key: cord-0870828-qubisv88
authors: Zhang, Zili; Lin, Fanjie; Liu, Fei; Li, Qiongqiong; Li, Yuanyuan; Zhu, Zhanbei; Guo, Hua; Liu, Lidong; Liu, Xiaoqing; Liu, Wei; Fang, Yaowei; Wei, Xinguang; Lu, Wenju
title: Proteomic profiling reveals a distinctive molecular signature for critically ill COVID-19 patients compared with asthma and COPD A distinctive molecular signature for critically ill COVID-19 patients
date: 2022-01-10
journal: Int J Infect Dis
DOI: 10.1016/j.ijid.2022.01.008
sha: 644dc84e0ad56e3d10d73d19a6661731317f9869
doc_id: 870828
cord_uid: qubisv88

Objective: The mortality rate for critically ill coronavirus disease 2019 (COVID-19) cases was more than 80%. Nonetheless, research about the effect of common respiratory diseases on critically ill COVID-19 expression and outcomes is scarce. Design: We performed proteomic analyses on airway mucus obtained by bronchoscopy from severe COVID-19 patients, or induced sputum from patients with chronic obstructive pulmonary disease (COPD), asthma, and healthy controls. Results: Out of the total identified and quantified proteins, 445 differentially expressed proteins (DEPs) were found in different comparison groups. In comparison to COPD, asthma, and controls, 11 proteins were uniquely present in COVID-19 patients. Apart from DEPs associated with COPD vs controls and asthma vs controls, there were a total of 59 DEPs specific to COVID-19 patients. Finally, the findings revealed that there were 8 overlapping proteins in COVID-19 patients, including C9, FGB, FGG, PRTN3, HBB, HBA1, IGLV3-19, and COTL1. Functional analyses revealed that the majority of them were associated with complement and coagulation cascades, platelet activation, or iron metabolism, and anemia-related pathways. Conclusions: This study provides fundamental data for identifying COVID-19-specific proteomic changes in comparison to COPD and asthma, which may suggest molecular targets for specialized therapy.

Coronavirus disease 2019 , which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a threat to global health and healthcare systems. Currently, the disease is spreading rapidly around the world. According to the World Health Organization's (WHO) situation report for June 9, 2021, there had been over 174,801,871 confirmed COVID-19 cases and approximately 3,756,350 COVID-19 related deaths worldwide. Additionally, the report revealed that the global severity rate of COVID-19 ranges between 5% to 20%, with the rates varying from region to region. For example, in New York, 1151 patients (20%) were diagnosed with severe COVID-19 and required mechanical ventilation (Richardson et al., 2020) .

In Italy, the proportion of intensive care unit (ICU) admissions was between 5% and 12% of the total COVID-19 cases (Livingston and Bucher, 2020) . According to the Chinese Center for Disease Control and Prevention, 19% of COVID-19 patients developed severe or critical illness, in a study encompassing 44, cases (Wu and McGoogan, 2020) . Surprisingly, the mortality rate of critically ill COVID-19 cases was above 80% .

To date, there are still gaps in the mechanistic understanding of the disease process as reported by Nalbandian A (2021). For instance, data about the biochemical and molecular alterations associated with the severe form of COVID-19 are scarce.

Additionally, there is evidence that chronic respiratory diseases, including chronic obstructive pulmonary disease (COPD) and asthma, may predispose patients to SARS-CoV-2 infection. Nevertheless, the effects of COPD and asthma on disease expression and outcomes, as well as the potential underlying processes are poorly investigated in COVID-19 patients.

The formation of mucus plugs has been observed in critically ill patients. Clinical findings show that the mucus plugs cause airway obstruction and respiratory failure in a significant proportion of affected patients (Lu et al., 2021 . In this study, it was speculated that this mucus is a mixture of secretions produced by airway and alveolar epithelial cells in response to viruses and inflammatory mediators, and the molecular changes may be indicative of the pathological changes of COVID-19. A previous study reported that COPD and asthma were associated with severe illness in COVID-19 patients (Gao et al., 2021) . In this study, proteomic analyses of airway mucus from severe COVID-19, chronic obstructive pulmonary disease (COPD), and asthma patients were performed. The study contributes fundamental information to the understanding of the pathogenesis of critically ill COVID-19 patients and their associated co-morbidities, which can be used to develop future targeted therapeutic approaches.

Five critically ill COVID-19 patients were diagnosed with laboratory-confirmed SARS-CoV-2 infection by the local health authorities. COVID-19 patients were classified into subgroups based on their different clinical manifestations using the Chinese Government Diagnosis and Treatment Guideline (Trial seventh version).

Severe patients were characterized by respiratory distress and a respiratory rate ≥ 30 times/min, which corresponds to an oxygen saturation ≤ 93% in resting state or arterial blood oxygen partial pressure (PaO2)/oxygen concentration (FiO2) ≤ 300 mmHg (1mmHg = 0.133kPa). Patients classified as critically ill were those who had respiratory failure requiring mechanical ventilation, experienced shock, or required ICU care. The COPD inclusion and exclusion criteria were adapted as previously described (Lu et al., 2016) . Asthma was defined according to the Global strategy for asthma management and prevention 2018(2018). The change in forced expiratory volume in 1s (FEV 1 ) was used as a diagnostic tool. An increase in FEV 1 , in response to bronchodilator reversibility (ΔFEV 1 BDR), following inhalation of 400 µg salbutamol was considered significant if it was ≥ 12% and ≥ 200mL in comparison to the initial FEV 1 .

Five participants who were negative for the SARS-CoV-2 nucleic acid test without any lung disease were included as healthy controls. Meanwhile, five COPD patients and five asthma patients were designated as disease controls. To aspirate the airway mucus, the critically ill COVID-19 patients presenting with expectoration difficulty and dyspnea underwent bronchoscopy using a PENTAX FB-15BS portable fiber bronchoscope (PENTAX Medical Shanghai Co, Ltd, Shanghai, China) via tracheal intubation. Airway mucus in COPD, asthma, and healthy control subjects was induced using hypertonic (3%) saline solution inhalation administered via an ultrasonic nebulizer.

Clinical charts, nursing records, laboratory findings, and chest imaging of the COVID-19 patients were reviewed from January 26, 2020, to February 15, 2020.

Electronic medical records were used to acquire epidemiological, clinical, laboratory, and radiological data. Two researchers independently reviewed the data collection forms to ensure that the collected data was accurate. All the procedures were approved by the Ethics Committee of the First Affiliated Hospital of Guangzhou Medical University (No.2020-65). While informed consent was obtained from all participants, it was waived for COVID-19 patients because their family members were quarantined.

The processing of airway mucus was conducted as previously described (Wang et al., 2019) . Two independent physicians who were blind to clinical data performed the procedures. Supplemental material 1 provides more information on airway mucus processing.

Airway mucus processing was performed as previously described .

Supplemental material 2 provides more information on protein extraction and trypsin digestion.

Proteomic data were quantified and analyzed as previously described . For label-free quantification, protein expression levels were estimated using the iBAQ (Intensity Based Absolute Quantification) algorithm embedded in MaxQuant (Schwanhausser et al., 2011) . Detailed information is provided in supplementary material 3.

The peptides were subjected to NSI source followed by tandem mass spectrometry (MS/MS) in Q ExactiveTM Plus (Thermo Fisher Scientific), which was connected online to the UPLC. Peptides were selected for MS/MS analysis using an NCE setting of 28, and the fragments were detected in the Orbitrap at a resolution of 17,500. A Principal Components Analysis (PCA) was performed to visualize the separation of COVID-19 patients, COPD, asthma, and healthy controls.

Differential gene expression analysis was performed in R (v3.2.0) using the empirical Bayesian algorithm in the limma package. Up-regulated and down-regulated genes were defined using a fold change of ≥1.5 or ≤ 0.67 and a p-value < 0.05. The cutoff value for fold-change was set at 1.2. The Gene Ontology (GO) annotation proteome was constructed using data from the UniProt-GOA database (http://www.ebi.ac. uk/GOA). The Kyoto Encyclopedia of Genes and Genomes (KEGG) database was used to identify the enriched pathways. Further hierarchical clustering based on DEPs functional classification was visualized using the "heatmap.2" function from the "gplots" in R-package. More information about pathway analysis is provided in supplementary material 4.

Continuous variables were presented as median (IQR). Categorical variables were presented as a (%) of the total sample (n). All analyses were performed using the GraphPad Prism 5 software, and two-sided p-values. Statistical significance was set at p-value < 0.05.

The clinical characteristics of COVID-19 patients, asthma, COPD, and healthy controls are shown in table 1. There was no significant difference in baseline characteristics (age, gender, and smoking status) between COVID-19, asthma, COPD, and healthy controls. In all COVID-19 patients, laboratory findings revealed characteristic clinical outcomes of SARS-CoV-2 infection, which were almost identical to those reported in previous studies.

Airway mucus samples were obtained from critically ill COVID-19 patients, asthma, COPD, as well as healthy control subjects. Label-free quantification of proteomic (PTMBiolabs) was used to analyze airway mucus from each participant. The airway mucus from COVID-19 patients exhibited distinct proteomic patterns compared to asthma, COPD, and healthy controls. 91 differentially expressed proteins (DEPs) were identified between COVID-19 and healthy controls, 78 between asthma and healthy controls, 66 between COPD and healthy controls, 69 between COVID-19 and asthma, and 143 between COVID-19 and COPD as shown in Figure S1A . There were 2257, 2169, 2093, and 2175 proteins identified and quantified in the airway mucus of COVID-19 patients, asthmatic patients, COPD patients, and healthy controls, respectively ( Figure S1B ). The proteomics datasets (including fold-change and p-values for the two groups' comparisons) are provided in table S1-S3. PCA, the median relative SD (RSD) of all internal standards in each sample, protein mass and coverage distribution, and protein sequence distribution were calculated as part of the quality control analysis ( Figure S1C -F). The current study's data were collected with a high degree of consistency and reproducibility. Figure S2 -S3 depicts a heatmap, GO enrichment analysis, and KEGG pathway analysis for each proteomics dataset.

When COVID-19 was compared to healthy controls, Venn diagrams, and volcano plots ( When COVID-19 was compared to asthma, venn and volcano plots (Figure 1) showed that there were 46 up-regulated and 46 down-regulated DEPs. The GO enrichment analysis revealed significant changes in molecular function terms such as serine-type peptidase activity, serine hydrolase activity, and (serine-type) endopeptidase activity. Significantly altered biological process terms included protein activation cascade, antimicrobial humoral response, immune response, and regulation of defense response. The majority of them were located in the vesicle lumen and granule lumen (Figure 2Ab ). The KEGG pathway analysis showed that these DEPs were significantly enriched in complement and coagulation cascades, as well as in propanoate metabolism (Figures 2Bb and 3B ).

The comparison between COVID-19 and COPD groups showed the presence of 143 DEPs (Figure 1 ) in the mucus obtained from COVID-19 patients, including 56 up-regulated and 87 down-regulated proteins. The GO functional enrichment analysis revealed that protein activation cascade, antimicrobial humoral response, cellular response to interleukin-1 (IL-1), immunoglobulin mediated immune response, B cell-mediated immunity, regulation of inflammatory response, and receptor-mediated response were all enriched. The majority of these proteins were located in the extracellular space, vesicle lumen, and the vacuolar lumen. The molecular functions of these proteins were primarily distributed among four-function processes: acetylgalactosaminyl transferase activity, endonuclease activity, carbohydrate-binding, and actin-binding ( Figure 2Ac ). According to KEGG pathway analysis, these DEPs were significantly enriched in the folate biosynthesis, hippo signaling pathway, glucagon signaling pathway, and tight junction (Figures 2Bc and 3C ).

A total of 11 overlapped DEPs were identified in COVID-19 patients. They were discovered from the intersection of COVID-19 vs controls, COVID-19 vs asthma, and COVID-19 vs COPD. As illustrated in figure 4A -B, pathway and network enrichment analyses revealed that these intersecting DEPs were primarily associated with complement and coagulation cascades, platelet activation, Staphylococcus aureus infection, nicotinate, and nicotinamide metabolism, and metabolic pathways.

According to differential significance levels, the COVID-19 specific proteins were IGLV3-19, IGLV3-1, FGB, FGG, C9, PRTN3, HBB, HBA1, COTL1, NAPRT, and BPIFB1 ( Figure 4C ).

A comparison between COVID-19 patients and controls revealed 91 DEPs as previously reported ( Figure 5, Figure 6Aa , 6Ba, and Figure 7A ). For asthma vs controls, 78 DEPs were significantly expressed, with 27 being up-regulated ( Figure 5 and Figure 7B ). GO enrichment analysis was performed to annotate the putative functional implications of these differently grouped DEPs. The results revealed that (L-) lactate dehydrogenase activity was enriched. Additionally, the majority of these proteins were located in the extracellular space and the tertiary granule lumen. The molecular function of these proteins was primarily distributed among three function processes: regulation of (ion) transmembrane transport, regulation of ion transport, and leukocyte migration (Figure 6Ab ). KEGG pathway analysis revealed that these DEPs were significantly enriched in the hippo signaling pathway and glucagon signaling pathway (Figure 6Bb ).

There were 66 DEPs found in COPD vs controls, with 46 up-regulated and 20 down-regulated proteins ( Figure 5 and Figure 7C ). GO enrichment analysis showed that the significantly altered molecular function terms were enriched in iron ion binding and proteoglycan binding. The biological process terms comprised granulocyte/neutrophil activation, neutrophil-mediated immunity, response to tumor necrosis factor, and antimicrobial humoral response. The majority of these proteins were found located within the organelle/membrane-enclosed/intracellular organelle lumen (Figure 6Ac ). KEGG pathway analysis revealed that there were two pathways enriched in salivary secretion, cysteine and methionine metabolism, antigen processing, and presentation (Figure 6Bc ).

There were 59 DEPs detected in the mucus of COVID-19 patients compared to controls, excluding any DEPs detected in COPD vs controls or asthma vs controls. As indicated in figure 8A , pathway and network enrichment analysis revealed that the intersected DEPs were largely associated with metabolic pathways, lysosome, phagosome, and NOD-like receptor signaling pathways. The selected proteins included CXCL1, DEFA3, HBB, ICAM1, LAMP2, RAC1, and TXN, were chosen because they were present in at least two pathways at a high frequency ( Figure 8B ).

COVID-19 patients' specific proteins were defined as the intersection of specific DEPs in COVID-19 samples compared to healthy/disease controls (COVID-19 vs controls, COVID-19 vs COPD, and COVID-19 vs asthma). Simultaneously, any DEPs found in COPD vs controls or asthma vs controls were excluded from the analysis. For example, the filtered COVID-19 specific proteins were differentially expressed between COVID-19 and controls but not between COPD and controls or asthma and controls.

Finally, as determined by the two aforementioned techniques, the eight overlapping differential proteins specific to COVID-19 patients were identified, including FGB, FGG, C9, PRTN3, HBB, HBA1, IGLV3-19, and COTL1 ( Figure S4 ).

The COVID-19 pandemic is a major threat to public health and the social-economic well-being of people globally. There is currently no effective treatment strategy to prevent the death of severely ill COVID-19 patients. Therefore, any lead to the discovery of therapeutic drug targets for critically ill COVID-19 patients is vital. In this study, compared to asthma and COPD, proteomic sequencing identified 8 key characteristics of the proteomic changes associated with hospitalized patients seriously infected with SARS-CoV-2.

About 20%-51% of COVID-19 patients were associated with at least one comorbidity (Guan et al., 2020b , Huang et al., 2020 . The three most prevalent comorbidities were hypertension, diabetes, and coronary heart disease, with frequencies ratios of 10%-30%, 10%-20%, and 7%-15%, respectively (Guan et al., 2020a , Wang et al., 2020 , Zhou et al., 2020 , which contributed to poorer clinical outcomes. It is reported that chronic respiratory disorders, including COPD and asthma, may predispose patients to SARS-CoV-2 infection (Guan et al., 2020b , Huang et al., 2020 . Alternatively, the poor recognition by the general population and the lack of spirometric testing may result in the under-diagnosis of respiratory diseases (Guan et al., 2020a) . For instance, it was reported that the frequencies of COVID-19 with COPD were 1.5%-5% (Grasselli et al., 2020 , Zhang et al., 2020 and for asthma 0%-12.5% 18 . Evidence suggests that the intrinsic pathophysiological features of COPD and asthma may modify the response to severe SARS-CoV-2 infection made possible by ACE2 expression (Song et al., 2021) . Therefore, it is necessary to understand the effects of SARS-CoV-2 on unique proteomic changes compared to COPD and asthma, which may imply further research of molecular targets directed at specific therapy.

In this study, the 8 overlapped differential specific proteins were found in COVID-19 cases after intersecting. There was up-regulation of proteins, including FGB, FGG, C9, PRTN3, HBB, HBA1, IGLV3-19, and down-regulation of COTL1 proteins in COVID-19 patients compared to the other groups. Pathway and network enrichment analysis revealed that the DEPs were mostly associated with complement and coagulation cascades, platelet activation pathways, or iron metabolism and anemia related. In the present study, an elevated complement system protein C9 was identified. It is reported that the complement system plays an important role in linking innate and adaptive immunity and that inflammation could further aggravate lung injury. Complement activation is detected cumulatively in conditions such as ARDS, pneumonia, asthma, pulmonary arterial hypertension (PAH), and COPD (Sarma et al., 2006) . Evidence suggests that suppression of complement system protein C9 appears to be effective immunotherapy for the SARS-infected mouse model (Gralinski et al., 2018) . Additionally, FGB and FGG are crucial for blood clot formation (coagulation), and this study revealed that the two proteins were up-regulated. Previous proteomic study of plasma exosomes demonstrated that FGG and FGB levels were significantly higher in the malignant pulmonary nodules group, compared to the benign group (Kuang et al., 2019) . FGB and FGG were two of the key epithelial-mesenchymal transition (EMT) effectors associated with cell adhesion and cellular communication in lung cancer. Therefore, we indicate that critically ill COVID-19 patients may benefit from the suppression of the complement and coagulation systems.

Iron metabolism and anemia may play pivotal roles in multiple organ dysfunction syndromes in COVID-19. The hemoglobin proteins (HBB, HBA1, and HBA2) combine to form the adult hemoglobin molecule (HbA), which is a heterotetramer of two α and two β-globin chains. The dysregulated hemoglobin proteins result in an imbalanced globin chain synthesis and consequently impaired erythropoiesis. The severity of COVID-19 is heavily influenced by the degree of chain imbalance. Survival is dependent on regular blood transfusions in the worst-case scenario, which results in transfusional iron overload and secondary multi-organ damage due to iron toxicity. Understanding the relationship between HBB and HBA1 proteins and the severity of COVID-19, as well as whether these associations differ by age, sex, and the presence of chronic conditions is critical in the management of COVID-19.

Mucus is an integral part of respiratory physiology. It protects the respiratory tract by forming a physical barrier to inhaled allergens and pathogens. This study established that mucus accumulation contributed to recurrent airway infection, resulting to further obstruction. The inflammatory cytokine storm greatly contributes to the more serious clinical manifestations and worse outcomes in COVID-19 patients.

It is particularly potent in accumulating mucus because it initiates many inflammatory cascades associated with mucus production. Numerous studies have demonstrated that the SARS-CoV-2 infection can result in an allergic reaction in the respiratory tract mucosa, which activates mucin secretion and modulates its chemical structure to enable the virus to enter the cells (Khan et al., 2021) . Mucus accumulation can contribute to worse co-morbidities indicated in COVID-19 patients, such as venous engorgement and pulmonary edema. Thus, it is important to understand the proteomic expression and functional changes of mucus to develop new therapeutic approaches.

Additionally, this retrospective study identified several risk factors for COVID-19 patients. For example, increased levels of white blood cell count, D-dimer, blood IL-6, and lactate dehydrogenase, as well as lymphocytopenia, were all observed in severely ill COVID-19 patients. These risk factors were associated with COVID-19 outcomes and corroborated previously published studies . In this study, there were no significant differences in age, gender, and smoking status among COVID-19, asthma, COPD, and healthy controls.

Our study has some limitations. Firstly, the airway mucus obtained from COVID-19 patients using bronchoscopy may be a mixture of secretions produced by airway and alveolar epithelial cells in response to the virus and inflammatory mediators. On the other hand, induced sputum was used for COPD, asthma, and control subjects, all of whom may have variable content and sputum, cell count.

Secondly, because the study design was retrospective, laboratory tests may have been underestimated in the medical records analyzed, making it difficult to investigate the effect on outcomes. Thirdly, information on medications, disease control status, and phenotypes of diseases before admissions was incomplete. Further, the effect of these factors on the risk of SASR-CoV-2 infection and disease expression needs further exploration. Finally, the sample size was relatively small. Prospect studies on a larger population should be conducted.

Airway mucus proteomic databases are highly valuable resources for elucidating the host proteomic changes associated with severe SARS-CoV-2 infection. This study analyzed proteins from COVID-19 patients, COPD, asthma, and controls, to identify the unique proteomic molecular signatures associated with SARS-CoV-2 infection.

This study contributes to our understanding of the pathological changes associated with COVID-19 and forms the basis for the development of potential therapeutic strategies.

All the procedures were approved by the Ethics Committee of the First Affiliated Hospital of Guangzhou Medical University (No. 2020-65) . Verbal informed consent were obtained from all participants because the family members were in quarantine. 

The authors have no conflict of interest to declare.

This study was supported by grants from the National Key R&D Program of China 

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We sincerely thank all the healthcare providers who are fighting this public health crisis, as well as all the patients who participated in the study. We extend our heartfelt sympathy and condolences to the victims and bereaved families.