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Monteiro MES, Lechuga GC, Napoleão-Pêgo P, Carvalho JPRS, Gomes LR, Morel CM, Provance DW, De-Simone SG. Humoral Immune Response to SARS-CoV-2 Spike Protein Receptor-Binding Motif Linear Epitopes. Vaccines (Basel) 2024; 12:342. [PMID: 38675725 PMCID: PMC11055068 DOI: 10.3390/vaccines12040342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 03/05/2024] [Accepted: 03/14/2024] [Indexed: 04/28/2024] Open
Abstract
The worldwide spread of SARS-CoV-2 has led to a significant economic and social burden on a global scale. Even though the pandemic has concluded, apprehension remains regarding the emergence of highly transmissible variants capable of evading immunity induced by either vaccination or prior infection. The success of viral penetration is due to the specific amino acid residues of the receptor-binding motif (RBM) involved in viral attachment. This region interacts with the cellular receptor ACE2, triggering a neutralizing antibody (nAb) response. In this study, we evaluated serum immunogenicity from individuals who received either a single dose or a combination of different vaccines against the original SARS-CoV-2 strain and a mutated linear RBM. Despite a modest antibody response to wild-type SARS-CoV-2 RBM, the Omicron variants exhibit four mutations in the RBM (S477N, T478K, E484A, and F486V) that result in even lower antibody titers. The primary immune responses observed were directed toward IgA and IgG. While nAbs typically target the RBD, our investigation has unveiled reduced seroreactivity within the RBD's crucial subregion, the RBM. This deficiency may have implications for the generation of protective nAbs. An evaluation of S1WT and S2WT RBM peptides binding to nAbs using microscale thermophoresis revealed a higher affinity (35 nM) for the S2WT sequence (GSTPCNGVEGFNCYF), which includes the FNCY patch. Our findings suggest that the linear RBM of SARS-CoV-2 is not an immunodominant region in vaccinated individuals. Comprehending the intricate dynamics of the humoral response, its interplay with viral evolution, and host genetics is crucial for formulating effective vaccination strategies, targeting not only SARS-CoV-2 but also anticipating potential future coronaviruses.
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Affiliation(s)
- Maria E. S. Monteiro
- Center for Technological Development in Health (CDTS), National Institute of Science and Technology for Innovation in Neglected Population Diseases (INCT-IDPN), Oswaldo Cruz Foundation, Rio de Janeiro 21040-900, RJ, Brazil; (M.E.S.M.); (G.C.L.); (P.N.-P.); (J.P.R.S.C.); (L.R.G.); (C.M.M.); (D.W.P.)
- Program of Post-Graduation on Parasitic Biology, Oswaldo Cruz Institute, Oswaldo Cruz Foundation, Rio de Janeiro 21040-900, RJ, Brazil
| | - Guilherme C. Lechuga
- Center for Technological Development in Health (CDTS), National Institute of Science and Technology for Innovation in Neglected Population Diseases (INCT-IDPN), Oswaldo Cruz Foundation, Rio de Janeiro 21040-900, RJ, Brazil; (M.E.S.M.); (G.C.L.); (P.N.-P.); (J.P.R.S.C.); (L.R.G.); (C.M.M.); (D.W.P.)
- Epidemiology and Molecular Systematics Laboratory (LEMS), Oswaldo Cruz Institute, Oswaldo Cruz Foundation, Rio de Janeiro 21040-900, RJ, Brazil
| | - Paloma Napoleão-Pêgo
- Center for Technological Development in Health (CDTS), National Institute of Science and Technology for Innovation in Neglected Population Diseases (INCT-IDPN), Oswaldo Cruz Foundation, Rio de Janeiro 21040-900, RJ, Brazil; (M.E.S.M.); (G.C.L.); (P.N.-P.); (J.P.R.S.C.); (L.R.G.); (C.M.M.); (D.W.P.)
- Epidemiology and Molecular Systematics Laboratory (LEMS), Oswaldo Cruz Institute, Oswaldo Cruz Foundation, Rio de Janeiro 21040-900, RJ, Brazil
| | - João P. R. S. Carvalho
- Center for Technological Development in Health (CDTS), National Institute of Science and Technology for Innovation in Neglected Population Diseases (INCT-IDPN), Oswaldo Cruz Foundation, Rio de Janeiro 21040-900, RJ, Brazil; (M.E.S.M.); (G.C.L.); (P.N.-P.); (J.P.R.S.C.); (L.R.G.); (C.M.M.); (D.W.P.)
- Program of Post-Graduation on Science and Biotechnology, Department of Molecular and Cellular Biology, Biology Institute, Federal Fluminense University, Niterói 22040-036, RJ, Brazil
| | - Larissa R. Gomes
- Center for Technological Development in Health (CDTS), National Institute of Science and Technology for Innovation in Neglected Population Diseases (INCT-IDPN), Oswaldo Cruz Foundation, Rio de Janeiro 21040-900, RJ, Brazil; (M.E.S.M.); (G.C.L.); (P.N.-P.); (J.P.R.S.C.); (L.R.G.); (C.M.M.); (D.W.P.)
| | - Carlos M. Morel
- Center for Technological Development in Health (CDTS), National Institute of Science and Technology for Innovation in Neglected Population Diseases (INCT-IDPN), Oswaldo Cruz Foundation, Rio de Janeiro 21040-900, RJ, Brazil; (M.E.S.M.); (G.C.L.); (P.N.-P.); (J.P.R.S.C.); (L.R.G.); (C.M.M.); (D.W.P.)
| | - David W. Provance
- Center for Technological Development in Health (CDTS), National Institute of Science and Technology for Innovation in Neglected Population Diseases (INCT-IDPN), Oswaldo Cruz Foundation, Rio de Janeiro 21040-900, RJ, Brazil; (M.E.S.M.); (G.C.L.); (P.N.-P.); (J.P.R.S.C.); (L.R.G.); (C.M.M.); (D.W.P.)
- Epidemiology and Molecular Systematics Laboratory (LEMS), Oswaldo Cruz Institute, Oswaldo Cruz Foundation, Rio de Janeiro 21040-900, RJ, Brazil
| | - Salvatore G. De-Simone
- Center for Technological Development in Health (CDTS), National Institute of Science and Technology for Innovation in Neglected Population Diseases (INCT-IDPN), Oswaldo Cruz Foundation, Rio de Janeiro 21040-900, RJ, Brazil; (M.E.S.M.); (G.C.L.); (P.N.-P.); (J.P.R.S.C.); (L.R.G.); (C.M.M.); (D.W.P.)
- Program of Post-Graduation on Parasitic Biology, Oswaldo Cruz Institute, Oswaldo Cruz Foundation, Rio de Janeiro 21040-900, RJ, Brazil
- Epidemiology and Molecular Systematics Laboratory (LEMS), Oswaldo Cruz Institute, Oswaldo Cruz Foundation, Rio de Janeiro 21040-900, RJ, Brazil
- Program of Post-Graduation on Science and Biotechnology, Department of Molecular and Cellular Biology, Biology Institute, Federal Fluminense University, Niterói 22040-036, RJ, Brazil
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Rahbar MR, Mubarak SMH, Hessami A, Khalesi B, Pourzardosht N, Khalili S, Zanoos KA, Jahangiri A. A unique antigen against SARS-CoV-2, Acinetobacter baumannii, and Pseudomonas aeruginosa. Sci Rep 2022; 12:10852. [PMID: 35760825 PMCID: PMC9237110 DOI: 10.1038/s41598-022-14877-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 06/14/2022] [Indexed: 02/07/2023] Open
Abstract
The recent outbreak of COVID-19 has increased hospital admissions, which could elevate the risk of nosocomial infections, such as A. baumannii and P. aeruginosa infections. Although effective vaccines have been developed against SARS-CoV-2, no approved treatment option is still available against antimicrobial-resistant strains of A. baumannii and P. aeruginosa. In the current study, an all-in-one antigen was designed based on an innovative, state-of-the-art strategy. In this regard, experimentally validated linear epitopes of spike protein (SARS-CoV-2), OmpA (A. baumannii), and OprF (P. aeruginosa) were selected to be harbored by mature OmpA as a scaffold. The selected epitopes were used to replace the loops and turns of the barrel domain in OmpA; OprF311–341 replaced the most similar sequence within the OmpA, and three validated epitopes of OmpA were retained intact. The obtained antigen encompasses five antigenic peptides of spike protein, which are involved in SARS-CoV-2 pathogenicity. One of these epitopes, viz. QTQTNSPRRARSV could trigger antibodies preventing super-antigenic characteristics of spike and alleviating probable autoimmune responses. The designed antigen could raise antibodies neutralizing emerging variants of SARS-CoV-2 since at least two epitopes are consensus. In conclusion, the designed antigen is expected to raise protective antibodies against SARS-CoV-2, A. baumannii, and P. aeruginosa.
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Affiliation(s)
- Mohammad Reza Rahbar
- Pharmaceutical Sciences Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Shaden M H Mubarak
- Department of Clinical Laboratory Science, Faculty of Pharmacy, University of Kufa, Najaf, Iraq
| | - Anahita Hessami
- School of Pharmacy, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Bahman Khalesi
- Department of Research and Production of Poultry Viral Vaccine, Razi Vaccine and Serum Research Institute, Agricultural Research Education and Extension Organization, Karaj, Iran
| | - Navid Pourzardosht
- Biochemistry Department, Guilan University of Medical Sciences, Rasht, Iran
| | - Saeed Khalili
- Department of Biology Sciences, Shahid Rajaee Teacher Training University, Tehran, Iran
| | - Kobra Ahmadi Zanoos
- Young Researchers Club, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Abolfazl Jahangiri
- Applied Microbiology Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Sciences, Vanak Sq. Molasadra St., P.O. Box 1435915371, Tehran, Iran.
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Abstract
The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) virus is continuously evolving, and this poses a major threat to antibody therapies and currently authorized Coronavirus Disease 2019 (COVID-19) vaccines. It is therefore of utmost importance to investigate and predict the putative mutations on the spike protein that confer immune evasion. Antibodies are key components of the human immune system’s response to SARS-CoV-2, and the spike protein is a prime target of neutralizing antibodies (nAbs) as it plays critical roles in host cell recognition, fusion, and virus entry. The potency of therapeutic antibodies and vaccines partly depends on how readily the virus can escape neutralization. Recent structural and functional studies have mapped the epitope landscape of nAbs on the spike protein, which illustrates the footprints of several nAbs and the site of escape mutations. In this review, we discuss (1) the emerging SARS-CoV-2 variants; (2) the structural basis for antibody-mediated neutralization of SARS-CoV-2 and nAb classification; and (3) identification of the RBD escape mutations for several antibodies that resist antibody binding and neutralization. These escape maps are a valuable tool to predict SARS-CoV-2 fitness, and in conjunction with the structures of the spike-nAb complex, they can be utilized to facilitate the rational design of escape-resistant antibody therapeutics and vaccines.
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Shurrab FM, Al-Sadeq DW, Abou-Saleh H, Al-Dewik N, Elsharafi AE, Hamaydeh FM, Halawa BYA, Jamaleddin TM, Hameed HMA, Nizamuddin PB, Amanullah FH, Daas HI, Abu-Raddad LJ, Nasrallah GK. Assessment of the Neutralizing Antibody Response of BNT162b2 and mRNA-1273 SARS-CoV-2 Vaccines in Naïve and Previously Infected Individuals: A Comparative Study. Vaccines (Basel) 2022; 10:vaccines10020191. [PMID: 35214650 PMCID: PMC8877530 DOI: 10.3390/vaccines10020191] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 01/20/2022] [Accepted: 01/23/2022] [Indexed: 02/01/2023] Open
Abstract
The currently authorized mRNA COVID-19 vaccines, Pfizer-BNT162b2 and Moderna-mRNA-1273, offer great promise for reducing the spread of the COVID-19 by generating protective immunity against SARS-CoV-2. Recently, it was shown that the magnitude of the neutralizing antibody (NAbs) response correlates with the degree of protection. However, the difference between the immune response in naïve mRNA-vaccinated and previously infected (PI) individuals is not well studied. We investigated the level of NAbs in naïve and PI individuals after 1 to 26 (median = 6) weeks of the second dose of BNT162b2 or mRNA-1273 vaccination. The naïve mRNA-1273 vaccinated group (n = 68) generated significantly higher (~2-fold, p ≤ 0.001) NAbs than the naïve BNT162b2 (n = 358) group. The P -vaccinated group (n = 42) generated significantly higher (~3-fold; p ≤ 0.001) NAbs levels than the naïve-BNT162b2 (n = 426). Additionally, the older age groups produced a significantly higher levels of antibodies than the young age group (<30) (p = 0.0007). Our results showed that mRNA-1273 generated a higher NAbs response than the BNT162b2 vaccine, and the PI group generated the highest level of NAbs response regardless of the type of vaccine.
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Affiliation(s)
- Farah M. Shurrab
- Biomedical Research Center, Qatar University, Doha P.O. Box 2713, Qatar; (F.M.S.); (D.W.A.-S.); (H.A.-S.); (P.B.N.); (F.H.A.)
| | - Duaa W. Al-Sadeq
- Biomedical Research Center, Qatar University, Doha P.O. Box 2713, Qatar; (F.M.S.); (D.W.A.-S.); (H.A.-S.); (P.B.N.); (F.H.A.)
- College of Medicine, QU Health, Qatar University, Doha P.O. Box 2713, Qatar
| | - Haissam Abou-Saleh
- Biomedical Research Center, Qatar University, Doha P.O. Box 2713, Qatar; (F.M.S.); (D.W.A.-S.); (H.A.-S.); (P.B.N.); (F.H.A.)
- Biological Science Program, Department of Biological and Environmental Sciences, College of Arts and Science, Qatar University, Doha P.O. Box 2713, Qatar;
| | - Nader Al-Dewik
- Women’s Wellness and Research Center (WWRC), Clinical and Metabolic Genetics Section, Pediatrics Department, Hamad General Hospital (HGH), Interim Translational Research Institute (iTRI), Hamad Medical Corporation (HMC), College of Health and Life Science (CHLS), Hamad Bin Khalifa University (HBKU), Doha P.O. Box 3050, Qatar;
| | - Amira E. Elsharafi
- Department of Biomedical Science, College of Health Sciences, QU Health, Qatar University, Doha P.O. Box 2713, Qatar; (A.E.E.); (F.M.H.); (T.M.J.); (H.M.A.H.)
| | - Fatima M. Hamaydeh
- Department of Biomedical Science, College of Health Sciences, QU Health, Qatar University, Doha P.O. Box 2713, Qatar; (A.E.E.); (F.M.H.); (T.M.J.); (H.M.A.H.)
| | - Bushra Y. Abo Halawa
- Biological Science Program, Department of Biological and Environmental Sciences, College of Arts and Science, Qatar University, Doha P.O. Box 2713, Qatar;
| | - Tala M. Jamaleddin
- Department of Biomedical Science, College of Health Sciences, QU Health, Qatar University, Doha P.O. Box 2713, Qatar; (A.E.E.); (F.M.H.); (T.M.J.); (H.M.A.H.)
| | - Huda M. Abdul Hameed
- Department of Biomedical Science, College of Health Sciences, QU Health, Qatar University, Doha P.O. Box 2713, Qatar; (A.E.E.); (F.M.H.); (T.M.J.); (H.M.A.H.)
| | - Parveen B. Nizamuddin
- Biomedical Research Center, Qatar University, Doha P.O. Box 2713, Qatar; (F.M.S.); (D.W.A.-S.); (H.A.-S.); (P.B.N.); (F.H.A.)
| | - Fathima Humaira Amanullah
- Biomedical Research Center, Qatar University, Doha P.O. Box 2713, Qatar; (F.M.S.); (D.W.A.-S.); (H.A.-S.); (P.B.N.); (F.H.A.)
| | - Hanin I. Daas
- College of Dental Medicine, QU Health, Qatar University, Doha P.O. Box 2713, Qatar;
| | - Laith J. Abu-Raddad
- Infectious Disease Epidemiology Group, Weill Cornell Medicine-Qatar, Cornell University, Qatar Foundation—Education City, Doha P.O. Box 24144, Qatar;
- World Health Organization Collaborating Centre for Disease Epidemiology Analytics on HIV/AIDS, Sexually Transmitted Infections, and Viral Hepatitis, Weill Cornell Medicine–Qatar, Cornell University, Qatar Foundation—Education City, Doha P.O. Box 24144, Qatar
- Department of Healthcare Policy and Research, Weill Cornell Medicine, Cornell University, New York, NY 14850, USA
| | - Gheyath K. Nasrallah
- Biomedical Research Center, Qatar University, Doha P.O. Box 2713, Qatar; (F.M.S.); (D.W.A.-S.); (H.A.-S.); (P.B.N.); (F.H.A.)
- Department of Biomedical Science, College of Health Sciences, QU Health, Qatar University, Doha P.O. Box 2713, Qatar; (A.E.E.); (F.M.H.); (T.M.J.); (H.M.A.H.)
- Correspondence:
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Arora G, Joshi J, Mandal RS, Shrivastava N, Virmani R, Sethi T. Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19. Pathogens 2021; 10:1048. [PMID: 34451513 PMCID: PMC8399076 DOI: 10.3390/pathogens10081048] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 08/11/2021] [Accepted: 08/11/2021] [Indexed: 12/15/2022] Open
Abstract
As of August 6th, 2021, the World Health Organization has notified 200.8 million laboratory-confirmed infections and 4.26 million deaths from COVID-19, making it the worst pandemic since the 1918 flu. The main challenges in mitigating COVID-19 are effective vaccination, treatment, and agile containment strategies. In this review, we focus on the potential of Artificial Intelligence (AI) in COVID-19 surveillance, diagnosis, outcome prediction, drug discovery and vaccine development. With the help of big data, AI tries to mimic the cognitive capabilities of a human brain, such as problem-solving and learning abilities. Machine Learning (ML), a subset of AI, holds special promise for solving problems based on experiences gained from the curated data. Advances in AI methods have created an unprecedented opportunity for building agile surveillance systems using the deluge of real-time data generated within a short span of time. During the COVID-19 pandemic, many reports have discussed the utility of AI approaches in prioritization, delivery, surveillance, and supply chain of drugs, vaccines, and non-pharmaceutical interventions. This review will discuss the clinical utility of AI-based models and will also discuss limitations and challenges faced by AI systems, such as model generalizability, explainability, and trust as pillars for real-life deployment in healthcare.
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Affiliation(s)
- Gunjan Arora
- Department of Internal Medicine, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Jayadev Joshi
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44106, USA;
| | - Rahul Shubhra Mandal
- Department of Cancer Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA;
| | - Nitisha Shrivastava
- Department of Pathology, Albert Einstein College of Medicine/Montefiore Medical Center, Bronx, NY 10461, USA;
| | - Richa Virmani
- Confo Therapeutics, Technologiepark 94, 9052 Ghent, Belgium;
| | - Tavpritesh Sethi
- Indraprastha Institute of Information Technology, New Delhi 110020, India;
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6
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Arora G, Joshi J, Mandal RS, Shrivastava N, Virmani R, Sethi T. Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19. Pathogens 2021; 10:1048. [PMID: 34451513 PMCID: PMC8399076 DOI: 10.3390/pathogens10081048,] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
As of August 6th, 2021, the World Health Organization has notified 200.8 million laboratory-confirmed infections and 4.26 million deaths from COVID-19, making it the worst pandemic since the 1918 flu. The main challenges in mitigating COVID-19 are effective vaccination, treatment, and agile containment strategies. In this review, we focus on the potential of Artificial Intelligence (AI) in COVID-19 surveillance, diagnosis, outcome prediction, drug discovery and vaccine development. With the help of big data, AI tries to mimic the cognitive capabilities of a human brain, such as problem-solving and learning abilities. Machine Learning (ML), a subset of AI, holds special promise for solving problems based on experiences gained from the curated data. Advances in AI methods have created an unprecedented opportunity for building agile surveillance systems using the deluge of real-time data generated within a short span of time. During the COVID-19 pandemic, many reports have discussed the utility of AI approaches in prioritization, delivery, surveillance, and supply chain of drugs, vaccines, and non-pharmaceutical interventions. This review will discuss the clinical utility of AI-based models and will also discuss limitations and challenges faced by AI systems, such as model generalizability, explainability, and trust as pillars for real-life deployment in healthcare.
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Affiliation(s)
- Gunjan Arora
- Department of Internal Medicine, Yale University School of Medicine, New Haven, CT 06520, USA
- Correspondence: or
| | - Jayadev Joshi
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44106, USA;
| | - Rahul Shubhra Mandal
- Department of Cancer Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA;
| | - Nitisha Shrivastava
- Department of Pathology, Albert Einstein College of Medicine/Montefiore Medical Center, Bronx, NY 10461, USA;
| | - Richa Virmani
- Confo Therapeutics, Technologiepark 94, 9052 Ghent, Belgium;
| | - Tavpritesh Sethi
- Indraprastha Institute of Information Technology, New Delhi 110020, India;
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