and inpatient trials and/or prospective cohort studies, a novel baseline proteomic signature, which predicts the development of moderate or severe (vs mild) disease in patients with COVID-19 from a proteomic analysis of 7000 + proteins. The secondary objective was exploratory, to identify (1) individual baseline protein levels and/or (2) protein level changes within the first 2 weeks of acute infection that are associated with the development of moderate/severe (vs mild) disease. For model development, samples collected from 2 randomized controlled trials were used. Plasma was isolated and the SomaLogic SomaScan platform was used to characterize protein levels for 7301 proteins of interest for all studies. We dichotomized 113 patients as having mild or moderate/severe COVID-19 disease. An elastic net approach was used to develop a predictive proteomic signature. For validation, we applied our signature to data from three independent prospective biomarker studies. We found 4110 proteins measured at baseline that significantly differed between patients with mild COVID-19 and those with moderate/severe COVID-19 after adjusting for multiple hypothesis testing. Baseline protein expression was associated with predicted disease severity with an error rate of 4.7% (AUC = 0.964). We also found that five proteins (Afamin, I-309, NKG2A, PRS57, LIPK) and patient age serve as a signature that separates patients with mild COVID-19 and patients with moderate/severe COVID-19 with an error rate of 1.77% (AUC = 0.9804). This panel was validated using data from 3 external studies with AUCs of 0.764 (Harvard University), 0.696 (University of Colorado), and 0.893 (Karolinska Institutet). In this study we developed and externally validated a baseline COVID-19 proteomic signature associated with disease severity for potential use in both outpatients and inpatients with COVID-19.

oFVSD: a Python package of optimized forward variable selection decoder for high-dimensional neuroimaging data

The complexity and high dimensionality of neuroimaging data pose problems for decoding information with machine learning (ML) models because the number of features is often much larger than the number of observations. Feature selection is one of the crucial steps for determining meaningful target features in decoding; however, optimizing the feature selection from such high-dimensional neuroimaging data has been challenging using conventional ML models. Here, we introduce an efficient and high-performance decoding package incorporating a forward variable selection (FVS) algorithm and hyper-parameter optimization that automatically identifies the best feature pairs for both classification and regression models, where a total of 18 ML models are implemented by default. First, the FVS algorithm evaluates the goodness-of-fit across different models using the k-fold cross-validation step that identifies the best subset of features based on a predefined criterion for each model. Next, the hyperparameters of each ML model are optimized at each forward iteration. Final outputs highlight an optimized number of selected features (brain regions of interest) for each model with its accuracy. Furthermore, the toolbox can be executed in a parallel environment for efficient computation on a typical personal computer. With the optimized forward variable selection decoder (oFVSD) pipeline, we verified the effectiveness of decoding sex classification and age range regression on 1,113 structural magnetic resonance imaging (MRI) datasets. Compared to ML models without the FVS algorithm and with the Boruta algorithm as a variable selection counterpart, we demonstrate that the oFVSD significantly outperformed across all of the ML models over the counterpart models without FVS (approximately 0.20 increase in correlation coefficient, r, with regression models and 8% increase in classification models on average) and with Boruta variable selection algorithm (approximately 0.07 improvement in regression and 4% in classification models). Furthermore, we confirmed the use of parallel computation considerably reduced the computational burden for the high-dimensional MRI data. Altogether, the oFVSD toolbox efficiently and effectively improves the performance of both classification and regression ML models, providing a use case example on MRI datasets. With its flexibility, oFVSD has the potential for many other modalities in neuroimaging. This open-source and freely available Python package makes it a valuable toolbox for research communities seeking improved decoding accuracy.

COVIDpro: Database for Mining Protein Dysregulation in Patients with COVID-19

The ongoing pandemic of the coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 still has limited treatment options. Our understanding of the molecular dysregulations that occur in response to infection remains incomplete. We developed a web application COVIDpro (https://www.guomics.com/covidPro/) that includes proteomics data obtained from 41 original studies conducted in 32 hospitals worldwide, involving 3077 patients and covering 19 types of clinical specimens, predominantly plasma and serum. The data set encompasses 53 protein expression matrices, comprising a total of 5434 samples and 14,403 unique proteins. We identified a panel of proteins that exhibit significant dysregulation, enabling the classification of COVID-19 patients into severe and non-severe disease categories. The proteomic signatures achieved promising results in distinguishing severe cases, with a mean area under the curve of 0.87 and accuracy of 0.80 across five independent test sets. COVIDpro serves as a valuable resource for testing hypotheses and exploring potential targets for novel treatments in COVID-19 patients.

A complement atlas identifies interleukin-6-dependent alternative pathway dysregulation as a key druggable feature of COVID-19

Improvements in COVID-19 treatments, especially for the critically ill, require deeper understanding of the mechanisms driving disease pathology. The complement system is not only a crucial component of innate host defense but can also contribute to tissue injury. Although all complement pathways have been implicated in COVID-19 pathogenesis, the upstream drivers and downstream effects on tissue injury remain poorly defined. We demonstrate that complement activation is primarily mediated by the alternative pathway, and we provide a comprehensive atlas of the complement alterations around the time of respiratory deterioration. Proteomic and single-cell sequencing mapping across cell types and tissues reveals a division of labor between lung epithelial, stromal, and myeloid cells in complement production, in addition to liver-derived factors. We identify IL-6 and STAT1/3 signaling as an upstream driver of complement responses, linking complement dysregulation to approved COVID-19 therapies. Furthermore, an exploratory proteomic study indicates that inhibition of complement C5 decreases epithelial damage and markers of disease severity. Collectively, these results support complement dysregulation as a key druggable feature of COVID-19.

Plasma proteome of Long-COVID patients indicates HIF-mediated vasculo-proliferative disease with impact on brain and heart function

AIMS

Long-COVID occurs after SARS-CoV-2 infection and results in diverse, prolonged symptoms. The present study aimed to unveil potential mechanisms, and to inform prognosis and treatment.

METHODS

Plasma proteome from Long-COVID outpatients was analyzed in comparison to matched acutely ill COVID-19 (mild and severe) inpatients and healthy control subjects. The expression of 3072 protein biomarkers was determined with proximity extension assays and then deconvoluted with multiple bioinformatics tools into both cell types and signaling mechanisms, as well as organ specificity.

RESULTS

Compared to age- and sex-matched acutely ill COVID-19 inpatients and healthy control subjects, Long-COVID outpatients showed natural killer cell redistribution with a dominant resting phenotype, as opposed to active, and neutrophils that formed extracellular traps. This potential resetting of cell phenotypes was reflected in prospective vascular events mediated by both angiopoietin-1 (ANGPT1) and vascular-endothelial growth factor-A (VEGFA). Several markers (ANGPT1, VEGFA, CCR7, CD56, citrullinated histone 3, elastase) were validated by serological methods in additional patient cohorts. Signaling of transforming growth factor-β1 with probable connections to elevated EP/p300 suggested vascular inflammation and tumor necrosis factor-α driven pathways. In addition, a vascular proliferative state associated with hypoxia inducible factor 1 pathway suggested progression from acute COVID-19 to Long-COVID. The vasculo-proliferative process predicted in Long-COVID might contribute to changes in the organ-specific proteome reflective of neurologic and cardiometabolic dysfunction.

CONCLUSIONS

Taken together, our findings point to a vasculo-proliferative process in Long-COVID that is likely initiated either prior hypoxia (localized or systemic) and/or stimulatory factors (i.e., cytokines, chemokines, growth factors, angiotensin, etc). Analyses of the plasma proteome, used as a surrogate for cellular signaling, unveiled potential organ-specific prognostic biomarkers and therapeutic targets.

Uncovering a neurological protein signature for severe COVID-19

Coronavirus disease of 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has sparked a global pandemic with severe complications and high morbidity rate. Neurological symptoms in COVID-19 patients, and neurological sequelae post COVID-19 recovery have been extensively reported. Yet, neurological molecular signature and signaling pathways that are affected in the central nervous system (CNS) of COVID-19 severe patients remain still unknown and need to be identified. Plasma samples from 49 severe COVID-19 patients, 50 mild COVID-19 patients, and 40 healthy controls were subjected to Olink proteomics analysis of 184 CNS-enriched proteins. By using a multi-approach bioinformatics analysis, we identified a 34-neurological protein signature for COVID-19 severity and unveiled dysregulated neurological pathways in severe cases. Here, we identified a new neurological protein signature for severe COVID-19 that was validated in different independent cohorts using blood and postmortem brain samples and shown to correlate with neurological diseases and pharmacological drugs. This protein signature could potentially aid the development of prognostic and diagnostic tools for neurological complications in post-COVID-19 convalescent patients with long term neurological sequelae.

The blood proteomic signature of prurigo nodularis reveals distinct inflammatory and neuropathic endotypes: A cluster analysis

BACKGROUND

Prurigo nodularis (PN) is an extremely pruritic, chronic inflammatory skin disease. Little is known about systemic inflammation in PN.

OBJECTIVE

To characterize plasma inflammatory biomarkers in patients with PN and investigate the presence of disease endotypes.

METHODS

In this cross-sectional study, Olink proteomic analysis was performed on plasma samples from patients with PN (n = 29) and healthy controls (n = 18).

RESULTS

Patients with PN had increased levels of 8 circulating biomarkers compared to controls, including tumor necrosis factor, C-X-C Motif Chemokine Ligand 9, interleukin-12B, and tumor necrosis factor receptor superfamily member 9 (P < .05). Two PN clusters were identified in cluster 1 (n = 13) and cluster 2 (n = 16). Cluster 2 had higher levels of 25 inflammatory markers than cluster 1. Cluster 1 had a greater percentage of patients with a history of myelopathy and spinal disc disease compared with cluster 2 (69% vs 25%, P = .03). Patients in cluster 2 were more likely to have a history of atopy (38% in cluster 2 vs 8% in cluster 1, P = .09).

LIMITATIONS

Small sample size precludes robust subgroup analyses.

CONCLUSION

This study provides evidence of neuroimmune-biased endotypes in PN and can aid clinicians in managing patients with PN that are nonresponsive to traditional therapies.

Proteomics Investigation of Diverse Serological Patterns in COVID-19

Serum antibodies IgM and IgG are elevated during Coronavirus Disease 2019 (COVID-19) to defend against viral attacks. Atypical results such as negative and abnormally high antibody expression were frequently observed whereas the underlying molecular mechanisms are elusive. In our cohort of 144 COVID-19 patients, 3.5% were both IgM and IgG negative, whereas 29.2% remained only IgM negative. The remaining patients exhibited positive IgM and IgG expression, with 9.3% of them exhibiting over 20-fold higher titers of IgM than the others at their plateau. IgG titers in all of them were significantly boosted after vaccination in the second year. To investigate the underlying molecular mechanisms, we classed the patients into four groups with diverse serological patterns and analyzed their 2-year clinical indicators. Additionally, we collected 111 serum samples for TMTpro-based longitudinal proteomic profiling and characterized 1494 proteins in total. We found that the continuously negative IgM and IgG expression during COVID-19 were associated with mild inflammatory reactions and high T cell responses. Low levels of serum IgD, inferior complement 1 activation of complement cascades, and insufficient cellular immune responses might collectively lead to compensatory serological responses, causing overexpression of IgM. Serum CD163 was positively correlated with antibody titers during seroconversion. This study suggests that patients with negative serology still developed cellular immunity for viral defense and that high titers of IgM might not be favorable to COVID-19 recovery.

Targeted plasma proteomics reveals signatures discriminating COVID-19 from sepsis with pneumonia

BACKGROUND

COVID-19 remains a major public health challenge, requiring the development of tools to improve diagnosis and inform therapeutic decisions. As dysregulated inflammation and coagulation responses have been implicated in the pathophysiology of COVID-19 and sepsis, we studied their plasma proteome profiles to delineate similarities from specific features.

METHODS

We measured 276 plasma proteins involved in Inflammation, organ damage, immune response and coagulation in healthy controls, COVID-19 patients during acute and convalescence phase, and sepsis patients; the latter included (i) community-acquired pneumonia (CAP) caused by Influenza, (ii) bacterial CAP, (iii) non-pneumonia sepsis, and (iv) septic shock patients.

RESULTS

We identified a core response to infection consisting of 42 proteins altered in both COVID-19 and sepsis, although higher levels of cytokine storm-associated proteins were evident in sepsis. Furthermore, microbiologic etiology and clinical endotypes were linked to unique signatures. Finally, through machine learning, we identified biomarkers, such as TRIM21, PTN and CASP8, that accurately differentiated COVID-19 from CAP-sepsis with higher accuracy than standard clinical markers.

CONCLUSIONS

This study extends the understanding of host responses underlying sepsis and COVID-19, indicating varying disease mechanisms with unique signatures. These diagnostic and severity signatures are candidates for the development of personalized management of COVID-19 and sepsis.

Elevated IFNA1 and suppressed IL12p40 associated with persistent hyperinflammation in COVID-19 pneumonia

Introduction

Despite of massive endeavors to characterize inflammation in COVID-19 patients, the core network of inflammatory mediators responsible for severe pneumonia stillremain remains elusive.

Methods

Here, we performed quantitative and kinetic analysis of 191 inflammatory factors in 955 plasma samples from 80 normal controls (sample n = 80) and 347 confirmed COVID-19 pneumonia patients (sample n = 875), including 8 deceased patients.

Results

Differential expression analysis showed that 76% of plasmaproteins (145 factors) were upregulated in severe COVID-19 patients comparedwith moderate patients, confirming overt inflammatory responses in severe COVID-19 pneumonia patients. Global correlation analysis of the plasma factorsrevealed two core inflammatory modules, core I and II, comprising mainly myeloid cell and lymphoid cell compartments, respectively, with enhanced impact in a severity-dependent manner. We observed elevated IFNA1 and suppressed IL12p40, presenting a robust inverse correlation in severe patients, which was strongly associated with persistent hyperinflammation in 8.3% of moderate pneumonia patients and 59.4% of severe patients.

Discussion

Aberrant persistence of pulmonary and systemic inflammation might be associated with long COVID-19 sequelae. Our comprehensive analysis of inflammatory mediators in plasmarevealed the complexity of pneumonic inflammation in COVID-19 patients anddefined critical modules responsible for severe pneumonic progression.

COVID-19 plasma proteome reveals novel temporal and cell-specific signatures for disease severity and high-precision disease management

Coronavirus disease 2019 (COVID-19) is a systemic inflammatory condition with high mortality that may benefit from personalized medicine and high-precision approaches. COVID-19 patient plasma was analysed with targeted proteomics of 1161 proteins. Patients were monitored from Days 1 to 10 of their intensive care unit (ICU) stay. Age- and gender-matched COVID-19-negative sepsis ICU patients and healthy subjects were examined as controls. Proteomic data were resolved using both cell-specific annotation and deep-analysis for functional enrichment. COVID-19 caused extensive remodelling of the plasma microenvironment associated with a relative immunosuppressive milieu between ICU Days 3-7, and characterized by extensive organ damage. COVID-19 resulted in (1) reduced antigen presentation and B/T-cell function, (2) increased repurposed neutrophils and M1-type macrophages, (3) relatively immature or disrupted endothelia and fibroblasts with a defined secretome, and (4) reactive myeloid lines. Extracellular matrix changes identified in COVID-19 plasma could represent impaired immune cell homing and programmed cell death. The major functional modules disrupted in COVID-19 were exaggerated in patients with fatal outcome. Taken together, these findings provide systems-level insight into the mechanisms of COVID-19 inflammation and identify potential prognostic biomarkers. Therapeutic strategies could be tailored to the immune response of severely ill patients.

Proteomic analysis identifies a signature of disease severity in the plasma of COVID-19 pneumonia patients associated to neutrophil, platelet and complement activation

Most patients infected with SARS-CoV-2 display mild symptoms with good prognosis, while 20% of patients suffer from severe viral pneumonia and up to 5% may require intensive care unit (ICU) admission due to severe acute respiratory syndrome, which could be accompanied by multiorgan failure.Plasma proteomics provide valuable and unbiased information about disease progression and therapeutic candidates. Recent proteomic studies have identified molecular changes in plasma of COVID-19 patients that implied significant dysregulation of several aspects of the inflammatory response accompanied by a general metabolic suppression. However, which of these plasma alterations are associated with disease severity remains only partly characterized.A known limitation of proteomic studies of plasma samples is the large difference in the macromolecule abundance, with concentration spanning at least 10 orders of magnitude. To improve the coverage of plasma contents, we performed a deep proteomic analysis of plasma from 10 COVID-19 patients with severe/fatal pneumonia compared to 10 COVID-19 patients with pneumonia who did not require ICU admission (non-ICU). To this aim, plasma samples were first depleted of the most abundant proteins, trypsin digested and peptides subjected to a high pH reversed-phase peptide fractionation before LC-MS analysis.These results highlighted an increase of proteins involved in neutrophil and platelet activity and acute phase response, which is significantly higher in severe/fatal COVID-19 patients when compared to non-ICU ones. Importantly, these changes are associated with a selective induction of complement cascade factors in severe/fatal COVID-19 patients. Data are available via ProteomeXchange with identifier PXD036491. Among these alterations, we confirmed by ELISA that higher levels of the neutrophil granule proteins DEFA3 and LCN2 are present in COVID-19 patients requiring ICU admission when compared to non-ICU and healthy donors.Altogether, our study provided an in-depth view of plasma proteome changes that occur in COVID-19 patients in relation to disease severity, which can be helpful to identify therapeutic strategies to improve the disease outcome.

Plasma proteomic signature predicts who will get persistent symptoms following SARS-CoV-2 infection

BACKGROUND

The majority of those infected by ancestral Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) during the UK first wave (starting March 2020) did not require hospitalisation. Most had a short-lived mild or asymptomatic infection, while others had symptoms that persisted for weeks or months. We hypothesized that the plasma proteome at the time of first infection would reflect differences in the inflammatory response that linked to symptom severity and duration.

METHODS

We performed a nested longitudinal case-control study and targeted analysis of the plasma proteome of 156 healthcare workers (HCW) with and without lab confirmed SARS-CoV-2 infection. Targeted proteomic multiple-reaction monitoring analysis of 91 pre-selected proteins was undertaken in uninfected healthcare workers at baseline, and in infected healthcare workers serially, from 1 week prior to 6 weeks after their first confirmed SARS-CoV-2 infection. Symptom severity and antibody responses were also tracked. Questionnaires at 6 and 12 months collected data on persistent symptoms.

FINDINGS

Within this cohort (median age 39 years, interquartile range 30-47 years), 54 healthcare workers (44% male) had PCR or antibody confirmed infection, with the remaining 102 (38% male) serving as uninfected controls. Following the first confirmed SARS-CoV-2 infection, perturbation of the plasma proteome persisted for up to 6 weeks, tracking symptom severity and antibody responses. Differentially abundant proteins were mostly coordinated around lipid, atherosclerosis and cholesterol metabolism pathways, complement and coagulation cascades, autophagy, and lysosomal function. The proteomic profile at the time of seroconversion associated with persistent symptoms out to 12 months. Data are available via ProteomeXchange with identifier PXD036590.

INTERPRETATION

Our findings show that non-severe SARS-CoV-2 infection perturbs the plasma proteome for at least 6 weeks. The plasma proteomic signature at the time of seroconversion has the potential to identify which individuals are more likely to suffer from persistent symptoms related to SARS-CoV-2 infection.

FUNDING INFORMATION

The COVIDsortium is supported by funding donated by individuals, charitable Trusts, and corporations including Goldman Sachs, Citadel and Citadel Securities, The Guy Foundation, GW Pharmaceuticals, Kusuma Trust, and Jagclif Charitable Trust, and enabled by Barts Charity with support from University College London Hospitals (UCLH) Charity. This work was additionally supported by the Translational Mass Spectrometry Research Group and the Biomedical Research Center (BRC) at Great Ormond Street Hospital.

COVIDpro: Database for mining protein dysregulation in patients with COVID-19

Background

The ongoing pandemic of the coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) still has limited treatment options partially due to our incomplete understanding of the molecular dysregulations of the COVID-19 patients. We aimed to generate a repository and data analysis tools to examine the modulated proteins underlying COVID-19 patients for the discovery of potential therapeutic targets and diagnostic biomarkers.

Methods

We built a web server containing proteomic expression data from COVID-19 patients with a toolset for user-friendly data analysis and visualization. The web resource covers expert-curated proteomic data from COVID-19 patients published before May 2022. The data were collected from ProteomeXchange and from select publications via PubMed searches and aggregated into a comprehensive dataset. Protein expression by disease subgroups across projects was compared by examining differentially expressed proteins. We also visualize differentially expressed pathways and proteins. Moreover, circulating proteins that differentiated severe cases were nominated as predictive biomarkers.

Findings

We built and maintain a web server COVIDpro ( https://www.guomics.com/covidPro/ ) containing proteomics data generated by 41 original studies from 32 hospitals worldwide, with data from 3077 patients covering 19 types of clinical specimens, the majority from plasma and sera. 53 protein expression matrices were collected, for a total of 5434 samples and 14,403 unique proteins. Our analyses showed that the lipopolysaccharide-binding protein, as identified in the majority of the studies, was highly expressed in the blood samples of patients with severe disease. A panel of significantly dysregulated proteins was identified to separate patients with severe disease from non-severe disease. Classification of severe disease based on these proteomic signatures on five test sets reached a mean AUC of 0.87 and ACC of 0.80.

Interpretation

COVIDpro is an online database with an integrated analysis toolkit. It is a unique and valuable resource for testing hypotheses and identifying proteins or pathways that could be targeted by new treatments of COVID-19 patients.

Funding

National Key R&D Program of China: Key PDPM technologies (2021YFA1301602, 2021YFA1301601, 2021YFA1301603), Zhejiang Provincial Natural Science Foundation for Distinguished Young Scholars (LR19C050001), Hangzhou Agriculture and Society Advancement Program (20190101A04), National Natural Science Foundation of China (81972492) and National Science Fund for Young Scholars (21904107), National Resource for Network Biology (NRNB) from the National Institute of General Medical Sciences (NIGMS-P41 GM103504).

Research in context

Evidence before this study: Although an increasing number of therapies against COVID-19 are being developed, they are still insufficient, especially with the rise of new variants of concern. This is partially due to our incomplete understanding of the disease’s mechanisms. As data have been collected worldwide, several questions are now worth addressing via meta-analyses. Most COVID-19 drugs function by targeting or affecting proteins. Effectiveness and resistance to therapeutics can be effectively assessed via protein measurements. Empowered by mass spectrometry-based proteomics, protein expression has been characterized in a variety of patient specimens, including body fluids (e.g., serum, plasma, urea) and tissue (i.e., formalin-fixed and paraffin-embedded (FFPE)). We expert-curated proteomic expression data from COVID-19 patients published before May 2022, from the largest proteomic data repository ProteomeXhange as well as from literature search engines. Using this resource, a COVID-19 proteome meta-analysis could provide useful insights into the mechanisms of the disease and identify new potential drug targets.Added value of this study: We integrated many published datasets from patients with COVID-19 from 11 nations, with over 3000 patients and more than 5434 proteome measurements. We collected these datasets in an online database, and generated a toolbox to easily explore, analyze, and visualize the data. Next, we used the database and its associated toolbox to identify new proteins of diagnostic and therapeutic value for COVID-19 treatment. In particular, we identified a set of significantly dysregulated proteins for distinguishing severe from non-severe patients using serum samples.Implications of all the available evidence: COVIDpro will support the navigation and analysis of patterns of dysregulated proteins in various COVID-19 clinical specimens for identification and verification of protein biomarkers and potential therapeutic targets.

Plasma protein profiling reveals dynamic immunomodulatory changes in multiple sclerosis patients during pregnancy

Multiple sclerosis (MS) is a chronic autoimmune neuroinflammatory and neurodegenerative disorder of the central nervous system. Pregnancy represents a natural modulation of the disease course, where the relapse rate decreases, especially in the 3^rd trimester, followed by a transient exacerbation after delivery. Although the exact mechanisms behind the pregnancy-induced modulation are yet to be deciphered, it is likely that the immune tolerance established during pregnancy is involved. In this study, we used the highly sensitive and specific proximity extension assay technology to perform protein profiling analysis of 92 inflammation-related proteins in MS patients (n=15) and healthy controls (n=10), longitudinally sampled before, during, and after pregnancy. Differential expression analysis was performed using linear models and p-values were adjusted for false discovery rate due to multiple comparisons. Our findings reveal gradual dynamic changes in plasma proteins that are most prominent during the 3^rd trimester while reverting post-partum. Thus, this pattern reflects the disease activity of MS during pregnancy. Among the differentially expressed proteins in pregnancy, several proteins with known immunoregulatory properties were upregulated, such as PD-L1, LIF-R, TGF-β1, and CCL28. On the other hand, inflammatory chemokines such as CCL8, CCL13, and CXCL5, as well as members of the tumor necrosis factor family, TRANCE and TWEAK, were downregulated. Further in-depth studies will reveal if these proteins can serve as biomarkers in MS and whether they are mechanistically involved in the disease amelioration and worsening. A deeper understanding of the mechanisms involved may identify new treatment strategies mimicking the pregnancy milieu.

Mapping the Serum Proteome of COVID-19 Patients; Guidance for Severity Assessment

Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), whose outbreak in 2019 led to an ongoing pandemic with devastating consequences for the global economy and human health. According to the World Health Organization, COVID-19 has affected more than 481 million people worldwide, with 6 million confirmed deaths. The joint efforts of the scientific community have undoubtedly increased the pace of production of COVID-19 vaccines, but there is still so much uncharted ground to cover regarding the mechanisms of SARS-CoV-2 infection, replication and host response. These issues can be approached by proteomics with unprecedented capacity paving the way for the development of more efficient strategies for patient care. In this study, we present a deep proteome analysis that has been performed on a cohort of 72 COVID-19 patients aiming to identify serum proteins assessing the dynamics of the disease at different age ranges. A panel of 53 proteins that participate in several functions such as acute-phase response and inflammation, blood coagulation, cell adhesion, complement cascade, endocytosis, immune response, oxidative stress and tissue injury, have been correlated with patient severity, suggesting a molecular basis for their clinical stratification. Eighteen protein candidates were further validated by targeted proteomics in an independent cohort of 84 patients including a group of individuals that had satisfactorily resolved SARS-CoV-2 infection. Remarkably, all protein alterations were normalized 100 days after leaving the hospital, which further supports the reliability of the selected proteins as hallmarks of COVID-19 progression and grading. The optimized protein panel may prove its value for optimal severity assessment as well as in the follow up of COVID-19 patients.