Nasopharyngeal Proteomic Profiles from Patients Hospitalized Due to COVID-19 in Manaus, Amazonas, Brazil
by
, , , , , , , , , , , and
Cláudia P. M. Araújo
1, 
Carolina M. Vieira
1,
Ketlen C. Ohse
1,
Alessandra S. Silva
1,
Sofia A. Cavalcante
1,
Felipe G. Naveca
1
,
Fernanda N. Oliveira
1,
James L. Crainey
1
,
Marcus V. G. Lacerda
1,2,3,
Gisely C. Melo
3,
Vanderson S. Sampaio
4,
Michel Batista
5,
Amanda C. Camillo-Andrade
6,
Marlon D. M. Santos
6,7,
Diogo B. Lima
8,
Juliana de S. G. Fischer
6,9,
Paulo C. Carvalho
6,9 and
Priscila F. Aquino
1,* 1
Instituto Leônidas & Maria Deane, Fiocruz-Amazônia, Manaus 69057-070, Brazil
2
Fundação de Medicina Tropical Dr Heitor Vieira Dourado, Manaus 69040-000, Brazil
3
Universidade do Estado do Amazonas, Manaus 69065-001, Brazil
4
Instituto Todos pela Saúde, São Paulo 01310-942, Brazil
5
Mass Spectrometry Platform-RPT02H, Instituto Carlos Chagas (Fiocruz-Paraná), Curitiba 81350-010, Brazil
6
Laboratory for Structural and Computational Proteomics, Instituto Carlos Chagas (Fiocruz-Paraná), Curitiba 81350-010, Brazil
7
Analytical Biochemistry and Proteomics Unit, Instituto de Investigaciones Biológicas Clemente Estable (Institut Pasteur de Montevideo), Montevideo 11400, Uruguay
8
Departament of Chemical Biology, Forchungsinstitut für Molekulare Pharmakologie (FMP), 13125 Berlin, Germany
9
Integrated Space Stem Cell Orbital Research (ISSCOR), University of California, San Diego, CA 92093, USA
*
Author to whom correspondence should be addressed.
COVID 2025, 5(11), 192; https://doi.org/10.3390/covid5110192
Submission received: 23 September 2025
/
Revised: 19 October 2025
/
Accepted: 25 October 2025
/
Published: 18 November 2025
(This article belongs to the Section Host Genetics and Susceptibility/Resistance)
Abstract
This study investigated proteomic differences in nasopharyngeal swabs of SARS-CoV-2-infected patients from Manaus (Brazil) who were hospitalized during the devastating first wave of the COVID-19 pandemic, before the emergence of the deadly P1 SARS-CoV-2 strain. LC-MS/MS proteomic analysis compared 16 matched COVID-19 patient profiles: eight survivors and eight fatalities. A total of 1604 proteins were identified in fatality swabs, and 981 in the swabs of survivors. Our study provides new insights into the cellular mechanisms underlying first-wave COVID-19 deaths from Manaus and identifies hypoxia-related HYOU1, endothelial injury-associated S100A10, and some viral replication proteins (DDX1/17, XPO1) as potential biomarkers of fatal infections. The proteomic profiles of the swabs taken from patients that died collectively suggest that many of the first wave COVID-19 fatalities in Manaus suffered immune-system collapse. Survivor patient swabs showed elevated levels of immune defense proteins (FN1, C4BPA, IGKV1-5), indicating effective antiviral responses. Gene ontology analysis revealed dysregulated secretory pathways in fatalities and did not detect the defense-response pathways in fatality-group datasets that were observed in survivor protein datasets. Interestingly, the NOS2 protein, previously associated with first-wave fatalities, was found exclusively in our fatality swabs.
1. Introduction
The COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, has resulted in over seven million deaths worldwide [1]. While most infections are mild or asymptomatic, a subset of patients develops severe disease, progressing to acute respiratory distress syndrome (ARDS), multiorgan failure, and death [2]. The factors determining disease severity remain incompletely understood, though host immune responses, viral load dynamics, and pre-existing comorbidities are known to play critical roles [3]. The first wave of COVID-19 in Manaus, Brazil, was among the most devastating outbreaks to occur during the pandemic. An overwhelming surge in COVID-19 cases during this period led to overcrowded emergency rooms and the rapid construction of mass graves, which attracted international attention [4].
Proteomic profiling of respiratory samples offers a powerful approach to understanding host–pathogen interactions in COVID-19. Some previous studies have analyzed bronchoalveolar lavage fluid (BALF) and nasopharyngeal swabs to identify proteins associated with disease severity [5,6,7]. However, most proteomic investigations have focused on blood, plasma or lower respiratory tract samples, which provide only a systemic picture of immune response and do not provide a clear picture of the host–parasite interactions at the primary site of initial viral replication [8]. Given that the nasal mucosa serves as the first line of defense against respiratory pathogens, its proteomic signature may hold critical clues to early immune responses and viral evasion mechanisms.
This study employed liquid chromatography-tandem mass spectrometry (LC-MS/MS) to analyze nasopharyngeal swabs from 16 SARS-CoV-2-positive patients in Manaus and compared the profiles of individuals who survived their infections with those who died. Our findings reveal stark contrasts between the two groups, provide mechanistic insights into COVID-19 severity, and suggest potential biomarkers for clinical risk assessment. Moreover, our study contributes to the growing body of proteomic data from respiratory infections, offering a foundation for future therapeutic strategies.
2. Methods
2.1. Patient Profiles and Sample Collection
Samples used for this study came from the biorepository at Instituto Leônidas & Maria Deane (ILMD/Fiocruz Amazônia), one of the designated laboratories for SARS-CoV-2 diagnosis in Amazonas and a member of the Fiocruz COVID-19 Genomics Surveillance Network. Our study used only a limited number of patient samples. A total of 16 nasal swabs were analyzed and categorized into two patient groups, referred to hereafter as “survivor” and “fatality” groups. The survivor group consisted of swabs from eight SARS-CoV-2-positive patients who survived their infections, four of whom had comorbidities and four of whom did not have comorbidities. The fatality group consisted of eight swabs from SARS-CoV-2-positive patients, four of whom had comorbidities, and four of whom did not have comorbidities. An equal number of male and female patients were selected for both the survivor and fatality groups; the mean age of the survivor group was 58.3, and the mean age of the mortality group was 48.2 (the difference between the two age groups not being statistically significant [p = 0.146; Welch’s t-test]). Further details of the patients’ sociodemographic characteristics are provided in Supplementary Table S1. Supplementary Figure S1 provides details of key patient blood biochemical markers, including hemoglobin, leukocyte count, lymphocyte count, neutrophil count, hematocrit, platelet count, creatinine, and urea that were taken when the patients were admitted to the hospital. Mann–Whitney tests were used to detect significant differences in these biochemical markers between our two study groups. Nasopharyngeal swab samples were collected from hospitalized Manaus residents who tested positive for SARS-CoV-2 by RT-qPCR between 29 April and 21 December 2020.
2.2. Nasal Swab Sample Preparation, LC-MS-MS Processing and Analysis
All 16 nasopharyngeal swab samples were individually immersed in 250 µL of a stock solution containing a viral transport medium before being inactivated at 65 °C for 40 min. Subsequently, the samples were lyophilized, and the proteins were extracted in 150 µL of RapiGest SF (Waters Corp., Milford, MA, USA) according to the manufacturer’s instructions [9]. Details of this and of how the Liquid chromatography tandem mass spectrometry (LC-MS-MS) was performed are provided in Supplementary Material File S1. Details of how the spectra generated from the LC-MS-MS workflow were analyzed are provided in Supplementary Material File S2.
3. Results
3.1. Biochemical Blood Markers of Patients Hospitalized
Patients who survived exhibited higher blood counts of lymphocytes and higher levels of hemoglobin and hematocrit, although these differences were not statistically significant (Supplementary Figure S1). Conversely, patients who succumbed to the disease presented slightly elevated neutrophil counts, platelet levels, and creatinine concentrations compared to those who survived. Only the leukocyte count (103/µL) and urea levels, however, showed a statistically significant difference between the groups (p = 0.038 for both). For the other blood markers, no significant differences were observed between the two study groups in our study.
3.2. General Proteomic Profile of Nasopharyngeal Smear Samples
PatternLab for Proteomics V software (version 5.0.0.198) analysis of our data identified, with high confidence (FDR < 1%), an average of 10,876 peptides and 1604 proteins from the fatality group patients’ tandem mass spectrometry spectra files, and an average of 6580 peptides and 981 proteins in the survivor group spectra. Secondary COVID-19 survivor analysis focused only on proteins that were detected in ≥50% of patient swab samples: 549 proteins from the fatality and 421 proteins from the survivor patient group. A comprehensive list of these proteins is provided in Supplementary Tables S2 and S3. Figure 1 shows a Venn diagram illustrating the overlap between the proteomic profiles obtained from nasopharyngeal swabs of both groups. The analysis did not identify any proteins that were unique to the survivor group; however, it did identify 128 proteins that were exclusively present in the fatality group and are thus of special interest as potential biomarkers for severe outcomes (Table 1). The analysis, therefore, also found a core set of 421 proteins associated with SARS-CoV-2 infections that were shared between both groups and are highlighted in Figure 1.
When the PatternLab T-Fold module was used to compare the relative abundance of these 421 shared proteins, just three proteins were significantly more abundant in the survivor group. All three of these proteins have roles in extracellular host immune defense against pathogens: Fibronectin 1 (FN1) which had a 4.38 fold-change; Complement Component 4 Binding Protein Alpha (C4BPA) which had a 3.05 fold-change; and Immunoglobulin Kappa Variable 1-5 (IGKV1-5) which had a 2.14 fold-change. The same analysis, however, found that 135 proteins were significantly more abundant in the fatality group (Figure 2). A complete list of the significantly overexpressed proteins found in the fatality group of nasal samples is provided in Table 2.
Enriched gene ontology processes (GOPs) were identified using the protein pathway analysis tool “PANTHER”. Figure 3 shows the most significantly enriched GOPs identified in the 549-protein dataset associated with our fatality group nasal swabs. Figure 4 shows the most significantly enriched GOPs identified from the 421-protein dataset that was found in at least 50% of swabs taken from patients that died and patients that survived their infections. Figure 5 shows the most significantly enriched GOPs found within the 135-protein dataset, representing proteins that were observed to be significantly more abundant in the fatality group swabs than in the survivor group swabs. Figure 6 shows the GOPs that were significantly enriched within our 128-protein dataset, representing the proteins that our analysis identified as exclusively associated with fatality group nasal swabs. Comparing the GOPs present in fatal group datasets (Figure 3, Figure 5 and Figure 6) with those present in the survival group dataset (Figure 4), just three GOPs were found to be common in all of fatality groups but absent from the survivor swabs (Secretory vesicle, Secretory granule and Cytoplasmic vesicle lumen), showing that signs of cellular secretion pathway dysregulation in nasopharyngeal swabs was a robust indicator that our COVID-19 patients were likely to die from their infection.
4. Discussion
The patient samples analyzed in this study were collected between March and May 2020, during the first year of the COVID-19 pandemic, coinciding with the first wave of cases in Manaus. During this period, the state of Amazonas reported up to 2763 confirmed cases per day, with 1723 cases in Manaus and 1040 in other municipalities across the state [10,11]. The profiles presented here thus all derive from patients who were hospitalized before the emergence of the deadly P1 SARS-CoV-2 strain that led to oxygen shortages and a complete collapse of the regional healthcare system [12,13].
4.1. The General Proteomic Profile of Nasopharyngeal Swabs
Our analysis identified a total of 1604 proteins in the nasopharyngeal swabs of our SARS-CoV-2 patients who died from their infections and a total of 981 in the nasopharyngeal swabs of our patient survivors. The nature of our analysis did not allow us to detect bacterial, fungal or other respiratory virus co-infections directly. However, the greater number and diversity (Table 2) of human proteins that were consistently detected in the patient swabs taken from fatalities might be partly explained by a more complex host immune response provoked by bacterial, fungal or respiratory viral co-infections which could be more common in fatal patient group swabs. Alternatively, the higher number of proteins detected in fatality patient swabs might be explained by a richer protein load in the mucus of fatal swabs or indeed other factors. Of the 421 identified proteins that were identified in the nasopharyngeal swabs of both our fatality and survivor groups, many belong to protein families that have previously been found in a range of SARS-CoV-2-infected samples. Not surprisingly, many of the shared proteins were linked to viral replication and immune responses as were most of the GOPs identified in this group of proteins (Supplementary Tables S2 and S3, Figure 4).
4.2. The Proteomic Profiles of Nasopharyngeal Swabs Associated with Fatalities
Previous studies have linked immune system dysregulation and T-cell exhaustion to COVID-19 mortality, and both our blood biochemistry analysis, which revealed significantly higher leukocyte counts in our survivor group, and our proteomic analysis of fatality samples from nasopharyngeal swabs support this [14,15,16,17]. The protein BPIFA1, an essential immune defense protein, was found in both our survivor and fatal group swabs but was significantly elevated in the swabs of patients who died (Table 2). Similarly, the major vault protein (MVP), a protein involved in immune modulation, was also upregulated (Table 2). Our analysis also identified proteins associated with epithelial lung damage, such as Keratin 14 (KRT14), which was upregulated in our fatality group swabs, as well as the Inducible Nitric Oxide Synthase (NOS2) protein—see Table 2 [18]. Interestingly, the upregulation of NOS2 has been previously reported to be associated with mortalities from first-wave SARS-CoV-2 infections, but not with deaths from subsequent COVID-19 waves [19]. Our discovery that this protein is upregulated in fatal case swabs can thus be seen as providing valuable corroborating proteomic data to support this observation (Table 2).
The proteins HSP90B1, CANX, EEF2, and MUC5AC, which were consistently detected in both fatality and survivor group swabs, are all linked to viral exploitation of patient cellular mechanics [20,21,22,23,24,25]. Our GO analysis also detected viral exploitation of host mechanisms by identifying RNA binding and cytoplasmic translation GOPs that are enriched in the 421 shared protein dataset (Supplementary Table S2; Figure 4). The significantly higher abundance of these proteins and related GOs within the fatality group patient swabs of our study agrees with findings from other similar proteomic studies, which have tied ribosomal hijacking to viral load escalation [20,21,22,23,24,25]. The detection of elevated levels of cytoskeletal proteins, like Vinculin (VCL), which are linked with epithelial barrier disruption, lung injury and viral cellular entry, can also be interpreted as evidence of elevated levels of viral activity being detected in the swabs of our fatality group patients and agrees with the findings from similar studies [20,21,22,23,24,25]. The detection of the viral replication-linked proteins DX1, DDX17, and XPO1 (Exportin-1) exclusively in our fatality dataset could be a consequence of these proteins being less abundantly used in SARS-CoV-2 replication and thus only detectable in seriously out-of-control infections that are likely to lead to death (Table 1).
Hypoxia Up-regulated Protein 1 (HYOU1) was found among the 128 proteins that our analysis classified as exclusive to our fatality group (Table 2). Oxygen deprivation is a key clinical pathology associated with COVID-19 mortality. Thus, a biomarker, like HYOU1, which detects this at the cellular level and cannot be detected in survivor swabs, represents a promising marker for disease progression or mortality risk. Similarly, endothelial injury and thrombosis are both strongly linked to COVID-19 mortality and are also related to the protein S100-A10 [2,15]. The S100-A10 protein, which was not among the proteins associated with survivor swabs but was detected in more than 50% of the swabs from our fatality group, also represents a promising biomarker for predicting disease progression.
4.3. The Proteomic Profiles of Nasopharyngeal Swabs Associated with Survival
In our study, only three proteins were found to be significantly more abundant in the group of patients who survived. These proteins: fibronectin 1 (FN1), C4b-binding protein alpha chain (C4BPA), and immunoglobulin kappa variable 1-5 (IGKV1-5) were identified as four-fold, three-fold, and two-fold more abundant in this group, respectively. IGKV1-5 is a predominant component of specific neutralizing antibodies targeting the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein [26]. The increased presence of IGKV1-5 in recovered patients suggests an enhanced humoral immune response, which may contribute to viral clearance and improved clinical outcomes. Our patient’s biochemical blood analysis also supported this interpretation by revealing significantly higher leukocyte counts in our survivor patient (Supplementary Figure S1). The most over-expressed of the three proteins, FN1, is an extracellular matrix (ECM) glycoprotein involved in cell adhesion, migration, proliferation, and apoptosis and more generally in immune response and tissue repair and thus, from a functional perspective, it is not surprising that it would be more abundant in our survivor group swabs [27]. However, FN1 levels have been reported to gradually increase in the blood plasma of COVID-19 patients as the disease progresses, leading to its consideration as a potential marker of disease severity [28]. The increased levels of FN1 observed in the swabs of patients who recovered from the disease in our study have two possible interpretations: either FN1 played a role in tissue repair, facilitating recovery, or its elevation indicates a predisposition to post-COVID-19 complications, such as pulmonary fibrosis. Either way, our study suggests that caution must be exercised when using FN1, at least in nasopharyngeal swabs, as a marker for disease progression. One influential previous study on the proteomic profiles of fatal COVID-19 infections reported that patients who succumbed to the disease exhibited elevated blood plasma levels of C4BPA. This immunomodulator regulates complement activation [29,30]. Our finding of high levels of C4BPA in nasopharyngeal swabs supports less influential blood plasma studies. It highlights the need for caution when interpreting patient immune responses to SARS-CoV-2 infections as well as the difficulty of identifying reliable markers for disease progression [31]. As our analysis did not identify any proteins that were unique to the nasopharyngeal swabs of our survivor group and only three unregulated proteins, the power of the gene ontology analysis we could do with the survivor group-associated proteins was limited. Nevertheless, our analysis still identified two defense response GOPs (GO:0051707 and GO:0009615) in the survivor swabs that were absent from the swabs taken from patients who died from their infections. The absence of these GOPs in the fatal swabs aligns with studies that have documented immune exhaustion in severe cases of COVID-19 [32]. Similarly, the absence of many viral-replication-associated GOPs from the swabs of those who survived their infection supports the hypothesis that effective immune responses limit viral replication [32].
4.4. Study Limitations
The shotgun proteomics approach in this study prioritized analytical depth over sample size. Our study, therefore, uses a smaller patient cohort than typically employed in a clinical investigation aiming to definitively link a single protein to a specific pathology. Consequently, our findings are more susceptible to sampling biases than those from a larger patient cohort. The analytical tools used to identify proteins and GO processes that were significantly more abundant in the swabs of patients who died from SARS-CoV-2 (compared to those who survived) all incorporate correction procedures to compensate for false discoveries. While these procedures are expected to drastically reduce the possibility of incorrectly identifying a non-overrepresented protein, they cannot eliminate it. Therefore, our findings are preliminary; they serve to identify candidate proteins for future, larger-scale studies rather than as definitively conclusive results.
5. Conclusions
This study found distinct proteomic signatures in nasopharyngeal swabs from patients who died and survived the first wave of the COVID-19 pandemic in Manaus (Brazil), before the emergence of the deadly P1 SARS-CoV-2 strain. Most proteomic studies of COVID-19 patients have hitherto focused on their blood and plasma profiles and thus provided insights into the systemic host immune response to infections, rather than a detailed picture of the immune response at the site of infection. In our study, proteins linked to hypoxia, endothelial injury, uncontrolled viral spread, and immune collapse were found to be more abundant in or exclusive to the fatality group swabs. In contrast, survivor swabs showed elevated levels of immune defense proteins (FN1, C4BPA, IGKV1-5), indicating robust antiviral responses. HYOU1 and S100A10 were identified as biomarkers for fatal outcomes, reflecting hypoxia and thrombosis. Interestingly and consistent with previous studies, our study found that the NOS2 protein, which was previously reported to be associated with first wave COVID deaths, was present only in our fatality swabs. Despite our study having a limited sample size, our findings highlight the utility of nasopharyngeal swabs for identifying disease progression biomarkers and provide valuable insights into the cellular mechanisms underlying COVID-19 deaths during the first wave in Manaus.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/covid5110192/s1. Supplementary Material File S1: Liquid chromatography tandem mass spectrometry (LC-MS-MS) procedures [9,33]. Supplementary Materials File S2: Protein spectrum analysis procedures [34,35,36,37,38,39,40,41,42]. Supplementary Table S1: Sociodemographic and clinical profiles of participating patients. Supplementary Table S2: Proteins (549) identified in >50% of the nasopharyngeal swabs of our fatality group patients, hospitalized with COVID-19 in Manaus 2020. Supplementary Table S3: Proteins (421) identified in >50% of the nasopharyngeal swabs of our survivor group patients, hospitalized with COVID-19 in Manaus 2020. Supplementary Figure S1: key patient blood biochemical markers.
Author Contributions
P.F.A. and P.C.C. designed the survey; C.P.M.A., P.F.A., K.C.O., M.D.M.S. and J.L.C. wrote the manuscript; F.G.N., F.N.O., G.C.M., V.S.S. and M.V.G.L. provided the biological samples used in this study; C.P.M.A., M.B., M.D.M.S., A.C.C.-A., D.B.L., J.d.S.G.F. and J.L.C. performed the analysis; C.P.M.A., A.S.S., S.A.C., C.M.V., M.D.M.S. and P.F.A. interpreted the results; P.F.A. and P.C.C. supervised the research. All authors have read and agreed to the published version of the manuscript.
Funding
This study was supported by Fundação de Amparo a Pesquisa do Estado do Amazonas (FAPEAM) with student stipends for C.P.M.A. and CMV and with the Project grant PCTI-EMERGESAÚDE/AM/FAPEAM: 062.00472/2020/FAPEAM. Financial support was also provided through an internal grant call entitled: “Fiocruz Inova: Geração de conhecimento—enfrentamento da pandemia e pós-pandemia COVID-19 encomendas estratégicas”.
Institutional Review Board Statement
The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Fundação de Medicina Tropical Dr Heitor Vieira Dourado (CAAE:30152620.1.0000.0005 and CAAE:30504220.5.0000.0005) for studies involving humans. The project received approval on 23 March 2020.
Informed Consent Statement
The patient samples used in this study were from Instituto de Pesquisa Clínica Carlos Borborema biorepository (IPCCB) of clinical samples, which was set-up with ethical approval from the Ethics Committee of Fundação de Medicina Tropical Dr Heitor Vieira Dourado (CAAE:30152620.1.0000.0005 and CAAE:30504220.5.0000.0005). Informed consent for use of IPCCB clinical samples in research studies is waived provided the studies they intend to be used in have received ethics approval and a trustee of the IPCCB deems the use of the samples appropriate. A letter signed by a IPCCB trustee approving the use of the samples for the project set out in this paper was obtained on the 19 August 2020.
Data Availability Statement
The original data presented in the study are openly available in the PRIDE data repository [https://www.ebi.ac.uk/pride] and can be accessed with the project accession code: PXD064171.
Acknowledgments
We thank Instituto Leônidas & Maria Deane (ILMD), the Mass Spectrometry Facility (RPT02H), and Instituto Carlos Chagas/Fiocruz for their technical and infrastructural support for this study. We also gratefully acknowledge support from the post-graduate program ‘PPGBIO’ and the scientific initiation program ‘PAIC’ of the Fiocruz, ILMD.
Conflicts of Interest
The authors declare no conflict of interest.
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Figure 1.
Venn diagram illustrating the distribution of shared and unique proteins identified in nasopharyngeal swab samples from patients who succumbed to COVID-19 and those who survived. Only proteins detected in at least four biological replicates per group were considered for this analysis. The diagram highlights 128 proteins that are exclusively present in the deceased patients, and no proteins were uniquely identified in the survivors.
Figure 1.
Venn diagram illustrating the distribution of shared and unique proteins identified in nasopharyngeal swab samples from patients who succumbed to COVID-19 and those who survived. Only proteins detected in at least four biological replicates per group were considered for this analysis. The diagram highlights 128 proteins that are exclusively present in the deceased patients, and no proteins were uniquely identified in the survivors.
Figure 2.
Horizontal bar graph depicting the primary protein families with increased abundance in COVID-19 patients who died from their infections. The identified families include Annexin, Thioredoxin, Proteasome, Serpin, S100, and Chaperone, each represented by distinct colors. The X-axis indicates fold change values, reflecting the variation in protein abundance within each family.
Figure 2.
Horizontal bar graph depicting the primary protein families with increased abundance in COVID-19 patients who died from their infections. The identified families include Annexin, Thioredoxin, Proteasome, Serpin, S100, and Chaperone, each represented by distinct colors. The X-axis indicates fold change values, reflecting the variation in protein abundance within each family.
Figure 3.
A horizontal bar graph summarizing the most enriched GO processes identified from the 549 proteins that our analysis found in ≥50% of the nasopharyngeal swabs of the hospitalized SARS-CoV-2-infected patients who succumbed to their infections. GO processes with similar biological functions have been grouped into categories and colored as indicated.
Figure 3.
A horizontal bar graph summarizing the most enriched GO processes identified from the 549 proteins that our analysis found in ≥50% of the nasopharyngeal swabs of the hospitalized SARS-CoV-2-infected patients who succumbed to their infections. GO processes with similar biological functions have been grouped into categories and colored as indicated.
Figure 4.
A horizontal bar graph summarizing the most enriched GO processes identified from the 421 proteins that our analysis found were in both ≥50% of the nasopharyngeal swabs of the hospitalized SARS-CoV-2 infected patients who died and ≥50% of the nasopharyngeal swabs of hospitalized SARS-CoV-2 infected patients who survived their infections. GO processes with similar biological functions have been grouped into categories and colored as indicated.
Figure 4.
A horizontal bar graph summarizing the most enriched GO processes identified from the 421 proteins that our analysis found were in both ≥50% of the nasopharyngeal swabs of the hospitalized SARS-CoV-2 infected patients who died and ≥50% of the nasopharyngeal swabs of hospitalized SARS-CoV-2 infected patients who survived their infections. GO processes with similar biological functions have been grouped into categories and colored as indicated.
Figure 5.
A horizontal bar graph summarizing the most enriched GO processes identified from the 135 proteins that our analysis found to be significantly more abundant in the swabs of patients who died from their SARS-CoV-2 infections than in the swabs of patients who survived their infections. GO processes with similar biological functions have been grouped into categories and colored as indicated. GO processes were classified as represented using the criteria of PANTHER software (version 19.0).
Figure 5.
A horizontal bar graph summarizing the most enriched GO processes identified from the 135 proteins that our analysis found to be significantly more abundant in the swabs of patients who died from their SARS-CoV-2 infections than in the swabs of patients who survived their infections. GO processes with similar biological functions have been grouped into categories and colored as indicated. GO processes were classified as represented using the criteria of PANTHER software (version 19.0).
Figure 6.
A horizontal bar graph summarizing the most enriched GO processes identified from the 128 proteins that our analysis found exclusively in the nasopharyngeal swabs of the hospitalized SARS-CoV-2-infected patients who died from their infections. GO processes with similar biological functions have been grouped into categories and colored as indicated.
Figure 6.
A horizontal bar graph summarizing the most enriched GO processes identified from the 128 proteins that our analysis found exclusively in the nasopharyngeal swabs of the hospitalized SARS-CoV-2-infected patients who died from their infections. GO processes with similar biological functions have been grouped into categories and colored as indicated.
Table 1.
A list of 135 proteins that our analysis found to be significantly more abundant in the swabs of patients who died from their SARS-CoV-2 infections than in the swabs of patients who survived their infections. Uniport identification numbers are given in the collum labeled locus. Proteins were classified as significantly overrepresented using the criteria employed by PatternLab T-fold software (version 5.0.0.198).
Table 1.
A list of 135 proteins that our analysis found to be significantly more abundant in the swabs of patients who died from their SARS-CoV-2 infections than in the swabs of patients who survived their infections. Uniport identification numbers are given in the collum labeled locus. Proteins were classified as significantly overrepresented using the criteria employed by PatternLab T-fold software (version 5.0.0.198).
| Locus | Gene | Description | Fold Change |
|---|---|---|---|
| P80188 | LCN2 | Neutrophil gelatinase-associated lipocalin | 2.01 |
| P06753 | TPM3 | Tropomyosin alpha-3 chain | 2.02 |
| P61626 | LYZ | Lysozyme C | 2.05 |
| Q96C19 | EFHD2 | EF-hand domain-containing protein D2 | 2.08 |
| P04083 | ANXA1 | Annexin A1 | 2.09 |
| P32119 | PRDX2 | Peroxiredoxin-2 | 2.12 |
| P13796 | LCP1 | Plastin-2 | 2.14 |
| P52566 | ARHGDIB | Rho GDP-dissociation inhibitor 2 | 2.15 |
| P06733 | ENO1 | Alpha-enolase | 2.18 |
| P47756 | CAPZB | F-actin-capping protein subunit beta | 2.21 |
| P06396 | GSN | Gelsolin | 2.22 |
| P05387 | RPLP2 | Large ribosomal subunit protein P2 | 2.22 |
| P28799 | GRN | Progranulin | 2.25 |
| P37802 | TAGLN2 | Transgelin-2 | 2.28 |
| Q06830 | PRDX1 | Peroxiredoxin-1 | 2.29 |
| P13987 | CD59 | CD59 glycoprotein | 2.33 |
| P52565 | ARHGDIA | Rho GDP-dissociation inhibitor 1 | 2.34 |
| P80303 | NUCB2 | Nucleobindin-2 | 2.35 |
| P37837 | TALDO1 | Transaldolase | 2.36 |
| P07737 | PFN1 | Profilin-1 | 2.44 |
| P04040 | CAT | Catalase | 2.46 |
| Q13510 | ASAH1 | Acid ceramidase | 2.47 |
| P04075 | ALDOA | Fructose-bisphosphate aldolase A | 2.52 |
| P62937 | PPIA | Peptidyl-prolyl cis-trans isomerase A | 2.52 |
| Q9UGM3 | DMBT1 | Scavenger receptor cysteine-rich domain-containing protein DMBT1 | 2.55 |
| P14618 | PKM | Pyruvate kinase PKM | 2.59 |
| P20700 | LMNB1 | Lamin-B1 | 2.61 |
| O00391 | QSOX1 | Sulfhydryl oxidase 1 | 2.62 |
| P31944 | CASP14 | Caspase-14 | 2.62 |
| O43707 | ACTN4 | Alpha-actinin-4 | 2.63 |
| P09960 | LTA4H | Leukotriene A-4 hydrolase | 2.64 |
| P26583 | HMGB2 | High mobility group protein B2 | 2.65 |
| P18669 | PGAM1 | Phosphoglycerate mutase 1 | 2.66 |
| P20061 | TCN1 | Transcobalamin-1 | 2.71 |
| P06744 | GPI | Glucose-6-phosphate isomerase | 2.72 |
| O15143 | ARPC1B | Actin-related protein 2/3 complex subunit 1B | 2.74 |
| Q9H299 | SH3BGRL3 | SH3 domain-binding glutamic acid-rich-like protein 3 | 2.74 |
| P09758 | TACSTD2 | Tumor-associated calcium signal transducer 2 | 2.76 |
| P17931 | LGALS3 | Galectin-3 | 2.78 |
| P60660 | MYL6 | Myosin light polypeptide 6 | 2.82 |
| P03973 | SLPI | Antileukoproteinase | 2.85 |
| P28066 | PSMA5 | Proteasome subunit alpha type-5 | 2.91 |
| Q06323 | PSME1 | Proteasome activator complex subunit 1 | 2.91 |
| P61604 | HSPE1 | 10 kDa heat shock protein, mitochondrial | 2.92 |
| P16401 | H1-5 | Histone H1.5 | 2.92 |
| P29401 | TKT | Transketolase | 3.04 |
| P07998 | RNASE1 | Ribonuclease pancreatic | 3.08 |
| P26038 | MSN | Moesin | 3.09 |
| P04632 | CAPNS1 | Calpain small subunit 1 | 3.11 |
| P04406 | GAPDH | Glyceraldehyde-3-phosphate dehydrogenase | 3.11 |
| P23528 | CFL1 | Cofilin-1 | 3.14 |
| P40121 | CAPG | Macrophage-capping protein | 3.17 |
| P19957 | PI3 | Elafin | 3.18 |
| P04792 | HSPB1 | Heat shock protein beta-1 | 3.19 |
| P10909 | CLU | Clusterin | 3.19 |
| P12273 | PIP | Prolactin-inducible protein | 3.21 |
| P63104 | YWHAZ | 14-3-3 protein zeta/delta | 3.23 |
| P40926 | MDH2 | Malate dehydrogenase, mitochondrial | 3.26 |
| P06703 | S100A6 | Protein S100-A6 | 3.30 |
| P07339 | CTSD | Cathepsin D | 3.35 |
| P00352 | ALDH1A1 | Aldehyde dehydrogenase 1A1 | 3.36 |
| P20810 | CAST | Calpastatin | 3.36 |
| Q09666 | AHNAK | Neuroblast differentiation-associated protein AHNAK | 3.39 |
| P23284 | PPIB | Peptidyl-prolyl cis-trans isomerase B | 3.41 |
| O14745 | NHERF1 | Na(+)/H(+) exchange regulatory cofactor NHE-RF1 | 3.42 |
| P07355 | ANXA2 | Annexin A2 | 3.47 |
| P07237 | P4HB | Protein disulfide-isomerase | 3.47 |
| P11413 | G6PD | Glucose-6-phosphate 1-dehydrogenase | 3.48 |
| O15144 | ARPC2 | Actin-related protein 2/3 complex subunit 2 | 3.50 |
| P61158 | ACTR3 | Actin-related protein 3 | 3.50 |
| P59998 | ARPC4 | Actin-related protein 2/3 complex subunit 4 | 3.57 |
| Q14508 | WFDC2 | WAP four-disulfide core domain protein 2 | 3.62 |
| Q01518 | CAP1 | Adenylyl cyclase-associated protein 1 | 3.62 |
| P07858 | CTSB | Cathepsin B | 3.63 |
| P00338 | LDHA | L-lactate dehydrogenase A chain | 3.65 |
| P52209 | PGD | 6-phosphogluconate dehydrogenase, decarboxylating | 3.66 |
| P80723 | BASP1 | Brain acid soluble protein 1 | 3.66 |
| P78417 | GSTO1 | Glutathione S-transferase omega-1 | 3.69 |
| P14314 | PRKCSH | Glucosidase 2 subunit beta | 3.72 |
| P15311 | EZR | Ezrin | 3.73 |
| P61981 | YWHAG | 14-3-3 protein gamma | 3.76 |
| P61916 | NPC2 | NPC intracellular cholesterol transporter 2 | 3.76 |
| P35579 | MYH9 | Myosin-9 | 3.81 |
| P27797 | CALR | Calreticulin | 3.81 |
| P11142 | HSPA8 | Heat shock cognate 71 kDa protein | 3.82 |
| Q8TDL5 | BPIFB1 | BPI fold-containing family B member 1 | 3.83 |
| P60174 | TPI1 | Triosephosphate isomerase | 3.84 |
| P07602 | PSAP | Prosaposin | 3.86 |
| P31949 | S100A11 | Protein S100-A11 | 3.92 |
| Q99497 | PARK7 | Parkinson disease protein 7 | 3.99 |
| P07195 | LDHB | L-lactate dehydrogenase B chain | 4.00 |
| P01011 | SERPINA3 | Alpha-1-antichymotrypsin | 4.06 |
| P12429 | ANXA3 | Annexin A3 | 4.10 |
| P50395 | GDI2 | Rab GDP dissociation inhibitor beta | 4.32 |
| P25788 | PSMA3 | Proteasome subunit alpha type-3 | 4.33 |
| O00299 | CLIC1 | Chloride intracellular channel protein 1 | 4.36 |
| P31946 | YWHAB | 14-3-3 protein beta/alpha | 4.37 |
| Q96DA0 | ZG16B | Pancreatic adenocarcinoma up-regulated factor | 4.43 |
| P00558 | PGK1 | Phosphoglycerate kinase 1 | 4.47 |
| P30101 | PDIA3 | Protein disulfide-isomerase A3 | 4.53 |
| P47929 | LGALS7 | Galectin-7 | 4.60 |
| P43490 | NAMPT | Nicotinamide phosphoribosyltransferase | 4.61 |
| P55072 | VCP | Transitional endoplasmic reticulum ATPase | 4.62 |
| Q6P5S2 | LEG1 | Protein LEG1 homolog | 4.75 |
| P62258 | YWHAE | 14-3-3 protein epsilon | 4.79 |
| P07900 | HSP90AA1 | Heat shock protein HSP 90-alpha | 4.81 |
| O75083 | WDR1 | WD repeat-containing protein 1 | 4.82 |
| P61978 | HNRNPK | Heterogeneous nuclear ribonucleoprotein K | 4.83 |
| P51149 | RAB7A | Ras-related protein Rab-7a | 4.87 |
| P22626 | HNRNPA2B1 | Heterogeneous nuclear ribonucleoproteins A2/B1 | 4.87 |
| P02545 | LMNA | Prelamin-A/C | 4.88 |
| P08238 | HSP90AB1 | Heat shock protein HSP 90-beta | 4.93 |
| P30041 | PRDX6 | Peroxiredoxin-6 | 5.05 |
| P08758 | ANXA5 | Annexin A5 | 5.10 |
| O95994 | AGR2 | Anterior gradient protein 2 homolog | 5.15 |
| O14818 | PSMA7 | Proteasome subunit alpha type-7 | 5.18 |
| P36952 | SERPINB5 | Serpin B5 | 5.36 |
| Q15084 | PDIA6 | Protein disulfide-isomerase A6 | 5.50 |
| P52907 | CAPZA1 | F-actin-capping protein subunit alpha-1 | 5.57 |
| P25705 | ATP5F1A | ATP synthase subunit alpha, mitochondrial | 5.57 |
| P08133 | ANXA6 | Annexin A6 | 5.64 |
| P46940 | IQGAP1 | Ras GTPase-activating-like protein IQGAP1 | 5.70 |
| P23396 | RPS3 | Small ribosomal subunit protein uS3 | 5.85 |
| P13667 | PDIA4 | Protein disulfide-isomerase A4 | 5.87 |
| P29508 | SERPINB3 | Serpin B3 | 6.20 |
| P26641 | EEF1G | Elongation factor 1-gamma | 6.40 |
| P98088 | MUC5AC | Mucin-5AC | 6.48 |
| P18206 | VCL | Vinculin | 6.57 |
| P27824 | CANX | Calnexin | 7.21 |
| P13639 | EEF2 | Elongation factor 2 | 7.24 |
| Q13813 | SPTAN1 | Spectrin alpha chain, non-erythrocytic 1 | 7.54 |
| Q14764 | MVP | Major vault protein | 8.05 |
| Q9NP55 | BPIFA1 | BPI fold-containing family A member 1 | 8.32 |
| Q00610 | CLTC | Clathrin heavy chain 1 | 9.54 |
| P14625 | HSP90B1 | Endoplasmin | 9.93 |
Table 2.
A complete list of the 128 proteins that were exclusively identified in the nasopharyngeal swabs of the hospitalized SARS-CoV-2 infected patients who succumbed to their infections.
Table 2.
A complete list of the 128 proteins that were exclusively identified in the nasopharyngeal swabs of the hospitalized SARS-CoV-2 infected patients who succumbed to their infections.
| Locus | Replicate Count | Description | Gene |
|---|---|---|---|
| P02533 | 4 | Keratin, type I cytoskeletal 14 | KRT14 |
| P35241 | 4 | Radixin | RDX |
| P00505 | 4 | Aspartate aminotransferase, mitochondrial | GOT2 |
| Q9BR76 | 4 | Coronin-1B | CORO1B |
| P47755 | 4 | F-actin-capping protein subunit alpha-2 | CAPZA2 |
| P46926 | 4 | Glucosamine-6-phosphate isomerase 1 | GNPDA1 |
| Q92499 | 4 | ATP-dependent RNA helicase DDX1 | DDX1 |
| Q16222 | 4 | UDP-N-acetylhexosamine pyrophosphorylase | UAP1 |
| O60716 | 4 | Catenin delta-1 | CTNND1 |
| P47897 | 4 | Glutamine-tRNA ligase | QARS1 |
| P36405 | 4 | ADP-ribosylation factor-like protein 3 | ARL3 |
| Q9UHL4 | 4 | Dipeptidyl peptidase 2 | DPP7 |
| P14866 | 4 | Heterogeneous nuclear ribonucleoprotein L | HNRNPL |
| Q04446 | 4 | 1,4-alpha-glucan-branching enzyme | GBE1 |
| P61313 | 4 | Large ribosomal subunit protein eL15 | RPL15 |
| P12268 | 4 | Inosine-5′-monophosphate dehydrogenase 2 | IMPDH2 |
| Q16698 | 4 | 2,4-dienoyl-CoA reductase [(3E)-enoyl-CoA-producing], mitochondrial | DECR1 |
| O43747 | 4 | AP-1 complex subunit gamma-1 | AP1G1 |
| P62495 | 4 | Eukaryotic peptide chain release factor subunit 1 | ETF1 |
| Q07666 | 4 | KH domain-containing, RNA-binding, signal transduction-associated protein 1 | KHDRBS1 |
| P26640 | 4 | Valine-tRNA ligase | VARS1 |
| P09496 | 4 | Clathrin light chain A | CLTA |
| P07814 | 4 | Bifunctional glutamate/proline-tRNA ligase | EPRS1 |
| Q15019 | 4 | Septin-2 | SEPTIN2 |
| P35228 | 4 | Nitric oxide synthase, inducible | NOS2 |
| Q92820 | 4 | Gamma-glutamyl hydrolase | GGH |
| P53999 | 4 | Activated RNA polymerase II transcriptional coactivator p15 | SUB1 |
| Q9Y277 | 4 | Voltage-dependent anion-selective channel protein 3 | VDAC3 |
| Q9BS26 | 4 | Endoplasmic reticulum resident protein 44 | ERP44 |
| Q9Y4L1 | 4 | Hypoxia up-regulated protein 1 | HYOU1 |
| O14980 | 4 | Exportin-1 | XPO1 |
| Q92945 | 4 | Far upstream element-binding protein 2 | KHSRP |
| P50914 | 4 | Large ribosomal subunit protein eL14 | RPL14 |
| O15231 | 4 | Zinc finger protein 185 | ZNF185 |
| P60903 | 4 | Protein S100-A10 | S100A10 |
| P31930 | 4 | Cytochrome b-c1 complex subunit 1, mitochondrial | UQCRC1 |
| O95433 | 4 | Activator of 90 kDa heat shock protein ATPase homolog 1 | AHSA1 |
| P15586 | 4 | N-acetylglucosamine-6-sulfatase | GNS |
| Q16543 | 4 | Hsp90 co-chaperone Cdc37 | CDC37 |
| Q9Y2Z0 | 4 | Protein SGT1 homolog | SUGT1 |
| Q00325 | 4 | Solute carrier family 25 member 3 | SLC25A3 |
| Q99623 | 4 | Prohibitin-2 | PHB2 |
| P20042 | 4 | Eukaryotic translation initiation factor 2 subunit 2 | EIF2S2 |
| P07954 | 4 | Fumarate hydratase, mitochondrial | FH |
| P20674 | 4 | Cytochrome c oxidase subunit 5A, mitochondrial | COX5A |
| Q02790 | 4 | Peptidyl-prolyl cis-trans isomerase FKBP4 | FKBP4 |
| P49748 | 4 | Very long-chain specific acyl-CoA dehydrogenase, mitochondrial | ACADVL |
| O00560 | 4 | Syntenin-1 | SDCBP |
| P05186 | 4 | Alkaline phosphatase, tissue-nonspecific isozyme | ALPL |
| Q99829 | 4 | Copine-1 | CPNE1 |
| P55064 | 4 | Aquaporin-5 | AQP5 |
| Q86UX7 | 4 | Fermitin family homolog 3 | FERMT3 |
| O75955 | 4 | Flotillin-1 | FLOT1 |
| P0CG39 | 4 | POTE ankyrin domain family member J | POTEJ |
| O75369 | 5 | Filamin-B | FLNB |
| P48147 | 5 | Prolyl endopeptidase | PREP |
| P05091 | 5 | Aldehyde dehydrogenase, mitochondrial | ALDH2 |
| Q9P2E9 | 5 | Ribosome-binding protein 1 | RRBP1 |
| P26639 | 5 | Threonine-tRNA ligase 1, cytoplasmic | TARS1 |
| Q01082 | 5 | Spectrin beta chain, non-erythrocytic 1 | SPTBN1 |
| P43034 | 5 | Platelet-activating factor acetylhydrolase IB subunit beta | PAFAH1B1 |
| P35221 | 5 | Catenin alpha-1 | CTNNA1 |
| Q15008 | 5 | 26S proteasome non-ATPase regulatory subunit 6 | PSMD6 |
| P17858 | 5 | ATP-dependent 6-phosphofructokinase, liver type | PFKL |
| O43242 | 5 | 26S proteasome non-ATPase regulatory subunit 3 | PSMD3 |
| P09622 | 5 | Dihydrolipoyl dehydrogenase, mitochondrial | DLD |
| P62195 | 5 | 26S proteasome regulatory subunit 8 | PSMC5 |
| Q13347 | 5 | Eukaryotic translation initiation factor 3 subunit I | EIF3I |
| Q92841 | 5 | Probable ATP-dependent RNA helicase DDX17 | DDX17 |
| Q9Y3C8 | 5 | Ubiquitin-fold modifier-conjugating enzyme 1 | UFC1 |
| P53004 | 5 | Biliverdin reductase A | BLVRA |
| Q13561 | 5 | Dynactin subunit 2 | DCTN2 |
| P05026 | 5 | Sodium/potassium-transporting ATPase subunit beta-1 | ATP1B1 |
| O95777 | 5 | U6 snRNA-associated Sm-like protein LSm8 | LSM8 |
| P46776 | 5 | Large ribosomal subunit protein uL15 | RPL27A |
| P22695 | 5 | Cytochrome b-c1 complex subunit 2, mitochondrial | UQCRC2 |
| P18621 | 5 | Large ribosomal subunit protein uL22 | RPL17 |
| Q99729 | 5 | Heterogeneous nuclear ribonucleoprotein A/B | HNRNPAB |
| P68402 | 5 | Platelet-activating factor acetylhydrolase IB subunit alpha2 | PAFAH1B2 |
| Q9Y6N5 | 5 | Sulfide:quinone oxidoreductase, mitochondrial | SQOR |
| Q99436 | 5 | Proteasome subunit beta type-7 | PSMB7 |
| O00233 | 5 | 26S proteasome non-ATPase regulatory subunit 9 | PSMD9 |
| P35232 | 5 | Prohibitin 1 | PHB1 |
| P48556 | 5 | 26S proteasome non-ATPase regulatory subunit 8 | PSMD8 |
| P68431 | 5 | Histone H3.1 | H3C1 |
| Q16836 | 5 | Hydroxyacyl-coenzyme A dehydrogenase, mitochondrial | HADH |
| Q15942 | 5 | Zyxin | ZYX |
| P68366 | 6 | Tubulin alpha-4A chain | TUBA4A |
| P48735 | 6 | Isocitrate dehydrogenase [NADP], mitochondrial | IDH2 |
| Q3LXA3 | 6 | Triokinase/FMN cyclase | TKFC |
| Q15149 | 6 | Plectin | PLEC |
| Q9BZQ8 | 6 | Protein Niban 1 | NIBAN1 |
| P10768 | 6 | S-formylglutathione hydrolase | ESD |
| Q9NTK5 | 6 | Obg-like ATPase 1 | OLA1 |
| P35998 | 6 | 26S proteasome regulatory subunit 7 | PSMC2 |
| P35606 | 6 | Coatomer subunit beta’ | COPB2 |
| P05388 | 6 | Large ribosomal subunit protein uL10 | RPLP0 |
| P46459 | 6 | Vesicle-fusing ATPase | NSF |
| P55884 | 6 | Eukaryotic translation initiation factor 3 subunit B | EIF3B |
| P62424 | 6 | Large ribosomal subunit protein eL8 | RPL7A |
| P53618 | 6 | Coatomer subunit beta | COPB1 |
| P30084 | 6 | Enoyl-CoA hydratase, mitochondrial | ECHS1 |
| P21796 | 6 | Non-selective voltage-gated ion channel VDAC1 | VDAC1 |
| P58107 | 6 | Epiplakin | EPPK1 |
| P61758 | 6 | Prefoldin subunit 3 | VBP1 |
| O14773 | 6 | Tripeptidyl-peptidase 1 | TPP1 |
| P04843 | 6 | Dolichyl-diphosphooligosaccharide-protein glycosyltransferase subunit 1 | RPN1 |
| O43852 | 6 | Calumenin | CALU |
| Q99102 | 6 | Mucin-4 | MUC4 |
| O43390 | 7 | Heterogeneous nuclear ribonucleoprotein R | HNRNPR |
| P11216 | 7 | Glycogen phosphorylase, brain form | PYGB |
| P67809 | 7 | Y-box-binding protein 1 | YBX1 |
| P22234 | 7 | Bifunctional phosphoribosylaminoimidazole carboxylase/phosphoribosylaminoimidazole succinocarboxamide synthetase | PAICS |
| P22061 | 7 | Protein-L-isoaspartate(D-aspartate) O-methyltransferase | PCMT1 |
| P62277 | 7 | Small ribosomal subunit protein uS15 | RPS13 |
| Q07960 | 7 | Rho GTPase-activating protein 1 | ARHGAP1 |
| P04066 | 7 | Tissue alpha-L-fucosidase | FUCA1 |
| Q02543 | 7 | Large ribosomal subunit protein eL20 | RPL18A |
| P55058 | 7 | Phospholipid transfer protein | PLTP |
| P22307 | 7 | Sterol carrier protein 2 | SCP2 |
| P49327 | 8 | Fatty acid synthase | FASN |
| P45974 | 8 | Ubiquitin carboxyl-terminal hydrolase 5 | USP5 |
| Q6XQN6 | 8 | Nicotinate phosphoribosyltransferase | NAPRT |
| P20073 | 8 | Annexin A7 | ANXA7 |
| Q15370 | 8 | Elongin-B | ELOB |
| Q15691 | 8 | Microtubule-associated protein RP/EB family member 1 | MAPRE1 |
| P13473 | 8 | Lysosome-associated membrane glycoprotein 2 | LAMP2 |
| Q9UII2 | 8 | ATPase inhibitor, mitochondrial | ATP5IF1 |
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MDPI and ACS Style
Araújo, C.P.M.; Vieira, C.M.; Ohse, K.C.; Silva, A.S.; Cavalcante, S.A.; Naveca, F.G.; Oliveira, F.N.; Crainey, J.L.; Lacerda, M.V.G.; Melo, G.C.; et al. Nasopharyngeal Proteomic Profiles from Patients Hospitalized Due to COVID-19 in Manaus, Amazonas, Brazil. COVID 2025, 5, 192. https://doi.org/10.3390/covid5110192
AMA Style
Araújo CPM, Vieira CM, Ohse KC, Silva AS, Cavalcante SA, Naveca FG, Oliveira FN, Crainey JL, Lacerda MVG, Melo GC, et al. Nasopharyngeal Proteomic Profiles from Patients Hospitalized Due to COVID-19 in Manaus, Amazonas, Brazil. COVID. 2025; 5(11):192. https://doi.org/10.3390/covid5110192
Chicago/Turabian StyleAraújo, Cláudia P. M., Carolina M. Vieira, Ketlen C. Ohse, Alessandra S. Silva, Sofia A. Cavalcante, Felipe G. Naveca, Fernanda N. Oliveira, James L. Crainey, Marcus V. G. Lacerda, Gisely C. Melo, and et al. 2025. "Nasopharyngeal Proteomic Profiles from Patients Hospitalized Due to COVID-19 in Manaus, Amazonas, Brazil" COVID 5, no. 11: 192. https://doi.org/10.3390/covid5110192
APA StyleAraújo, C. P. M., Vieira, C. M., Ohse, K. C., Silva, A. S., Cavalcante, S. A., Naveca, F. G., Oliveira, F. N., Crainey, J. L., Lacerda, M. V. G., Melo, G. C., Sampaio, V. S., Batista, M., Camillo-Andrade, A. C., Santos, M. D. M., Lima, D. B., Fischer, J. d. S. G., Carvalho, P. C., & Aquino, P. F. (2025). Nasopharyngeal Proteomic Profiles from Patients Hospitalized Due to COVID-19 in Manaus, Amazonas, Brazil. COVID, 5(11), 192. https://doi.org/10.3390/covid5110192
