Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 Nov 1;21(1):61.
doi: 10.1186/s12014-024-09512-6.

Serum proteomics for the identification of biomarkers to flag predilection of COVID19 patients to various organ morbidities

Madhan Vishal Rajan  1 , Vipra Sharma  1 , Neelam Upadhyay  1 , Ananya Murali  2 , Sabyasachi Bandyopadhyay  3 , Gururao Hariprasad  4
Affiliations

Serum proteomics for the identification of biomarkers to flag predilection of COVID19 patients to various organ morbidities

Madhan Vishal Rajan et al. Clin Proteomics. .

Abstract

Background: COVID19 is a pandemic that has affected millions around the world since March 2020. While many patients recovered completely with mild illness, many patients succumbed to various organ morbidities. This heterogeneity in the clinical presentation of COVID19 infection has posed a challenge to clinicians around the world. It is therefore crucial to identify specific organ-related morbidity for effective treatment and better patient outcomes. We have carried out serum-based proteomic experiments to identify protein biomarkers that can flag organ dysfunctions in COVID19 patients.

Methods: COVID19 patients were screened and tested at various hospitals across New Delhi, India. 114 serum samples from these patients, with and without organ morbidities were collected and annotated based on clinical presentation and treatment history. Of these, 29 samples comprising of heart, lung, kidney, gastrointestinal, liver, and neurological morbidities were considered for the discovery phase of the experiment. Proteins were isolated, quantified, trypsin digested, and the peptides were subjected to liquid chromatography assisted tandem mass spectrometry analysis. Data analysis was carried out using Proteome Discoverer software. Fold change analysis was carried out on MetaboAnalyst. KEGG, Reactome, and Wiki Pathway analysis of differentially expressed proteins were carried out using the STRING database. Potential biomarker candidates for various organ morbidities were validated using ELISA.

Results: 254 unique proteins were identified from all the samples with a subset of 12-31 differentially expressed proteins in each of the clinical phenotypes. These proteins establish complement and coagulation cascade pathways in the pathogenesis of the organ morbidities. Validation experiments along with their diagnostic parameters confirm Secreted Protein Acidic and Rich in Cysteine, Cystatin C, and Catalase as potential biomarker candidates that can flag cardiovascular disease, renal disease, and respiratory disease, respectively.

Conclusions: Label free serum proteomics shows differential protein expression in COVID19 patients with morbidity as compared to those without morbidity. Identified biomarker candidates hold promise to flag organ morbidities in COVID19 for efficient patient care.

Keywords: Biomarkers; COVID19; Clinical proteomics; Coagulation factors; Complement factors; Differential protein expression; Organ morbidity.

PubMed Disclaimer

Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Flowchart depicting the summary of methodology and outcome of this study
Fig. 2
Fig. 2
Partial least-squares discriminant analysis (PLS-DA) based on abundance of proteins identified in each of the COVID19 biological replicates: (A) Control and Cardiovascular morbidity phenotype. (B) Control and Pulmonary morbidity phenotype. (C) Control and Renal morbidity phenotype. (D) Control and Gastrointestinal morbidity phenotype. (E) Control and Neurological morbidity phenotype. (F) Control and Hepatic morbidity phenotype
Fig. 3
Fig. 3
Volcano plots showing differentially expressed proteins between control and organ morbidity phenotypes in COVID19. (A) Control and Cardiovascular morbidity phenotype. (B) Control and Pulmonary morbidity phenotype. (C) Control and Renal morbidity phenotype. (D) Control and Gastrointestinal morbidity phenotype. (E) Control and Neurological morbidity phenotype. (F) Control and Hepatic morbidity phenotype. With respect to organ morbidity phenotypes, Red dots represent proteins with increased fold change (> 2); blue dots represent proteins with decreased fold change (< 0.5); and grey dots represent proteins with no significant change
Fig. 4
Fig. 4
Pathway analysis of differentially expressed proteins in organ morbidity phenotypes in COVID19. (A) Control and Cardiovascular morbidity phenotype. (B) Control and Neurological morbidity phenotype. (C) Control and Renal morbidity phenotype. (D) Control and Gastrointestinal morbidity phenotype. (E) Control and Hepatic morbidity phenotype. KEGG pathways are represented in green; Reactome pathways are represented in blue; and Wiki pathways are represented in purple. x-axis represents the different pathways. Left y-axis represents number of proteins annotated from previous studies in each of the pathways and percentage corresponding (blue) to total number of input proteins is shown for the top values in the axis. Right y-axis represents the FDR value
Fig. 5
Fig. 5
ELISA of candidate biomarker proteins. (A) SPARC. (B) Cystatin C. (C) Catalase. The Scatter plots represent the individual concentrations of all the samples in the group as dots. Mean of the values is indicated by horizontal lines. * indicates p < 0.05; and *** indicates p < 0.001
Fig. 6
Fig. 6
ROC of candidate biomarker proteins. (A) SPARC. (B) Cystatin C. (C) Catalase. Area under the curve (AUC) and their 95% confidence intervals (blue area) are depicted. Red dot indicates optimal cut-off for the best sensitivity and specificity values
See this image and copyright information in PMC

Similar articles

See all similar articles

References

    1. Gallo Marin B, Aghagoli G, Lavine K, Yang L, Siff EJ, Chiang SS, et al. Predictors of COVID-19 severity: a literature review. Rev Med Virol. 2021;31:e2146. - PMC - PubMed
    1. Ciotti M, Ciccozzi M, Terrinoni A, Jiang W-C, Wang C-B, Bernardini S. The COVID-19 pandemic. Crit Rev Clin Lab Sci. 2020;57:365–88. - PubMed
    1. Huang C, Huang L, Wang Y, Li X, Ren L, Gu X, et al. 6-month consequences of COVID-19 in patients discharged from hospital: a cohort study. Lancet. 2023;401:e21–33. - PMC - PubMed
    1. Nalbandian A, Sehgal K, Gupta A, Madhavan MV, McGroder C, Stevens JS, et al. Post-acute COVID-19 syndrome. Nat Med. 2021;27:601–15. - PMC - PubMed
    1. Gupta A, Madhavan MV, Sehgal K, Nair N, Mahajan S, Sehrawat TS, et al. Extrapulmonary manifestations of COVID-19. Nat Med. 2020;26:1017–32. - PubMed

LinkOut - more resources