A serum proteome signature to predict mortality in severe COVID-19 patients

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.

Inter-alpha-trypsin inhibitor heavy chain H3 is a potential biomarker for disease activity in myasthenia gravis

Myasthenia gravis is a chronic antibody-mediated autoimmune disease disrupting neuromuscular synaptic transmission. Informative biomarkers remain an unmet need to stratify patients with active disease requiring intensified monitoring and therapy; their identification is the primary objective of this study. We applied mass spectrometry-based proteomic serum profiling for biomarker discovery. We studied an exploration and a prospective validation cohort consisting of 114 and 140 anti-acetylcholine receptor antibody (AChR-Ab)-positive myasthenia gravis patients, respectively. For downstream analysis, we applied a machine learning approach. Protein expression levels were confirmed by ELISA and compared to other myasthenic cohorts, in addition to myositis and neuropathy patients. Anti-AChR-Ab levels were determined by a radio receptor assay. Immunohistochemistry and immunofluorescence of intercostal muscle biopsies were employed for validation in addition to interactome studies of inter-alpha-trypsin inhibitor heavy chain H3 (ITIH3). Machine learning identified ITIH3 as potential serum biomarker reflective of disease activity. Serum levels correlated with disease activity scores in the exploration and validation cohort and were confirmed by ELISA. Lack of correlation between anti-AChR-Ab levels and clinical scores underlined the need for biomarkers. In a subgroup analysis, ITIH3 was indicative of treatment responses. Immunostaining of muscle specimens from these patients demonstrated ITIH3 localization at the neuromuscular endplates in myasthenia gravis but not in controls, thus providing a structural equivalent for our serological findings. Immunoprecipitation of ITIH3 and subsequent proteomics lead to identification of its interaction partners playing crucial roles in neuromuscular transmission. This study provides data on ITIH3 as a potential pathophysiological-relevant biomarker of disease activity in myasthenia gravis. Future studies are required to facilitate translation into clinical practice.

Proteomic lung analysis revealed hyper-activation of neutrophil extracellular trap formation in cases of fatal COVID-19

The molecular pathology of lung injury in patients with Corona Virus Disease 2019 (COVID-19) remain unclear. In this study, we performed a proteomic study of lung tissues from seven patients with COVID-19, and eight without. Lung parenchymal tissues with COVID-19 were obtained from autopsy samples, while control samples were obtained from paracancerous tissues. Proteins were extracted using phenol extraction. A tandem mass tag-based quantitative proteomic approach combined with bioinformatic analysis was used to detect proteomic changes in the SARS-CoV-2-infected lung tissues. A total of 6,602, and 6,549 proteins were identified in replicates 1 and 2, respectively. Of these, 307, and 278, respectively, were identified as differentially expressed proteins (DEPs). In total, 216 DEPs were identified in this study. These proteins were enriched in 189 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The downregulated proteins are mainly involved in focal adhesion (n = 5), and the PI3K-Akt signaling pathway (n = 4). The upregulated proteins were related to neutrophil extracellular trap (NET) formation (n = 16), and the phagosome pathway (n = 11). The upregulated proteins in these two pathways interact with one another. Further immunohistochemistry verified NET enrichment in the tissues with COVID-19 compared to the controls. Our results systematically outlined the proteomic profiles of the lung's response to SARS-CoV-2 infection and indicated that NET formation was hyper-activated. These results will hopefully provide new evidence for understanding the mechanism behind fatal COVID-19.

Longitudinal Fluctuations in Protein Concentrations and Higher-Order Structures in the Plasma Proteome of Kidney Failure Patients Subjected to a Kidney Transplant

Using proteomics and complexome profiling, we evaluated in a year-long study longitudinal variations in the plasma proteome of kidney failure patients, prior to and after a kidney transplantation. The post-transplant period was complicated by bacterial infections, resulting in dramatic changes in the proteome, attributed to an acute phase response (APR). As positive acute phase proteins (APPs), being elevated upon inflammation, we observed the well-described C-reactive protein and Serum Amyloid A (SAA), but also Fibrinogen, Haptoglobin, Leucine-rich alpha-2-glycoprotein, Lipopolysaccharide-binding protein, Alpha-1-antitrypsin, Alpha-1-antichymotrypsin, S100, and CD14. As negative APPs, being downregulated upon inflammation, we identified the well-documented Serotransferrin and Transthyretin, but added Kallistatin, Heparin cofactor 2, and interalpha-trypsin inhibitor heavy chain H1 and H2 (ITIH1, ITIH2). For the patient with the most severe APR, we performed plasma complexome profiling by SEC-LC-MS on all longitudinal samples. We observed that several plasma proteins displaying alike concentration patterns coelute and form macromolecular complexes. By complexome profiling, we expose how SAA1 and SAA2 become incorporated into high-density lipid particles, replacing largely Apolipoprotein (APO)A1 and APOA4. Overall, our data highlight that the combination of in-depth longitudinal plasma proteome and complexome profiling can shed further light on correlated variations in the abundance of several plasma proteins upon inflammatory events.

Higher-order structure and proteoforms of co-occurring C4b-binding protein assemblies in human serum

The complement is a conserved cascade that plays a central role in the innate immune system. To maintain a delicate equilibrium preventing excessive complement activation, complement inhibitors are essential. One of the major fluid-phase complement inhibitors is C4b-binding protein (C4BP). Human C4BP is a macromolecular glycoprotein composed of two distinct subunits, C4BPα and C4BPβ. These associate with vitamin K-dependent protein S (ProS) forming an ensemble of co-occurring higher-order structures. Here, we characterize these C4BP assemblies. We resolve and quantify isoforms of purified human serum C4BP using distinct single-particle detection techniques: charge detection mass spectrometry, and mass photometry accompanied by high-speed atomic force microscopy. Combining cross-linking mass spectrometry, glycoproteomics, and structural modeling, we report comprehensive glycoproteoform profiles and full-length structural models of the endogenous C4BP assemblies, expanding knowledge of this key complement inhibitor's structure and composition. Finally, we reveal that an increased C4BPα to C4BPβ ratio coincides with elevated C-reactive protein levels in patient plasma samples. This observation highlights C4BP isoform variation and affirms a distinct role of co-occurring C4BP assemblies upon acute phase inflammation.

Longitudinal plasma proteomic analysis of 1117 hospitalized patients with COVID-19 identifies features associated with severity and outcomes

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is characterized by highly heterogeneous manifestations ranging from asymptomatic cases to death for still incompletely understood reasons. As part of the IMmunoPhenotyping Assessment in a COVID-19 Cohort study, we mapped the plasma proteomes of 1117 hospitalized patients with COVID-19 from 15 hospitals across the United States. Up to six samples were collected within ~28 days of hospitalization resulting in one of the largest COVID-19 plasma proteomics cohorts with 2934 samples. Using perchloric acid to deplete the most abundant plasma proteins allowed for detecting 2910 proteins. Our findings show that increased levels of neutrophil extracellular trap and heart damage markers are associated with fatal outcomes. Our analysis also identified prognostic biomarkers for worsening severity and death. Our comprehensive longitudinal plasma proteomics study, involving 1117 participants and 2934 samples, allowed for testing the generalizability of the findings of many previous COVID-19 plasma proteomics studies using much smaller cohorts.

Integrated multiomics implicates dysregulation of ECM and cell adhesion pathways as drivers of severe COVID-associated kidney injury

COVID-19 has been a significant public health concern for the last four years; however, little is known about the mechanisms that lead to severe COVID-associated kidney injury. In this multicenter study, we combined quantitative deep urinary proteomics and machine learning to predict severe acute outcomes in hospitalized COVID-19 patients. Using a 10-fold cross-validated random forest algorithm, we identified a set of urinary proteins that demonstrated predictive power for both discovery and validation set with 87% and 79% accuracy, respectively. These predictive urinary biomarkers were recapitulated in non-COVID acute kidney injury revealing overlapping injury mechanisms. We further combined orthogonal multiomics datasets to understand the mechanisms that drive severe COVID-associated kidney injury. Functional overlap and network analysis of urinary proteomics, plasma proteomics and urine sediment single-cell RNA sequencing showed that extracellular matrix and autophagy-associated pathways were uniquely impacted in severe COVID-19. Differentially abundant proteins associated with these pathways exhibited high expression in cells in the juxtamedullary nephron, endothelial cells, and podocytes, indicating that these kidney cell types could be potential targets. Further, single-cell transcriptomic analysis of kidney organoids infected with SARS-CoV-2 revealed dysregulation of extracellular matrix organization in multiple nephron segments, recapitulating the clinically observed fibrotic response across multiomics datasets. Ligand-receptor interaction analysis of the podocyte and tubule organoid clusters showed significant reduction and loss of interaction between integrins and basement membrane receptors in the infected kidney organoids. Collectively, these data suggest that extracellular matrix degradation and adhesion-associated mechanisms could be a main driver of COVID-associated kidney injury and severe outcomes.

Genotype-dependent N-glycosylation and newly exposed O-glycosylation affect plasmin-induced cleavage of histidine-rich glycoprotein (HRG)

Histidine-rich glycoprotein (HRG) is an abundant plasma protein harboring at least three N-glycosylation sites. HRG integrates many biological processes, such as coagulation, antiangiogenic activity, and pathogen clearance. Importantly, HRG is known to exhibit five genetic variants with minor allele frequencies of more than 10%. Among them, Pro204Ser can induce a fourth N-glycosylation site (Asn202). Considerable efforts have been made to reveal the biological function of HRG, whereas data on HRG glycosylation are scarcer. To close this knowledge gap, we used C18-based LC-MS/MS to study the glycosylation characteristics of six HRG samples from different sources. We used endogenous HRG purified from human plasma and compared its glycosylation to that of the recombinant HRG produced in Chinese hamster ovary cells or human embryonic kidney 293 cells, targeting distinct genotypic isoforms. In endogenous plasma HRG, every N-glycosylation site was occupied predominantly with a sialylated diantennary complex-type glycan. In contrast, in the recombinant HRGs, all glycans showed different antennarities, sialylation, and core fucosylation, as well as the presence of oligomannose glycans, LacdiNAcs, and antennary fucosylation. Furthermore, we observed two previously unreported O-glycosylation sites in HRG on residues Thr273 and Thr274. These sites together showed more than 90% glycan occupancy in all HRG samples studied. To investigate the potential relevance of HRG glycosylation, we assessed the plasmin-induced cleavage of HRG under various conditions. These analyses revealed that the sialylation of the N- and O-glycans as well as the genotype-dependent N-glycosylation significantly influenced the kinetics and specificity of plasmin-induced cleavage of HRG.

Into the Dark Serum Proteome: Personalized Features of IgG1 and IgA1 Repertoires in Severe COVID-19 Patients

Serum proteomics has matured and is now able to monitor hundreds of proteins quantitatively in large cohorts of patients. However, the fine characteristics of some of the most dominant proteins in serum, the immunoglobulins, are in these studies often ignored, due to their vast, and highly personalized, diversity in sequences. Here, we focus exclusively on these personalized features in the serum proteome and distinctively chose to study individual samples from a low diversity population: elderly donors infected by severe acute respiratory syndrome corona virus 2 (SARS-CoV-2). By using mass spectrometry-based methods, immunoglobulin IgG1 and IgA1 clonal repertoires were monitored quantitatively and longitudinally in more than 50 individual serum samples obtained from 17 Corona virus disease 2019 patients admitted to intensive care units. These clonal profiles were used to examine how each patient reacted to a severe SARS-CoV-2 infection. All 17 donors revealed unique polyclonal repertoires and substantial changes over time, with several new clones appearing following the infection, in a few cases leading to a few, very high, abundant clones dominating their repertoire. Several of these clones were de novo sequenced through combinations of top-down, middle-down, and bottom-up proteomics approaches. This revealed sequence features in line with sequences deposited in the SARS-CoV-specific antibody database. In other patients, the serological Ig profiles revealed the treatment with tocilizumab, that subsequently dominated their serological IgG1 repertoire. Tocilizumab clearance could be monitored, and a half-life of approximately 6 days was established. Overall, our longitudinal monitoring of IgG1 and IgA1 repertoires of individual donors reveals that antibody responses are highly personalized traits of each patient, affected by the disease and the chosen clinical treatment. The impact of these observations argues for a more personalized and longitudinal approach in patients' diagnostics, both in serum proteomics as well as in monitoring immune responses.

CD5L is a canonical component of circulatory IgM

Immunoglobulin M (IgM) is an evolutionary conserved key component of humoral immunity, and the first antibody isotype to emerge during an immune response. IgM is a large (1 MDa), multimeric protein, for which both hexameric and pentameric structures have been described, the latter additionally containing a joining (J) chain. Using a combination of single-particle mass spectrometry and mass photometry, proteomics, and immunochemical assays, we here demonstrate that circulatory (serum) IgM exclusively exists as a complex of J-chain-containing pentamers covalently bound to the small (36 kDa) protein CD5 antigen-like (CD5L, also called apoptosis inhibitor of macrophage). In sharp contrast, secretory IgM in saliva and milk is principally devoid of CD5L. Unlike IgM itself, CD5L is not produced by B cells, implying that it associates with IgM in the extracellular space. We demonstrate that CD5L integration has functional implications, i.e., it diminishes IgM binding to two of its receptors, the FcαµR and the polymeric Immunoglobulin receptor. On the other hand, binding to FcµR as well as complement activation via C1q seem unaffected by CD5L integration. Taken together, we redefine the composition of circulatory IgM as a J-chain containing pentamer, always in complex with CD5L.

Foresight in clinical proteomics: current status, ethical considerations, and future perspectives

With the advent of robust and high-throughput mass spectrometric technologies and bioinformatics tools to analyze large data sets, proteomics has penetrated broadly into basic and translational life sciences research. More than 95% of FDA-approved drugs currently target proteins, and most diagnostic tests are protein-based. The introduction of proteomics to the clinic, for instance to guide patient stratification and treatment, is already ongoing. Importantly, ethical challenges come with this success, which must also be adequately addressed by the proteomics and medical communities. Consortium members of the H2020 European Union-funded proteomics initiative: European Proteomics Infrastructure Consortium-providing access (EPIC-XS) met at the Core Technologies for Life Sciences (CTLS) conference to discuss the emerging role and implementation of proteomics in the clinic. The discussion, involving leaders in the field, focused on the current status, related challenges, and future efforts required to make proteomics a more mainstream technology for translational and clinical research. Here we report on that discussion and provide an expert update concerning the feasibility of clinical proteomics, the ethical implications of generating and analyzing large-scale proteomics clinical data, and recommendations to ensure both ethical and effective implementation in real-world applications.

Mitochondrial dysfunction, lipids metabolism, and amino acid biosynthesis are key pathways for COVID-19 recovery

The metabolic alterations caused by SARS-CoV-2 infection reflect disease progression. To analyze molecules involved in these metabolic changes, a multiomics study was performed using plasma from 103 patients with different degrees of COVID-19 severity during the evolution of the infection. With the increased severity of COVID-19, changes in circulating proteomic, metabolomic, and lipidomic profiles increased. Notably, the group of severe and critical patients with high HRG and ChoE (20:3) and low alpha-ketoglutaric acid levels had a high chance of unfavorable disease evolution (AUC = 0.925). Consequently, patients with the worst prognosis presented alterations in the TCA cycle (mitochondrial dysfunction), lipid metabolism, amino acid biosynthesis, and coagulation. Our findings increase knowledge regarding how SARS-CoV-2 infection affects different metabolic pathways and help in understanding the future consequences of COVID-19 to identify potential therapeutic targets.

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.

Integrative multi-omics approach for identifying molecular signatures and pathways and deriving and validating molecular scores for COVID-19 severity and status

BACKGROUND

There is still more to learn about the pathobiology of COVID-19. A multi-omic approach offers a holistic view to better understand the mechanisms of COVID-19. We used state-of-the-art statistical learning methods to integrate genomics, metabolomics, proteomics, and lipidomics data obtained from 123 patients experiencing COVID-19 or COVID-19-like symptoms for the purpose of identifying molecular signatures and corresponding pathways associated with the disease.

RESULTS

We constructed and validated molecular scores and evaluated their utility beyond clinical factors known to impact disease status and severity. We identified inflammation- and immune response-related pathways, and other pathways, providing insights into possible consequences of the disease.

CONCLUSIONS

The molecular scores we derived were strongly associated with disease status and severity and can be used to identify individuals at a higher risk for developing severe disease. These findings have the potential to provide further, and needed, insights into why certain individuals develop worse outcomes.

Analysis of Plasma Proteins Involved in Inflammation, Immune Response/Complement System, and Blood Coagulation upon Admission of COVID-19 Patients to Hospital May Help to Predict the Prognosis of the Disease

The development of new approaches allowing for the early assessment of COVID-19 cases that are likely to become critical and the discovery of new therapeutic targets are urgently required. In this prospective cohort study, we performed proteomic and laboratory profiling of plasma from 163 COVID-19 patients admitted to Bauru State Hospital (Brazil) between 4 May 2020 and 4 July 2020. Plasma samples were collected upon admission for routine laboratory analyses and shotgun quantitative label-free proteomics. Based on the course of the disease, the patients were divided into three groups: (a) mild (n = 76) and (b) severe (n = 56) symptoms, whose patients were discharged without or with admission to an intensive care unit (ICU), respectively, and (c) critical (n = 31), a group consisting of patients who died after admission to an ICU. Based on our data, potential therapies for COVID-19 should target proteins involved in inflammation, the immune response and complement system, and blood coagulation. Other proteins that could potentially be employed in therapies against COVID-19 but that so far have not been associated with the disease are CD5L, VDBP, A1BG, C4BPA, PGLYRP2, SERPINC1, and APOH. Targeting these proteins' pathways might constitute potential new therapies or biomarkers of prognosis of the disease.

Serum proteomics hint at an early T-cell response and modulation of SARS-CoV-2-related pathogenic pathways in COVID-19-ARDS treated with Ruxolitinib

Background

Acute respiratory distress syndrome (ARDS) in corona virus disease 19 (COVID-19) is triggered by hyperinflammation, thus providing a rationale for immunosuppressive treatments. The Janus kinase inhibitor Ruxolitinib (Ruxo) has shown efficacy in severe and critical COVID-19. In this study, we hypothesized that Ruxo's mode of action in this condition is reflected by changes in the peripheral blood proteome.

Methods

This study included 11 COVID-19 patients, who were treated at our center's Intensive Care Unit (ICU). All patients received standard-of-care treatment and n = 8 patients with ARDS received Ruxo in addition. Blood samples were collected before (day 0) and on days 1, 6, and 10 of Ruxo treatment or, respectively, ICU admission. Serum proteomes were analyzed by mass spectrometry (MS) and cytometric bead array.

Results

Linear modeling of MS data yielded 27 significantly differentially regulated proteins on day 1, 69 on day 6 and 72 on day 10. Only five factors (IGLV10-54, PSMB1, PGLYRP1, APOA5, WARS1) were regulated both concordantly and significantly over time. Overrepresentation analysis revealed biological processes involving T-cells only on day 1, while a humoral immune response and complement activation were detected at day 6 and day 10. Pathway enrichment analysis identified the NRF2-pathway early under Ruxo treatment and Network map of SARS-CoV-2 signaling and Statin inhibition of cholesterol production at later time points.

Conclusion

Our results indicate that the mechanism of action of Ruxo in COVID-19-ARDS can be related to both known effects of this drug as a modulator of T-cells and the SARS-CoV-2-infection.

Deletion of DP148R, DP71L, and DP96R Attenuates African Swine Fever Virus, and the Mutant Strain Confers Complete Protection against Homologous Challenges in Pigs

The African swine fever virus (ASFV) has caused a devastating pandemic in domestic and wild swine, causing economic losses to the global swine industry. Recombinant live attenuated vaccines are an attractive option for ASFV treatment. However, safe and effective vaccines against ASFV are still scarce, and more high-quality experimental vaccine strains need to be developed. In this study, we revealed that deletion of the ASFV genes DP148R, DP71L, and DP96R from the highly virulent isolate ASFV CN/GS/2018 (ASFV-GS) substantially attenuated virulence in swine. Pigs infected with 104 50% hemadsorbing doses of the virus with these gene deletions remained healthy during the 19-day observation period. No ASFV infection was detected in contact pigs under the experimental conditions. Importantly, the inoculated pigs were protected against homologous challenges. Additionally, RNA sequence analysis showed that deletion of these viral genes induced significant upregulation of the host histone H3.1 gene (H3.1) and downregulation of the ASFV MGF110-7L gene. Knocking down the expression of H3.1 resulted in high levels of ASFV replication in primary porcine macrophages in vitro. These findings indicate that the deletion mutant virus ASFV-GS-Δ18R/NL/UK is a novel potential live attenuated vaccine candidate and one of the few experimental vaccine strains reported to induce full protection against the highly virulent ASFV-GS virus strain. IMPORTANCE Ongoing outbreaks of African swine fever (ASF) have considerably damaged the pig industry in affected countries. Thus, a safe and effective vaccine is important to control African swine fever spread. Here, an ASFV strain with three gene deletions was developed by knocking out the viral genes DP148R (MGF360-18R), NL (DP71L), and UK (DP96R). The results showed that the recombinant virus was completely attenuated in pigs and provided strong protection against parental virus challenge. Additionally, no viral genomes were detected in the sera of pigs housed with animals infected with the deletion mutant. Furthermore, transcriptome sequencing (RNA-seq) analysis revealed significant upregulation of histone H3.1 in virus-infected macrophage cultures and downregulation of the ASFV MGF110-7L gene after viral DP148R, UK, and NL deletion. Our study provides a valuable live attenuated vaccine candidate and potential gene targets for developing strategies for anti-ASFV treatment.

Mass spectrometry-based high-throughput proteomics and its role in biomedical studies and systems biology

There are multiple reasons why the next generation of biological and medical studies require increasing numbers of samples. Biological systems are dynamic, and the effect of a perturbation depends on the genetic background and environment. As a consequence, many conditions need to be considered to reach generalizable conclusions. Moreover, human population and clinical studies only reach sufficient statistical power if conducted at scale and with precise measurement methods. Finally, many proteins remain without sufficient functional annotations, because they have not been systematically studied under a broad range of conditions. In this review, we discuss the latest technical developments in mass spectrometry (MS)-based proteomics that facilitate large-scale studies by fast and efficient chromatography, fast scanning mass spectrometers, data-independent acquisition (DIA), and new software. We further highlight recent studies which demonstrate how high-throughput (HT) proteomics can be applied to capture biological diversity, to annotate gene functions or to generate predictive and prognostic models for human diseases.

Normal Alpha-1-Antitrypsin Variants Display in Serum Allele-Specific Protein Levels

Alpha-1-antitrypsin (A1AT or SERPINA1) has been proposed as a putative biomarker distinguishing healthy from diseased donors throughout several proteomics studies. However, the SERPINA1 gene displays high variability of frequent occurring genotypes among the general population. These different genotypes may affect A1AT expression and serum protein concentrations, and this is often not known, ignored, and/or not reported in serum proteomics studies. Here, we address allele-specific protein serum levels of A1AT in donors carrying the normal M variants of A1AT by measuring the proteoform profiles of purified A1AT from 81 serum samples, originating from 52 donors. When focusing on heterozygous donors, our data clearly reveal a statistically relevant difference in allele-specific protein serum levels of A1AT. In donors with genotype PI*M1VM1A, the experimentally observed ratio was approximately 1:1 (M1V/M1A, 1.00:0.96 ± 0.07, n = 17). For individuals with genotype PI*M1VM2, this ratio was 1:1.28 (M1V/M2, 1.00:1.31, ±0.19, n = 7). For genotypes PI*M1VM3 and PI*M1AM3, a significant higher amount of M3 was observed compared to the M1-subtypes (M1V/M3, 1.00:1.84 ± 0.35, n = 8; M1A/M3, 1.00:1.61 ± 0.33, n = 5). We argue that these observations are important and should be considered when analyzing serum A1AT levels before proposing A1AT as a putative serum biomarker.

Plasma proteomics identify potential severity biomarkers from COVID-19 associated network

PURPOSE

Coronavirus disease 2019 (COVID-19) continues to threaten public health globally. Severe acute respiratory coronavirus type 2 (SARS-CoV-2) infection-dependent alterations in the host cell signaling network may unveil potential target proteins and pathways for therapeutic strategies. In this study, we aim to define early severity biomarkers and monitor altered pathways in the course of SARS-CoV-2 infection.

EXPERIMENTAL DESIGN

We systematically analyzed plasma proteomes of COVID-19 patients from Turkey by using mass spectrometry. Different severity grades (moderate, severe, and critical) and periods of disease (early, inflammatory, and recovery) are monitored. Significant alterations in protein expressions are used to reconstruct the COVID-19 associated network that was further extended to connect viral and host proteins.

RESULTS

Across all COVID-19 patients, 111 differentially expressed proteins were found, of which 28 proteins were unique to our study mainly enriching in immunoglobulin production. By monitoring different severity grades and periods of disease, CLEC3B, MST1, and ITIH2 were identified as potential early predictors of COVID-19 severity. Most importantly, we extended the COVID-19 associated network with viral proteins and showed the connectedness of viral proteins with human proteins. The most connected viral protein ORF8, which has a role in immune evasion, targets many host proteins tightly connected to the deregulated human plasma proteins.

CONCLUSIONS AND CLINICAL RELEVANCE

Plasma proteomes from critical patients are intrinsically clustered in a distinct group than severe and moderate patients. Importantly, we did not recover any grouping based on the infection period, suggesting their distinct proteome even in the recovery phase. The new potential early severity markers can be further studied for their value in the clinics to monitor COVID-19 prognosis. Beyond the list of plasma proteins, our disease-associated network unravels altered pathways, and the possible therapeutic targets in SARS-CoV-2 infection by connecting human and viral proteins. Follow-up studies on the disease associated network that we propose here will be useful to determine molecular details of viral perturbation and to address how the infection affects human physiology.

Plasma Proteomic Variables Related to COVID-19 Severity: An Untargeted nLC-MS/MS Investigation

Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) infection leads to a wide range of clinical manifestations and determines the need for personalized and precision medicine. To better understand the biological determinants of this heterogeneity, we explored the plasma proteome of 43 COVID-19 patients with different outcomes by an untargeted liquid chromatography-mass spectrometry approach. The comparison between asymptomatic or pauci-symptomatic subjects (MILDs), and hospitalised patients in need of oxygen support therapy (SEVEREs) highlighted 29 proteins emerged as differentially expressed: 12 overexpressed in MILDs and 17 in SEVEREs. Moreover, a supervised analysis based on a decision-tree recognised three proteins (Fetuin-A, Ig lambda-2chain-C-region, Vitronectin) that are able to robustly discriminate between the two classes independently from the infection stage. In silico functional annotation of the 29 deregulated proteins pinpointed several functions possibly related to the severity; no pathway was associated exclusively to MILDs, while several only to SEVEREs, and some associated to both MILDs and SEVEREs; SARS-CoV-2 signalling pathway was significantly enriched by proteins up-expressed in SEVEREs (SAA1/2, CRP, HP, LRG1) and in MILDs (GSN, HRG). In conclusion, our analysis could provide key information for 'proteomically' defining possible upstream mechanisms and mediators triggering or limiting the domino effect of the immune-related response and characterizing severe exacerbations.

Longitudinal change of serum inter-alpha-trypsin inhibitor heavy chain H4, and its correlation with inflammation, multiorgan injury, and death risk in sepsis

BACKGROUND

Inter-alpha-trypsin inhibitor heavy chain H4 (ITIH4) inhibits infection-induced inflammation and multiorgan injury through several methods. The present study aimed to estimate the association of serum ITIH4 with inflammatory cytokines, multiorgan injury, and death risk in sepsis patients.

METHODS

Serum samples were collected to detect ITIH4 by enzyme-linked immunosorbent assay in 127 sepsis patients at admission (baseline), day (D)1, D3, and D7 after admission, as well as in 30 healthy controls (HCs). Additionally, 28-day mortality was recorded in sepsis patients.

RESULTS

ITIH4 was reduced in sepsis patients versus HCs (median [interquartile range]: 147.9 [78.2-208.8] vs. 318.8 [237.2-511.4] ng/ml) (p < 0.001). In sepsis patients, ITIH4 was associated with the absence of cardiovascular and cerebrovascular disease history (p = 0.021). Additionally, ITIH4 was negatively correlated with tumor necrosis factor-α (p < 0.001), interleukin (IL)-1β (p < 0.001), IL-6 (p = 0.019), IL-17A (p = 0.002), and C-reactive protein (p = 0.001), but positively related to IL-10 (p = 0.007). Moreover, ITIH4 was also inversely associated with Acute Physiology and Chronic Health Evaluation II score (p = 0.002), Sequential Organ Failure Assessment (SOFA) score (p < 0.001), SOFA-respiratory system score (p = 0.023), and SOFA-renal system score (p = 0.007). Interestingly, ITIH4 gradually increased from baseline to D7 (p < 0.001); besides, ITIH4 at baseline (p = 0.009), D1 (p = 0.002), D3 (p < 0.001), and D7 (p = 0.015) were all decreased in sepsis deaths versus sepsis survivors.

CONCLUSION

Serum ITIH4 is raised from baseline to D7 after disease onset, and it reflects the reduction of systemic inflammation, disease severity, and 28-day mortality for sepsis. However, further verification is required.

Role of different types of RNA molecules in the severity prediction of SARS-CoV-2 patients

SARS-CoV-2 pandemic is the current threat of the world with enormous number of deceases. As most of the countries have constraints on resources, particularly for intensive care and oxygen, severity prediction with high accuracy is crucial. This prediction will help the medical society in the selection of patients with the need for these constrained resources. Literature shows that using clinical data in this study is the common trend and molecular data is rarely utilized in this prediction. As molecular data carry more disease related information, in this study, three different types of RNA molecules ( lncRNA, miRNA and mRNA) of SARS-COV-2 patients are used to predict the severity stage and treatment stage of those patients. Using seven different machine learning algorithms along with several feature selection techniques shows that in both phenotypes, feature importance selected features provides the best accuracy along with random forest classifier. Further to this, it shows that in the severity stage prediction miRNA and lncRNA give the best performance, and lncRNA data gives the best in treatment stage prediction. As most of the studies related to molecular data uses mRNA data, this is an interesting finding.

Plasma proteome profiling of healthy subjects undergoing bed rest reveals unloading-dependent changes linked to muscle atrophy

BACKGROUND

Inactivity and unloading induce skeletal muscle atrophy, loss of strength and detrimental metabolic effects. Bed rest is a model to study the impact of inactivity on the musculoskeletal system. It not only provides information for bed-ridden patients care, but it is also a ground-based spaceflight analogue used to mimic the challenges of long space missions for the human body. In both cases, it would be desirable to develop a panel of biomarkers to monitor muscle atrophy in a minimally invasive way at point of care to limit the onset of muscle loss in a personalized fashion.

METHODS

We applied mass spectrometry-based proteomics to measure plasma protein abundance changes in response to 10 days of bed rest in 10 young males. To validate the correlation between muscle atrophy and the significant hits emerging from our study, we analysed in parallel, with the same pipeline, a cohort of cancer patients with or without cachexia and age-matched controls. Our analysis resulted in the quantification of over 500 proteins.

RESULTS

Unloading affected plasma concentration of proteins of the complement cascade, lipid carriers and proteins derived from tissue leakage. Among the latter, teneurin-4 increased 1.6-fold in plasma at bed rest day 10 (BR10) compared with BR0 (6.E9 vs. 4.3E9, P = 0.02) and decreased to 0.6-fold the initial abundance after 2 days of recovery at normal daily activity (R + 2, 2.7E9, P = 3.3E-4); the extracellular matrix protein lumican was decreased to 0.7-fold (1.2E9 vs. 8.5E8, P = 1.5E-4) at BR10 and remained as low at R + 2. We identified six proteins distinguishing subjects developing unloading-mediated muscle atrophy (decrease of >4% of quadriceps cross-sectional area) from those largely maintaining their initial muscle mass. Among them, transthyretin, a thyroid hormone-binding protein, was significantly less abundant at BR10 in the plasma of subjects with muscle atrophy compared with those with no atrophy (1.6E10 vs. 2.6E10, P = 0.001). Haptoglobin-related protein was also significantly reduced in the serum of cancer patients with cachexia compared with that of controls.

CONCLUSIONS

Our findings highlight a combination or proteomic changes that can be explored as potential biomarkers of muscle atrophy occurring under different conditions. The panel of significant proteomic differences distinguishing atrophy-prone and atrophy-resistant subjects after 10 days of bed rest need to be tested in a larger cohort to validate their potential to predict inactivity-triggered muscle loss in humans.

Identification of common and distinct origins of human serum and breastmilk IgA1 by mass spectrometry-based clonal profiling

The most abundant immunoglobulin present in the human body is IgA. It has the highest concentrations at the mucosal lining and in biofluids such as milk and is the second most abundant class of antibodies in serum. We assessed the structural diversity and clonal repertoire of IgA1-containing molecular assemblies longitudinally in human serum and milk from three donors using a mass spectrometry-based approach. IgA-containing molecules purified from serum or milk were assessed by the release and subsequent analysis of their Fab fragments. Our data revealed that serum IgA1 consists of two distinct structural populations, namely monomeric IgA1 (∼80%) and dimeric joining (J-) chain coupled IgA1 (∼20%). Also, we confirmed that IgA1 in milk is present solely as secretory (S)IgA, consisting of two (∼50%), three (∼33%) or four (∼17%) IgA1 molecules assembled with a J-chain and secretory component (SC). Interestingly, the serum and milk IgA1-Fab repertoires were distinct between monomeric, and J-chain coupled dimeric IgA1. The serum dimeric J-chain coupled IgA1 repertoire contained several abundant clones also observed in the milk IgA1 repertoire. The latter repertoire had little to no overlap with the serum monomeric IgA1 repertoire. This suggests that human IgA1s have (at least) two distinct origins; one of these produces dimeric J-chain coupled IgA1 molecules, shared in human serum and milk, and another produces monomeric IgA1 ending up exclusively in serum.

Consistently low levels of histidine-rich glycoprotein as a new prognostic biomarker for sepsis: A multicenter prospective observational study

BACKGROUND

Few sepsis biomarkers accurately predict severity and mortality. Previously, we had reported that first-day histidine-rich glycoprotein (HRG) levels were significantly lower in patients with sepsis and were associated with mortality. Since the time trends of HRG are unknown, this study focused on the time course of HRG in patients with sepsis and evaluated the differences between survivors and non-survivors.

METHODS

A multicenter prospective observational study was conducted involving 200 patients with sepsis in 16 Japanese hospitals. Blood samples were collected on days 1, 3, 5, and 7, and 28-day mortality was used for survival analysis. Plasma HRG levels were determined using a modified quantitative sandwich enzyme-linked immunosorbent assay.

RESULTS

First-day HRG levels in non-survivors were significantly lower than those in survivors (mean, 15.7 [95% confidence interval (CI), 13.4-18.1] vs 20.7 [19.5-21.9] μg/mL; P = 0.006). Although there was no time × survivors/non-survivors interaction in the time courses of HRG (P = 0.34), the main effect of generalized linear mixed models was significant (P < 0.001). In a univariate Cox proportional hazards model with each variable as a time-dependent covariate, higher HRG levels were significantly associated with a lower risk of mortality (hazard ratio, 0.85 [95% CI, 0.78-0.92]; P < 0.001). Furthermore, presepsin levels (P = 0.02) and Sequential Organ Function Assessment scores (P < 0.001) were significantly associated with mortality. Harrell's C-index values for the 28-day mortality effect of HRG, presepsin, procalcitonin, and C-reactive protein were 0.72, 0.70, 0.63, and 0.59, respectively.

CONCLUSIONS

HRG levels in non-survivors were consistently lower than those in survivors during the first seven days of sepsis. Repeatedly measured HRG levels were significantly associated with mortality. Furthermore, the predictive power of HRG for mortality may be superior to that of other singular biomarkers, including presepsin, procalcitonin, and C-reactive protein.

Proteomic Investigation of COVID-19 Severity During the Tsunamic Second Wave in Mumbai

Maharashtra was severely affected during the noxious second wave of COVID-19, with the highest number of cases recorded across India. The emergence of new symptoms and dysregulation of multiple organs resulted in high disease severity during the second wave which led to increased difficulties in understanding the molecular mechanisms behind the disease pathology. Exploring the underlying factors can help to relieve the burden on the medical communities to some extent by prioritizing the patients and, at the same time, opening avenues for improved treatments. In the current study, we have performed a mass-spectrometry-based proteomic analysis to investigate the disease pathology using nasopharyngeal swab samples collected from the COVID-19 patients in the Mumbai region of Maharashtra over the period of March-June 2021, the peak of the second wave. A total of 59 patients, including 32 non-severe and 27 severe cases, were considered for this proteomic study. We identified 23 differentially regulated proteins in severe patients as a host response to infection. In addition to the previously identified innate mechanisms of neutrophil and platelet degranulation, this study revealed significant alterations of anti-microbial peptide pathways in severe conditions, illustrating its role in the severity of the infectious strain of COVID-19 during the second wave. Furthermore, myeloperoxidase, cathepsin G, and profilin-1 were identified as potential therapeutic targets of the FDA-approved drugs dabrafenib, ZINC4097343, and ritonavir. This study has enlightened the role of the anti-microbial peptide pathway associated with the second wave in India and proposed its importance in potential therapeutics for COVID-19.

Serum Proteomic Analysis for New Types of Long-Term Persistent COVID-19 Patients in Wuhan

The emergence of a new type of COVID-19 patients, who were retested positive after hospital discharge with long-term persistent SARS-CoV-2 infection but without COVID-19 clinical symptoms (hereinafter, LTPPs), poses novel challenges to COVID-19 treatment and prevention. Why was there such a contradictory phenomenon in LTPPs? To explore the mechanism underlying this phenomenon, we performed quantitative proteomic analyses using the sera of 12 LTPPs (Wuhan Pulmonary Hospital), with the longest carrying history of 132 days, and mainly focused on 7 LTPPs without hypertension (LTPPs-NH). The results showed differential serum protein profiles between LTPPs/LTPPs-NH and health controls. Further analysis identified 174 differentially-expressed-proteins (DEPs) for LTPPs, and 165 DEPs for LTPPs-NH, most of which were shared. GO and KEGG analyses for these DEPs revealed significant enrichment of "coagulation" and "immune response" in both LTPPs and LTPPs-NH. A unity of contradictory genotypes in the 2 aspects were then observed: some DEPs showed the same dysregulated expressed trend as that previously reported for patients in the acute phase of COVID-19, which might be caused by long-term stimulation of persistent SARS-CoV-2 infection in LTPPs, further preventing them from complete elimination; in contrast, some DEPs showed the opposite expression trend in expression, so as to retain control of COVID-19 clinical symptoms in LTPPs. Overall, the contrary effects of these DEPs worked together to maintain the balance of LTPPs, further endowing their contradictory steady-state with long-term persistent SARS-CoV-2 infection but without symptoms. Additionally, our study revealed some potential therapeutic targets of COVID-19. Further studies on these are warranted. IMPORTANCE This study reported a new type of COVID-19 patients and explored the underlying molecular mechanism by quantitative proteomic analyses. DEPs were significantly enriched in "coagulation" and "immune response". Importantly, we identified 7 "coagulation system"- and 9 "immune response"-related DEPs, the expression levels of which were consistent with those previously reported for patients in the acute phase of COVID-19, which appeared to play a role in avoiding the complete elimination of SARS-CoV-2 in LTPPs. On the contrary, 6 "coagulation system"- and 5 "immune response"-related DEPs showed the opposite trend in expression. The 11 inconsistent serum proteins seem to play a key role in the fight against long-term persistent SARS-CoV-2 infection, further retaining control of COVID-19 clinical symptom of LTPPs. The 26 proteins can serve as potential therapeutic targets and are thus valuable for the treatment of LTPPs; further studies on them are warranted.

Exploratory analysis of interleukin-38 in hospitalized COVID-19 patients

INTRODUCTION

A major contributor to coronavirus disease 2019 (COVID-19) progression and severity is a dysregulated innate and adaptive immune response. Interleukin-38 (IL-38) is an IL-1 family member with broad anti-inflammatory properties, but thus far little is known about its role in viral infections. Recent studies have shown inconsistent results, as one study finding an increase in circulating IL-38 in COVID-19 patients in comparison to healthy controls, whereas two other studies report no differences in IL-38 concentrations.

METHODS

Here, we present an exploratory, retrospective cohort study of circulating IL-38 concentrations in hospitalized COVID-19 patients admitted to two Dutch hospitals (discovery n = 148 and validation n = 184) and age- and sex-matched healthy subjects. Plasma IL-38 concentrations were measured by enzyme-linked immunosorbent assay, disease-related proteins by proximity extension assay, and clinical data were retrieved from hospital records.

RESULTS

IL-38 concentrations were stable during hospitalization and similar to those of healthy control subjects. IL-38 was not associated with rates of intensive care unit admission or mortality. Only in men in the discovery cohort, IL-38 concentrations were positively correlated with hospitalization duration. A positive correlation between IL-38 and the inflammatory biomarker d-dimer was observed in men of the validation cohort. In women of the validation cohort, IL-38 concentrations correlated negatively with thrombocyte numbers. Furthermore, plasma IL-38 concentrations in the validation cohort correlated positively with TNF, TNFRSF9, IL-10Ra, neurotrophil 3, polymeric immunoglobulin receptor, CHL1, CD244, superoxide dismutase 2, and fatty acid binding protein 2, and negatively with SERPINA12 and cartilage oligomeric matrix protein.

CONCLUSIONS

These data indicate that IL-38 is not associated with disease outcomes in hospitalized COVID-19 patients. However, moderate correlations between IL-38 concentrations and biomarkers of disease were identified in one of two cohorts. While we demonstrate that IL-38 concentrations are not indicative of COVID-19 severity, its anti-inflammatory effects may reduce COVID-19 severity and should be experimentally investigated.

Levels of soluble complement regulators predict severity of COVID-19 symptoms

The SARS-CoV-2 virus continues to cause significant morbidity and mortality worldwide from COVID-19. One of the major challenges of patient management is the broad range of symptoms observed. While the majority of individuals experience relatively mild disease, a significant minority of patients require hospitalisation, with COVID-19 still proving fatal for some. As such, there remains a desperate need to better understand what drives this severe disease, both in terms of the underlying biology, but also to potentially predict at diagnosis which patients are likely to require further interventions, thus enabling better outcomes for both patients and healthcare systems. Several lines of evidence have pointed to dysregulation of the complement cascade as a major factor in severe COVID-19 outcomes. How this is underpinned mechanistically is not known. Here, we have focussed on the role of the soluble complement regulators Complement Factor H (FH), its splice variant Factor H-like 1 (FHL-1) and five Factor H-Related proteins (FHR1-5). Using a targeted mass spectrometry approach, we quantified these proteins in a cohort of 188 plasma samples from controls and SARS-CoV-2 patients taken at diagnosis. This analysis revealed significant elevations in all FHR proteins, but not FH, in patients with more severe disease, particularly FHR2 and FHR5 (FHR2: 1.97-fold, p&lt;0.0001; FHR5: 2.4-fold, p&lt;0.0001). Furthermore, for a subset of 77 SARS-CoV-2 +ve patients we also analysed time course samples taken approximately 28 days post-diagnosis. Here, we see complement regulator levels drop in all individuals with asymptomatic or mild disease, but regulators remain high in those with more severe outcomes, with elevations in FHR2 over baseline levels in this group. These data support the hypothesis that elevation of circulating levels of the FHR family of proteins could predict disease severity in COVID-19 patients, and that the duration of elevation (or lack of immune activation resolution) may be partly responsible for driving poor outcomes in COVID-19.

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.

The human host response to monkeypox infection: a proteomic case series study

The rapid rise of monkeypox (MPX) cases outside previously endemic areas prompts for a better understanding of the disease. We studied the plasma proteome of a group of MPX patients with a similar infection history and clinical manifestation typical for the current outbreak. We report that MPX in this case series is associated with a strong plasma proteomic response among nutritional and acute phase response proteins. Moreover, we report a correlation between plasma proteins and disease severity. Contrasting the MPX host response with that of COVID‐19, we find a range of similarities, but also important differences. For instance, CFHR1 is induced in COVID‐19, but suppressed in MPX, reflecting the different roles of the complement system in the two infectious diseases. Of note, the spatial overlap in response proteins suggested that a COVID‐19 biomarker panel assay could be repurposed for MPX. Applying a targeted protein panel assay provided encouraging results and distinguished MPX cases from healthy controls. Hence, our results provide a first proteomic characterization of the MPX human host response and encourage further research on protein‐panel assays in emerging infectious diseases.

COVID-19 Salivary Protein Profile: Unravelling Molecular Aspects of SARS-CoV-2 Infection

COVID-19 is the most impacting global pandemic of all time, with over 600 million infected and 6.5 million deaths worldwide, in addition to an unprecedented economic impact. Despite the many advances in scientific knowledge about the disease, much remains to be clarified about the molecular alterations induced by SARS-CoV-2 infection. In this work, we present a hybrid proteomics and in silico interactomics strategy to establish a COVID-19 salivary protein profile. Data are available via ProteomeXchange with identifier PXD036571. The differential proteome was narrowed down by the Partial Least-Squares Discriminant Analysis and enrichment analysis was performed with FunRich. In parallel, OralInt was used to determine interspecies Protein-Protein Interactions between humans and SARS-CoV-2. Five dysregulated biological processes were identified in the COVID-19 proteome profile: Apoptosis, Energy Pathways, Immune Response, Protein Metabolism and Transport. We identified 10 proteins (KLK 11, IMPA2, ANXA7, PLP2, IGLV2-11, IGHV3-43D, IGKV2-24, TMEM165, VSIG10 and PHB2) that had never been associated with SARS-CoV-2 infection, representing new evidence of the impact of COVID-19. Interactomics analysis showed viral influence on the host immune response, mainly through interaction with the degranulation of neutrophils. The virus alters the host’s energy metabolism and interferes with apoptosis mechanisms.

Proteomics of fibrin amyloid microclots in long COVID/post-acute sequelae of COVID-19 (PASC) shows many entrapped pro-inflammatory molecules that may also contribute to a failed fibrinolytic system

Background

Post-acute sequelae of COVID-19 (PASC), also now known as long COVID, has become a major global health and economic burden. Previously, we provided evidence that there is a significant insoluble fibrin amyloid microclot load in the circulation of individuals with long COVID, and that these microclots entrap a substantial number of inflammatory molecules, including those that might prevent clot breakdown. Scientifically, the most challenging aspect of this debilitating condition is that traditional pathology tests such as a serum CRP (C-reactive protein) may not show any significant abnormal inflammatory markers, albeit these tests measure only the soluble inflammatory molecules. Elevated, or abnormal soluble biomarkers such as IL-6, D-Dimer or fibrinogen indicate an increased risk for thrombosis or a host immune response in COVID-19. The absence of biomarkers in standard pathology tests, result in a significant amount of confusion for patients and clinicians, as patients are extremely sick or even bed-ridden but with no regular identifiable reason for their disease. Biomarkers that are currently available cannot detect the molecules present in the microclots we identified and are therefore unable to confirm their presence or the mechanisms that drive their formation.

Methods

Here we analysed the protein content of double-digested microclots of 99 long COVID patients and 29 healthy controls. The patients suffering from long COVID reported their symptoms through a questionnaire completed by themselves or their attending physician.

Results

Our long COVID cohort’s symptoms were found to be in line with global findings, where the most prevalent symptoms were constant fatigue (74%,) cognitive impairment (71%) and depression and anxiety (30%). Our most noteworthy findings were a reduced level of plasma Kallikrein compared to our controls, an increased level of platelet factor 4 (PF4) von Willebrand factor (VWF), and a marginally increased level of α-2 antiplasmin (α-2-AP). We also found a significant presence of antibodies entrapped inside these microclots.

Conclusion

Our results confirm the presence of pro-inflammatory molecules that may also contribute to a failed fibrinolysis phenomenon, which could possibly explain why individuals with long COVID suffer from chronic fatigue, dyspnoea, or cognitive impairment. In addition, significant platelet hyperactivation was noted. Hyperactivation will result in the granular content of platelets being shed into the circulation, including PF4. Overall, our results provide further evidence of both a failed fibrinolytic system in long COVID/PASC and the entrapment of many proteins whose presence might otherwise go unrecorded. These findings might have significant implications for individuals with pre-existing comorbidities, including cardiovascular disease and type 2 diabetes.

Novel aspects of sepsis pathophysiology: NETs, plasma glycoproteins, endotheliopathy and COVID-19

In 2016, sepsis was newly defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. Sepsis remains one of the crucial medical problems to be solved worldwide. Although the world health organization has made sepsis a global health priority, there remain no specific and effective therapy for sepsis so far. Indeed, over the previous decades almost all attempts to develop novel drugs have failed. This may be partly ascribable to the multifactorial complexity of the septic cascade and the resultant difficulties of identifying drug targets. In addition, there might still be missing links among dysregulated host responses in vital organs. In this review article, recent advances in understanding of the complex pathophysiology of sepsis are summarized, with a focus on neutrophil extracellular traps (NETs), the significant role of NETs in thrombosis/embolism, and the functional roles of plasma proteins, histidine-rich glycoprotein (HRG) and inter-alpha-inhibitor proteins (IAIPs). The specific plasma proteins that are markedly decreased in the acute phase of sepsis may play important roles in the regulation of blood cells, vascular endothelial cells and coagulation. The accumulating evidence may provide us with insights into a novel aspect of the pathophysiology of sepsis and septic ARDS, including that in COVID-19.

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.

The Impact of Severe COVID-19 on Plasma Antioxidants

Several studies suggested the association of COVID-19 with systemic oxidative stress, in particular with lipid peroxidation and vascular stress. Therefore, this study aimed to evaluate the antioxidant signaling in the plasma of eighty-eight patients upon admission to the Clinical Hospital Dubrava in Zagreb, of which twenty-two died within a week, while the other recovered. The differences between the deceased and the survivors were found, especially in the reduction of superoxide dismutases (SOD-1 and SOD-2) activity, which was accompanied by the alteration in glutathione-dependent system and the intensification of the thioredoxin-dependent system. Reduced levels of non-enzymatic antioxidants, especially tocopherol, were also observed, which correlated with enhanced lipid peroxidation (determined by 4-hydroxynonenal (4-HNE) and neuroprostane levels) and oxidative modifications of proteins assessed as 4-HNE-protein adducts and carbonyl groups. These findings confirm the onset of systemic oxidative stress in patients with severe SARS-CoV-2, especially those who died from COVID-19, as manifested by strongly reduced tocopherol level and SOD activity associated with lipid peroxidation. Therefore, we propose that preventive and/or supplementary use of antioxidants, especially of lipophilic nature, could be beneficial for the treatment of COVID-19 patients.

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.

Integration of protein context improves protein-based COVID-19 patient stratification

BACKGROUND

Classification of disease severity is crucial for the management of COVID-19. Several studies have shown that individual proteins can be used to classify the severity of COVID-19. Here, we aimed to investigate whether integrating four types of protein context data, namely, protein complexes, stoichiometric ratios, pathways and network degrees will improve the severity classification of COVID-19.

METHODS

We performed machine learning based on three previously published datasets. The first was a SWATH (sequential window acquisition of all theoretical fragment ion spectra) MS (mass spectrometry) based proteomic dataset. The second was a TMTpro 16plex labeled shotgun proteomics dataset. The third was a SWATH dataset of an independent patient cohort.

RESULTS

Besides twelve proteins, machine learning also prioritized two complexes, one stoichiometric ratio, five pathways, and five network degrees, resulting a 25-feature panel. As a result, a model based on the 25 features led to effective classification of severe cases with an AUC of 0.965, outperforming the models with proteins only. Complement component C9, transthyretin (TTR) and TTR-RBP (transthyretin-retinol binding protein) complex, the stoichiometric ratio of SAA2 (serum amyloid A proteins 2)/YLPM1 (YLP Motif Containing 1), and the network degree of SIRT7 (Sirtuin 7) and A2M (alpha-2-macroglobulin) were highlighted as potential markers by this classifier. This classifier was further validated with a TMT-based proteomic data set from the same cohort (test dataset 1) and an independent SWATH-based proteomic data set from Germany (test dataset 2), reaching an AUC of 0.900 and 0.908, respectively. Machine learning models integrating protein context information achieved higher AUCs than models with only one feature type.

CONCLUSION

Our results show that the integration of protein context including protein complexes, stoichiometric ratios, pathways, network degrees, and proteins improves phenotype prediction.

Early Prediction of COVID-19 Patient Survival by Targeted Plasma Multi-Omics and Machine Learning

The recent surge of coronavirus disease 2019 (COVID-19) hospitalizations severely challenges healthcare systems around the globe and has increased the demand for reliable tests predictive of disease severity and mortality. Using multiplexed targeted mass spectrometry assays on a robust triple quadrupole MS setup which is available in many clinical laboratories, we determined the precise concentrations of hundreds of proteins and metabolites in plasma from hospitalized COVID-19 patients. We observed a clear distinction between COVID-19 patients and controls and, strikingly, a significant difference between survivors and nonsurvivors. With increasing length of hospitalization, the survivors' samples showed a trend toward normal concentrations, indicating a potential sensitive readout of treatment success. Building a machine learning multi-omic model that considers the concentrations of 10 proteins and five metabolites, we could predict patient survival with 92% accuracy (area under the receiver operating characteristic curve: 0.97) on the day of hospitalization. Hence, our standardized assays represent a unique opportunity for the early stratification of hospitalized COVID-19 patients.

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.

Histidine-rich glycoprotein as a novel predictive biomarker of postoperative complications in intensive care unit patients: a prospective observational study

Background

Decrease in histidine-rich glycoprotein (HRG) was reported as a cause of dysregulation of the coagulation-fibrinolysis and immune systems, leading to multi-organ failure, and it may be a biomarker for sepsis, ventilator-associated pneumonia, preeclampsia, and coronavirus disease 2019. However, the usefulness of HRG in perioperative management remains unclear. This study aimed to assess the usefulness of HRG as a biomarker for predicting postoperative complications.

Methods

This was a single-center, prospective, observational study of 150 adult patients who were admitted to the intensive care unit after surgery. Postoperative complications were defined as those having a grade II or higher in the Clavien–Dindo classification, occurring within 7 days after surgery. The primary outcome was HRG levels in the patients with and without postoperative complications. The secondary outcome was the ability of HRG, white blood cell, C-reactive protein, procalcitonin, and presepsin to predict postoperative complications. Data are presented as number and median (interquartile range).

Results

The incidence of postoperative complications was 40%. The HRG levels on postoperative day 1 were significantly lower in patients who developed postoperative complications (n = 60; 21.50 [18.12–25.74] µg/mL) than in those who did not develop postoperative complications (n = 90; 25.46 [21.05–31.63] µg/mL). The Harrell C-index scores for postoperative complications were HRG, 0.65; white blood cell, 0.50; C-reactive protein, 0.59; procalcitonin, 0.73; and presepsin, 0.73. HRG was independent predictor of postoperative complications when adjusted for age, the presence of preoperative cardiovascular comorbidities, American Society of Anesthesiologists Physical Status Classification, operative time, and the volume of intraoperative bleeding (adjusted hazard ratio = 0.94; 95% confidence interval, 0.90–0.99).

Conclusions

The HRG levels on postoperative day 1 could predict postoperative complications. Hence, HRG may be a useful biomarker for predicting postoperative complications.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12871-022-01774-7.

Integrated plasma proteomic and single-cell immune signaling network signatures demarcate mild, moderate, and severe COVID-19

The biological determinants underlying the range of coronavirus 2019 (COVID-19) clinical manifestations are not fully understood. Here, over 1,400 plasma proteins and 2,600 single-cell immune features comprising cell phenotype, endogenous signaling activity, and signaling responses to inflammatory ligands are cross-sectionally assessed in peripheral blood from 97 patients with mild, moderate, and severe COVID-19 and 40 uninfected patients. Using an integrated computational approach to analyze the combined plasma and single-cell proteomic data, we identify and independently validate a multi-variate model classifying COVID-19 severity (multi-class area under the curve [AUC]training = 0.799, p = 4.2e-6; multi-class AUCvalidation = 0.773, p = 7.7e-6). Examination of informative model features reveals biological signatures of COVID-19 severity, including the dysregulation of JAK/STAT, MAPK/mTOR, and nuclear factor κB (NF-κB) immune signaling networks in addition to recapitulating known hallmarks of COVID-19. These results provide a set of early determinants of COVID-19 severity that may point to therapeutic targets for prevention and/or treatment of COVID-19 progression.

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.

A multiplex protein panel assay for severity prediction and outcome prognosis in patients with COVID-19: An observational multi-cohort study

BACKGROUND

Global healthcare systems continue to be challenged by the COVID-19 pandemic, and there is a need for clinical assays that can help optimise resource allocation, support treatment decisions, and accelerate the development and evaluation of new therapies.

METHODS

We developed a multiplexed proteomics assay for determining disease severity and prognosis in COVID-19. The assay quantifies up to 50 peptides, derived from 30 known and newly introduced COVID-19-related protein markers, in a single measurement using routine-lab compatible analytical flow rate liquid chromatography and multiple reaction monitoring (LC-MRM). We conducted two observational studies in patients with COVID-19 hospitalised at Charité - Universitätsmedizin Berlin, Germany before (from March 1 to 26, 2020, n=30) and after (from April 4 to November 19, 2020, n=164) dexamethasone became standard of care. The study is registered in the German and the WHO International Clinical Trials Registry (DRKS00021688).

FINDINGS

The assay produces reproducible (median inter-batch CV of 10.9%) absolute quantification of 47 peptides with high sensitivity (median LLOQ of 143 ng/ml) and accuracy (median 96.8%). In both studies, the assay reproducibly captured hallmarks of COVID-19 infection and severity, as it distinguished healthy individuals, mild, moderate, and severe COVID-19. In the post-dexamethasone cohort, the assay predicted survival with an accuracy of 0.83 (108/130), and death with an accuracy of 0.76 (26/34) in the median 2.5 weeks before the outcome, thereby outperforming compound clinical risk assessments such as SOFA, APACHE II, and ABCS scores.

INTERPRETATION

Disease severity and clinical outcomes of patients with COVID-19 can be stratified and predicted by the routine-applicable panel assay that combines known and novel COVID-19 biomarkers. The prognostic value of this assay should be prospectively assessed in larger patient cohorts for future support of clinical decisions, including evaluation of sample flow in routine setting. The possibility to objectively classify COVID-19 severity can be helpful for monitoring of novel therapies, especially in early clinical trials.

FUNDING

This research was funded in part by the European Research Council (ERC) under grant agreement ERC-SyG-2020 951475 (to M.R) and by the Wellcome Trust (IA 200829/Z/16/Z to M.R.). The work was further supported by the Ministry of Education and Research (BMBF) as part of the National Research Node 'Mass Spectrometry in Systems Medicine (MSCoresys)', under grant agreements 031L0220 and 161L0221. J.H. was supported by a Swiss National Science Foundation (SNSF) Postdoc Mobility fellowship (project number 191052). This study was further supported by the BMBF grant NaFoUniMedCOVID-19 - NUM-NAPKON, FKZ: 01KX2021. The study was co-funded by the UK's innovation agency, Innovate UK, under project numbers 75594 and 56328.

A Novel Secreted Protein-Related Gene Signature Predicts Overall Survival and Is Associated With Tumor Immunity in Patients With Lung Adenocarcinoma

Secreted proteins are important proteins in the human proteome, accounting for approximately one-tenth of the proteome. However, the prognostic value of secreted protein-related genes has not been comprehensively explored in lung adenocarcinoma (LUAD). In this study, we screened 379 differentially expressed secretory protein genes (DESPRGs) by analyzing the expression profile in patients with LUAD from The Cancer Genome Atlas database. Following univariate Cox regression and least absolute shrinkage and selection operator method regression analysis, 9 prognostic SPRGs were selected to develop secreted protein-related risk score (SPRrisk), including CLEC3B, C1QTNF6, TCN1, F2, FETUB, IGFBP1, ANGPTL4, IFNE, and CCL20. The prediction accuracy of the prognostic models was determined by Kaplan–Meier survival curve analysis and receiver operating characteristic curve analysis. Moreover, a nomogram with improved accuracy for predicting overall survival was established based on independent prognostic factors (SPRrisk and clinical stage). The DESPRGs were validated by quantitative real-time PCR and enzyme-linked immunosorbent assay by using our clinical samples and datasets. Our results demonstrated that SPRrisk can accurately predict the prognosis of patients with LUAD. Patients with a higher risk had lower immune, stromal, and ESTIMATE scores and higher tumor purity. A higher SPRrisk was also negatively associated with the abundance of CD8⁺ T cells and M1 macrophages. In addition, several genes of the human leukocyte antigen family and immune checkpoints were expressed in low levels in the high-SPRrisk group. Our results provided some insights into assessing individual prognosis and choosing personalized treatment modalities.

Molecular signature of postmortem lung tissue from COVID-19 patients suggests distinct trajectories driving mortality

To elucidate the molecular mechanisms that manifest lung abnormalities during severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections, we performed whole-transcriptome sequencing of lung autopsies from 31 patients with severe COVID-19 and ten uninfected controls. Using metatranscriptomics, we identified the existence of two distinct molecular signatures of lethal COVID-19. The dominant 'classical' signature (n=23) showed upregulation of the unfolded protein response, steroid biosynthesis and complement activation, supported by massive metabolic reprogramming leading to characteristic lung damage. The rarer signature (n=8) that potentially represents 'cytokine release syndrome' (CRS) showed upregulation of cytokines such as IL1 and CCL19, but absence of complement activation. We found that a majority of patients cleared SARS-CoV-2 infection, but they suffered from acute dysbiosis with characteristic enrichment of opportunistic pathogens such as Staphylococcus cohnii in 'classical' patients and Pasteurella multocida in CRS patients. Our results suggest two distinct models of lung pathology in severe COVID-19 patients, which can be identified through complement activation, presence of specific cytokines and characteristic microbiome. These findings can be used to design personalized therapy using in silico identified drug molecules or in mitigating specific secondary infections.

Tissue chaperoning—the expanded functions of fetuin-A beyond inhibition of systemic calcification

Traditionally, fetuin-A embodies the prototype anti-calcification protein in the blood, preventing cardiovascular calcification. Low serum fetuin-A is generally associated with mineralization dysbalance and enhanced mortality in end stage renal disease. Recent evidence indicates that fetuin-A is a crucial factor moderating tissue inflammation and fibrosis, as well as a systemic indicator of acute inflammatory disease. Here, the expanded function of fetuin-A is discussed in the context of mineralization and inflammation biology. Unbalanced depletion of fetuin-A in this context may be the critical event, triggering a vicious cycle of progressive calcification, inflammation, and tissue injury. Hence, we designate fetuin-A as tissue chaperone and propose the potential use of exogenous fetuin-A as prophylactic agent or emergency treatment in conditions that are associated with acute depletion of endogenous protein.

Plasma Metabolomic Alterations Induced by COVID-19 Vaccination Reveal Putative Biomarkers Reflecting the Immune Response

Vaccination is currently the most effective strategy for the mitigation of the COVID-19 pandemic. mRNA vaccines trigger the immune system to produce neutralizing antibodies (NAbs) against SARS-CoV-2 spike proteins. However, the underlying molecular processes affecting immune response after vaccination remain poorly understood, while there is significant heterogeneity in the immune response among individuals. Metabolomics have often been used to provide a deeper understanding of immune cell responses, but in the context of COVID-19 vaccination such data are scarce. Mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR)-based metabolomics were used to provide insights based on the baseline metabolic profile and metabolic alterations induced after mRNA vaccination in paired blood plasma samples collected and analysed before the first and second vaccination and at 3 months post first dose. Based on the level of NAbs just before the second dose, two groups, "low" and "high" responders, were defined. Distinct plasma metabolic profiles were observed in relation to the level of immune response, highlighting the role of amino acid metabolism and the lipid profile as predictive markers of response to vaccination. Furthermore, levels of plasma ceramides along with certain amino acids could emerge as predictive biomarkers of response and severity of inflammation.