At least 58% of U.S. population has natural antibodies from previous Covid infection, CDC says

Authors: Spencer Kimball PUBLISHED TUE, APR 26 2022 CNBC

KEY POINTS

  • Three out of every 5 people in the U.S. now have antibodies from a previous Covid-19 infection, according to a new CDC analysis.
  • The proportion is even higher among children, demonstrating how widespread the virus was during the winter omicron surge.
  • CDC officials told reporters on a call Tuesday that the study did not measure whether people with prior infections had high enough antibody levels to protect against reinfection and severe illness.
  • However, CDC Director Dr. Rochelle Walensky said health officials believe there is a lot of protection against the virus in communities from vaccination, boosting and infection taken together.

Three out of every 5 people in the U.S. now have antibodies from a previous Covid-19 infection with the proportion even higher among children, demonstrating how widespread the virus was during the winter omicron surge, according to data from the Centers for Disease Control and Prevention.

The proportion of people with natural Covid antibodies increased substantially from about 34% of the population in December to about 58% in February during the unprecedent wave of infection driven by the highly contagious omicron variant. The CDC’s analysis didn’t factor in people who had antibodies from vaccination.

The CDC published the data in its Morbidity and Mortality Weekly Report on Tuesday.

The increase in antibody prevalence was most pronounced among children, indicating a high rate of infection among kids during the winter omicron wave. About 75% of children and teenagers now have antibodies from past Covid infections, up from about 45% in December.

The high rate of infection among children is likely due to lower vaccination rates than adults. Only 28% of children 5- to 11-years-old and 59% of teens 12- to 17-years-old were fully vaccinated as of April. Children under 5-years-old are not yet eligible for vaccination.

About 33% of people ages 65 and older, the group with the highest vaccination rate, had antibodies from infection. Roughly 64% of adults ages 18 to 49 and 50% of people 50 to 64 had the antibodies.

The CDC analyzed about 74,000 blood samples every month from September through January from a national commercial lab network. The sample size decreased to about 46,000 in February. The CDC tested the samples for a specific type of antibody that is produced in response to Covid infection, not from vaccination.

CDC officials told reporters on a call Tuesday that the study did not measure whether people with prior infections had high enough antibody levels to protect against reinfection and severe illness. However, CDC Director Dr. Rochelle Walensky said health officials believe there is a lot of protection in communities across the country from vaccination, boosting and infection taken together, while cautioning that vaccination is the safest strategy to protect yourself against the virus.

“Those who have detectable antibody from prior infection, we still continue to encourage them to get vaccinated,” Walensky told reporters during the call. “We don’t know when that infection was. We don’t know whether that protection has waned. We don’t know as much about that level of protection than we do about the protection we get from both vaccines and boosters.”

Scientists in Qatar affiliated with Cornell University found that natural infection provides about 73% protection against hospitalization if a person is reinfected with BA.2. However, three doses of Pfizer’s vaccine provided much higher protection against hospitalization at 98%. The study, published in March, has not undergone peer-review.

About 66% of the U.S. population is fully vaccinated and 77% have received at least one dose, according to data from the CDC.

Infections and hospitalizations have dropped more than 90% from the peak of the omicron wave in January when infections in the U.S. soared to an average of more than 800,000 a day. New cases are rising again due to the BA.2 subvariant. Another subvariant, BA.2.12.1, is now gaining ground in the U.S., representing about 29% of new infections, according to CDC data. Walensky said the public health agency believes BA.2.12.1 spreads about 25% faster than BA.2. However, she said the CDC does not expect to see more severe disease from BA.2.12.1though studies are ongoing.

More than 98% of the U.S. population lives in areas where they do not need to wear masks indoors under CDC guidance due to low Covid community levels, which takes into account both infections and hospitalizations. A U.S. district judge last week struck down the CDC’s mask mandate for public transportation, though the Justice Department has filed an appeal. Walensky said the CDC continues to recommend that people wear masks on public transportation.

Counties With Highest Vaccination Rates See More COVID-19 Cases Than Least Vaccinated

Authors: Petr Svab April 4, 2022 Updated: April 5, 2022 THE EPOCH TIMES

U.S. counties with the highest rates of vaccination against COVID-19 are currently experiencing more cases than those with the lowest vaccination rates, according to data collected by the Centers for Disease Control and Prevention (CDC).

The 500 counties where 62 to 95 percent of the population has been vaccinated detected more than 75 cases per 100,000 residents on average in the past week. Meanwhile, the 500 counties where 11 to 40 percent of the population has been vaccinated averaged about 58 cases per 100,000 residents.

The data is skewed by the fact that the CDC suppresses figures for counties with very low numbers of detected cases (one to nine) for privacy purposes. The Epoch Times calculated the average case rates by assuming the counties with the suppressed numbers had five cases each on average.

The least vaccinated counties tended to be much smaller, averaging less than 20,000 in population. The most vaccinated counties had an average population of over 330,000. More populous counties, however, weren’t more likely to have higher case rates.

Even when comparing counties of similar population, the ones with the most vaccinations tended to have higher case rates than those that reported the least vaccinations.

Among counties with populations of 1 million or more, the 10 most vaccinated had a case rate more than 27 percent higher than the 10 least vaccinated. In counties with populations of 500,000 to 1 million, the 10 most vaccinated had a case rate almost 19 percent higher than the 10 least vaccinated.

In counties with populations of 200,000 to 500,000, the 10 most vaccinated had case rates around 55 percent higher than the 10 least vaccinated.

The difference was more than 200 percent for counties with populations of 100,000 to 200,000.

For counties with smaller populations, the comparison becomes increasingly difficult because so much of the data is suppressed.

Another problem is that the prevalence of testing for COVID-19 infections isn’t uniform. A county may have a low case number on paper because its residents are tested less often.

The massive spike in infections during the winter appears to have abated in recent weeks. Detected infections are down to less than 30,000 per day from a high of over 800,000 per day in mid-January, according to CDC data. The seven-day average of currently hospitalized dropped to about 11,000 on April 1, from nearly 150,000 in January.

The most recent wave of COVID-19 has been attributed to the Omicron virus variant, which is more transmissible but less virulent. The variant also seems more capable of overcoming any protection offered by the vaccines, though, according to the CDC, the vaccines still reduce the risk of severe disease.

Bone Marrow Suppression Secondary to the COVID-19 Booster Vaccine: A Case Report

TAuthors: oral Shastri 1Navkiran Randhawa 2Ragia Aly 3Masood Ghouse 3 PMID: 35210894PMCID: PMC8863340DOI: 10.2147/JBM.S350290 J Blood Med.  2022; 13: 69–74.Published online 2022 Feb 18. doi: 10.2147/JBM.S350290

Abstract

As of September 2021, SARS-CoV-2 booster shots became widely available in the US to ensure continued protection against the virus. A temporal relationship has been previously reported between the first or second dose of the COVID-19 vaccine and the development of thrombocytopenia. However, adverse events related to the third COVID-19 vaccine are still being reported and studied. We report a 74-year-old male who developed bone marrow suppression and pancytopenia recorded seven days after receiving the Pfizer SARS-CoV-2 vaccine. During his hospital stay, the patient’s hemoglobin, white blood cell, and platelet levels continued to trend downwards. However, all three levels showed improvement one week after discharge without robust intervention. Global vaccination is of utmost importance, as is understanding and documenting post-vaccination reactions including bone marrow suppression. Prompt evaluation and patient education are imperative to improve patient outcomes and combat hesitancy against vaccine administration.

Introduction

Since its emergence in December of 2019, the rapid spread of severe acute respiratory syndrome coronavirus (SARS-CoV-2) has quickly affected millions of lives across every continent.1 This highly transmittable and pathogenic viral infection has led to massive mitigation efforts and allocation of resources to prevent the spread of transmission and high mortality related to complications.2 The establishment of higher levels of community (herd) immunity and protection against SARS-CoV-2 via the widespread deployment of effective vaccines has become a global effort.3 In December of 2020, the FDA issued an Emergency use Authorization for the Pfizer-BioNTech and Moderna COVID-19 Vaccine as a two-dose series.4 In September 2021, booster vaccines became widely administered in the US due to waning immunity of the COVID-19 vaccines against variants of SARS-CoV-2 along with ensuring continued protection against the virus.5

Serious adverse events such as anaphylaxis, Guillain-Barre Syndrome, myocarditis, pericarditis, thrombocytopenia, and death have been previously reported following the first and/or second dose of vaccine.6 To our knowledge, no cases have been reported regarding bone marrow suppression related to the third COVID-19 vaccine. Adverse events reported between August 12-September 19, 2021 from the COVID-19 booster vaccine supported similar reactions to those after dose two.7 According to the Centers for Disease Control and Prevention (CDC), these initial findings indicate no unexpected patterns of adverse reactions after an additional dose of COVID-19 vaccination.7 However, adverse events related to the COVID-19 booster are still being reported and studied.6 This report presents a case of bone marrow suppression occurring after the third COVID-19 vaccine without a similar reaction after the first or second dose.Go to:

Case Report

A 74-year-old male with a history of polychondritis and hypothyroidism presented to the hospital after falling out of his chair and inability to ambulate. The patient was found to be mildly confused upon arrival to the emergency room, limiting our ability to obtain a full verbal history. Chart review revealed the patient had received his third Pfizer Covid vaccine shot seven days before admission followed by fatigue, decreased appetite, fever, and chills. The patient had received the second Pfizer Covid-19 shot nine months before the booster. No reactions to the previous two shots were noted.

Upon initial evaluation, vital signs were within normal limits and a physical exam revealed significant tenderness in the upper arm and no gross bleeding (Figure 1). Computed tomography (CT) imaging (Figure 2) was significant for enhancement of the left axillary lymph node. The patient’s initial complete blood count (CBC) was remarkable for a hemoglobin count of 9.9 g/dl and platelet count of 84 x 109/L; both values lower than his prior hemoglobin count of 13.7 g/dl and platelet count of 180 x 109/L from December of 2020. His mean corpuscular volume (MCV) was elevated at 101.3 femtolitres from his prior MCV value of 95.8 femtolitres in December of 2020. His white blood cell (WBC) count was recorded at 7.6 x 109/L.

An external file that holds a picture, illustration, etc.
Object name is JBM-13-69-g0001.jpg

Figure 1

The patient’s upper arm showed erythema with no gross bleeding near the injection site

An external file that holds a picture, illustration, etc.
Object name is JBM-13-69-g0002.jpg

Figure 2

The patient’s CT imaging of the thoracic region showed enhancement of the left axillary lymph node.

The hemoglobin, WBC, and platelet count further down trended from his baseline (Figures 3​5).5). Anemia labs including ferritin levels (554 ng/mL), vitamin B12 (253 pg/mL), total bilirubin (0.5 mg/dL), and reticulocyte count (0.8%) were nonsignificant during the patient’s hospital stay. The patient’s left shoulder presented with extensive bruising, erythema, papular rash, warmth, and tenderness on palpation during the hospitalization. An improvement in WBC and platelet levels was noted on day 4 of hospitalization.

An external file that holds a picture, illustration, etc.
Object name is JBM-13-69-g0003.jpg

Figure 3

The patient’s hemoglobin count throughout his hospital course and 6 days after discharge.

An external file that holds a picture, illustration, etc.
Object name is JBM-13-69-g0004.jpg

Figure 4

The patient’s WBC count throughout his hospital course and 6 days after discharge.

An external file that holds a picture, illustration, etc.
Object name is JBM-13-69-g0005.jpg

Figure 5

The patient’s platelet count throughout his hospital course and 6 days after discharge.

Before discharge, the patient was fully alert and oriented and reported improvement in his symptoms. Examination of his lateral left arm showed decreased erythema and bruising with slight petechiae. The patient was discharged due to stabilization of labs and encouraged to take oral vitamin B12 supplements. During his outpatient follow-up six days after hospitalization, his hemoglobin increased to 10.5 g/dl, WBC count increased to 4.9 x 109/L, and platelets increased to 101 x 109/L.

Discussion

This paper presents a patient with pancytopenia recorded seven days after receiving the Pfizer booster vaccine. Interestingly, this patient did not report any reactions after the first or second dose of the Pfizer vaccine against SARS-CoV-2. Pancytopenia refers to a decrease in all peripheral bloodlines and is present when all three cell lines are below the normal reference range.8 The patient’s physical exam showed no signs of active bleeding along with his labs indicating no evidence of hemolysis. The patient’s hemoglobin, platelet, and white blood cell count presented below baseline followed by a decrease and slight improvement during his hospital stay. Six days after hospitalization, all three cell lines showed improvement. The temporal association with the booster vaccine and negative infectious disease workup raised suspicion for vaccine-induced bone marrow suppression. In addition, the patient’s reticulocyte count and lactate dehydrogenase value were consistent with hypoproliferation within the bone marrow.

Currently, there is a gap in knowledge of adverse events specific to the third vaccine against SARS-CoV-2 due to the recent initiation of administration and ongoing reporting of events.6 To our knowledge, bone marrow suppression after any dose of vaccine against SARS-CoV-2 has not been previously reported. However, a prior case of pancytopenia after the third vaccination with a recombinant hepatitis B vaccine has previously been reported.9 The patient’s bone marrow biopsy within this case displayed a paucity of late myeloid elements and CD8+ T cells.9 It was believed the patient’s CD8+T cells were causing excessive production of IFN-γ; a stimulant of negative regulators of hematopoiesis such as tumor necrosis factor and lymphotoxin.10 IFN-γ has also previously been reported to create immunological effects comprising an upregulation of histocompatibility gene transcription and alteration in class I and II antigen expression at the cell surface.11 It was predicted these changes resulted in an autoimmune reaction causing suppression of maturation of hematopoietic progenitor cells and pancytopenia.9 Via a similar mechanism, we believe that our patient’s pancytopenia was immune-mediated, potentially triggered by the vaccination.

Vaccines against SARS-CoV-2 (first or second dose) and the induction of Idiopathic Thrombocytopenic Purpura (ITP) have also been recently acknowledged in multiple cases.12 Our patient presented with low platelet levels and associated petechiae and purpura at the site of the vaccination. However, the patient’s presentation of low hemoglobin and white blood cells along with normal reticulocyte levels was more indicative of pancytopenia secondary to bone marrow suppression. In patients presenting with pancytopenia, the history and the physical exam should help assess the severity of the pancytopenia and comorbid illnesses that may complicate the disorder.13 In addition, suspicious medications and exposure to toxic agents should be ruled out.13 Initial screening laboratory evaluation should include the patient’s complete blood count, peripheral blood smear examination, reticulocyte count, complete metabolic panel, prothrombin time/partial thromboplastin time, and blood type and screen. Common interventions to alleviate bone marrow suppression and pancytopenia include treating the underlying cause and utilizing supplements to boost red blood cell production if indicated.

Vaccines against SARS-CoV-2 undergo continuous safety monitoring; adverse events are very rare.14 However, vaccine hesitancy remains a barrier towards full population inoculation against SARS-CoV-2 and is influenced by misinformation regarding vaccine safety.15 One study using an anonymous online questionnaire found a person’s trust in the effectiveness of the vaccine was a major facilitative factor affecting willingness to vaccinate.16 The same study also found that 66.7% of unvaccinated participants thought the vaccine’s safety was not enough, making it the main reason for reluctance or hesitance to be vaccinated.16 Therefore, education of adverse events and available interventions post-vaccination is imperative to prevent the spread of misinformation and combat hesitancy towards vaccination.15

As of September 19, 2021, about 2.2 million people in the United States received a third vaccine against SARS-CoV-2.17 Among those who received the vaccine, 22,000 people reported the effects of the vaccine with no unexpected patterns of adverse reactions.17 Our patient demonstrates abnormal pancytopenia first recorded seven days after receiving the booster vaccine, possibly indicating a rare adverse event from the vaccination given the temporal relationship. While additional studies and observations are indicated to verify bone marrow suppression as an adverse reaction, this case report provides an opportunity for patient education and treatment planning before symptoms arise.

Conclusion

Our case report highlights pancytopenia secondary to bone marrow suppression following Pfizer vaccination against SARS-CoV-2. It is important to consider the possibility of bone marrow suppression following the third vaccine against SARS-CoV-2. Although additional studies are indicated to determine the risk factors and pathogenesis of vaccine-induced bone marrow suppression, prompt evaluation and initiation of interventions can improve patient outcomes.

References

1. Fernandes A, Chaudhari S, Jamil N, Gopalakrishnan G. COVID-19 vaccine. Endocr Pract. 2021;27(2):170–172. doi:10.1016/j.eprac.2021.01.013 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

2. Johansson MA, Quandelacy TM, Kada S, et al. SARS-CoV-2 transmission from people without COVID-19 symptoms. JAMA Network Open. 2021;4(1):e2035057–e2035057. doi:10.1001/jamanetworkopen.2020.35057 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

3. Graham BS. Rapid COVID-19 vaccine development. Science. 2020;368(6494):945–946. doi:10.1126/science.abb8923 [PubMed] [CrossRef] [Google Scholar]

4. Gee J, Marquez P, Su J, et al. First month of COVID-19 vaccine safety monitoring—United States, December 14, 2020–January 13. Morb Mortal Wkly Rep. 2021;70(8):283. doi:10.15585/mmwr.mm7008e3 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

5. Mahase E. Covid-19 booster vaccines: what we know and who’s doing what. BMJ. 2021. doi: 10.1136/bmj.n2082 [PubMed] [CrossRef] [Google Scholar]

6. Centers for Disease Control and Prevention. Selected adverse events reported after COVID-19 vaccination. Centers for Disease Control and Prevention. Available from: https://www.cdc.gov/coronavirus/2019-ncov/vaccines/safety/adverse-events.html. Accessed November 8, 2021. [Google Scholar]

7. Hause AM. Safety monitoring of an additional dose. Centers for Disease Control and Prevention; 2021. Available from: https://www.cdc.gov/mmwr/volumes/70/wr/mm7039e4.htm. Accessed February 11, 2022. [Google Scholar]

8. Valent P. Low blood counts: immune mediated, idiopathic, or myelodysplasia. Hematology. 2012;2012(1):485–491. doi:10.1182/asheducation.V2012.1.485.3798522 [PubMed] [CrossRef] [Google Scholar]

9. Viallard JF, Boiron JM, Parrens M, et al. Severe pancytopenia triggered by recombinant hepatitis B vaccine. Br J Haematol. 2000;110(1):230–233. doi:10.1046/j.1365-2141.2000.02171.x [PubMed] [CrossRef] [Google Scholar]

10. Collart MA, Belin D, Vassalli JD, De Kossodo S, Vassalli P. Gamma interferon enhances macrophage transcription of the tumor necrosis factor/cachectin, interleukin 1, and urokinase genes, which are controlled by short-lived repressors. J Exp Med. 1986;164(6):2113–2118. doi:10.1084/jem.164.6.2113 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

11. Wallach D, Fellous M, Revel M. Preferential effect of gamma interferon on the synthesis of HLA antigens and their mRNAs in human cells. Nature. 1982;299(5886):833–836. doi:10.1038/299833a0 [PubMed] [CrossRef] [Google Scholar]

12. Shah SRA, Dolkar S, Mathew J, et al. COVID-19 vaccination associated severe immune thrombocytopenia. Exp Hematol Oncol. 2021;10:42. doi:10.1186/s40164-021-00235-0 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

13. Elizabeth P, Weinzierl MD, Daniel A, Arber MD. The differential diagnosis and bone marrow evaluation of new-onset pancytopenia. Am J Clin Pathol. 2013;139(1):9–29. doi:10.1309/AJCP50AEEYGREWUZ [PubMed] [CrossRef] [Google Scholar]

14. Centers for Disease Control and Prevention. COVID-19 vaccination; 2020. Available from: https://www.cdc.gov/coronavirus/2019-ncov/vaccines/safety/safety-of-vaccines.html. Accessed February 11, 2022.

15. Dror AA, Eisenbach N, Taiber S, et al. Vaccine hesitancy: the next challenge in the fight against COVID-19. Eur J Epidemiol. 2020;35:775–779. doi:10.1007/s10654-020-00671-y [PMC free article] [PubMed] [CrossRef] [Google Scholar]

16. Gan L, Chen Y, Hu P, et al. Willingness to receive SARS-CoV-2 vaccination and associated factors among Chinese adults: a cross sectional survey. Int J Environ Res Public Health. 2021;18(4):1993. doi:10.3390/ijerph18041993 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

17. STAT. Early data suggest side effects after Covid booster similar to second dose; 2021. Available from: https://www.statnews.com/2021/09/28/side-effect-rates-from-a-third-covid-19-vaccine-dose-similar-to-those-after-second-shot-early-data-indicate/.

Severe aplastic anemia after COVID-19 mRNA vaccination: Causality or coincidence?

Authors: Shotaro Tabata 1Hiroki Hosoi 2Shogo Murata 1Satomi Takeda 1Toshiki Mushino 1Takashi Sonoki 1PMID: 34920343

PMCID: PMC8668346I: 10.1016/j.jaut.2021.102782 J Autoimmun. 2022 Jan; 126: 102782.Published online 2021 Dec 14. doi: 10.1016/j.jaut.2021.102782

Abstract

The development of various autoimmune diseases has been reported after COVID-19 infections or vaccinations. However, no method for assessing the relationships between vaccines and the development of autoimmune diseases has been established. Aplastic anemia (AA) is an immune-mediated bone marrow failure syndrome. We report a case of severe AA that arose after the administration of a COVID-19 vaccine (the Pfizer-BioNTech mRNA vaccine), which was treated with allogeneic hematopoietic stem cell transplantation (HSCT). In this patient, antibodies against the SARS-CoV-2 spike protein were detected both before and after the HSCT. After the patient’s hematopoietic stem cells were replaced through HSCT, his AA improved despite the presence of anti-SARS-CoV-2 antibodies. In this case, antibodies derived from the COVID-19 vaccine may not have been directly involved in the development of AA. This case suggests that the measurement of vaccine antibody titers before and after allogeneic HSCT may provide clues to the pathogenesis of vaccine-related autoimmune diseases. Although causality was not proven in this case, further evaluations are warranted to assess the associations between vaccines and AA.

1. Introduction

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global pandemic of coronavirus disease 2019 (COVID-19). The introduction of SARS-CoV-2 vaccines has drastically reduced the transmission rate of the disease. Studies have confirmed the safety and efficacy of the available SARS-CoV-2 vaccines. However, rare cases of adverse immunological reactions to SARS-CoV-2 vaccines have been reported, including cases involving immune-mediated disease [[1][2][3]]. Although evaluating the associations between SARS-CoV-2 vaccines and the development of autoimmune diseases is important, no method for assessing such relationships has been established. Aplastic anemia (AA), a bone marrow failure syndrome, appears to be immune-mediated [4,5]. In addition to T lymphocytes and cytokines, autoantibodies are involved in the development of AA as immunological factors [4]. Here, we report a case of AA that developed after the administration of a SARS-CoV-2 vaccine and discuss the association between AA and vaccination.

2. Case description

A previously healthy 56-year-old male, who was not taking any medication, was referred to a clinic because of bleeding in the oral cavity after dental therapy. Laboratory tests showed that his white blood cell count (1.6 × 109/l) and platelet count (11 × 109/l) were decreased. Four days before his visit to the clinic, he had received a second dose of the Pfizer-BioNTech mRNA vaccine (three weeks after his first dose). He was admitted to our hospital due to progressive pancytopenia (Supplementary Table 1). He had no history of COVID-19 infection. The Elecsys® anti-SARS-CoV-2 immunoassay (Roche, Basel, Switzerland), which is used to detect anti-SARS-CoV-2 nucleocapsid protein antibodies, produced a negative result. Tests for immunoglobulin G against cytomegalovirus and Epstein-Barr virus produced positive results, but were not indicative of virus reactivation. Serological tests for hepatitis B, hepatitis C, and human immunodeficiency virus produced negative results. A bone marrow biopsy revealed a hypocellular marrow (Fig. 1 ). The patient was diagnosed with very severe AA [6]. Human leukocyte antigen (HLA) testing showed DRB1 04:05 04:05, which is not associated with a high frequency of AA. The administration of granulocyte-colony stimulating factor had no effect on his neutropenia. In spite of the administration of cyclosporine and eltrombopag, his pancytopenia progressed.

Fig. 1

Fig. 1

Histological findings of the bone marrow biopsy specimen at diagnosis. Panel A: Hematoxylin and eosin (H.E.) staining (x40) of the bone marrow after the administration of a SARS-CoV-2 vaccine showed a markedly hypocellular marrow. Panel B: H.E. staining (x400) showed the replacement of hematopoietic cells by fat and a few nucleated cells.

He underwent an allogeneic hematopoietic stem cell transplantation (HSCT) from an HLA haploidentical related donor (Fig. 2 ). The donor had no history of COVID-19 infection and had not received a SARS-CoV-2 vaccine. The conditioning regimen consisted of 120 mg/m2 fludarabine, 100 mg/kg cyclophosphamide, 2.5 mg/kg anti-thymocyte globulin, and 2 Gy of total body irradiation. Tacrolimus and short-term methotrexate were used as a prophylaxis against graft-versus-host disease (GVHD). Post-transplant cyclophosphamide was not administered because the patient’s HLA-A, C, and DR were homologous, which would not increase the risk of GVHD. The transplanted cells collected from the donor’s bone marrow were transfused into the patient after the removal of red blood cells and plasma. Twenty-one days after the HSCT, neutrophil engraftment was achieved. Chimerism analysis performed on day 29 after the HSCT revealed complete chimerism in the peripheral blood. The patient developed acute GVHD (skin grade 1), which was ameliorated with a topical corticosteroid alone.

Fig. 2

Fig. 2

Evaluation of neutrophil count, The X-axis indicates the number of days after the 2nd dose of the COVID-19 vaccine was administered. The allogeneic BMT was conducted at 34 days after the 2nd dose of the COVID-19 vaccine was administered. The gray boxes indicate the titers of antibodies against the SARS-CoV-2 spike protein (log scale). BMT, bone marrow transplantation; COVID-19, coronavirus disease 2019; CyA, cyclosporine A; TAC, tacrolimus.

The titers of antibodies against SARS-CoV-2 were measured before and after the HSCT to examine the association between the SARS-CoV-2 vaccine the patient received and the development of AA. The measurement of anti-SARS-CoV-2 spike protein antibody titers was performed by SRL, Inc. (Tokyo, Japan) using the Elecsys® anti-SARS-CoV-2 S immunoassay (Roche, Basel, Switzerland). The titers of antibodies against SARS-CoV-2 before the conditioning regimen and 63 days after the HSCT were 540 and 34.9 U/mL (reference range, <0.8 U/mL), respectively (Fig. 2). These results suggest that the AA was ameliorated by the allogeneic HSCT even though anti-SARS-CoV-2 spike protein antibodies continued to be detected after the HSCT.Go to:

3. Discussion

We report a case in which AA developed after the administration of a SARS-CoV-2 vaccine. No association between new-onset AA and SARS-CoV-2 vaccines has been reported. The patient in this case underwent allogeneic HSCT. In this patient, antibodies against the SARS-CoV-2 spike protein were detected both before and after the HSCT. After the allogeneic HSCT, the patient’s AA was ameliorated despite the presence of antibodies against SARS-CoV-2. Our results did not reveal a direct association between antibodies derived from the SARS-CoV-2 vaccine and the development of AA. Further studies are needed to investigate the impairment of hematopoiesis induced by immune reactions after SARS-CoV-2 vaccine administration.

One of the most feared adverse reactions to vaccines is the development of autoimmune disease. To the best of our knowledge, only six cases of newly diagnosed acquired AA have been reported after vaccination [[7][8][9][10][11]] (Table 1 ). However, in general, AA is not recognized as a vaccine-related adverse event [12]. The mRNA vaccines against SARS-CoV-2 have a novel mechanism of action. Therefore, it is important to collect information about their adverse events. Various cases of autoimmune disease have been reported after SARS-CoV-2 vaccine administration, including autoimmune hepatitis, type 1 diabetes mellitus, immune thrombocytopenia, and acquired hemophilia [3,[13][14][15]]. Patients with AA after COVID-19 infection were also reported [16,17]. Further epidemiological evaluations of the incidence of AA after COVID-19 infection and SARS-CoV-2 vaccination are warranted.

Table 1

Reported cases of newly diagnosed aplastic anemia after vaccinations.

Age (years)SexVaccineTime to symptom onsetTreatmentOutcomeReference
16FRecombinant hepatitis B3 weeks after 3rd doseCorticosteroidImprovedViallard et al. [7]
19FRecombinant hepatitis B10 days after 3rd doseCorticosteroidImprovedAshok Shenoy et al. [8]
25MHepatitis B7 days after 2nd doseAllogeneic HSCTN.A.Shah et al. [9]
19MAnthrax1 monthAllogeneic HSCTN.A.Shah et al. [9]
1.5FVaricella zoster3 weeksNoneImprovedAngelini et al. [10]
25MH1N1 influenza2 weeksAllogeneic HSCTImprovedDonnini et al. [11]
56MSARS-Cov-24 days after 2nd doseAllogeneic HSCTImprovedThis case

Open in a separate window

HSCT, hematopoietic stem cell transplantation; N.A., not applicable; SARS-Cov-2, severe acute respiratory syndrome coronavirus 2.

Various cases of vaccine-related autoimmune disease have been reported. Most of these reports have linked vaccination to the development of autoimmune disease based on clinical observations of temporal associations. There is no established method for examining the relationships between vaccines and the development of autoimmune diseases. The pathogenetic mechanisms by which vaccines cause the development of autoimmune disease are still unclear. The major hypotheses relating to such immunological reactions involve epitope mimicry [18,19]. For example, it has been reported that vaccine-derived antibodies may exhibit structural similarities with autoantibodies [18,19]. There is significant evidence that AA is an immune-mediated condition, mainly based on the effectiveness of immunosuppressive therapy against AA. In addition to T cells and cytokines, autoantibodies are one of the factors that contribute to the pathogenesis of AA [4]. However, autoantibodies specific to AA and the role of autoantibodies for the pathogenesis of AA are unclear. The allogeneic HSCT replaces the recipient’s hematopoietic and associated immune systems with those of the donor. The measurement of vaccine antibody titers before and after allogeneic HSCT may provide a clue to the pathogenesis of vaccine-related autoimmune diseases. The clonal expansion of effector T cells was also reported to occur following vaccination [20]. To understand the link between COVID-19 vaccination and the development of AA, the following needs to be examined: the exploration of autoantibodies against stem cells, the role for molecular mimicry between mRNA vaccine encoded antigens and stem cells, and T-cell subset dynamics after vaccination.

In conclusion, the administered SARS-CoV-2 mRNA vaccine may have contributed to the pathogenesis of AA in this case. However, it is not clear whether antibodies derived from the SARS-CoV-2 vaccine directly contributed to the development of AA because the anti-SARS-CoV-2 antibodies remained after the patient’s pancytopenia had been ameliorated by the allogeneic HSCT. Further evaluations in large cohorts are warranted to elucidate the associations between AA and SARS-CoV-2 vaccines.Go to:

Authors’ contributions

Shotaro Tabata: Data curation, Investigation, Writing – original draft; Hiroki Hosoi: Conceptualization, Data curation, Investigation, Writing – original draft and Review & Editing; Shogo Murata: Investigation, Writing – review & editing; Satomi Takeda: Data curation, Writing – review & editing; Toshiki Mushino: Writing – review & editing; Takashi Sonoki: Writing – review & editing, Supervision.Go to:

Declaration of competing interest

There are no funding sources associated with the writing of this manuscript. Written consent for publication was obtained from the patient.Go to:

Acknowledgements

We thank the patients and clinical staff at Wakayama Medical University Hospital for their participation in this study. We also wish to thank Dr. Takashi Ozaki and Mr. Masaya Morimoto from Kinan Hospital for their helpful diagnostic support.Go to:

Footnotes

Appendix ASupplementary data to this article can be found online at https://doi.org/10.1016/j.jaut.2021.102782.Go to:

Appendix A. Supplementary data

The following is the Supplementary data to this article:Multimedia component 1:Click here to view.(12K, xlsx)Multimedia component 1Go to:

References

1. Arepally G.M., Ortel T.L. Vaccine-induced immune thrombotic thrombocytopenia: what we know and do not know. Blood. 2021;138:293–298. https://doi:10.1182/blood.2021012152 [PMC free article] [PubMed] [Google Scholar]

2. Lee E.J., Cines D.B., Gernsheimer T., Kessler C., Michel M., Tarantino M.D., et al. Thrombocytopenia following Pfizer and Moderna SARS-CoV-2 vaccination. Am. J. Hematol. 2021;96:534–537. https://doi:10.1002/ajh.26132 [PMC free article] [PubMed] [Google Scholar]

3. Vuille-Lessard E., Montani M., Bosch J., Semmo N. Autoimmune hepatitis triggered by SARS-CoV-2 vaccination. J. Autoimmun. 2021;123 https://doi:10.1016/j.jaut.2021.102710 [PMC free article] [PubMed] [Google Scholar]

4. Dolberg O.J., Levy Y. Idiopathic aplastic anemia: diagnosis and classification. Autoimmun. Rev. 2014;13:569–573. https://doi:10.1016/j.autrev.2014.01.014 [PubMed] [Google Scholar]

5. Young N.S. Aplastic Anemia. N. Engl. J. Med. 2018;379:1643–1656. https://doi:10.1056/NEJMra1413485 [PMC free article] [PubMed] [Google Scholar]

6. Killick S.B., Bown N., Cavenagh J., Dokal I., Foukaneli T., Hill A., et al. Guidelines for the diagnosis and management of adult aplastic anaemia. Br. J. Haematol. 2016;172:187–207. https://doi:10.1111/bjh.13853 [PubMed] [Google Scholar]

7. Viallard J.F., Boiron J.M., Parrens M., Moreau J.F., Ranchin V., Reiffers J., et al. Severe pancytopenia triggered by recombinant hepatitis B vaccine. Br. J. Haematol. 2000;110:230–233. https://doi:10.1046/j.1365-2141.2000.02171.x [PubMed] [Google Scholar]

8. Ashok Shenoy K., Prabha Adhikari M.R., Chakrapani M., Shenoy D., Pillai A. Pancytopenia after recombinant hepatitis B vaccine–an Indian case report. Br. J. Haematol. 2001;114 https://doi:10.1046/j.1365-2141.2001.03006-2.x [PubMed] [Google Scholar]

9. Shah C., Lemke S., Singh V., Gentile T. Case reports of aplastic anemia after vaccine administration. Am. J. Hematol. 2004;77 https://doi:10.1002/ajh.20153 [PubMed] [Google Scholar]

10. Angelini P., Kavadas F., Sharma N., Richardson S.E., Tipples G., Roifman C., et al. Aplastic anemia following varicella vaccine. Pediatr. Infect. Dis. J. 2009;28:746–748. https://doi:10.1097/INF.0b013e31819b6c1f [PubMed] [Google Scholar]

11. Donnini I., Scappini B., Guidi S., Longo G., Bosi A. Acquired severe aplastic anemia after H1N1 influenza virus vaccination successfully treated with allogeneic bone marrow transplantation. Ann. Hematol. 2012;91:475–476. https://doi:10.1007/s00277-011-1278-0 [PubMed] [Google Scholar]

12. Dudley M.Z., Halsey N.A., Omer S.B., Orenstein W.A., O’Leary S.T., Limaye R.J., et al. The state of vaccine safety science: systematic reviews of the evidence. Lancet Infect. Dis. 2020;20:e80–e89. https://doi:10.1016/S1473-3099(20)30130-4 [PubMed] [Google Scholar]

13. Patrizio A., Ferrari S.M., Antonelli A., Fallahi P. A case of Graves’ disease and type 1 diabetes mellitus following SARS-CoV-2 vaccination. J. Autoimmun. 2021;125 https://doi:10.1016/j.jaut.2021.102738 [PMC free article] [PubMed]  [Google Scholar]

14. Tarawneh O., Tarawneh H. Immune thrombocytopenia in a 22-year-old post Covid-19 vaccine. Am. J. Hematol. 2021;96:E133–E134. https://doi:10.1002/ajh.26106 [PMC free article] [PubMed] [Google Scholar]

15. Radwi M., Farsi S. A case report of acquired hemophilia following COVID-19 vaccine. J. Thromb. Haemostasis. 2021;19:1515–1518. https://doi:10.1111/jth.15291 [PMC free article] [PubMed] [Google Scholar]

16. Avenoso D., Marsh J.C.W., Potter V., Pagliuca A., Slade S., Dignan F., et al. SARS-CoV-2 Infection in Aplastic Anaemia. Haematologica. 2021  https://doi:10.3324/haematol.2021.279928 [PMC free article] [PubMed] [Google Scholar]

17. Chakravarthy R., Murphy M.L., Ann Thompson M., McDaniel H.L., Zarnegar-Lumley S., Borinstein S.C. SARS-CoV-2 infection coincident with newly diagnosed severe aplastic anemia: a report of two cases. Pediatr. Blood Cancer. 2021 https://doi:10.1002/pbc.29433 [PMC free article] [PubMed] [Google Scholar]

18. Wraith D.C., Goldman M., Lambert P.H. Vaccination and autoimmune disease: what is the evidence? Lancet. 2003;362:1659–1666. https://doi:10.1016/S0140-6736(03)14802-7 [PubMed] [Google Scholar]

19. Vadala M., Poddighe D., Laurino C., Palmieri B. Vaccination and autoimmune diseases: is prevention of adverse health effects on the horizon? EPMA J. 2017;8:295–311. https://doi:10.1007/s13167-017-0101-y [PMC free article] [PubMed] [Google Scholar]

20. Ritz C., Meng W., Stanley N.L., Baroja M.L., Xu C., Yan P., et al. Postvaccination graft dysfunction/aplastic anemia relapse with massive clonal expansion of autologous CD8+ lymphocytes. Blood Adv. 2020;4:1378–1382. https://doi:10.1182/bloodadvances. 2019000853 [PMC free article] [PubMed] [Google Scholar]

Bone Marrow Suppression Secondary to the COVID-19 Booster Vaccine: A Case Report

Authors: Toral Shastri 1Navkiran Randhawa 2Ragia Aly 3Masood Ghouse 3

PMID: 35210894 PMCID: PMC8863340DOI: 10.2147/JBM.S350290

Abstract

As of September 2021, SARS-CoV-2 booster shots became widely available in the US to ensure continued protection against the virus. A temporal relationship has been previously reported between the first or second dose of the COVID-19 vaccine and the development of thrombocytopenia. However, adverse events related to the third COVID-19 vaccine are still being reported and studied. We report a 74-year-old male who developed bone marrow suppression and pancytopenia recorded seven days after receiving the Pfizer SARS-CoV-2 vaccine. During his hospital stay, the patient’s hemoglobin, white blood cell, and platelet levels continued to trend downwards. However, all three levels showed improvement one week after discharge without robust intervention. Global vaccination is of utmost importance, as is understanding and documenting post-vaccination reactions including bone marrow suppression. Prompt evaluation and patient education are imperative to improve patient outcomes and combat hesitancy against vaccine administration.

Introduction

Since its emergence in December of 2019, the rapid spread of severe acute respiratory syndrome coronavirus (SARS-CoV-2) has quickly affected millions of lives across every continent.1 This highly transmittable and pathogenic viral infection has led to massive mitigation efforts and allocation of resources to prevent the spread of transmission and high mortality related to complications.2 The establishment of higher levels of community (herd) immunity and protection against SARS-CoV-2 via the widespread deployment of effective vaccines has become a global effort.3 In December of 2020, the FDA issued an Emergency use Authorization for the Pfizer-BioNTech and Moderna COVID-19 Vaccine as a two-dose series.4 In September 2021, booster vaccines became widely administered in the US due to waning immunity of the COVID-19 vaccines against variants of SARS-CoV-2 along with ensuring continued protection against the virus.5

Serious adverse events such as anaphylaxis, Guillain-Barre Syndrome, myocarditis, pericarditis, thrombocytopenia, and death have been previously reported following the first and/or second dose of vaccine.6 To our knowledge, no cases have been reported regarding bone marrow suppression related to the third COVID-19 vaccine. Adverse events reported between August 12-September 19, 2021 from the COVID-19 booster vaccine supported similar reactions to those after dose two.7 According to the Centers for Disease Control and Prevention (CDC), these initial findings indicate no unexpected patterns of adverse reactions after an additional dose of COVID-19 vaccination.7 However, adverse events related to the COVID-19 booster are still being reported and studied.6 This report presents a case of bone marrow suppression occurring after the third COVID-19 vaccine without a similar reaction after the first or second dose.Go to:

Case Report

A 74-year-old male with a history of polychondritis and hypothyroidism presented to the hospital after falling out of his chair and inability to ambulate. The patient was found to be mildly confused upon arrival to the emergency room, limiting our ability to obtain a full verbal history. Chart review revealed the patient had received his third Pfizer Covid vaccine shot seven days before admission followed by fatigue, decreased appetite, fever, and chills. The patient had received the second Pfizer Covid-19 shot nine months before the booster. No reactions to the previous two shots were noted.

Upon initial evaluation, vital signs were within normal limits and a physical exam revealed significant tenderness in the upper arm and no gross bleeding (Figure 1). Computed tomography (CT) imaging (Figure 2) was significant for enhancement of the left axillary lymph node. The patient’s initial complete blood count (CBC) was remarkable for a hemoglobin count of 9.9 g/dl and platelet count of 84 x 109/L; both values lower than his prior hemoglobin count of 13.7 g/dl and platelet count of 180 x 109/L from December of 2020. His mean corpuscular volume (MCV) was elevated at 101.3 femtolitres from his prior MCV value of 95.8 femtolitres in December of 2020. His white blood cell (WBC) count was recorded at 7.6 x 109/L.

An external file that holds a picture, illustration, etc.
Object name is JBM-13-69-g0001.jpg

Figure 1

The patient’s upper arm showed erythema with no gross bleeding near the injection site

An external file that holds a picture, illustration, etc.
Object name is JBM-13-69-g0002.jpg

Figure 2

The patient’s CT imaging of the thoracic region showed enhancement of the left axillary lymph node.

The hemoglobin, WBC, and platelet count further down trended from his baseline (Figures 3​5).5). Anemia labs including ferritin levels (554 ng/mL), vitamin B12 (253 pg/mL), total bilirubin (0.5 mg/dL), and reticulocyte count (0.8%) were nonsignificant during the patient’s hospital stay. The patient’s left shoulder presented with extensive bruising, erythema, papular rash, warmth, and tenderness on palpation during the hospitalization. An improvement in WBC and platelet levels was noted on day 4 of hospitalization.

An external file that holds a picture, illustration, etc.
Object name is JBM-13-69-g0003.jpg

Figure 3

The patient’s hemoglobin count throughout his hospital course and 6 days after discharge.

An external file that holds a picture, illustration, etc.
Object name is JBM-13-69-g0004.jpg

Figure 4

The patient’s WBC count throughout his hospital course and 6 days after discharge.

An external file that holds a picture, illustration, etc.
Object name is JBM-13-69-g0005.jpg

Figure 5

The patient’s platelet count throughout his hospital course and 6 days after discharge.

Before discharge, the patient was fully alert and oriented and reported improvement in his symptoms. Examination of his lateral left arm showed decreased erythema and bruising with slight petechiae. The patient was discharged due to stabilization of labs and encouraged to take oral vitamin B12 supplements. During his outpatient follow-up six days after hospitalization, his hemoglobin increased to 10.5 g/dl, WBC count increased to 4.9 x 109/L, and platelets increased to 101 x 109/L.

Discussion

This paper presents a patient with pancytopenia recorded seven days after receiving the Pfizer booster vaccine. Interestingly, this patient did not report any reactions after the first or second dose of the Pfizer vaccine against SARS-CoV-2. Pancytopenia refers to a decrease in all peripheral bloodlines and is present when all three cell lines are below the normal reference range.8 The patient’s physical exam showed no signs of active bleeding along with his labs indicating no evidence of hemolysis. The patient’s hemoglobin, platelet, and white blood cell count presented below baseline followed by a decrease and slight improvement during his hospital stay. Six days after hospitalization, all three cell lines showed improvement. The temporal association with the booster vaccine and negative infectious disease workup raised suspicion for vaccine-induced bone marrow suppression. In addition, the patient’s reticulocyte count and lactate dehydrogenase value were consistent with hypoproliferation within the bone marrow.

Currently, there is a gap in knowledge of adverse events specific to the third vaccine against SARS-CoV-2 due to the recent initiation of administration and ongoing reporting of events.6 To our knowledge, bone marrow suppression after any dose of vaccine against SARS-CoV-2 has not been previously reported. However, a prior case of pancytopenia after the third vaccination with a recombinant hepatitis B vaccine has previously been reported.9 The patient’s bone marrow biopsy within this case displayed a paucity of late myeloid elements and CD8+ T cells.9 It was believed the patient’s CD8+T cells were causing excessive production of IFN-γ; a stimulant of negative regulators of hematopoiesis such as tumor necrosis factor and lymphotoxin.10 IFN-γ has also previously been reported to create immunological effects comprising an upregulation of histocompatibility gene transcription and alteration in class I and II antigen expression at the cell surface.11 It was predicted these changes resulted in an autoimmune reaction causing suppression of maturation of hematopoietic progenitor cells and pancytopenia.9 Via a similar mechanism, we believe that our patient’s pancytopenia was immune-mediated, potentially triggered by the vaccination.

Vaccines against SARS-CoV-2 (first or second dose) and the induction of Idiopathic Thrombocytopenic Purpura (ITP) have also been recently acknowledged in multiple cases.12 Our patient presented with low platelet levels and associated petechiae and purpura at the site of the vaccination. However, the patient’s presentation of low hemoglobin and white blood cells along with normal reticulocyte levels was more indicative of pancytopenia secondary to bone marrow suppression. In patients presenting with pancytopenia, the history and the physical exam should help assess the severity of the pancytopenia and comorbid illnesses that may complicate the disorder.13 In addition, suspicious medications and exposure to toxic agents should be ruled out.13 Initial screening laboratory evaluation should include the patient’s complete blood count, peripheral blood smear examination, reticulocyte count, complete metabolic panel, prothrombin time/partial thromboplastin time, and blood type and screen. Common interventions to alleviate bone marrow suppression and pancytopenia include treating the underlying cause and utilizing supplements to boost red blood cell production if indicated.

Vaccines against SARS-CoV-2 undergo continuous safety monitoring; adverse events are very rare.14 However, vaccine hesitancy remains a barrier towards full population inoculation against SARS-CoV-2 and is influenced by misinformation regarding vaccine safety.15 One study using an anonymous online questionnaire found a person’s trust in the effectiveness of the vaccine was a major facilitative factor affecting willingness to vaccinate.16 The same study also found that 66.7% of unvaccinated participants thought the vaccine’s safety was not enough, making it the main reason for reluctance or hesitance to be vaccinated.16 Therefore, education of adverse events and available interventions post-vaccination is imperative to prevent the spread of misinformation and combat hesitancy towards vaccination.15

As of September 19, 2021, about 2.2 million people in the United States received a third vaccine against SARS-CoV-2.17 Among those who received the vaccine, 22,000 people reported the effects of the vaccine with no unexpected patterns of adverse reactions.17 Our patient demonstrates abnormal pancytopenia first recorded seven days after receiving the booster vaccine, possibly indicating a rare adverse event from the vaccination given the temporal relationship. While additional studies and observations are indicated to verify bone marrow suppression as an adverse reaction, this case report provides an opportunity for patient education and treatment planning before symptoms arise.

Conclusion

Our case report highlights pancytopenia secondary to bone marrow suppression following Pfizer vaccination against SARS-CoV-2. It is important to consider the possibility of bone marrow suppression following the third vaccine against SARS-CoV-2. Although additional studies are indicated to determine the risk factors and pathogenesis of vaccine-induced bone marrow suppression, prompt evaluation and initiation of interventions can improve patient outcomes

Consent for Publication

Institutional approval was not required to publish the case details. The publication of this study has been consented to by the patient.

Disclosure

The authors report no conflicts of interest in this work.

1. Fernandes A, Chaudhari S, Jamil N, Gopalakrishnan G. COVID-19 vaccine. Endocr Pract. 2021;27(2):170–172. doi:10.1016/j.eprac.2021.01.013 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

2. Johansson MA, Quandelacy TM, Kada S, et al. SARS-CoV-2 transmission from people without COVID-19 symptoms. JAMA Network Open. 2021;4(1):e2035057–e2035057. doi:10.1001/jamanetworkopen.2020.35057 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

3. Graham BS. Rapid COVID-19 vaccine development. Science. 2020;368(6494):945–946. doi:10.1126/science.abb8923 [PubMed] [CrossRef] [Google Scholar]

4. Gee J, Marquez P, Su J, et al. First month of COVID-19 vaccine safety monitoring—United States, December 14, 2020–January 13. Morb Mortal Wkly Rep. 2021;70(8):283. doi:10.15585/mmwr.mm7008e3 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

5. Mahase E. Covid-19 booster vaccines: what we know and who’s doing what. BMJ. 2021. doi: 10.1136/bmj.n2082 [PubMed] [CrossRef] [Google Scholar]

6. Centers for Disease Control and Prevention. Selected adverse events reported after COVID-19 vaccination. Centers for Disease Control and Prevention. Available from: https://www.cdc.gov/coronavirus/2019-ncov/vaccines/safety/adverse-events.html. Accessed November 8, 2021. [Google Scholar]

7. Hause AM. Safety monitoring of an additional dose. Centers for Disease Control and Prevention; 2021. Available from: https://www.cdc.gov/mmwr/volumes/70/wr/mm7039e4.htm. Accessed February 11, 2022. [Google Scholar]

8. Valent P. Low blood counts: immune mediated, idiopathic, or myelodysplasia. Hematology. 2012;2012(1):485–491. doi:10.1182/asheducation.V2012.1.485.3798522 [PubMed] [CrossRef] [Google Scholar]

9. Viallard JF, Boiron JM, Parrens M, et al. Severe pancytopenia triggered by recombinant hepatitis B vaccine. Br J Haematol. 2000;110(1):230–233. doi:

10.1046/j.1365-2141.2000.02171.x [PubMed] [CrossRef] [Google Scholar]10. Collart MA, Belin D, Vassalli JD, De Kossodo S, Vassalli P. Gamma interferon enhances macrophage transcription of the tumor necrosis factor/cachectin, interleukin 1, and urokinase genes, which are controlled by short-lived repressors. J Exp Med. 1986;164(6):2113–2118. doi:10.1084/jem.164.6.2113 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

11. Wallach D, Fellous M, Revel M. Preferential effect of gamma interferon on the synthesis of HLA antigens and their mRNAs in human cells. Nature. 1982;299(5886):833–836. doi:10.1038/299833a0 [PubMed] [CrossRef] [Google Scholar]

12. Shah SRA, Dolkar S, Mathew J, et al. COVID-19 vaccination associated severe immune thrombocytopenia. Exp Hematol Oncol. 2021;10:42. doi:10.1186/s40164-021-00235-0 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

13. Elizabeth P, Weinzierl MD, Daniel A, Arber MD. The differential diagnosis and bone marrow evaluation of new-onset pancytopenia. Am J Clin Pathol. 2013;139(1):9–29. doi:10.1309/AJCP50AEEYGREWUZ [PubMed] [CrossRef] [Google Scholar]

14. Centers for Disease Control and Prevention. COVID-19 vaccination; 2020. Available from: https://www.cdc.gov/coronavirus/2019-ncov/vaccines/safety/safety-of-vaccines.html. Accessed February 11, 2022.

15. Dror AA, Eisenbach N, Taiber S, et al. Vaccine hesitancy: the next challenge in the fight against COVID-19. Eur J Epidemiol. 2020;35:775–779. doi:10.1007/s10654-020-00671-y [PMC free article] [PubMed] [CrossRef] [Google Scholar]

16. Gan L, Chen Y, Hu P, et al. Willingness to receive SARS-CoV-2 vaccination and associated factors among Chinese adults: a cross sectional survey. Int J Environ Res Public Health. 2021;18(4):1993. doi:10.3390/ijerph18041993 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

17. STAT. Early data suggest side effects after Covid booster similar to second dose; 2021. Available from: https://www.statnews.com/2021/09/28/side-effect-rates-from-a-third-covid-19-vaccine-dose-similar-to-those-after-second-shot-early-data-indicate/.

SARS-CoV-2 infection induces long-lived bone marrow plasma cells in humans

Authors: Jackson S. TurnerWooseob KimElizaveta KalaidinaCharles W. GossAdriana M. RauseoAaron J. SchmitzLena HansenAlem HaileMichael K. KlebertIskra PusicJane A. O’HalloranRachel M. Presti & Ali H. Ellebedy 

Nature volume 595, pages421–425 (2021)

Abstract

Long-lived bone marrow plasma cells (BMPCs) are a persistent and essential source of protective antibodies1,2,3,4,5,6,7. Individuals who have recovered from COVID-19 have a substantially lower risk of reinfection with SARS-CoV-28,9,10. Nonetheless, it has been reported that levels of anti-SARS-CoV-2 serum antibodies decrease rapidly in the first few months after infection, raising concerns that long-lived BMPCs may not be generated and humoral immunity against SARS-CoV-2 may be short-lived11,12,13. Here we show that in convalescent individuals who had experienced mild SARS-CoV-2 infections (n = 77), levels of serum anti-SARS-CoV-2 spike protein (S) antibodies declined rapidly in the first 4 months after infection and then more gradually over the following 7 months, remaining detectable at least 11 months after infection. Anti-S antibody titres correlated with the frequency of S-specific plasma cells in bone marrow aspirates from 18 individuals who had recovered from COVID-19 at 7 to 8 months after infection. S-specific BMPCs were not detected in aspirates from 11 healthy individuals with no history of SARS-CoV-2 infection. We show that S-binding BMPCs are quiescent, which suggests that they are part of a stable compartment. Consistently, circulating resting memory B cells directed against SARS-CoV-2 S were detected in the convalescent individuals. Overall, our results indicate that mild infection with SARS-CoV-2 induces robust antigen-specific, long-lived humoral immune memory in humans.

Main

Reinfections by seasonal coronaviruses occur 6 to 12 months after the previous infection, indicating that protective immunity against these viruses may be short-lived14,15. Early reports documenting rapidly declining antibody titres in the first few months after infection in individuals who had recovered from COVID-19 suggested that protective immunity against SARS-CoV-2 might be similarly transient11,12,13. It was also suggested that infection with SARS-CoV-2 could fail to elicit a functional germinal centre response, which would interfere with the generation of long-lived plasma cells3,4,5,7,16. More recent reports analysing samples that were collected approximately 4 to 6 months after infection indicate that SARS-CoV-2 antibody titres decline more slowly than in the initial months after infection8,17,18,19,20,21. Durable serum antibody titres are maintained by long-lived plasma cells—non-replicating, antigen-specific plasma cells that are detected in the bone marrow long after the clearance of the antigen1,2,3,4,5,6,7. We sought to determine whether they were detectable in convalescent individuals approximately 7 months after SARS-CoV-2 infection.

Biphasic decay of anti-S antibody titres

Blood samples were collected approximately 1 month after the onset of symptoms from 77 individuals who were convalescing from COVID-19 (49% female, 51% male, median age 49 years), the majority of whom had experienced mild illness (7.8% hospitalized, Extended Data Tables 12). Follow-up blood samples were collected three times at approximately three-month intervals. Twelve convalescent participants received either the BNT162b2 (Pfizer) or the mRNA-1273 (Moderna) SARS-CoV-2 vaccine between the last two time points; these post-vaccination samples were not included in our analyses. In addition, bone marrow aspirates were collected from 18 of the convalescent individuals at 7 to 8 months after infection and from 11 healthy volunteers with no history of SARS-CoV-2 infection or vaccination. Follow-up bone marrow aspirates were collected from 5 of the 18 convalescent individuals and from 1 additional convalescent donor approximately 11 months after infection (Fig. 1a, Extended Data Tables 34). We first performed a longitudinal analysis of circulating anti-SARS-CoV-2 serum antibodies. Whereas anti-SARS-CoV-2 spike protein (S) IgG antibodies were undetectable in blood from control individuals, 74 out of the 77 convalescent individuals had detectable serum titres approximately 1 month after the onset of symptoms. Between 1 and 4 months after symptom onset, overall anti-S IgG titres decreased from a mean loge-transformed half-maximal dilution of 6.3 to 5.7 (mean difference 0.59 ± 0.06, P < 0.001). However, in the interval between 4 and 11 months after symptom onset, the rate of decline slowed, and mean titres decreased from 5.7 to 5.3 (mean difference 0.44 ± 0.10, P < 0.001; Fig. 1a). In contrast to the anti-S antibody titres, IgG titres against the 2019–2020 inactivated seasonal influenza virus vaccine were detected in all control individuals and individuals who were convalescing from COVID-19, and declined much more gradually, if at all over the course of the study, with mean titres decreasing from 8.0 to 7.9 (mean difference 0.16 ± 0.06, P = 0.042) and 7.9 to 7.8 (mean difference 0.02 ± 0.08, P = 0.997) across the 1-to-4-month and 4-to-11-month intervals after symptom onset, respectively (Fig. 1b).

figure 1
Fig. 1: SARS-CoV-2 infection elicits durable serum anti-S antibody titres.

Induction of S-binding long-lived BMPCs

The relatively rapid early decline in the levels of anti-S IgG, followed by a slower decrease, is consistent with a transition from serum antibodies being secreted by short-lived plasmablasts to secretion by a smaller but more persistent population of long-lived plasma cells generated later in the immune response. The majority of this latter population resides in the bone marrow1,2,3,4,5,6. To investigate whether individuals who had recovered from COVID-19 developed a virus-specific long-lived BMPC compartment, we examined bone marrow aspirates obtained approximately 7 and 11 months after infection for anti-SARS-CoV-2 S-specific BMPCs. We magnetically enriched BMPCs from the aspirates and then quantified the frequencies of those secreting IgG and IgA directed against the 2019–2020 influenza virus vaccine, the tetanus–diphtheria vaccine and SARS-CoV-2 S by enzyme-linked immunosorbent spot assay (ELISpot) (Fig. 2a). Frequencies of influenza- and tetanus–diphtheria-vaccine-specific BMPCs were comparable between control individuals and convalescent individuals. IgG- and IgA-secreting S-specific BMPCs were detected in 15 and 9 of the 19 convalescent individuals, respectively, but not in any of the 11 control individuals (Fig. 2b). Notably, none of the control individuals or convalescent individuals had detectable S-specific antibody-secreting cells in the blood at the time of bone marrow sampling, indicating that the detected BMPCs represent bone-marrow-resident cells and not contamination from circulating plasmablasts. Frequencies of anti-S IgG BMPCs were stable among the 5 convalescent individuals who were sampled a second time approximately 4 months later, and frequencies of anti-S IgA BMPCs were stable in 4 of these 5 individuals but had decreased to below the limit of detection in one individual (Fig. 2c). Consistent with their stable BMPC frequencies, anti-S IgG titres in the 5 convalescent individuals remained consistent between 7 and 11 months after symptom onset. IgG titres measured against the receptor-binding domain (RBD) of the S protein—a primary target of neutralizing antibodies—were detected in 4 of the 5 convalescent individuals and were also stable between 7 and 11 months after symptom onset (Fig. 2d). Frequencies of anti-S IgG BMPCs showed a modest but significant correlation with circulating anti-S IgG titres at 7–8 months after the onset of symptoms in convalescent individuals, consistent with the long-term maintenance of antibody levels by these cells (r = 0.48, P = 0.046). In accordance with previous reports22,23,24, frequencies of influenza-vaccine-specific IgG BMPCs and antibody titres exhibited a strong and significant correlation (r = 0.67, P < 0.001; Fig. 2e). Nine of the aspirates from control individuals and 12 of the 18 aspirates that were collected 7 months after symptom onset from convalescent individuals yielded a sufficient number of BMPCs for additional analysis by flow cytometry. We stained these samples intracellularly with fluorescently labelled S and influenza virus haemagglutinin (HA) probes to identify and characterize antigen-specific BMPCs. As controls, we also intracellularly stained peripheral blood mononuclear cells (PBMCs) from healthy volunteers one week after vaccination against SARS-CoV-2 or seasonal influenza virus (Fig. 3a, Extended Data Fig. 1a–c). Consistent with the ELISpot data, low frequencies of S-binding BMPCs were detected in 10 of the 12 samples from convalescent individuals, but not in any of the 9 control samples (Fig. 3b). Although both recently generated circulating plasmablasts and S- and HA-binding BMPCs expressed BLIMP-1, the BMPCs were differentiated by their lack of expression of Ki-67—indicating a quiescent state—as well as by higher levels of CD38 (Fig. 3c).

figure 2
Fig. 2: SARS-CoV-2 infection elicits S-binding long-lived BMPCs.
figure 3
Fig. 3: SARS-CoV-2 S-binding BMPCs are quiescent and distinct from circulating plasmablasts.

Robust S-binding memory B cell response

Memory B cells form the second arm of humoral immune memory. After re-exposure to an antigen, memory B cells rapidly expand and differentiate into antibody-secreting plasmablasts. We examined the frequency of SARS-CoV-2-specific circulating memory B cells in individuals who were convalescing from COVID-19 and in healthy control individuals. We stained PBMCs with fluorescently labelled S probes and determined the frequency of S-binding memory B cells among isotype-switched IgDloCD20+ memory B cells by flow cytometry. For comparison, we co-stained the cells with fluorescently labelled influenza virus HA probes (Fig. 4a, Extended Data Fig. 1d). S-binding memory B cells were identified in convalescent individuals in the first sample that was collected approximately one month after the onset of symptoms, with comparable frequencies to influenza HA-binding memory B cells (Fig. 4b). S-binding memory B cells were maintained for at least 7 months after symptom onset and were present at significantly higher frequencies relative to healthy controls—comparable to the frequencies of influenza HA-binding memory B cells that were identified in both groups (Fig. 4c).

figure 4
Fig. 4: SARS-CoV-2 infection elicits a robust memory B cell response.

Discussion

This study sought to determine whether infection with SARS-CoV-2 induces antigen-specific long-lived BMPCs in humans. We detected SARS-CoV-2 S-specific BMPCs in bone marrow aspirates from 15 out of 19 convalescent individuals, and in none from the 11 control participants. The frequencies of anti-S IgG BMPCs modestly correlated with serum IgG titres at 7–8 months after infection. Phenotypic analysis by flow cytometry showed that S-binding BMPCs were quiescent, and their frequencies were largely consistent in 5 paired aspirates collected at 7 and 11 months after symptom onset. Notably, we detected no S-binding cells among plasmablasts in blood samples collected at the same time as the bone marrow aspirates by ELISpot or flow cytometry in any of the convalescent or control samples. Together, these data indicate that mild SARS-CoV-2 infection induces a long-lived BMPC response. In addition, we showed that S-binding memory B cells in the blood of individuals who had recovered from COVID-19 were present at similar frequencies to those directed against influenza virus HA. Overall, our results are consistent with SARS-CoV-2 infection eliciting a canonical T-cell-dependent B cell response, in which an early transient burst of extrafollicular plasmablasts generates a wave of serum antibodies that decline relatively quickly. This is followed by more stably maintained levels of serum antibodies that are supported by long-lived BMPCs.

Although this overall trend captures the serum antibody dynamics of the majority of participants, we observed that in three participants, anti-S serum antibody titres increased between 4 and 7 months after the onset of symptoms, after having initially declined between 1 and 4 months. This could be stochastic noise, could represent increased net binding affinity as early plasmablast-derived antibodies are replaced by those from affinity-matured BMPCs, or could represent increases in antibody concentration from re-encounter with the virus (although none of the participants in our cohort tested positive a second time). Although anti-S IgG titres in the convalescent cohort were relatively stable in the interval between 4 and 11 months after symptom onset, they did measurably decrease, in contrast to anti-influenza virus vaccine titres. It is possible that this decline reflects a final waning of early plasmablast-derived antibodies. It is also possible that the lack of decline in influenza titres was due to boosting through exposure to influenza antigens. Our data suggest that SARS-CoV-2 infection induces a germinal centre response in humans because long-lived BMPCs are thought to be predominantly germinal-centre-derived7. This is consistent with a recent study that reported increased levels of somatic hypermutation in memory B cells that target the RBD of SARS-CoV-2 S in convalescent individuals at 6 months compared to 1 month after infection20.

To our knowledge, the current study provides the first direct evidence for the induction of antigen-specific BMPCs after a viral infection in humans. However, we do acknowledge several limitations. Although we detected anti-S IgG antibodies in serum at least 7 months after infection in all 19 of the convalescent donors from whom we obtained bone marrow aspirates, we failed to detect S-specific BMPCs in 4 donors. Serum anti-S antibody titres in those four donors were low, suggesting that S-specific BMPCs may potentially be present at very low frequencies that are below the limit of detection of the assay. Another limitation is that we do not know the fraction of the S-binding BMPCs detected in our study that encodes neutralizing antibodies. SARS-CoV-2 S protein is the main target of neutralizing antibodies17,25,26,27,28,29,30 and a correlation between serum anti-S IgG binding and neutralization titres has been documented17,31. Further studies will be required to determine the epitopes that are targeted by BMPCs and memory B cells, as well as their clonal relatedness. Finally, although our data document a robust induction of long-lived BMPCs after infection with SARS-CoV-2, it is critical to note that our convalescent individuals mostly experienced mild infections. Our data are consistent with a report showing that individuals who recovered rapidly from symptomatic SARS-CoV-2 infection generated a robust humoral immune response32. It is possible that more-severe SARS-CoV-2 infections could lead to a different outcome with respect to long-lived BMPC frequencies, owing to dysregulated humoral immune responses. This, however, has not been the case in survivors of the 2014 Ebola virus outbreak in West Africa, in whom severe viral infection induced long-lasting antigen-specific serum IgG antibodies33.

Long-lived BMPCs provide the host with a persistent source of preformed protective antibodies and are therefore needed to maintain durable immune protection. However, the longevity of serum anti-S IgG antibodies is not the only determinant of how durable immune-mediated protection will be. Isotype-switched memory B cells can rapidly differentiate into antibody-secreting cells after re-exposure to a pathogen, offering a second line of defence34. Encouragingly, the frequency of S-binding circulating memory B cells at 7 months after infection was similar to that of B cells directed against contemporary influenza HA antigens. Overall, our data provide strong evidence that SARS-CoV-2 infection in humans robustly establishes the two arms of humoral immune memory: long-lived BMPCs and memory B cells. These findings provide an immunogenicity benchmark for SARS-CoV-2 vaccines and a foundation for assessing the durability of primary humoral immune responses that are induced in humans after viral infections.

Methods

Data reporting

No statistical methods were used to predetermine sample size. The experiments were not randomized and the investigators were not blinded during outcome assessment.

Sample collection, preparation and storage

All studies were approved by the Institutional Review Board of Washington University in St Louis. Written consent was obtained from all participants. Seventy-seven participants who had recovered from SARS-CoV-2 infection and eleven control individuals without a history of SARS-CoV-2 infection were enrolled (Extended Data Tables 14). Blood samples were collected in EDTA tubes and PBMCs were enriched by density gradient centrifugation over Ficoll 1077 (GE) or Lymphopure (BioLegend). The remaining red blood cells were lysed with ammonium chloride lysis buffer, and cells were immediately used or cryopreserved in 10% dimethyl sulfoxide in fetal bovine serum (FBS). Bone marrow aspirates of approximately 30 ml were collected in EDTA tubes from the iliac crest of 18 individuals who had recovered from COVID-19 and the control individuals. Bone marrow mononuclear cells were enriched by density gradient centrifugation over Ficoll 1077, and the remaining red blood cells were lysed with ammonium chloride buffer (Lonza) and washed with phosphate-buffered saline (PBS) supplemented with 2% FBS and 2 mM EDTA. Bone marrow plasma cells were enriched from bone marrow mononuclear cells using the CD138 Positive Selection Kit II (Stemcell) and immediately used for ELISpot or cryopreserved in 10% dimethyl sulfoxide in FBS.

Antigens

Recombinant soluble spike protein (S) and its receptor-binding domain (RBD) derived from SARS-CoV-2 were expressed as previously described35. In brief, mammalian cell codon-optimized nucleotide sequences coding for the soluble version of S (GenBank: MN908947.3, amino acids (aa) 1–1,213) including a C-terminal thrombin cleavage site, T4 foldon trimerization domain and hexahistidine tag cloned into the mammalian expression vector pCAGGS. The S protein sequence was modified to remove the polybasic cleavage site (RRAR to A) and two stabilizing mutations were introduced (K986P and V987P, wild-type numbering). The RBD, along with the signal peptide (aa 1–14) plus a hexahistidine tag were cloned into the mammalian expression vector pCAGGS. Recombinant proteins were produced in Expi293F cells (Thermo Fisher Scientific) by transfection with purified DNA using the ExpiFectamine 293 Transfection Kit (Thermo Fisher Scientific). Supernatants from transfected cells were collected 3 (for S) or 4 (for RBD) days after transfection, and recombinant proteins were purified using Ni-NTA agarose (Thermo Fisher Scientific), then buffer-exchanged into PBS and concentrated using Amicon Ultracel centrifugal filters (EMD Millipore). For flow cytometry staining, recombinant S was labelled with Alexa Fluor 647- or DyLight 488-NHS ester (Thermo Fisher Scientific); excess Alexa Fluor 647 and DyLight 488 were removed using 7-kDa and 40-kDa Zeba desalting columns, respectively (Pierce). Recombinant HA from A/Michigan/45/2015 (aa 18–529, Immune Technology) was labelled with DyLight 405-NHS ester (Thermo Fisher Scientific); excess DyLight 405 was removed using 7-kDa Zeba desalting columns. Recombinant HA from A/Brisbane/02/2018 (aa 18–529) and B/Colorado/06/2017 (aa 18–546) (both Immune Technology) were biotinylated using the EZ-Link Micro NHS-PEG4-Biotinylation Kit (Thermo Fisher Scientific); excess biotin was removed using 7-kDa Zeba desalting columns.

ELISpot

Plates were coated with Flucelvax Quadrivalent 2019/2020 seasonal influenza virus vaccine (Sequiris), tetanus–diphtheria vaccine (Grifols), recombinant S or anti-human Ig. Direct ex vivo ELISpot was performed to determine the number of total, vaccine-binding or recombinant S-binding IgG- and IgA-secreting cells present in BMPC and PBMC samples using IgG/IgA double-colour ELISpot Kits (Cellular Technology) according to the manufacturer’s instructions. ELISpot plates were analysed using an ELISpot counter (Cellular Technology).

ELISA

Assays were performed in 96-well plates (MaxiSorp, Thermo Fisher Scientific) coated with 100 μl of Flucelvax 2019/2020 or recombinant S in PBS, and plates were incubated at 4 °C overnight. Plates were then blocked with 10% FBS and 0.05% Tween-20 in PBS. Serum or plasma were serially diluted in blocking buffer and added to the plates. Plates were incubated for 90 min at room temperature and then washed 3 times with 0.05% Tween-20 in PBS. Goat anti-human IgG–HRP (Jackson ImmunoResearch, 1:2,500) was diluted in blocking buffer before adding to wells and incubating for 60 min at room temperature. Plates were washed 3 times with 0.05% Tween-20 in PBS, and then washed 3 times with PBS before the addition of o-phenylenediamine dihydrochloride peroxidase substrate (Sigma-Aldrich). Reactions were stopped by the addition of 1 M HCl. Optical density measurements were taken at 490 nm. The half-maximal binding dilution for each serum or plasma sample was calculated using nonlinear regression (GraphPad Prism v.8). The limit of detection was defined as 1:30.

Statistics

Spearman’s correlation coefficients were estimated to assess the relationship between 7-month anti-S and anti-influenza virus vaccine IgG titres and the frequencies of BMPCs secreting IgG specific for S and for influenza virus vaccine, respectively. Means and pairwise differences of antibody titres at each time point were estimated using a linear mixed model analysis with a first-order autoregressive covariance structure. Time since symptom onset was treated as a categorical fixed effect for the 4 different sample time points spaced approximately 3 months apart. P values were adjusted for multiple comparisons using Tukey’s method. All analyses were conducted using SAS v.9.4 (SAS Institute) and Prism v.8.4 (GraphPad), and P values of less than 0.05 were considered significant.

Flow cytometry

Staining for flow cytometry analysis was performed using cryo-preserved magnetically enriched BMPCs and cryo-preserved PBMCs. For BMPC staining, cells were stained for 30 min on ice with CD45-A532 (HI30, Thermo Fisher Scientific, 1:50), CD38-BB700 (HIT2, BD Horizon, 1:500), CD19-PE (HIB19, 1:200), CXCR5-PE-Dazzle 594 (J252D4, 1:50), CD71-PE-Cy7 (CY1G4, 1:400), CD20-APC-Fire750 (2H7, 1:400), CD3-APC-Fire810 (SK7, 1:50) and Zombie Aqua (all BioLegend) diluted in Brilliant Stain buffer (BD Horizon). Cells were washed twice with 2% FBS and 2 mM EDTA in PBS (P2), fixed for 1 h using the True Nuclear permeabilization kit (BioLegend), washed twice with perm/wash buffer, stained for 1h with DyLight 405-conjugated recombinant HA from A/Michigan/45/2015, DyLight 488- and Alexa 647-conjugated S, Ki-67-BV711 (Ki-67, 1:200, BioLegend) and BLIMP-1-A700 (646702, 1:50, R&D), washed twice with perm/wash buffer, and resuspended in P2. For memory B cell staining, PBMCs were stained for 30 min on ice with biotinylated recombinant HAs diluted in P2, washed twice, then stained for 30 min on ice with Alexa 647-conjugated S, IgA-FITC (M24A, Millipore, 1:500), IgG-BV480 (goat polyclonal, Jackson ImmunoResearch, 1:100), IgD-SB702 (IA6-2, Thermo Fisher Scientific, 1:50), CD38-BB700 (HIT2, BD Horizon, 1:500), CD20-Pacific Blue (2H7, 1:400), CD4-BV570 (OKT4, 1:50), CD24-BV605 (ML5, 1:100), streptavidin-BV650, CD19-BV750 (HIB19, 1:100), CD71-PE (CY1G4, 1:400), CXCR5-PE-Dazzle 594 (J252D4, 1:50), CD27-PE-Cy7 (O323, 1:200), IgM-APC-Fire750 (MHM-88, 1:100), CD3-APC-Fire810 (SK7, 1:50) and Zombie NIR (all BioLegend) diluted in Brilliant Stain buffer (BD Horizon), and washed twice with P2. Cells were acquired on an Aurora using SpectroFlo v.2.2 (Cytek). Flow cytometry data were analysed using FlowJo v.10 (Treestar). In each experiment, PBMCs were included from convalescent individuals and control individuals.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this paper.

Data availability

Relevant data are available from the corresponding author upon reasonable request.

References

  1. Benner, R., Meima, F., van der Meulen, G. M. & van Muiswinkel, W. B. Antibody formation in mouse bone marrow. I. Evidence for the development of plaque-forming cells in situ. Immunology 26, 247–255 (1974).CAS PubMed PubMed Central Google Scholar 
  2. Manz, R. A., Thiel, A. & Radbruch, A. Lifetime of plasma cells in the bone marrow. Nature 388, 133–134 (1997).ADS CAS Article Google Scholar 
  3. Slifka, M. K., Antia, R., Whitmire, J. K. & Ahmed, R. Humoral immunity due to long-lived plasma cells. Immunity 8, 363–372 (1998).CAS Article Google Scholar 
  4. Hammarlund, E. et al. Duration of antiviral immunity after smallpox vaccination. Nat. Med9, 1131–1137 (2003).CAS Article Google Scholar 
  5. Halliley, J. L. et al. Long-lived plasma cells are contained within the CD19CD38hiCD138+ subset in human bone marrow. Immunity 43, 132–145 (2015).CAS Article Google Scholar 
  6. Mei, H. E. et al. A unique population of IgG-expressing plasma cells lacking CD19 is enriched in human bone marrow. Blood 125, 1739–1748 (2015).CAS Article Google Scholar 
  7. Nutt, S. L., Hodgkin, P. D., Tarlinton, D. M. & Corcoran, L. M. The generation of antibody-secreting plasma cells. Nat. Rev. Immunol15, 160–171 (2015).CAS Article Google Scholar 
  8. Hall, V. J. et al. SARS-CoV-2 infection rates of antibody-positive compared with antibody-negative health-care workers in England: a large, multicentre, prospective cohort study (SIREN). Lancet 397, 1459–1469 (2021).CAS Article Google Scholar 
  9. Houlihan, C. F. et al. Pandemic peak SARS-CoV-2 infection and seroconversion rates in London frontline health-care workers. Lancet 396, e6–e7 (2020).CAS Article Google Scholar 
  10. Lumley, S. F. et al. Antibodies to SARS-CoV-2 are associated with protection against reinfection. Preprint at https://doi.org/10.1101/2020.11.18.20234369 (2020).
  11. Long, Q.-X. et al. Clinical and immunological assessment of asymptomatic SARS-CoV-2 infections. Nat. Med26, 1200–1204 (2020).CAS Article Google Scholar 
  12. Ibarrondo, F. J. et al. Rapid decay of anti-SARS-CoV-2 antibodies in persons with mild Covid-19. N. Engl. J. Med383, 1085–1087 (2020).Article Google Scholar 
  13. Seow, J. et al. Longitudinal observation and decline of neutralizing antibody responses in the three months following SARS-CoV-2 infection in humans. Nat. Microbiol5, 1598–1607 (2020).CAS Article Google Scholar 
  14. Edridge, A. W. D. et al. Seasonal coronavirus protective immunity is short-lasting. Nat. Med26, 1691–1693 (2020).Article Google Scholar 
  15. Callow, K. A., Parry, H. F., Sergeant, M. & Tyrrell, D. A. The time course of the immune response to experimental coronavirus infection of man. Epidemiol. Infect105, 435–446 (1990).CAS Article Google Scholar 
  16. Kaneko, N. et al. Loss of Bcl-6-expressing T follicular helper cells and germinal centers in COVID-19. Cell 183, 143–157 (2020).CAS Article Google Scholar 
  17. Wajnberg, A. et al. Robust neutralizing antibodies to SARS-CoV-2 infection persist for months. Science 370, 1227–1230 (2020).ADS CAS Article Google Scholar 
  18. Isho, B. et al. Persistence of serum and saliva antibody responses to SARS-CoV-2 spike antigens in COVID-19 patients. Sci. Immunol5, eabe5511 (2020).Article Google Scholar 
  19. Dan, J. M. et al. Immunological memory to SARS-CoV-2 assessed for up to 8 months after infection. Science 371, eabf4063 (2021).CAS Article Google Scholar 
  20. Gaebler, C. et al. Evolution of antibody immunity to SARS-CoV-2. Nature 591, 639–644 (2021).ADS CAS Article Google Scholar 
  21. Rodda, L. B. et al. Functional SARS-CoV-2-specific immune memory persists after mild COVID-19. Cell 184, 169–183 (2021).CAS Article Google Scholar 
  22. Davis, C. W. et al. Influenza vaccine-induced human bone marrow plasma cells decline within a year after vaccination. Science 370, 237–241 (2020).ADS CAS Article Google Scholar 
  23. Turesson, I. Distribution of immunoglobulin-containing cells in human bone marrow and lymphoid tissues. Acta Med. Scand199, 293–304 (1976).CAS Article Google Scholar 
  24. Pritz, T. et al. Plasma cell numbers decrease in bone marrow of old patients. Eur. J. Immunol45, 738–746 (2015).CAS Article Google Scholar 
  25. Shi, R. et al. A human neutralizing antibody targets the receptor-binding site of SARS-CoV-2. Nature 584, 120–124 (2020).ADS CAS Article Google Scholar 
  26. Cao, Y. et al. Potent neutralizing antibodies against SARS-CoV-2 identified by high-throughput single-cell sequencing of convalescent patients’ B cells. Cell 182, 73–84 (2020).CAS Article Google Scholar 
  27. Robbiani, D. F. et al. Convergent antibody responses to SARS-CoV-2 in convalescent individuals. Nature 584, 437–442 (2020).ADS CAS Article Google Scholar 
  28. Kreer, C. et al. Longitudinal isolation of potent near-germline SARS-CoV-2-neutralizing antibodies from COVID-19 patients. Cell 182, 843–854 (2020).CAS Article Google Scholar 
  29. Alsoussi, W. B. et al. A potently neutralizing antibody protects mice against SARS-CoV-2 infection. J. Immunol205, 915–922 (2020).CAS Article Google Scholar 
  30. Wang, C. et al. A human monoclonal antibody blocking SARS-CoV-2 infection. Nat. Commun11, 2251 (2020).ADS CAS Article Google Scholar 
  31. Wang, K. et al. Longitudinal dynamics of the neutralizing antibody response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Infection. Clin. Infect. Dis2020, ciaa1143 (2020).Article Google Scholar 
  32. Chen, Y. et al. Quick COVID-19 healers sustain anti-SARS-CoV-2 antibody production. Cell 183, 1496–1507 (2020).CAS Article Google Scholar 
  33. Davis, C. W. et al. Longitudinal analysis of the human B Cell response to ebola virus infection. Cell 177, 1566–1582 (2019).CAS Article Google Scholar 
  34. Ellebedy, A. H. et al. Defining antigen-specific plasmablast and memory B cell subsets in human blood after viral infection or vaccination. Nat. Immunol17, 1226–1234 (2016).CAS Article Google Scholar 
  35. Stadlbauer, D. et al. SARS-CoV-2 seroconversion in humans: a detailed protocol for a serological assay, antigen production, and test setup. Curr. Protoc. Microbiol57, e100 (2020).CAS Article Google Scholar

A review: Antibody-dependent enhancement in COVID-19: The not so friendly side of antibodies

Authors: Gabriela Athziri Sánchez-Zuno,1,†Mónica Guadalupe Matuz-Flores,1,†Guillermo González-Estevez,1Ferdinando Nicoletti,2Francisco Javier Turrubiates-Hernández,1Katia Mangano,2 and José Francisco Muñoz-Valle1

Int J Immunopathol Pharmacol. 2021 Jan-Dec; 35: 20587384211050199.Published online 2021 Oct 10. doi: 10.1177/20587384211050199PMCID: PMC8512237PMID: 34632844

Abstract

The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), represents an unprecedented global public health emergency with economic and social consequences. One of the main concerns in the development of vaccines is the antibody-dependent enhancement phenomenon, better known as ADE. In this review, we provide an overview of SARS-CoV-2 infection as well as the immune response generated by the host. On the bases of this principle, we also describe what is known about the ADE phenomenon in various viral infections and its possible role as a limiting factor in the development of new vaccines and therapeutic strategies.

Keywords: COVID-19, SARS-CoV-2, ADE, vaccine, antibody-dependent enhancement

Introduction

The first cases of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), were identified in December 2019 in China. The spread of this disease occurred rapidly throughout the world mainly due to its forms of transmission, being the most important the contact with respiratory fluids (exposure to respiratory droplets carrying infectious viruses). 1

Following the outbreak in China, a trend of increasing cases grew exponentially as it was observed. As a consequence of this rapid spread, the World Health Organization (WHO) declared COVID-19 as an international public health emergency, and in March 2020, it was declared a pandemic. As a result of this unexpected progression, health authorities around the world entered into a state of alert facing the need to implement unprecedented sanitation and isolation protocols. 2

The state of pandemic has caused a great impact on both the economic and public health level around the world, because of social distancing, border closures, and the performance of essential activities only. According to the WHO, in the week of April 12th to April 20th, 2021, more than 140 million cases and more than 3 million deaths have already been reported worldwide, new cases continued to rise globally, in the past week to over 5.2 million new reported cases. Possible reasons for this increase include the continued spread of more transmissible variants of concern (VOCs). The countries such as India, the United States of America, Brazil, Turkey, and France reported the highest number of cases. 2

Regarding the health implications of this disease, it has been described that the majority of patients infected by COVID-19 have symptoms of a common cold such as fever, cough, fatigue, headache, and muscle pain as well as diarrhea. In some cases, severe shortness of breath can also occur. 3 Although most patients have a favorable prognosis, in some cases this may not be the scenario. A poor prognosis has been associated with the presence of some chronic diseases and comorbidities including hypertension, diabetes, coronary heart disease, and obesity. In the case of diabetes, patients are more susceptible to developing the so-called “cytokine storm” that leads to a rapid deterioration of COVID-19.4,5

Another important aspect regarding the pathogenesis of COVID-19 is the occurrence of the phenomenon called antibody-dependent enhancement (ADE). This mechanism involves endocytosis of virus–antibody immune complexes into cells through interaction of the antibody Fc region with cellular Fc receptors (FcRs). In this event, pre-existing non-neutralizing or sub-neutralizing antibodies to viral surface proteins that were generated during a previous infection can promote the subsequent entry of viruses into the cell and therefore intensify the inflammatory process during a secondary infection with any antigenic-related virus.68 The occurrence of ADE may represent one of the greatest challenges for scientists working on the development of a safe vaccine against COVID-19.

For the aforementioned, in this review, we provide an overview of SARS-CoV-2 infection as well as the immune response generated by the host. On the bases of this principle, we also describe what is known about the ADE phenomenon in various viral infections and its role as a limiting factor in the development of new vaccines and therapeutic strategies.

Structure and pathogenesis of SARS-CoV-2

The Coronaviruses (CoVs) are viruses that show morphological similarity to a solar corona appearance under an electron microscope due to the presence of “spike” glycoproteins. These CoVs belong to the large family Coronaviridae, which consists of two subfamilies: Orthocoronavirinae and Torovirinae. The Orthocoronavirinae subfamily is classified into four genera: alpha coronaviruses, beta coronaviruses, gamma coronaviruses, and delta coronaviruses. Among these, the beta genus is the one that has been described as capable of causing severe illness and even death among infected individuals.9,10

The genome of this beta-CoV has been classified as a single-stranded ribonucleic acid (RNA) virus consisting of 26–32 kbp and contains 7–10 open reading frames (ORF). Two-thirds of the genome encodes the replicase-transcriptase proteins, and a third part encodes the four structural proteins: spike (S), envelope, membrane, and nucleoprotein. The S-glycoproteins on the surface of CoVs comprise the receptor-binding domain(s) and contribute for host cell binding, host–viral cell membrane fusion, and virus internalization while the M-glycoprotein plays a role in the virion envelope formation and assembly.912 Therefore, the entry of the coronavirus into susceptible cells is a complex process that requires receptor binding and proteolytic processing of protein S to promote virus–cell fusion. As anticipated above, SARS-CoV-2 is acquired by exposure to respiratory fluids of infected individuals and less through contact with fomites. 13

SARS CoV interacts directly with angiotensin-converting enzyme 2 (ACE2) to enter target cells. At the onset of the infection, SARS-CoV-2 targets mainly host cells that express ACE2, including bronchial cells, airway epithelial cells, alveolar epithelial cells, macrophages in the lung, and vascular endothelial cells. 14

After the recognition and binding of the SARS-CoV-2 S-glycoprotein with ACE2 in the host cells, the S-protein is cleaved by transmembrane protease serine 2 (TMPRSS2) to reveal the S2 domain necessary for the fusion of the viral membrane–host cell and the entry of the virus. Once the viral content is released into host cells, the viral RNA that enters, begins its replication, production, and release of new viral particles (Figure 1(a)).14,15

An external file that holds a picture, illustration, etc.
Object name is 10.1177_20587384211050199-fig1.jpg

Open in a separate windowFigure 1.

The Immune Response and Immunopathology of COVID-19. (a) The entry of SARS-CoV-2 into cells is mediated by the binding of TMPRSS2 and S-glycoprotein with the ACE2 acting as a receptor that facilitates viral binding to the membrane of the host cells. The virus enters by endocytosis and releases its RNA, replicates and creates new virions that cause a rapid progression of the infection. (b) Bronchial epithelial cells, type I and type II alveolar pneumocytes, and capillary endothelial cells become infected and a response occurs that leads to recruitment of macrophages, monocytes, neutrophils, and cytokine production in response to virus entry. (c) Sub-epithelial dendritic cells recognize the virus antigen and present them to CD4 + T cells that induce the differentiation of B cells into plasma cells that promote the production of virus-specific antibodies. Neutralizing antibodies can interact with phagocytes and NK cells and enhance antibody-mediated clearance of SARS-CoV. (d) A dysfunctional immune response leads to excessive cell infiltration, cytokine storm, inflammation, apoptosis, and multi-organ damage. Ab, antibody; ACE2, angiotensin-converting enzyme 2; FcγR, Fcγ receptor; IL, interleukin; MHC, major histocompatibility complex; TCR, T-cell receptor; TMPRSS2, transmembrane protease serine 2; TNF-a, tumor necrosis factor.

Innate immune response

After SARS-CoV-2 enters the host cells, it is recognized by pattern recognition receptors (PRRs) such as Toll-like receptor-7 (TLR7) and TLR8, which are expressed by epithelial cells that activate the local immune response, recruiting macrophages and monocytes that respond to infection (Figure 1(b)).

Once SARS-CoV-2 binds to PRRs, the recruited adapter proteins activate transcription factors. This includes interferon regulatory factor (IRF) and Nuclear factor κB (NF-κB), that lead to the production of antiviral type I interferon (IFN), and cytokines that induce an alarm signal in neighboring cells to attract other cells of innate immunity including polymorphonuclear cells, natural killer cells (NKs), dendritic cells, and monocytes.16,17 One of the signature features of this disease in patients with worst prognosis is the high serum levels of cytokines such as IL-1β, IL-6, TNF, IL1RA, and IL-8. These cytokines have an important role in the exacerbation of the inflammatory process and lead to the recruitment of other immune cells such as neutrophils and T cells. Among infiltrated innate cells, neutrophils can promote the destruction of viruses, but they can also worsen disease progression by inducing severe lung lesions.18,19

Types I and III IFNs are considered to be crucial in the antiviral response, and SARS-CoV-2 has been shown to be sensitive to pretreatment with IFN-I and III in vitro assays.20,21 The IFN timing and location are a key factor for an effective response against the virus. A study of the Middle East respiratory syndrome (MERS) in mice demonstrated that blockade of IFN signaling leads to a delayed virus clearance with increased neutrophil infiltration and alteration in T cell response. Conversely, 1 day of IFN-I administration protected mice from lethal infection, meanwhile, delayed IFN treatment failed to inhibit the replication of the virus. 22

One of the most important questions that arises in relation to innate immunity is how the SARS-CoV-2 evades the immune response. In a recent study, Kaneko et al., propose that the evasion of the antiviral aspects of innate immunity and the inflammatory process as a consequence of the virus can probably result in an alteration of the environment that leads to the attenuation of immunity of CD8 + T cells. In addition, there is an absence of germinal centers with reduction of B cells; therefore, it gives rise to a memory with a short duration and to B cells without high affinity. So far, it is still a very difficult question to answer. 23 However, it has been shown that patients with COVID-19 with worst prognosis showed poor IFN-I signals compared to patients with a favorable prognosis. 24

Additionally, various evasion mechanisms have been described for CoVs, with viral factors that antagonize pathways from PRR detection, cytokine secretion, and IFN signal induction. CoVs are able to evade PRRs by protecting the double strand RNA (dsRNA) with membrane-bound compartments formed during viral replication. Furthermore, SARS-CoV-2 is protected with guanosine and methylated by nonstructural proteins. They resemble host mRNA to promote translation, prevent degradation, and avoid detection of RIG-I-like receptors (RLRs).2527

Adaptive immune response

The main mechanisms for decreasing viral replication, limit virus spread, and inflammation include the production of various pro-inflammatory cytokines, the activation of CD4 + and CD8 + T cells.28,29

The mechanism for the presentation of viral peptides occurs once the virus is inside respiratory cells. They are presented through the major histocompatibility complex (MHC) class I for cytotoxic CD8+ T cells which are essential to mediate elimination of cells infected by the virus. Additionally, the virus and its viral particles can be presented in the context of MHC class II by means of antigen-presenting cells, including dendritic cells and macrophages. They are in charge of presenting viral proteins to CD4+ T cells that provide the signals necessary for the induction of B cells and differentiation of plasma cells producing virus-specific neutralizing antibodies (Figure 1(c)).28,29

However, in patients with COVID-19, a low count of lymphocytes, CD4+ T cells, CD8+ T cells, B cells, and NK cells has been shown. Likewise, severe cases have presented lower levels of these cells compared to mild cases. 30 Secretion of type I IFNs dramatically increases the response of CD8 + T cells against viruses, but SARS-CoV-2 has been shown to possess nonstructural proteins that induce a decreased response to type I interferon (IFN) in infected cells. Therefore, the decrease in type I IFNs by different non-structural proteins of SARS-CoV-2 could explain the marked absence of CD8+ T cell response in COVID-19 patients.3133

Kaneko et al., evaluated subsets of CD4+ T cells in lymph nodes and the spleen and observed that TH1 cells increase steadily at the beginning and end in lymph nodes and the spleen, also, a constant decrease in TH2 cells was described. Furthermore, FOXP3 + T reg cells make up a large part of the CD4+ T cell population at the end of disease. 23 Furthermore, it was shown that patients with significant decreases in T cell counts, especially CD8+ T cells, have elevated levels of IL-6, IL-10, IL-2, and IFN-γ in the peripheral blood. 34

Elevated cytokine secretion promotes cell infiltration inflammatory by establishing an aberrant inflammatory feedback loop that can cause damage to the lung. It can also cause damage through the secretion of proteases and reactive oxygen species (ROS) with subsequent alveolar damage and desquamation of alveolar cells. This results in inefficient gas exchange in the lung, which is reflected in low oxygen levels in patients. 35

Overall, impaired acquired immune responses and uncontrolled innate inflammatory responses to SARS-CoV-2 can cause cytokine storms that are associated with COVID-19 severity states and can lead to migration to different organs, causing multi-organ damage (Figure 1(d)). 36

Antibody responses in COVID-19 patients occur in conjunction with CD4+ T cell responses that induce B cells to differentiate into plasma cells and subsequently produce antibodies. In patients with SARS-CoV infection, the main target of neutralizing antibodies is the virus S glycoprotein, particularly with its receptor-binding domain (RBD), which is responsible for the binding of the virus to the ACE2 in host cells. 37 Neutralizing antibody responses to protein S possibly begin to develop in week two, and in most patients, antibody titers are detected by the third week.38,39

A recent study conducted by Ni et al., 2020 40 showed the presence of specific IgM and IgG antibodies for the structural proteins N (nuclear) and S-RBD in serum of recently negative COVID-19 patients compared to healthy donors. The IgG anti-SARS-CoV-2 was also higher in titers than IgM in follow-up patients compared to healthy donors. This indicates that patients with COVID-19 have IgG- and IgM-mediated responses to SARS-CoV-2 proteins, especially N and S-RBD. It also proposes that previously infected patients could maintain their IgG levels for at least 2 weeks after receiving a negative COVID-19 test result. 40Go to:

The devil in disguise: What happens when antibodies go bad

All viruses initiate infection by adhering to host cells through the interaction between viral proteins and receptor/coreceptor molecules on target cells (Figure 2(a)) As mentioned above, the host’s humoral response is responsible for generating specific antibodies to surface proteins that inhibit this step of the infection cycle, resulting in virus neutralization. Conversely, in some cases, these antibodies may paradoxically favor the infection process as part of a phenomenon better known as antibody-dependent enhancement (ADE). 41

An external file that holds a picture, illustration, etc.
Object name is 10.1177_20587384211050199-fig2.jpg

Figure 2.

ADE phenomenon. (a) The conventional mechanism of infection by SARS-CoV 2 consists of the binding of its S-protein to the cellular receptor ACE2. After the union of the SARS-CoV-2 virus to the receptor, a conformational change occurs in the S-protein necessary for the fusion of the viral envelope with the cell membrane for subsequent endocytosis. Subsequently, SARS-CoV-2 releases its genetic material into the host cell. The RNA of the viral genome is then translated into proteins necessary for the subsequent assembly of viriomes in the ER and Golgi. These visions are then transported through vesicles outside the cell by exocytosis. The ADE phenomenon can be classified as two different mechanisms: ADE through enhanced infection and ADE through enhanced immune activation. (b) In ADE through increased infection, antibodies of a non-neutralizing or sub-neutralizing nature cause viral infection through FcγRIIa-mediated endocytosis, resulting in a more severe disease phenotype. (c) In ADE via enhanced immune activation, non-neutralizing antibodies can form immune complexes with viral antigens inside airway tissues, resulting in the secretion of pro-inflammatory cytokines, immune cell recruitment, and activation of the complement cascade within lung tissue. ADE, antibody-dependent enhancement; ACE2, angiotensin-converting enzyme 2; CR, compliment receptor; ER, endoplasmic reticulum; FcγRIIa, Fc γ receptor IIa; IFN-a, interferon a; IL, interleukin; IRF, interferon regulatory factors; iNOS, inducible nitric oxide synthase; PGE2, prostaglandin E2, RNA, ribonucleic acid; TNF-a, tumor necrosis factor.

Regarding the mechanism of ADE, it has been described that it involves endocytosis of virus–antibody immune complexes into cells through interaction of the antibody Fc region with cellular Fc receptors (FcRs). It is well known that the FcγRI (CD64) binds with high affinity to IgG monomerically while FcγRII (CD32) and FcγRIII (CD16) do so with low affinity and are activated by immune complexes. 42 In this regard, it is postulated that myeloid cells that express FcRs such as monocytes and macrophages, dendritic cells, and certain granulocytes can promote ADE through phagocytic uptake of the immune complexes. Although ADE is principally mediated by IgG antibodies, IgM along with complement, and IgA antibodies have also been described as capable of ADE 43

The phenomenon of ADE is an event that occurs in some viruses, where pre-existing non-neutralizing or sub-neutralizing antibodies to viral surface proteins that were generated during a previous infection can promote the subsequent entry of viruses into the cell and therefore intensify the inflammatory process during a secondary infection with any antigenic-related virus.6,8

ADE was first described in 1964 by Hawkes, who demonstrated increased infectivity of various arboviruses such as Japanese encephalitis virus, West Nile virus, Murray Valley encephalitis virus, and Murray Valley virus and Getah virus under in vitro conditions. 6 Prior to that, there were also previous reports positing pre-existing non-neutralizing antibodies as responsible for increased infection with various human and animal viruses, including dengue virus (DENV), Zika virus (ZIKV), Ebola virus, human immunodeficiency virus (HIV), Aleutian mink disease parvovirus, Coxsackie B virus, equine infectious anemia virus, feline infectious peritonitis virus, simian hemorrhagic fever virus, caprine arthritis virus, respiratory syndrome virus, and reproductive disease and African swine fever virus. To date, ADE has also been demonstrated with models using monoclonal antibodies and in vitro models of polyclonal sera using cells expressing the Fc receptor, including K562 and U937 cell lines, as well as primary human monocytes, macrophages, and dendritic cells. 44

Molecular mechanism of ADE

In order to clearly understand ADE, it has been broadly categorized into two different mechanisms; when the specific antibody enhances viral entry into host monocytes/macrophages and granulocytes or when it promotes viral infection in cells through interaction with FcR and/or complement receptor. Although these mechanisms are not mutually exclusive, their classification was proposed in order to understand the biological process involved at the molecular level.8,45

ADE via enhanced infection

As mentioned earlier, FcRs are mostly expressed by immune cells and are receptors directed to Fc portion of an antibody. In ADE, via enhanced infection, non-neutralizing or sub-neutralizing antibodies bind to the viral surface and traffic virions directly to macrophages, this complex is internalized by Fc-receptor-bearing cells, including monocytes/macrophages and dendritic cells and subsequently leads to the phosphorylation of Syk and PI3K that triggers signaling for FcγR-mediated phagocytosis. Alternatively, activating FcγR can concentrate immune complexes on the surface of the cell. The virion can then bind to its receptor to enter the cell via receptor-mediated endocytosis. These processes culminate in an increased virus load and disease severity (Figure 2(b)).8,44,46

It is also worth mentioning that this mechanism can be abrogated in the absence of the Fc receptor. The activation of Fc receptors triggers signaling molecules that also induce IFN-stimulated gene (ISG) expression, independent of type-I IFN. Because ISGs have powerful antiviral effects, viruses must develop tools to suppress these antiviral responses in target cells for ADE to occur. For example, in DENV infection, the ADE phenomenon requires the binding of DENV to the leukocyte immunoglobulin receptor B1 (LILRB1). As a result, LILRB1 signaling can inhibit the pathway that induces ISG expression.47,48

ADE via enhanced immune activation

The second, recently described and less studied mechanism, through which ADE can occur, is well represented by pathogens that cause respiratory infections. In these conditions, Fc-mediated antibody effector functions are capable of enhancing respiratory disease by initiating a strong immune cascade that results in severe lung pathology (Figure 2(c)). 45

This mechanism can also be induced when virus–antibody C1q complexes promote fusion between the viral capsule and the cell membrane by deposition of C1q and its receptor. This complex binds to the C1q receptor in cells and initiates the intracellular signaling pathway. The classical complement pathway is then initiated, leading to the activation of C3, whose fragment can be covalently linked to the bound antibodies or the surface of the virus particles then favors the binding of the virus and its receptor, as well as the subsequent endocytosis.10,43

Interestingly, another mechanism for the ADE phenomenon that has been rather described in the multisystemic inflammatory syndrome in children is that mediated by mast cells; these cells are capable of degranulating both IgE and IgG antibodies bound to Fc receptors. 49

In this sense, a model of multisystemic inflammatory syndrome in children has been proposed in babies with maternally transferred antibodies against SARS-CoV-2 in which the activation and degranulation of mast cells with SARS-CoV-2 antibodies bound to the Fc receptor lead to an increase in histamine levels. In this model, the binding of the SARS-CoV-2 nucleocapsid to the PTGS2 promoter results in prostaglandin E2 (PGE2) which may be driving overactive mast cells as an alternative mechanism that drives increased histamine levels in older children and adults. 49

The best known so far (but also misunderstood) ADE phenomenon: ADE in DENV infection

The DENV is a mosquito-borne virus of the Flaviviridae family (with four serotypes identified DENV1-4) capable of causing classic dengue (DF), dengue hemorrhagic fever (DHF), and dengue shock syndrome (DSS) showing tropism for monocytes, macrophages, and dendritic cells.42,48,50

There exists no cross-antibody protection for the four serotypes, which means the antibodies induced by each serotype cannot work on others. In the case of a secondary infection, if infected by the virus of same serotype, the antibodies produced in previous infections are capable of effectively neutralizing the virus. On the contrary, these antibodies will not only neutralize viruses, but may also even facilitate viral entry through Fc portions of antibodies and will increase viral load in vivo. 8

According to this hypothesis of ADE, the antibodies produced in a DENV infection can recognize and bind to a different serotype of DENV than that of the primary infection but are not able to neutralize it. Instead, these antibodies facilitate the entry of non-neutralized virus–antibody complexes (immune complexes), primarily through FcγR into phagocytic mononuclear cells (MPCs). 48

The DENV represents the best documented example of clinical ADE via enhanced infection. After ligation of FcR, DENV activates IL-10 production at an early phase of infection. The suppressor activity of IL-10 during ADE infection induces Th2 bias and inhibits the JAK-STAT signaling pathway through the suppressor cytokine signaling system (SOCS). ADE also results in a higher rate of virus internalization by increasing the number of fusions per cell.44,45,51

Since many antibodies to different dengue serotypes are cross-reactive, secondary infections with heterologous strains can lead to increased viral replication and more severe disease. Typically, both DHF and DSS occur in this setting, presenting more severe forms of symptoms, such as thrombocytopenia, fever, and hemorrhagic manifestations. It has also been shown that the presence of these cross-reactivated non-neutralizing antibodies can predispose to more severe disease and even the development of DHF and DSS.45,52

SARS CoV-2 and ADE, what is known and what remains to be known?

Despite all reports generated in recent months in response to the pandemic, there is still no detailed information regarding the mechanism of the ADE that occurs in SARS-CoV-2 infection. One of the best accepted hypotheses so far is that in the SARS-CoV-2 infection, pre-existing CoV-specific antibodies are capable of promoting viral entry into FcR-expressing cells. ADE is mediated by the binding of FcRs, mainly CD32 expressed in different immune cells, including monocytes, macrophages, and B cells. The infection of CD32+ cells is a key step in the development of the COVID-19 and its progression from mild to severe form.53,54

A potential hypothesis states that circulating non-neutralizing antibodies, instead of helping to eliminate circulating SARS-CoV-2, can then bind to viral particles and thus contribute to the worsening of COVID-19 by promoting its Fc-mediated internalization by pulmonary epithelial cells and infiltrating monocytes, as it has been observed in previously mentioned diseases such as SARS-CoV-1. 55

One particularity about this mechanism is that ADE of SARS-CoV does not use endosomal/lysosomal pathway as used by ACE2 during normal virus transport into the cell, but instead it has been described as a possible mechanism for viral entry where non-neutralizing antibodies recognizing the RBD of the S-protein of the coronavirus bind to the Fc receptor and allow virus entry. The non-neutralizing antibodies–Fc receptor complex mimics the cell surface virus receptor and favors virus entry pathways into IgG Fc receptor-expressing cells.6,52

This phenomenon could also explain the observed impairment of immune regulation such as apoptosis of immune cells leading to the development of T-cell lymphopenia, an inflammatory cascade, as well as a storm of cytokines.8,54

An important difference between the ADE phenomenon previously described for DENV and SARS-CoV is that there is no evidence that ADE facilitates the spread of SARS-CoV in infected hosts. Therefore, ADE in this disease would be best described as “ADE of viral entry” which does not necessarily result in a productive viral infection, meaning that ADE of viral entry in vitro does not predict ADE of infection and ADE of disease. 56

Antibodies are capable of promoting virus attachment and entry into the immune cell, where they start to replicate without production of viable virions. This pseudo infection may be due to the inability of macrophages to express the serine proteases necessary for virion activation. For their part, immune complexes (virus–antibody) can promote an infectious process after being internalized through the FcRs. Furthermore, pulmonary epithelial cells have been reported to express high levels of FcγRIIa. The virus introduced into the endosome through this pathway will likely involve TLR3, TLR7, and TLR8 capable of recognizing RNA. SARS-CoV infection by ADE in macrophages leads to elevated production of TNF and IL-6. It was also observed in a murine SARS-CoV model that ADE is associated with a decrease in the levels of the anti-inflammatory cytokines IL-10 and TGFβ and increased levels of the pro-inflammatory chemokines CCL2 and CCL3.7,53,54

ADE in the case of SARS-CoV-2 can occur due to the priming caused by other CoVs, leading to development of non-neutralizing or poorly neutralizing antibodies. It is known that antibodies to the S-proteins of SARS-CoV and SARS-CoV-2—and, to a much lesser extent, MERS-CoV—can cross-react, and both high-potency neutralizing antibodies that also mediate antibody-dependent cytotoxicity and antibody-dependent cellular phagocytosis, as well as non-neutralizing antibodies, can be elicited against conserved S-epitopes. Despite the above, the limited spread of SARS-CoV and MERS-CoV means that it is not feasible that antibodies with cross-reactivity due to another coronavirus infection are the responsible element for the development of ADE, but rather those that were generated during a first infection or after passive immunization.8,57

The ADE hypothesis is further supported by the results of a study on viral kinetics and antibody responses in patients with COVID-19 where it was found that stronger antibody response was associated with delayed viral clearance and increased disease severity. Patients with an elevated IgG response showed only 9% of virus shedding on day 7 after IgG developed. In the case of weak IgG patients, 57% shed the virus. Furthermore, an association was found between a more severe disease phenotype and earlier IgG response, concurrently with IgM and higher IgG antibody titers. 58

The hypotheses regarding ADE are however conflictive and somehow even contradictory. As stated by Jaume et al., it was observed in an in vitro analysis that ADE infection promoted viral gene transcription and the production of viral gene protein synthesis and intermediate species, which can be then recognized by immune sensors and potentiate an immune response. Therefore, proposing a possible participation of immune-mediated enhanced disease during SARS pathogenesis suggests very little clinical significance for this mechanism. In this same study, it was observed in a different cell line, (Raji cells, derived from a Burkitt’s lymphoma patient) that ADE infected cells did not support replication of SARS-CoV-1, ultimately ending in an abortive viral cycle without the detectable release of progeny virus. 59

In addition to the above, recent reports indicate that the percentage of patients with COVID-19 that develop cross-reactive antibodies is significant. In a study by Shrock et al., a serological profile of patients with and without previous COVID-19 infection was performed. In this study, it was found that the studied patients presented cross-reactive antibody titers, and it is suggested that this may have various effects on the disease, from a less severe prognosis when they were able to neutralize the virus to a serious infection when ADE is developed. 60

Another important aspect that needs to be studied further is the relationship between ADE-epitopes. This was previously reported for DENV and ZIKV.61,62 In the case of SARS-CoV-2, this association was reported for the first time in the article by Zhou et al., where monoclonal cells were isolated from memory B cells, later a group of non-overlapping receptor-binding domain was identified. (RBD) epitopes that were directly associated with ADE and favored the entry of the virus into Raji cells via an Fcg receptor-dependent mechanism. 56 This is of utmost importance especially when considering the design of vaccines, which, as mentioned later, must be capable of triggering a strong neutralizing response, which is why the epitopes to which they will be directed must be carefully selected.

Finally, it is also important to take into account that detailed research is lacking to elucidate the possible mechanism of ADE in SARS-CoV-2 infection, mainly due to the fact that the studies carried out at present have been carried out in viral infections (such as DENV) with differences in their pathological mechanisms as well as in animal models (such as the feline infectious peritonitis virus [FIPV]) where mechanisms of pathogenesis in the human host differ among viruses, therefore difficult to translate the mechanisms of infection. 57

ADE as a possible threat to vaccine efficacy

All vaccines have the objective of generating a response from the host against an antigen that is not capable of causing a disease but of provoking a response against that antigen that will be effective in subsequent encounters with it. As we have been discussing, the mechanism of ADE makes vaccine development particularly difficult due to similarity to a natural infection. Vaccines against one specific serotype produce cross-reactive non-neutralizing antibodies against other serotypes, predisposing the enhanced illness in secondary heterotypic infection. 52

The immune mechanisms of this phenomenon involve from ADE of infection to the formation of immune complexes by antibodies, although accompanied by various coordinated cellular responses, such as Th2 T-cell skewing. 63 Another important point to consider is that not only sub-neutralizing or non-neutralizing antibodies are associated with the development of ADE; according to the study by Liu et al., 55 anti-spike IgG (S-IgG), in productively infected lungs, causes severe ALI by skewing inflammation-resolving response.

To avoid the development of ADE, the strategy used in the development of current vaccines was to target the immunodominant epitope, in this case, that corresponds to the S-protein. The S1 subunit presents two highly immunogenic domains, the N-terminal domain (NTD) and the RBD, which are the major targets of polyclonal and monoclonal neutralizing antibodies.64,65 Because the S-proteins of SARS-CoV-2 are accessible and play an essential role in the entry of the virus into the host cell, and therefore the mechanism of infection, they are considered to be prime antibody targets. 66

Understanding the structure of SARS-CoV-2 epitopes, particularly within S, provides essential information for the development of vaccines that favors the production of neutralizing antibodies rather than antibodies that could exacerbate the severity of ADE infection. 60 In general, RNA viruses are known to be highly susceptible to random mutations due to the lack of exonuclease proofreading activity of virus-encoded RNA-dependent RNA polymerases (RdRp) 67 with some exceptions such as Nidovirales order (to which the Coronavirus genus belongs). In SARS-CoV, an exonuclease activity with proofreading function has been described for the nsp14 (ExoN), and a homologue nsp14 protein is found in the SARS-CoV-2 as well. 68

The high error rate and subsequent rapid evolution of virus populations, which could lead to the accumulation of amino acid mutations, could affect the virus’ transmissibility, its cellular tropism, and even its pathogenicity. 69

Although several vaccines have gained (emergence) regulatory approval and are being distributed worldwide, we cannot ignore the possibility that the evolution of the virus, based on natural selection, can directly affect the S-protein to which these vaccines are directed, and therefore the newly mutated virus can escape antibody-mediated protection induced by previous infection or vaccination. 70

Amino acid sequences of SARS-CoV-2 are available from NCBI GenBank and by the Global Initiative on Sharing All Influenza Data (GISAID). The first complete genome sequence of SARS-CoV-2 was released on NCBI GenBank (NC 045512.2). 67 According to these reported sequences, the linear genome of the SARS-CoV-2 virus is 29,903 bases long and houses 25 genes. 71 To date, 4150 mutations have been identified in the S-gene of SARS-CoV-2 isolated from humans, resulting in 1246 changes in amino acids, including 187 RBD substitutions compared to the reference genome. 72

The main variants identified that seem to have high relevance in the immunogenicity of the virus are D614G, N501Y, and E484K mutations of the RBD.7376

The D614G mutation in protein S represents a change from nucleotide A to G at position 23,403 in the first Wuhan reference strain. The D614G change is commonly detected along with three other mutations: a C to T change in the UTR 5 ‘ (position 241 relative to the Wuhan reference sequence), a silent mutation from C to T at position 3037, and a C-to-T mutation at position 14,408 that results in an amino acid change in the RdRp P323L. This, comprised the four aforementioned mutations, represented the dominant global form as of May (78% of a total of 12,194 sequences). 73 The D614G mutation has been reported as capable of improving the replication capacity of SARS-CoV-2 in the upper respiratory tract through increased virion infectivity, this was demonstrated in the human lung cell line Calu-3 and the primary tissues of the human upper respiratory tract. 73

It was also observed that patients infected with the G614 variant of the virus developed higher levels of viral RNA in nasopharyngeal smears than those with the D614 virus but did not develop a more severe disease. This suggests that despite affecting the replication capacity of the virus, this mutation did not influence the severity of the infection. 77

The N501Y variant was identified in the UK as VUI-2020/01 or lineage B.1.1.7. This lineage is composed of 14 defining mutations in protein S. This variant has a mutation in the RBD of the peak protein at position 501, where the amino acid asparagine (N) has been replaced by tyrosine (Y). The N501Y mutation is one of the six key contact residues within the RBD. 78

This change in different fundamental residues in the binding site could affect the fusion of the host cells–virus and, therefore, the infectivity of the virus. 79 As of December 28, 2020, this variant accounted for approximately 28% of cases of SARS-CoV-2 infection in England. 74

The E484K mutation in the S-protein of the virus has been identified in the South African (B.1.351) and Brazilian (B.1.1.28) variants and has been reported to be an escape mutation from the immune response. 80

This variant consists of a change in codon 484 in that of the RBD where a negatively charged amino acid (E, glutamic acid) is substituted with a positively charged amino acid (K, lysine). 81

Due to the location of this mutation, like the other variants, it has been directly associated with changes in the mechanism of infection of the virus and even on the efficacy of the immune response of the organism or that induced by a vaccine to the virus. 80 Studies have also shown that the presence of this variant directly affects the average binding of convalescent sera (>10 times) reducing the neutralization activity of some individuals. 75

Recently, the BNT162b2 nucleoside modified RNA vaccine encoding the full-length SARS-CoV-2 protein (S) was reported to be effective in inducing neutralizing geometric mean titers of antibodies against SARS-CoV-2 virus constructs containing key peak mutations of the newly emerging UK (UK) and South African (SA) variants: N501Y from the UK and South Africa; Deletion 69/70 + N501Y + D614G from the UK; and E484K + N501Y + D614G de SA, thus suggesting that the efficacy of this vaccine is not significantly affected by these variants. 82

Recently, the delta variant (B.1.617.2) was described, which is characterized by mutations in the peak protein P681R, T19R, D614G, L452R, T478K, Δ157-158, and D950N, first detected in India in December 2020. 83 According to what is believed, these mutations directly affect key antigenic regions of RBD. This variant also appears to cause mutations at sites that trigger an increase in viral replication and therefore an increase in viral load. 84 This variant and its rapid transmission capacity represent an imminent threat to the population and a concern about the effectiveness of vaccines. In this sense, in the study by Lopez-Bernal et al., it was reported that the effectiveness after a dose of vaccine (BNT162b2 or ChAdOx1 nCoV-19) was lower among people with the delta variant (30.7%) than among those with the alpha variant (48.7%). With the BNT162b2 vaccine, the effectiveness of two doses was 93.7% among people with the alpha variant and 88.0% among people with the delta variant. With the ChAdOx1 nCoV-19 vaccine, the two-dose efficacy was 74.5% among people with the alpha variant and 67.0% among people with the delta variant. 84

In addition to this, there are reports regarding the kinetics of natural immunity in patients who had COVID-19. In a study, 85 the humoral response was evaluated in a total of 76 patients (IgM and IgG antibodies that recognized the nucleocapsid protein or the RBD of the S-protein). In these patients 1 year after infection, approximately 90% of recovered patients still had detectable SARS-CoV-2-specific IgG antibodies recognizing N and RBD-S. However, when evaluating the neutralizing capacity, it was only detected in ∼43% of patients. 85

In addition to concerns regarding natural immunity, there are also reports about the duration of the humoral immune response in response to a vaccine. In a study in health personnel vaccinated with BNT162b2, it was observed that the antibody response was greater in seropositive participants compared to seronegative participants. In both seropositive and seronegative subjects, a significant decrease in antibodies was observed at 3 months compared to maximum response. 86 Similar results were found by our work group in the study by Morales-Nuñez et al., where it was observed that after the second dose with this same vaccine, individuals developed antibodies with high neutralizing capacity. 87 In a study by Pegu et al., 2021, the efficacy of the immune response generated by the mRNA-1273 vaccine was evaluated, in this work the impact of the variants B.1.1.7 (Alpha), B.1.351 (Beta), P.1 was also evaluated (Gamma), B.1.429 (Epsilon), B.1.526 (Iota) for SARS-CoV-2, and B.1.617.2 (Delta) on binding, neutralization, and ACE2-competing antibodies elicited by this vaccine for 7 months. The results of this study turned out to be interesting because all included individuals responded to all variants. Binding and functional antibodies against variants persisted in most subjects, albeit at low levels, for 6 months after the primary series of mRNA-1273 vaccine. 88

The imminent risk that may be triggered by a vaccine-mediated antibody response is that the mechanism of ADE occurs and places vaccinated individuals at greater risk of a more severe disease phenotype compared to unvaccinated individuals. Closely monitoring of these mutations is essential for the scientists in charge of the design and development of vaccines to make the necessary modifications that go hand in hand with the high mutation rate of SARS-CoV-2. 63

The light at the end of the tunnel; an inhibitor as a possible therapeutic alternative

As described above in the presence of cross-reactive antibodies (responsible for the ADE phenomenon), the entry of the virus is promoted in monocytes/macrophages through the FcR. Once inside the cell, the viruses are replicated and released in large quantities after escaping the immune response. The exacerbated activation of macrophages and mass liberation of cytokines support a hypothesis that states that the so-called cytokine storm is the secondary event of the activation of macrophages, mainly mediated by the ADE phenomenon, reason why its specific blockade will provide therapeutic potentials for patients suffering from severe COVID-19. 89

In this context, it has been stated that the mammalian Target of Rapamycin (mTOR) is one of the main signaling pathways involved in the exacerbated immune response triggered by SARS-CoV2. 90 mTOR is a serine-threonine kinase family protein, a key regulator in protein synthesis, and cellular metabolism that forms two major complexes, mTORC1 with Raptor and mTORC2 with Rictor and plays a pivotal role in cell proliferation and cellular metabolism; therefore, inhibition of mTOR has shown to suppress virus growth and replication. 91

In this regard, in a recent study, a specific set of biological pathways was described in the primary human pulmonary epithelium of SARS-COV-2 infection, among them the mTOR signaling pathway was identified. 92 It has also been stated that the mTOR pathway plays an important role in B‐cell development; mTORC1 controls BCL6 expression and controlling the fate of B cells in the germinal center reaction, therefore contributing in an essential way to the development of ADE by favoring the production of cross-reactive or sub-neutralizing antibodies. 89

These findings propose that selective inhibition of mTOR by an inhibitory agent, such as rapamycin, could have detrimental effects over memory B cell activation and therefore beneficial effects over the characteristic immune response of COVID-19 92

The mechanism of action of rapamycin consists of its ability to bind to the FK506 Immunophilin-binding protein (FKBP12A) and to inhibit the activity of mTORC1 as well as to interrupt the interaction between Raptor and mTOR. The inhibition of mTORC1 by rapamycin then leads to autophagy of infected cells and inhibition of translation of SARS‐CoV‐2 viral polymerase and structural proteins. 90

Overall, it is suggested that the antiviral action of rapamycin, together with its immunomodulatory potential that reduces the excessive production of pro-inflammatory cytokines, would justify clinical studies in patients with COVID-19.90,91

Conclusions

The outbreak and rapid spread of SARS-CoV-2 are a health threat with unprecedented consequences throughout the world. Considering the great economic and health burden of the COVID-19 pandemic, any means to improve the condition of patients, accelerate their recovery, and reduce the risk of deterioration and death would be considered of significant clinical and economic importance. With respect to the immune response generated by the host, the specific neutralizing antibodies generated against the virus are considered essential in the control of virus infections in various ways. However, in some cases, the presence of specific antibodies can be beneficial for the virus. This activity known as antibody-dependent enhancement (ADE) of virus infection enhances virus entry and in some cases virus replication into host cells through interaction with Fc and/or complement receptors. It has been also reported in data from previous CoV research studies that ADE may play a role in the virus’s pathology.

Even though several vaccines have been approved from regulatory bodies under emergency conditions and are distributed worldwide, we cannot rule out the possibility that the evolution of the virus can directly affect its targets, and therefore, the newly mutated virus can escape antibody-mediated protection induced by previous infection or vaccination.

If the vaccines are not capable of generating neutralizing antibodies against the possible mutagenic variants to mount a response, the result may lead to the generation of sub-neutralizing antibodies that will even be capable of facilitating uptake by macrophages that express FcR, with the subsequent stimulation of macrophages and production of pro-inflammatory cytokines.

One advantage of the current pandemics is the unprecedented availability of scientific and technological means to face COVID-19, on these bases, careful design and testing of vaccines will be necessary to evaluate which viral mutations can escape from antibodies-mediated neutralization as well as which one significantly affects the efficacy of the currently approved vaccines.Go to:

Acknowledgments

The figures were created with BioRender.comGo to:

Appendix

Abbreviations

ACE2Angiotensin-converting enzyme 2ADEAntibody-dependent enhancementCoVsCoronavirusesCOVID-19Coronavirus-19DENVDengue virusDFDengue feverDHFDengue hemorrhagic feverDSSDengue shock syndromeEREndoplasmic reticulumFcRsFc receptorsGISAIDGlobal Initiative on Sharing All Influenza DataHIVHuman immunodeficiency virusIDIntradermalIFNInterferonILInterleukinIMIntramusculariNOSInducible nitric oxide synthaseIRFInterferon regulatory factorISGIFN stimulated geneLILRB1Leukocyte immunoglobulin-like receptor B1MERSMiddle East respiratory syndromeMHCMajor histocompatibility complexMPCsMononuclear phagocytic cellsmTORMammalian Target of RapamycinNF-KBNuclear factor kBNKsNatural killer cellsNTDN-terminal domainORFOpen reading framesPGE2Prostaglandin E2PRRsPattern recognition receptorsRBDReceptor-binding domainRdRpRNA-dependent RNA polymeraseRLRRIG-I-like receptorsRNARibonucleic acidROSReactive oxygen speciesRSVRespiratory Syncytial VirusSOCSSuppressor of cytokine signallingThT helper cellTLRToll-like receptorTMPRRS2Serine protease transmembrane type 2TNF-αTumor necrosis factorWHOWorld Health OrganizationZIKVZika virusGo to:

Footnotes

Author contributions: Gabriela Athziri Sánchez-Zuno, Mónica Guadalupe Matuz-Flores, and José Francisco Muñoz-Valle conceived, drafted, and finalized the manuscript.José Francisco Muñoz-Valle, Francisco Javier Turrubiates-Hernández, and Guillermo González-Estevez critically reviewed the draft of the manuscript and approved the final version.All the authors contributed significantly and agreed to the published version of the manuscript.

Declaration of conflicting interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded by the National Council of Science and Technology (CONACYT Ciencia Básica grant number A1-S-8774) and the Universidad de Guadalajara through Fortalecimiento de la Investigación y el Posgrado 2020.Go to:

ORCID iDs

Francisco Javier Turrubiates-Hernández https://orcid.org/0000-0001-9637-168X

José Francisco Muñoz-Valle https://orcid.org/0000-0002-2272-9260Go to:

References

1. National Center for Immunization and Respiratory Diseases (NCIRD), Division of Viral Diseases (2020) Scientific Brief: SARS-CoV-2 Transmission. In CDC COVID-19 Science Briefs Atlanta (GA): Centers for Disease Control and Prevention (US). [Google Scholar]

2. Coronavirus Disease (COVID-19) Situation reports. [online] Available at: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports (accessed April 2021).

3. Baloch S, Baloch MA, Zheng T, et al. (2020) The Coronavirus Disease 2019 (COVID-19) pandemic. Tohoku J Exp Med 250: 271–278. doi:10.1620/tjem.250.271. [PubMed] [CrossRef] [Google Scholar]

4. Zhou Y, Chi J, Lv W, et al. (2021) Obesity and diabetes as high‐risk factors for severe coronavirus disease 2019 (Covid‐19). Diabetes Metab Res Rev 37(2): e3377. doi:10.1002/dmrr.3377. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

5. Mehta P, McAuley DF, Brown M, et al. (2020) COVID-19: consider cytokine storm syndromes and immunosuppression. Lancet 395: 1033–1034. doi:10.1016/S0140-6736(20)30628-0. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

6. Karthik K, Senthilkumar TMA, Udhayavel S, et al. (2020) Role of antibody-dependent enhancement (ADE) in the virulence of SARS-CoV-2 and its mitigation strategies for the development of vaccines and immunotherapies to counter COVID-19. Hum Vaccines Immunother 16(12): 3055–3060. doi:10.1080/21645515.2020.1796425. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

7. Peron JPS, Nakaya H. (2020) Susceptibility of the elderly to SARS-CoV-2 infection: ACE-2 overexpression, shedding, and antibody-dependent enhancement (ADE). Clinics 75, e1912. doi:10.6061/clinics/2020/e1912. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

8. Wen J, Cheng Y, Ling R, et al. (2020) Antibody-dependent enhancement of coronavirus. Int J Infect Dis 100: 483–489. doi:10.1016/j.ijid.2020.09.015. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

9. Ujike M, Taguchi F. (2015) Incorporation of spike and membrane glycoproteins into coronavirus virions. Viruses 7: 1700–1725. doi:10.3390/v7041700. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

10. Comas-Garcia M. (2019) Packaging of genomic RNA in positive-sense single-stranded RNA viruses: a complex story. Viruses 11: 253. doi:10.3390/v11030253. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

11. Kirchdoerfer RN, Cottrell CA, Wang N, et al. (2016) Pre-fusion structure of a human coronavirus spike protein. Nature 531; 118–121. doi:10.1038/nature17200. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

12. Song W, Gui M, Wang X, et al. (2018) Cryo-EM structure of the SARS coronavirus spike glycoprotein in complex with its host cell receptor ACE2. PLOS Pathog 14: e1007236. doi:10.1371/journal.ppat.1007236. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

13. National Center for Immunization and Respiratory Diseases (NCIRD), Division of Viral Diseases (2020) Science brief: SARS-CoV-2 and surface (Fomite) transmission for indoor community environments. In CDC COVID-19 Science Briefs. Atlanta (GA): Centers for Disease Control and Prevention (US). [Google Scholar]

14. Li W, Moore MJ, Vasilieva N, et al. (2003) Angiotensin-converting enzyme 2 is a functional receptor for the SARS coronavirus. Nature 426: 450–454. doi:10.1038/nature02145. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

15. HCA Lung Biological Network. Sungnak W, Huang N, Bécavin C, et al. (2020) SARS-CoV-2 entry factors are highly expressed in nasal epithelial cells together with innate immune genes. Nat Med 26: 681–687. doi:10.1038/s41591-020-0868-6. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

16. Fung TS, Liu DX. (2019) Human coronavirus: host-pathogen interaction. Annu Rev Microbiol 73: 529–557. doi:10.1146/annurev-micro-020518-115759. [PubMed] [CrossRef] [Google Scholar]

17. Hur S. (2019) Double-stranded RNA sensors and modulators in innate immunity. Annu Rev Immunol 37: 349–375. doi:10.1146/annurev-immunol-042718-041356. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

18. Young RE, Thompson RD, Larbi KY, et al. (2004) Neutrophil Elastase (NE)-deficient mice demonstrate a nonredundant role for NE in neutrophil migration, generation of proinflammatory mediators, and phagocytosis in response to zymosan particles in vivo. J Immunol 172: 4493–4502. doi:10.4049/jimmunol.172.7.4493. [PubMed] [CrossRef] [Google Scholar]

19. Liu S, Su X, Pan P, et al. (2016) Neutrophil extracellular traps are indirectly triggered by lipopolysaccharide and contribute to acute lung injury. Sci Rep 6: 37252. doi:10.1038/srep37252. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

20. Lokugamage KG, Hage A, de Vries M, et al. (2020) Type I Interferon Susceptibility Distinguishes SARS-CoV-2 from SARS-CoV. J Virol 94(23): e01410–e01420. [PMC free article] [PubMed] [Google Scholar]

21. Stanifer ML, Kee C, Cortese M, et al. (2020) Critical role of type III interferon in controlling SARS-CoV-2 infection, replication and spread in primary human intestinal epithelial cells. Cell Rep 32(1): 107863. [PMC free article] [PubMed] [Google Scholar]

22. Channappanavar R, Fehr AR, Zheng J, et al. (2019) IFN-I response timing relative to virus replication determines MERS coronavirus infection outcomes. J Clin Invest 129: 3625–3639. doi:10.1172/JCI126363. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

23. Kaneko N, Kuo H-H, Boucau J, et al. (2020) Loss of Bcl-6-expressing T follicular helper cells and germinal centers in COVID-19. Cell 183: 143–157.e13. doi:10.1016/j.cell.2020.08.025. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

24. Hadjadj J, Yatim N, Barnabei L, et al. (2020) Impaired type I interferon activity and exacerbated inflammatory responses in severe covid-19 patients. Science 369(6504): 718–724. [PMC free article] [PubMed] [Google Scholar]

25. Knoops K, Kikkert M, Van den Worm SHE, et al. (2008) SARS-coronavirus replication is supported by a reticulovesicular network of modified endoplasmic reticulum. PLoS Biol 6: e226. doi:10.1371/journal.pbio.00602

26. [PMC free article] [PubMed] [CrossRef] [Google Scholar]26. Chen Y, Cai H, Pan J, et al. (2009) Functional screen reveals SARS coronavirus nonstructural Protein Nsp14 as a Novel Cap N7 methyltransferase. Proc Natl Acad Sci Unit States Am 106: 3484–3489. doi:10.1073/pnas.0808790106. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

27. Bouvet M, Debarnot C, Imbert I, et al. (2010) Vitro reconstitution of SARS-coronavirus MRNA cap methylation. PLoS Pathog 6: e1000863. doi:10.1371/journal.ppat.1000863. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

28. Jansen JM, Gerlach T, Elbahesh H, et al. (2009) Influenza virus-specific CD4+ and CD8+ T cell-mediated immunity induced by infection and vaccination. J Clin Virol 119: 44–52. doi:10.1016/j.jcv.2019.08.009. [PubMed] [CrossRef] [Google Scholar]

29. Long Q-X, Liu B-Z, Deng H-J, et al. (2020) Antibody responses to SARS-CoV-2 in patients with COVID-19. Nat Med 26: 845–848. doi:10.1038/s41591-020-0897-1. [PubMed] [CrossRef] [Google Scholar]

30. Wang F, Nie J, Wang H, et al. (2020) Characteristics of peripheral lymphocyte subset alteration in COVID-19 pneumonia. J Infect Dis 221: 1762–1769. doi:10.1093/infdis/jiaa150. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

31. Fung S-Y, Yuen K-S, Ye Z-W, et al. (2020) A tug-of-war between severe acute respiratory syndrome coronavirus 2 and host antiviral defence: lessons from other pathogenic viruses. Emerg Microb Infect 9: 558–570, doi:10.1080/22221751.2020.1736644. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

32. Welsh RM, Bahl K, Marshall HD, et al. (2012) Type 1 interferons and antiviral CD8 T-cell responses. PLoS Pathog 8: e1002352. doi:10.1371/journal.ppat.1002352. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

33. Sallard E, Lescure F-X, Yazdanpanah Y, et al. (2020) Type 1 interferons as a potential treatment against COVID-19. Antivir Res 178: 104791. doi:10.1016/j.antiviral.2020.104791. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

34. Liu J, Li S, Liu J, et al. (2020) Longitudinal characteristics of lymphocyte responses and cytokine profiles in the peripheral blood of SARS-CoV-2 infected patients. EBioMedicine 55: 102763. doi:10.1016/j.ebiom.2020.102763. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

35. Qin C, Zhou L, Hu Z, et al. (2020) Dysregulation of immune response in patients with coronavirus 2019 (COVID-19) in Wuhan, China. Clin Infect Dis 71: 762–768. doi:10.1093/cid/ciaa248. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

36. Hu B, Huang S, Yin L. (2021) The cytokine storm and COVID‐19. J Med Virol 93(1): 250–256. doi:10.1002/jmv.26232. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

37. Zhu Z, Chakraborti S, He Y, et al. (2007) Potent cross-reactive neutralization of SARS coronavirus isolates by human monoclonal antibodies. Proc Natl Acad Sci Unit States Am 104: 12123–12128. doi:10.1073/pnas.0701000104. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

38. Temperton NJ, Chan PK, Simmons G, et al. (2007) Longitudinally profiling neutralizing antibody response to SARS coronavirus with pseudotypes. Emerg Infect Dis 11: 411–416. doi:10.3201/eid1103.040906. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

39. Yuchun N, Guangwen W, Xuanling S, et al. (2004) Neutralizing antibodies in patients with severe acute respiratory syndrome-associated coronavirus infection. J Infect Dis 190: 1119–1126. doi:10.1086/423286. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

40. Ni L, Ye F, Cheng M-L, et al. (2020) Detection of SARS-CoV-2-specific humoral and cellular immunity in COVID-19 convalescent individuals. Immunity 52: 971–977.e3. doi:10.1016/j.immuni.2020.04.023. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

41. Takada A, Kawaoka Y. (2003) Antibody-dependent enhancement of viral infection: molecular mechanisms andin vivo implications. Rev Med Virol 13: 387–398. doi:10.1002/rmv.405. [PubMed] [CrossRef] [Google Scholar]

42. Cloutier M, Nandi M, Ihsan AU, et al. (2020) ADE and hyperinflammation in SARS-CoV2 infection- comparison with dengue hemorrhagic fever and feline infectious peritonitis. Cytokine 136: 155256. doi:10.1016/j.cyto.2020.155256. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

43. Kulkarni R. (2020) Antibody-dependent enhancement of viral infections. In: Bramhachari PV. (ed) Dynamics of Immune Activation in Viral Diseases. Singapore: Springer Singapore. pp. 9–41. ISBN 9789811510441. [Google Scholar]

44. Khandia R, Munjal A, Dhama K, et al. (2018) Modulation of Dengue/Zika virus pathogenicity by antibody-dependent enhancement and strategies to protect against enhancement in zika virus infection. Front Immunol 9: 597. doi:10.3389/fimmu.2018.00597. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

45. Lee WS, Wheatley AK, Kent SJ, et al. (2020) Antibody-dependent enhancement and SARS-CoV-2 vaccines and therapies. Nat Microbiol 5: 1185–1191. doi:10.1038/s41564-020-00789-5. [PubMed] [CrossRef] [Google Scholar]

46. Gan ES, Ting DHR, Chan KR. (2017) The mechanistic role of antibodies to dengue virus in protection and disease pathogenesis. Expert Rev Anti Infect Ther 15: 111–119. doi:10.1080/14787210.2017.1254550. [PubMed] [CrossRef] [Google Scholar]

47. Wan Y, Shang J, Sun S, et al. (2020) Molecular mechanism for antibody-dependent enhancement of coronavirus entry. J Virol 94: e02015–e02019. doi:10.1128/JVI.02015-19. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

48. Langerak T, Mumtaz N, Tolk VI, et al. (2019) The possible role of cross-reactive dengue virus antibodies in zika virus pathogenesis. PLOS Pathog 15: e1007640. doi:10.1371/journal.ppat.1007640. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

49. Ricke DO, et al. (2021) Two different antibody-dependent enhancement (ADE) risks for SARS-CoV-2 antibodies. Front Immunol 12: 640093. doi:10.3389/fimmu.2021.640093. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

50. Pang X, Zhang R, Cheng G. (2017) Progress towards understanding the pathogenesis of dengue hemorrhagic fever. Virol Sin 32: 16–22. doi:10.1007/s12250-016-3855-9. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

51. Halstead SB, Mahalingam S, Marovich MA, et al. (2010) Intrinsic antibody-dependent enhancement of microbial infection in macrophages: disease regulation by immune complexes. Lancet Infect Dis 10: 712–722. doi:10.1016/S1473-3099(10)70166-3. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

52. Ulrich H, Pillat MM, Tárnok A, et al. (2020) Dengue fever, COVID ‐19 (SARS‐CoV ‐2), and antibody‐dependent enhancement (ADE): a perspective. Cytometry 97: 662–667. doi:10.1002/cyto.a.24047. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

53. Iwasaki A, Yang Y. (2020) The potential danger of suboptimal antibody responses in COVID-19. Nat Rev Immunol 20: 339–341. doi:10.1038/s41577-020-0321-6. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

54. Zaichuk TA, Nechipurenko YD, Adzhubey AA, et al. (2020) The challenges of vaccine development against betacoronaviruses: antibody dependent enhancement and sendai virus as a possible vaccine vector. Mol Biol 54(6): 922–938. doi:10.1134/S0026893320060151. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

55. Liu L, Wei Q, Lin Q, et al. (2019) Anti–spike IgG causes severe acute lung injury by skewing macrophage responses during Acute SARS-CoV infection. JCI Insight 4: e123158. doi:10.1172/jci.insight.123158. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

56. Zhou Y, Liu Z, Li S, et al. (2021) Enhancement versus neutralization by SARS-CoV-2 antibodies from a convalescent donor associates with distinct epitopes on the RBD. Cell Rep 34: 108699. doi:10.1016/j.celrep.2021.108699. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

57. Arvin AM, Fink K, Schmid MA, et al. (2020) A perspective on potential antibody-dependent enhancement of SARS-CoV-2. Nature 584: 353–363. doi:10.1038/s41586-020-2538-8. [PubMed] [CrossRef] [Google Scholar]

58. Fierz W, Walz B. (2020) Antibody dependent enhancement due to original antigenic sin and the development of SARS. Front Immunol 11: 1120. doi:10.3389/fimmu.2020.01120. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

59. Jaume M, Yip MS, Cheung CY, et al. (2011) Anti-severe acute respiratory syndrome coronavirus spike antibodies trigger infection of human immune cells via a PH- and cysteine protease-independent Fc R pathway. J Virol 85: 10582–10597. doi:10.1128/JVI.00671-11. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

60. Shrock E, Fujimura E, Kula T, et al. (2020) Viral epitope profiling of COVID-19 patients reveals cross-reactivity and correlates of severity. Science 370(6520): eabd4250. doi:10.1126/science.abd4250. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

61. Shukla R, Ramasamy V, Shanmugam RK, et al. (2020) Antibody-dependent enhancement: a challenge for developing a safe dengue vaccine. Front Cell Infect Microbiol 10: 572681. doi:10.3389/fcimb.2020.572681. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

62. Cui G, Si L, Wang Y, et al. (2021) Antibody‐dependent enhancement (ADE) of dengue virus: identification of the key amino acid that is vital in denv vaccine research. J Gene Med 23(2): e3297. doi:10.1002/jgm.3297. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

63. Cardozo T, Veazey R. (2021) Informed consent disclosure to vaccine trial subjects of risk of COVID‐19 vaccines worsening clinical disease. Int J Clin Pract 75(3): e13795. doi:10.1111/ijcp.13795. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

64. Andreano E, Piccini G, Licastro D, et al. (2020) SARS-CoV-2 escape in vitro from a highly neutralizing COVID-19 convalescent plasma. bioRxiv, in press. [PMC free article] [PubMed] [Google Scholar]

65. Letko M, Marzi A, Munster V. (2020) Functional assessment of cell entry and receptor usage for SARS-CoV-2 and other lineage B betacoronaviruses. Nat. Microbiol 5: 562–569. doi:10.1038/s41564-020-0688-y. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

66. Weisblum Y, Schmidt F, Zhang F, et al. (2020) Escape from neutralizing antibodies by SARS-CoV-2 spike protein variants. eLife 9: e61312. doi:10.7554/eLife.61312. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

67. Chen J, Wang R, Wang M, et al. (2020) Mutations strengthened SARS-CoV-2 infectivity. J Mol Biol 432: 5212–5226. doi:10.1016/j.jmb.2020.07.009. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

68. Pachetti M, Marini B, Benedetti F, et al. (2020) Emerging SARS-CoV-2 mutation hot spots include a novel RNA-dependent-RNA polymerase variant. J Transl Med 18: 179. doi:10.1186/s12967-020-02344-6. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

69. Giovanetti M, Benedetti F, Campisi G, et al. (2021) Evolution patterns of SARS-CoV-2: snapshot on its genome variants. Biochem Biophys Res Commun 538: 88–91. doi:10.1016/j.bbrc.2020.10.102. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

70. Jangra S, Ye C, Rathnasinghe R, et al. (2021) The E484K mutation in the SARS-CoV-2 spike protein reduces but does not abolish neutralizing activity of human convalescent and post-vaccination sera. medRxiv, in press. [Google Scholar]

71. Nagy Á, Pongor S, Győrffy B. (2021) Different mutations in SARS-CoV-2 associate with severe and mild outcome. Int J Antimicrob Agents 57: 106272. doi:10.1016/j.ijantimicag.2020.106272. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

72. Liu Z, VanBlargan LA, Bloyet L-M, et al. (2021) Identification of SARS-CoV-2 spike mutations that attenuate monoclonal and serum antibody neutralization. Cell Host Microbe 29: 477–488.e4. doi:10.1016/j.chom.2021.01.014. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

73. Korber B, Fischer WM, Gnanakaran S, et al. (2020) Tracking changes in SARS-CoV-2 spike: evidence that D614G increases infectivity of the COVID-19 virus. Cell 182: 812–827.e19. doi:10.1016/j.cell.2020.06.043. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

74. Davies NG, Abbott S, Barnard RC, et al. (2020) Estimated transmissibility and impact of SARS-CoV-2 lineage B.1.1.7 in England. Science 372(6538): eabg3055. [PMC free article] [PubMed] [Google Scholar]

75. Greaney AJ, Loes AN, Crawford KHD, et al. (2021) Comprehensive mapping of mutations to the SARS-CoV-2 receptor-binding domain that affect recognition by polyclonal human serum antibodies. Cell Host Microbe 29(3): 463–476.e6. [PMC free article] [PubMed] [Google Scholar]

76. National Institute of Infectious Diseases , JAPAN (2021) Brief report: new variant strain of SARS-CoV-2 identified in travelers from Brazil. January 12, 2021. [Online] Available at: https://www.niid.go.jp/niid/en/2019-ncov-e/10108-covid19-33-en.html (accessed September 2021).

77. Plante JA, Liu Y, Liu J, et al. (2021) Spike mutation D614G alters SARS-CoV-2 fitness. Nature 592: 116–121. doi:10.1038/s41586-020-2895-3. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

78. Erol A. (2021) Are the emerging SARS-COV-2 mutations friend or foe? Immunol Lett 230: 63–64. doi:10.1016/j.imlet.2020.12.014. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

79. Ali F, Kasry A, Amin M. (2021) The new SARS-CoV-2 strain shows a stronger binding affinity to ACE2 due to N501Y mutant. Med. Drug Discov 10: 100086. doi:10.1016/j.medidd.2021.100086. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

80. Wise J. (2021) Covid-19: the E484K mutation and the risks it poses. BMJ 372: n359. doi:10.1136/bmj.n359. [PubMed] [CrossRef] [Google Scholar]

81. da Silva Francisco R, Jr, Benites LF, Lamarca AP, et al. (2021) Pervasive transmission of E484K and emergence of VUI-NP13L with evidence of SARS-CoV-2 Co-infection events by two different lineages in Rio Grande Do Sul, Brazil. Virus Res 296: 198345. doi:10.1016/j.virusres.2021.198345. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

82. Xie X, Liu Y, Liu J, et al. (2021) Neutralization of SARS-CoV-2 Spike 69/70 Deletion, E484K, and N501Y Variants by BNT162b2 Vaccine-Elicited Sera. Nat Med 27(4): 620–621. [PubMed] [Google Scholar]

83. European Centre for Disease Prevention and Control (2021) Emergence of SARS-CoV-2 B.1.617 variants in India and situation in the EU/EEA– 11 May 2021. Stockholm: ECDC [Google Scholar]

84. Lopez Bernal J, Andrews N, Gower C, et al. (2021) Effectiveness of Covid-19 vaccines against the B.1.617.2 (Delta) variant. N Engl J Med 385: 585–594. doi:10.1056/NEJMoa2108891. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

85. Xiang T, Liang B, Fang Y, et al. (2021) Declining levels of neutralizing antibodies against SARS-CoV-2 in convalescent COVID-19 patients one year post symptom onset. Front Immunol 12: 708523. doi:10.3389/fimmu.2021.708523. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

86. Favresse J, Bayart J-L, Mullier F, et al. (2021) Antibody titres decline 3-month post-vaccination with BNT162b2. Emerg Microb Infect 10: 1495–1498. doi:10.1080/22221751.2021.1953403. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

87. Morales-Núñez JJ, Muñoz-Valle JF, Meza-López C, et al. (2021) Neutralizing antibodies titers and side effects in response to BNT162b2 vaccine in healthcare workers with and without prior SARS-CoV-2 infection. Vaccines 9: 742. doi:10.3390/vaccines9070742. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

88. Pegu A, O’Connell S, Schmidt SD, et al. (2021) Durability of MRNA-1273 vaccine–induced antibodies against SARS-CoV-2 variants. Science 2021: eabj4176. doi:10.1126/science.abj4176. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

89. Zheng Y, Li R, Liu S. (2020) Immunoregulation with MTOR inhibitors to prevent COVID‐19 severity: a novel intervention strategy beyond vaccines and specific antiviral medicines. J Med Virol 92: 1495–1500. doi:10.1002/jmv.26009. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

90. Karam BS, Morris RS, Bramante CT, et al. (2021) MTOR inhibition in COVID‐19: a commentary and review of efficacy in RNA viruses. J Med Virol 93: 1843–1846. doi:10.1002/jmv.26728. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

91. Ramaiah MJ. (2020) MTOR inhibition and P53 activation, MicroRNAs: the possible therapy against pandemic COVID-19. Gene Rep 20: 100765. doi:10.1016/j.genrep.2020.100765. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

92. Fagone P, Ciurleo R, Lombardo SD, et al. (2020) Transcriptional landscape of SARS-CoV-2 infection dismantles pathogenic pathways activated by the virus, proposes unique sex-specific differences and predicts tailored therapeutic strategies. Autoimmun Rev 19: 102571. doi:10.1016/j.autrev. 2020. 102571. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

Immunoglobulin signature predicts risk of post-acute COVID-19 syndrome

Nature Communications volume 13, Article number: 446 (2022) 

Abstract

Following acute infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) a significant proportion of individuals develop prolonged symptoms, a serious condition termed post-acute coronavirus disease 2019 (COVID-19) syndrome (PACS) or long COVID. Predictors of PACS are needed. In a prospective multicentric cohort study of 215 individuals, we study COVID-19 patients during primary infection and up to one year later, compared to healthy subjects. We discover an immunoglobulin (Ig) signature, based on total IgM and IgG3 levels, which – combined with age, history of asthma bronchiale, and five symptoms during primary infection – is able to predict the risk of PACS independently of timepoint of blood sampling. We validate the score in an independent cohort of 395 individuals with COVID-19. Our results highlight the benefit of measuring Igs for the early identification of patients at high risk for PACS, which facilitates the study of targeted treatment and pathomechanisms of PACS.

Introduction

Infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can cause asymptomatic or symptomatic coronavirus disease 2019 (COVID-19). As of October 25, 2021, more than 244 million SARS-CoV-2 infections have been confirmed worldwide that have caused at least 5 million deaths. Symptoms of acute SARS-CoV-2 infection can include fever, fatigue, myalgia, weakness, headache, rhinorrhea, dry cough, shortness of breath (dyspnea), change in smell or taste, nausea, vomiting, and diarrhea1. Following infection, a rapid systemic immune response is mounted against SARS-CoV-2, characterized by increased serum concentrations of chemokines and pro-inflammatory cytokines, such as interleukin (IL)-6 and tumor necrosis factor (TNF), and the appearance of activated monocytes, followed by SARS-CoV-2-specific immunoglobulin M (IgM), IgA, and IgG antibodies and interferon-γ-producing T cells2,3,4,5,6,7. This concerted action of the immune system controls the replication of SARS-CoV-2, and infectious SARS-CoV-2 cannot be isolated from the respiratory tract after 3 weeks8. This typically coincides with the recovery of most individuals with symptomatic COVID-19.

However, about one-third of individuals report one or more COVID-19-related symptoms that last for more than 4 weeks (i.e. 29 days and more) after the onset of the first COVID-19-related symptom9,10, a condition termed post-acute COVID-19 syndrome (PACS) or long COVID. Community prevalence of PACS has been estimated in most studies to lie between 10% and 60%, which depends on the definition of PACS used and patient care level10,11. PACS can be further subdivided into subacute COVID-19 when COVID-19-related symptoms last 12 weeks (84 days) or less versus post-COVID-19 syndrome, which defines patients with COVID-19-related symptoms persisting for more than 84 days after onset of their first symptoms of COVID-1912. The most frequent symptoms of PACS are reported to be fatigue, dyspnea, and cognitive impairment (also termed “brain fog”, which includes loss of concentration and memory), as well as pain and aches at different sites (including headache), cough, change in smell or taste, and diarrhea12,13. As PACS is increasingly recognized as a serious consequence of SARS-CoV-2 infection, early identification of individuals at risk of developing PACS is needed.

A recent study analyzed PACS in individuals who self-reported their symptoms by using an app. The authors found PACS to correlate with increased hospitalization rate and comorbidities, such as lung disease, asthma bronchiale, and heart disease, and they concluded that age, female sex, and number of symptoms during the first week of disease could be used to estimate an individual’s risk for PACS14. However, self-reported data and telehealth are at risk for bias, and risk factors associated with a severe course of primary SARS-CoV-2 infection complicate the detection of underlying risk factors for PACS independent of disease severity. To address these issues, we have characterized a prospective cohort of 215 individuals by clinical visits and laboratory analyses up to one year of follow-up. We found distinct patterns of total immunoglobulin (Ig) levels in patients with COVID-19 and integrated these in a clinical prediction score, which allowed early identification of both outpatients and hospitalized individuals with COVID-19 that were at high risk for PACS.

Results

Characteristics of COVID-19 patients with and without PACS

Our multicentric cohort included 175 individuals with reverse-transcriptase quantitative polymerase chain reaction-confirmed SARS-CoV-2 infection as well as 40 healthy controls without acute symptoms and negative SARS-CoV-2-specific immunoassays. A total of 134 individuals were followed up, including 123 patients at about 6 months and 50 patients at one year after primary SARS-CoV-2 infection (Fig. 1). Based on the classification by the World Health Organization15, we distinguished 89 mild and 45 severe COVID-19 cases attending follow-up and further subclassified them into four cases of asymptomatic disease, 76 mild illness, nine mild pneumonia, 20 severe pneumonia, and 25 acute respiratory distress syndrome (ARDS), including five mild, 10 moderate, and 10 severe ARDS cases (Table 1).

figure 1
Fig. 1: Flow chart of COVID-19 patients and healthy controls enrolled in the study.

Table 1 Participant characteristics at inclusion and 6-month follow-up.Full size table

53.9% of mild COVID-19 cases and 82.2% of patients that developed severe COVID-19 had PACS, defined—as aforementioned—by the persistence of one or more COVID-19-related symptoms for more than four weeks (i.e. 29 days and more) after the onset of the first COVID-19-related symptom (Table 1). Conversely, only 8.6% of healthy controls experienced one or more symptoms for more than 28 days during the one-year follow-up period (Table 1). The most common prolonged symptoms were fatigue, dyspnea, a change in smell or taste, and anxiety or depression. Symptoms of PACS were about 2–6.5-fold more frequent in severe compared to mild COVID-19 cases overall, with the exception of smell or taste disorders (Table 1).

In patients with severe disease, laboratory values taken at primary infection showed signs of lymphopenia and systemic inflammation, including increased concentrations of C-reactive protein (CRP), IL-6, and TNF, and some of these inflammatory markers remained perturbed at 6-month follow-up (Table 1).

When studying the above-mentioned demographic characteristics, comorbidities, and laboratory values at primary SARS-CoV-2 infection in individuals experiencing PACS, we observed several differences (Table 2). Compared to individuals without PACS, the group of patients experiencing PACS contained a larger percentage of severe COVID-19 cases (odds ratio 3.87; p = 0.001), showed more COVID-19-related symptoms during primary infection (odds ratio 1.81; p = 0.001), were of higher age (odds ratio 1.67; p = 0.008), and more often required hospitalization (odds ratio 2.55; p = 0.014) (Table 2). Sex distribution between the groups of our cohort with and without PACS was similar (p = 0.840). Moreover, we observed an association of risk of developing PACS with a history of lung disease (odds ratio 6.29; p = 0.004) and, particularly, asthma bronchiale (odds ratio 9.74; p = 0.003) (Table 2). Furthermore, CRP and TNF concentrations were slightly higher at primary SARS-CoV-2 infection in individuals later developing PACS, although the inflammatory parameters did not have largely increased odds ratios (odds ratios 1.01 and 1.07; p = 0.022 and 0.049, respectively) (Table 2). Collectively, we observed that several determinants of severe COVID-19, including age, hospitalization, and an increase of certain inflammatory markers, present during primary infection correlated with an increased risk of developing PACS.Table 2 Characteristics of patients during primary SARS-CoV-2 infection correlating with post-acute COVID-19 syndrome (PACS).Full size table

Distinct immunoglobulin signature correlating with development of PACS

We assessed serum concentrations of IgA and IgG antibodies specific for the SARS-CoV-2 spike protein subunit 1 (S1) and of total Igs. Compared to healthy controls, we detected increased serum titers of SARS-CoV-2 S1-specific IgA and IgG, in both mild and severe COVID-19 cases, with higher titers found in severe COVID-19 cases (Table 1), confirming the previous findings6. Comparison of individuals with and without PACS revealed that at primary infection S1-specific IgA and IgG values were similar between these two groups (Table 2).

On measuring total serum concentrations of different Igs, we made several interesting findings. Compared to healthy controls, IgM and IgG1 were indifferent in COVID-19 patients, whereas IgG3 was significantly increased in COVID-19 patients (Fig. 2a). Differentiating mild versus severe COVID-19, IgM was lower in severe compared to mild COVID-19 patients and healthy controls, both at primary infection and 6-month follow-up. IgG1 was indifferent, whereas IgG3 was higher in both mild and severe COVID-19 cases, compared to healthy controls (Fig. 2b and Supplementary Fig. 1a). IgM levels negatively correlated with age, whereas none of the IgG subclasses showed a significant trend with age (Fig. 2c).

figure 2
Fig. 2: Specific and total immunoglobulins at primary infection and follow-up.

In individuals developing PACS, we detected decreased IgM, both at primary infection and 6-month follow-up (Fig. 2d). Whereas IgG1 was unaltered, IgG3 tended to be lower in patients with PACS (Fig. 2d), which was contrary to the increased IgG3 concentrations in both mild and severe COVID-19 cases (Fig. 2a). IgA, IgG2, and IgG4 were neither significantly different in patients with PACS compared to without PACS nor did they show a trend that differed from the one observed in mild and severe COVID-19 cases (Supplementary Fig. 1b–e). Assessment of temporal changes in COVID-19 patients, of whom we had blood samples at primary infection, 6-month, and 1-year follow-up, showed that these total serum Ig concentrations remained stable over time (Fig. 2e and Supplementary Fig. 1f).

In notable contrast to the increased IgG3 concentrations in both mild and severe COVID-19 cases (Fig. 2b), IgG3 showed a trend to being lower in patients developing PACS (Fig. 2d, f). This discrepancy in IgG3 was also evident when analyzing the proportion of IgG3 within total IgG during primary infection, with severe COVID-19 patients without PACS demonstrating increased IgG3, whereas severe COVID-19 patients developing PACS failed to show such increase in IgG3 (Fig. 2g). Other IgG subclasses did not show such changes (Supplementary Fig. 1g).

Notably, individuals with either low IgM or low IgG3 had an increased risk of developing PACS, whereas patients with both high IgM and high IgG3 were less likely to develop PACS (Fig. 2h). In line with this finding, we observed in healthy controls that contracted COVID-19 during the course of this study (Supplementary Table 1), those developing PACS had low IgM prior to SARS-CoV-2 infection, which remained low during the observation period (Supplementary Fig. 1h).

Building of an immunoglobulin signature-based score predicting PACS

We extended the identified Ig signature to comprise additional parameters readily available during primary infection. Building on a previously published prediction model14, we considered patient age and number of symptoms during primary infection. For all continuous variables a linear relationship with the outcome PACS was accepted (Supplementary Fig. 2a). We found patient age and number of symptoms were increased in patients developing PACS (Fig. 3a and Supplementary Fig. 2b), whereas sex was not (Table 2). The symptom count during primary infection correlated with the maximal followed-up disease severity of COVID-19 patients (Fig. 3b). Vice versa, disease severity was associated with an increased risk of PACS (Supplementary Fig. 2c).

figure 3
Fig. 3: Prediction of post-acute COVID-19 syndrome (PACS) based on clinical features and immunoglobulin signature.

Regardless of their COVID-19 severity, 94% of individuals with a history of asthma bronchiale developed PACS and 71% developed post-COVID-19 syndrome defined as prolonged symptoms for more than 12 weeks after symptom onset. This is in stark contrast to 59% of individuals without a history of asthma bronchiale developing PACS and 42% developing post-COVID-19 syndrome (Fig. 3c). Interestingly, healthy controls and COVID-19 patients with a history of asthma bronchiale had lower serum IgG3 concentrations compared to their counterparts (Fig. 3d).

We applied our data obtained during primary infection to test different models predicting PACS. Use of a symptom-based score14, reliant on age, sex, and a number of symptoms during primary infection revealed an area under the curve (AUC) value of the receiver operating characteristic curve of 68% (CI 59–78%) and moderately underestimated the risk for PACS with a calibration-in-the-large of 1.76, a calibration slope of 0.57 and a Brier score of 0.328 (Fig. 3e). Based on our findings, we assessed previously identified predictors, such as patient age, sex, number of symptoms, body-mass-index, comorbidities, disease severity, and level of care as well as different combinations of serum Ig concentrations during primary infection to support development of a model predicting PACS (Supplementary Table 2). By combining patient age, number of symptoms during primary infection, history of asthma bronchiale, and an Ig signature consisting of IgM and IgG3 during primary infection, we were able to calculate a risk score—which we termed PACS score—that resulted in an AUC value of 77% (CI 69–85%) with a calibration-in-the-large of 0, a calibration slope of 1 and a Brier score of 0.185. To minimize overfitting, we modified the PACS score by shrinkage of the estimated coefficients. In a sensitivity analysis, the PACS score demonstrated, using the corresponding 6-month follow-up Ig measurements of our COVID-19 patients, a preserved calibration and ability to identify individuals with PACS with an AUC of 74% (CI 65–84%), a calibration-in-the-large of 0, a calibration slope of 1.2 and a Brier score of 0.191 (Fig. 3f). The addition of an interaction term between IgM and IgG3 significantly improved our PACS score (ANOVA; p = 0.02) compared to a model without interaction of IgM and IgG3 (Fig. 3g and Supplementary Tables 2 and 3).

Comparison of our PACS score to a recently published symptom-based score by Sudre et al.14 showed optimal performance of our PACS score in hospitalized patients of our cohort (Fig. 3h, i). We used our PACS score in an independent validation cohort of 395 individuals with confirmed COVID-19, including a small subgroup of hospitalized COVID-19 cases. This validated the improved predictive performance of our PACS score in the subgroup of hospitalized patients, resulting in an AUC of 99%, while the use of our PACS scores in the entire validation cohort resulted in an AUC of 64% (CI 58–69%) with a calibration-in-the-large of –0.3, a calibration slope of 0.8, and a Brier score of 0.239 (Fig. 3j and Supplementary Table 2). The PACS score performed well in the validation cohort, which consisted mainly of outpatients that showed a tendency to low IgG3 in individuals that had not recovered after 6 months (Supplementary Fig. 3a, b). Consistent with optimal performance in hospitalized patients, when applied to mild and severe COVID-19 patients, the PACS score performed better in the latter across all grades of severe COVID-19 (Supplementary Fig. 4a). Moreover, sensitivity analysis using different definitions of PACS showed a maintained ability of the PACS score to identify patients developing post-COVID-19 syndrome with symptoms lasting for more than 12 weeks and patients of the validation cohort that had not recovered after 6 months (Supplementary Fig. 4b).

Finally, we performed decision curve analyses, thus weighing the relative harms of false-positive and false-negative predictions. These decision curve analyses assessed the clinical utility of our PACS score and identified a range of threshold probabilities, in which the model could support clinical decision making compared to alternative intervention strategies, e.g. treating nobody or treating everybody with COVID-1916. The PACS score showed the best clinical utility within threshold probability ranges of 40% and 100% and an independently validated utility in ranges between 40% and 60% (Fig. 3k). Subgroup analysis in hospitalized patients revealed best clinical utility within probability threshold ranges of 35–100% and 55–100% in the derivation and validation cohort, respectively (Fig. 3l). Next, we calculated two probability thresholds as rule-in cut-offs for different clinical settings with the disparate prevalence of PACS. One threshold (0.52) was selected as optimal cut-off maximizing both sensitivity and specificity in the validation cohort. A second threshold (0.75) was calculated as the optimal cut-off for hospitalized patients of both derivation or validation cohorts independently (Supplementary Table 4). With a positive predictive value (PPV) of 0.88 in the derivation cohort and 0.90 in hospitalized patients, the upper threshold of 0.75 identifies with high specificity patients at very high risk for developing PACS. Conversely, with a PPV of 0.76 in the derivation cohort and 0.67 in outpatients, the lower threshold differentiates between patients at moderate versus high risk for developing PACS, while maintaining high sensitivity and negative predictive value (NPV) (Fig. 3m and Supplementary Table 4).

Discussion

Collectively, we demonstrate that the development of PACS correlates with a distinct Ig signature as well as patient age, history of asthma bronchiale, and a number of symptoms, all measured during primary infection. We translated these findings into a model, termed PACS score. When applied to our cohort comprising 134 followed-up and extensively characterized COVID-19 patients, the PACS score performed better than a symptom-based score14, was independent of timepoint of testing and sex, and only required broadly available Ig measurements rather than specialized tests, such as SARS-CoV-2-specific immunoassays. Despite previous reports on female sex as a risk factor for PACS, male sex is associated with a worse outcome of acute COVID-19, and a sex-independent prediction score benefits from improved applicability to different healthcare settings1,14.

Compared to symptom-based prediction scores, the measurement of an Ig signature allows the identification of patients at risk for developing PACS, particularly, in hospitalized patients. This finding suggests a possible pathomechanism distinct from merely increased inflammation and immune activation. Moreover, unspecific Ig levels are stable over time, unlike inflammatory markers that only transiently increase early in the disease course. This biological stability of Igs further increases their utility as biomarkers, as independence of sampling timepoint facilitates clinical application and Ig signatures can be determined already before infection.

Limitations of our study comprise a non-excludable selection bias of patients enrolled in our study affecting the transferability of our findings to all SARS-CoV-2 RT-qPCR-positive patients, a non-excludable selection bias of patients agreeing to follow-up despite a high follow-up rate of 77%, as well as a limited number of hospitalized patients and differences in study design of the validation cohort. Moreover, our study included only a small number of participants of non-white ethnicity due to Central European demographics, potentially affecting the transferability of our findings.

Based on decision curve analyses we determined the highest clinical benefit of our PACS score to lie between threshold probability ranges of 40–60%, and above 55% in hospitalized patients, meaning that a clinician would advise preventive measures against PACS if the probability of PACS were above 55%. Thus, depending on future intervention strategies, associated side effects, and costs, our PACS score can be applied in a setting where false-positive predictions are of greater harm than false negatives. This would enable clinical studies and prevention strategies targeting high specificity patients at very high risk for developing PACS. We, therefore, suggest our PACS score can be applied to identify outpatients at risk, high-risk asthmatic patients, and hospitalized patients, the latter of which are already at high risk for developing PACS. Reliable identification of high-risk patients not only allows precise recommendation of early medical consultations but also facilitates the study of preventive treatment strategies, such as the use of inhaled corticosteroids in asthmatic and non-asthmatic patients and possibly intravenous Ig therapies17,18. Early measurement of Ig titers upon hospitalization of COVID-19 patients can support clinical decision-making and personalized treatment strategies.

In reflecting on the association of the identified Ig signature correlating with increased risk of PACS, the following aspects are worth considering. IgM and, particularly, IgG3 secretion by B cells is induced by interferons and antagonized by IL-4 signals19,20,21. Thus, reduced production of type I interferons, as proposed to occur in poorly controlled SARS-CoV-2 infection22,23, or a predisposition to secreting increased IL-4 concentrations, as present in asthma bronchiale24, may contribute to a failure to efficiently induce Ig isotype switching to IgG3. This hypothesis is consistent with our finding of low IgG3 in asthma bronchiale patients. Conversely, immune responses dominated by IgG3 can occur with similar temporal dynamics as IgM responses and have been associated with viral infections at mucosal tissues25,26. Thus, the reduced IgG3 concentrations in patients with PACS might support a role for IgG3 in Fc receptor-dependent viral control. Low IgG3 levels have also been linked to chronic fatigue syndrome, a debilitating condition resembling certain symptoms of PACS, as well as an increased rate of respiratory infections18,27.

PACS has been proposed to result from tissue damage due to direct effects of the virus, excessive inflammation, or thrombotic events; alternatively, PACS could be the consequence of bystander or virus-mediated activation of autoreactive T and B cells28. Recent observations of PACS resolution after SARS-CoV-2 vaccination might hint at the depletion of persisting viral reservoirs29. Our results highlight the benefit of measuring Igs for the early identification of patients at high risk for PACS, which in turn is crucial for understanding the pathomechanisms of PACS and identification of preventive measures for treatment and care.

Methods

Experimental study design

Adult individuals were included in the study and visited between April 2020 and August 2021. The study was approved by the Cantonal Ethics Committee of Zurich (BASEC #2016-01440). The majority of participants were of white ethnicity.

Coronavirus disease 2019 (COVID-19) patients

Following written informed consent, 175 patients with quantitative reverse-transcriptase quantitative polymerase chain reaction (RT-qPCR)-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection were recruited for clinical evaluation and sampling of blood. Patients were included based on the selection criteria of SARS-CoV-2 PCR positivity and experiencing acute COVID-19. The multicentric study design comprised patient recruitment at four different hospitals in the area of Zurich, Switzerland, including the University Hospital Zurich (n = 111), the City Hospital Triemli Zurich (n = 35), the Limmattal Hospital (n = 15), and the Uster Hospital (n = 14). No exclusion criteria were applied on the analysis of the 175 COVID-19 patients, with the aim of generating a broadly applicable prediction score. Thus, SARS-CoV-2-specific RT-qPCR-positive individuals were included independently of comorbidities and medication. COVID-19 patients were sampled a first time during their primary SARS-CoV-2 infection (termed “primary infection”), a second time 6 months, and a third time one year after the initial blood sampling (Fig. 1). 39 patients declined follow-up or were not reachable and two patients were deceased. Eight patients only declined laboratory testing at 6-month follow-up and 12 patients at 1-year follow-up, whereas their medical history could be obtained by phone. In all followed-up COVID-19 patients (n = 134) medical history was obtained at least 3.5 months (105 days) after symptom onset to detect the presence or absence of PACS. Blood samples of COVID-19 patients were obtained during primary infection, around six months after symptom onset (n = 115) at an average of 199 days after symptom onset (interquartile range 185–216) and around one year after symptom onset (n = 38) at an average time point of 383 days (interquartile range 371–397) after symptom onset (Supplementary Fig. 5). The follow-up rate was 77% and followed-up patients are considered representative of the larger population of patients initially enrolled in the study (Supplementary Table 5).

Definitions

COVID-19 patients were grouped according to the World Health Organization classification criteria into (a) mild cases, comprising asymptomatic and symptomatic cases of mild illness and mild pneumonia, versus (b) severe cases, including severe pneumonia and acute respiratory distress syndrome (ARDS). Mild illness was defined as patients with uncomplicated respiratory tract infection and/or non-specific symptoms. Pneumonia was defined as the presence of respiratory symptoms, abnormal vital signs such as fever, and pathological lung examination findings, whereas patients with mild pneumonia showed no signs of severe pneumonia and did not require supplemental oxygen therapy. Severe pneumonia was defined as respiratory infection or fever with an observed respiratory rate greater than 30 breaths per minute, severe respiratory distress, and/or a SpO2 ≤ 93% on room air. Patients with severe pneumonia mostly required supplemental oxygen therapy. ARDS classification relied on measured oxygenation impairments (PaO2/FiO2a in mild ARDS ≤ 300 mmHg, moderate ARDS ≤ 200 mmHg, and severe ARDS ≤ 100 mmHg)15,30,31. Our COVID-19 derivation cohort did not contain any patients with sepsis or septic shock. For the validation cohort, patients with severe COVID-19 were identified as hospitalized patients reporting supplemental oxygen therapy during hospitalization. If not otherwise specified, all analyses were performed using the maximal followed-up disease severity of COVID-19 patients. We defined patients with PACS as individuals with PCR-confirmed COVID-19 experiencing one or more symptoms associated with COVID-19 that lasted for more than 4 weeks (i.e. 29 days and more) after the onset of the first COVID-19-related symptom12. Symptoms were assessed in a standardized manner by trained study physicians, both during primary infection (acute COVID-19) and at follow-up visits. During primary infection, five symptoms (fever, fatigue, cough, dyspnea, and gastrointestinal symptoms) were recorded systematically, which were subsequently used for our PACS prediction model. All five symptoms were patient-reported symptoms and, based on a standardized questionnaire, individually asked by a trained physician whether they were present during primary infection. Patient-reported temperature can be inaccurate for various reasons, including individual body temperature norms that vary with patient age as well as method and timepoint of measurement. Therefore, the following was considered as patient-reported “fever”: (i) reported increase of body temperature, (ii) fever chills, or (iii) sweating32,33. Gastrointestinal symptoms were counted as one symptom, also when multiple gastrointestinal symptoms were reported, including nausea, loss of appetite, heartburn, abdominal pain, flatulence, diarrhea, and obstipation. The severity of symptoms was not assessed. During follow-up visits, patients were asked whether and when they recovered from COVID-19 and which symptoms persisted. A total of nine symptoms were recorded systematically at follow-up visits (fever, cough, dyspnea, fatigue, gastrointestinal symptoms, headache, chest pain, anxiety and/or depression, and disorders of smell and/or taste; Table 1). Additional patient-reported prolonged symptoms were also recorded. Symptom severity was not assessed. When using the more stringent definition of PACS as symptoms lasting for more than 12 weeks, termed post-COVID-19 syndrome12, we found the preserved performance of our PACS prediction model (Supplementary Fig. 4b). For our validation cohort, we used the same definition of PACS as for our derivation cohort, i.e. patient-reported COVID-19-related symptoms lasting longer than four weeks, and we performed a sensitivity analysis showing preserved model performance using a different outcome based on whether patients had recovered after six months (Supplementary Fig. 4b).

Healthy controls

Following written informed consent, we additionally included 40 healthy controls who had no history of SARS-CoV-2 infection-associated symptoms, such as fever, rhinorrhea, respiratory symptoms (e.g. dry cough or shortness of breath), change in smell or taste, nausea, vomiting, and diarrhea1 and had a negative SARS-CoV-2 spike S1 protein-specific immunoassay, whereby individuals with borderline and positive values were excluded. Moreover, our healthy controls had no acute or active illness prior to or at blood sampling and no history of autoimmune disorder. We obtained a medical history from all 40 healthy controls at their blood sampling and at least 6 months thereafter in 35 healthy controls. Five healthy controls got infected with SARS-CoV-2 during the follow-up period and were therefore excluded from clinical follow-up (Supplementary Table 1). Participants were not compensated.

Validation cohort

Prognostic models were validated in a separate cohort of 395 PCR-confirmed COVID-19 patients that were enrolled at diagnosis between 06 August 2020 and 19 January 2021 and prospectively followed-up for 6 months after infection34. All serum samples were obtained during primary infection and in 98% of patients at 2 weeks after the diagnosis of COVID-19 with a median sampling time point of 19 days (interquartile range 17–22 days) after the onset of the first COVID-19-related symptom (Table 1). Pre-existing comorbidities and COVID-19 symptoms were recorded at baseline using standardized, self-administered, electronic questionnaires. Details regarding relevant medical history were clarified via phone by trained study personnel. Patient-reported symptoms were reassessed 1, 3, and 6 months after diagnosis. After 6 months, patients were asked whether they had recovered from COVID-19 or not.

Immunoassays

All laboratory tests were performed in accredited laboratories at the University Hospital Zurich. Blood samples were collected in BD Vacutainer CAT serum tubes (Becton Dickinson; Cat# 367896). Different serum immunoglobulins subsets and IgG subclasses were measured using the commercially available turbidimetric Optilite® assays using an Optilite® analyzer (The Binding Site Group Ltd; Cat# NK004.OPT, NK006–NK010.OPT, NK012.OPT). Laboratory reference values are as follows (g/l): IgM (0.4–2.8), IgA (0.7–4.0), IgG (7.0–16.0), IgG1 (2.8–8.0), IgG2 (1.15–5.70), IgG3 (0.24–1.25), IgG4 (0.052–1.25). SARS-CoV-2-specific IgA and IgG antibodies were measured, as previously established6, by using a commercial enzyme-linked immunosorbent assay (ELISA) specific for the SARS-CoV-2 spike S1 protein (Euroimmun SARS-CoV-2 IgA and IgG immunoassay; Cat# EI 2606-9601A and G). Interleukin IL-6 and tumor necrosis factor (TNF) were quantified using R&D Systems assays (Cat# S6050 and LHSCM210, respectively). Antibody dilutions were prepared according to the manufacturer’s instructions. Blood samples obtained after SARS-CoV-2 vaccination were excluded from comparisons of SARS-CoV-2-specific Ig titers. We observed no sex differences in the measured total Igs and S1-specific antibody titers (Supplementary Fig. 6).

Clinical prediction model

The sample for the development of our prediction model was obtained by including and following up all consecutive patients between April 2020 and August 2021 and resulted in a total of 134 followed-up patients. The number of outcome events was 85, which corresponds to the number of patients experiencing PACS. The required sample size for the development of clinical prediction models is a matter of active discussion and research. Our PACS score was developed using 14.2 events per predictor parameter, which is in line with the rule of thumb of 15 events per predictor parameter as well as several other recommendations on the required number of events per predictor parameter for accurate modeling in logistic regression analysis35. More precise estimates of the required sample size could be calculated based on published parameters of the previous studies36. However, as only one previous model for PACS prediction was available at the time of our study, and as definitions and prevalence of PACS in different populations vary significantly, we were unable to calculate precise requirements for model development. This might be reflected by some optimism in predictor effect estimates of our model (yielding a global shrinkage factor of 0.72) and a small overestimation of the overall risk for PACS (after shrinkage) in the validation cohort, that might be promoted by shrinkage of predictor effect estimates37.

Thus, the sample size was considered adequate to develop a prediction model with six predictor variables. These predictor variables were based on previous publications (age + number of symptoms during primary infection + history of asthma bronchiale)14,38 and include two new variables (total IgM + total IgG3) as well as one interaction term (total IgM * total IgG3), yielding 14.2 events per predictor parameter35,39,40,41,42,43. The validation cohort amounted to a sample size of 395 and counted 216 events, which was in line with a suggested sample size of 400 and an outcome event size of 200 in order to obtain precise calibration curves44.

The symptom-based prediction score was calculated using a previously published model14 and modified by applying it on five recorded symptoms instead of 14. The following five symptoms were recorded during primary infection: fever, fatigue, cough, shortness of breath (dyspnea), and gastrointestinal symptoms.

Our prediction model (PACS score) was built on a published prediction model14 that was based on “age + sex + number of symptoms during primary infection”. We have evaluated the three suggested predictors, together with other reported risk factors for PACS, such as asthma bronchiale14,38. Selection of new variables was a hypothesis-driven process based on the observation that total immunoglobulins are altered in COVID-19 patients experiencing long-term symptoms (Fig. 2), and previous studies connecting low total IgG3 levels to chronic fatigue syndrome and increased susceptibility to infection18,45. As some variables such as “age” represent risk factors for severe COVID-19 disease, a risk factor for PACS itself38, we further explored the influence of COVID-19 disease severity as well as associated risk factors (Table 2, Supplementary Table 2, and Supplementary Fig. 2c).

Moreover, we modified the estimated coefficients of the PACS score by shrinkage. As prognostic models tend to describe optimally the evaluated dataset but may perform less well in other datasets, we addressed this phenomenon of overfitting by applying the statistical method of shrinkage. Estimated coefficients of the generalized linear model were multiplied with a global shrinkage factor (0.72) that was calculated using the dfbeta-method46,47. Original and regression coefficients after shrinkage are summarized in Supplementary Table 3 with 95% confidence intervals (CI) and corresponding p values. Areas under the curve (AUC) of receiver operating characteristic (ROC) curves and calibration plots were calculated as previously described48,49. The PACS score was validated in a separate validation cohort using the same patient-centered outcome definition for PACS as in the derivation cohort. The PACS score (after shrinkage) can be calculated and the logistic regression model reproduced using the following R code: PACS_score < - glm(PACS_score ~ –1 + offset(–0.981011 + 0.2616998*scale(age)+0.3307986*number of symptoms during primary infection + 1.896502*history of asthma bronchiale + 0.8429766*total IgM (g/l) + 1.3716198*total IgG3 (g/l)–1.5316550*IgM*IgG3), family = binomial, data = patient_data_to_test), with the variables “age” in years, “number of symptoms during primary infection” ranging from zero to five, and “history of asthma bronchiale” as number zero if absent and number one if present. Individual risk for PACS can further be predicted using the following R code: predictions < −predict(PACS_score, patient_data_to_test, type = “response”). The number of symptoms can be determined by counting the occurrence of the following five symptom categories in tested COVID-19 patients (all self-reported): fever, fatigue, cough, shortness of breath (dyspnea), and gastrointestinal symptoms.

Statistics

Descriptive statistics for the followed-up healthy controls, COVID-19 patients, and validation cohort are presented as numbers and percentages of the total for categorical variables, as well as the median and interquartile range (IQR) for continuous variables. Comparison of variables was performed using non-parametric Wilcoxon’s rank-sum test if not otherwise specified. Evidence was quantified on a continuous scale, as results were considered exploratory. Thus, p values are to be interpreted as quantified evidence of the hypothesis without specified significance thresholds. In Table 2, odds ratios of categorical variables were calculated by median-unbiased estimation and odds ratios of continuous variables were calculated using univariate, unadjusted regression models for the outcome PACS. Horizontal lines in split violin plots indicate median values. Wedge sizes of radar plots visualize median values of measured immunoglobulins in patients with or without PACS, normalized by dividing the respective median with the overall median measured in COVID-19 patients. Microsoft Office Excel (version 2102) was used for data collection. Statistical analyses were performed with R (version 4.1.2) and using the packages “biostatUZH” (version 1.8.0), “CalibrationCurves” (version 0.1.2), “dcurves” (version 0.2.0), “epitools” (version 0.5-10.1), “interactions” (version 1.1.5), “gbm” (version 2.1.8), “ggstatsplot” (version 0.9.0), “interactions”, “pROC” (version 1.18.0), “mgcv” (version 1.8-38), “shrink” (version 1.2.1), and “sjPlot” (version 2.8.9), and missing values were omitted. The present study is reported according to the STROBE (Statement for reporting cohort studies) and TRIPOD (Statement for reporting clinical prediction models) guidelines50,51.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

All relevant data generated in this study are provided in the Supplementary Information. A PACS score calculator is accessible online (www.pacs-score.com). Source data are provided with this paper.

Code availability

R code for immunoglobulin signature analysis and prediction model development is provided in the Supplementary Software 1.

References

  1. 1.Wiersinga, W. J., Rhodes, A., Cheng, A. C., Peacock, S. J. & Prescott, H. C. Pathophysiology, transmission, diagnosis, and treatment of coronavirus disease 2019 (COVID-19): a review. JAMA 324, 782 (2020).CAS Article Google Scholar 
  2. 2.Silvin, A. et al. Elevated calprotectin and abnormal myeloid cell subsets discriminate severe from mild COVID-19. Cell 182, 1401 (2020).CAS Article Google Scholar 
  3. 3.Schulte-Schrepping, J. et al. Severe COVID-19 is marked by a dysregulated myeloid cell compartment. Cell 182, 1419 (2020).CAS Article Google Scholar 
  4. 4.Chevrier, S. et al. A distinct innate immune signature marks progression from mild to severe COVID-19. Cell Rep. Med. 2, 100166 (2021).Article Google Scholar 
  5. 5.To, K. K. et al. Temporal profiles of viral load in posterior oropharyngeal saliva samples and serum antibody responses during infection by SARS-CoV-2: an observational cohort study. Lancet Infect. Dis. 20, 565 (2020).CAS Article Google Scholar 
  6. 6.Cervia, C. et al. Systemic and mucosal antibody responses specific to SARS-CoV-2 during mild versus severe COVID-19. J. Allergy Clin. Immunol. 147, 545 (2021).CAS Article Google Scholar 
  7. 7.Blanco-Melo, D. et al. Imbalanced host response to SARS-CoV-2 drives development of COVID-19. Cell 181, 1036 (2020).CAS Article Google Scholar 
  8. 8.van Kampen, J. J. A. et al. Duration and key determinants of infectious virus shedding in hospitalized patients with coronavirus disease-2019 (COVID-19). Nat. Commun. 12, 267 (2021).Article Google Scholar 
  9. 9.Office for National Statistics (ONS). Prevalence of Long COVID Symptoms and COVID-19 Complications https://www.ons.gov.uk/peoplepopulationandcommunity/healthandsocialcare/healthandlifeexpectancies/datasets/prevalenceoflongcovidsymptomsandcovid19complications (2020).
  10. 10.National Institute for Health Research (NIHR). Living with Covid 19—Second Review https://evidence.nihr.ac.uk/themedreview/living-with-covid19-second-review/https://doi.org/10.3310/themedreview_45225 (2021).
  11. 11.Menges, D. et al. Burden of post-COVID-19 syndrome and implications for healthcare service planning: a Population-based Cohort Study. PLoS ONE https://doi.org/10.1371/journal.pone.0254523 (2021).
  12. 12.Shah, W., Hillman, T., Playford, E. D. & Hishmeh, L. Managing the long term effects of covid-19: summary of NICE, SIGN, and RCGP rapid guideline. BMJ 372, (2021) https://doi.org/10.1136/bmj.n136.
  13. 13.Lambert, N. et al. COVID-19 survivors’ reports of the timing, duration, and health impacts of post-acute sequelae of SARS-CoV-2 (PASC) infection. Preprint at medRxiv https://doi.org/10.1101/2021.03.22.21254026 (2021).
  14. 14.Sudre, C. H. et al. Attributes and predictors of long COVID. Nat. Med. https://doi.org/10.1038/s41591-021-01292-y (2021).
  15. 15.WHO. COVID-19 Clinical management: living guidance. World Health Organization www.who.int/publications/i/item/WHO-2019-nCoV-clinical-2021-1 (2021).
  16. 16.Vickers, A. J. & Elkin, E. B. Decision curve analysis: a novel method for evaluating prediction models. Med. Decis. Mak. 26, 565 (2006).Article Google Scholar 
  17. 17.Ramakrishnan, S. et al. Inhaled budesonide in the treatment of early COVID-19 (STOIC): a phase 2, open-label, randomised controlled trial. Lancet Respir. Med. 9, 763 (2021).CAS Article Google Scholar 
  18. 18.Scheibenbogen, C. et al. Tolerability and efficacy of s.c. IgG self-treatment in ME/CFS patients with IgG/IgG subclass deficiency: a Proof-of-Concept Study. J. Clin. Med. 10, 2420 (2021).Article Google Scholar 
  19. 19.Snapper, C. M. et al. Induction of IgG3 secretion by interferon gamma: a model for T cell-independent class switching in response to T cell-independent type 2 antigens. J. Exp. Med. 175, 1367 (1992).CAS Article Google Scholar 
  20. 20.Le Bon, A. et al. Type I interferons potently enhance humoral immunity and can promote isotype switching by stimulating dendritic cells in vivo. Immunity 14, 461 (2001).Article Google Scholar 
  21. 21.Deenick, E. K., Hasbold, J. & Hodgkin, P. D. Decision criteria for resolving isotype switching conflicts by B cells. Eur. J. Immunol. 35, 2949 (2005).CAS Article Google Scholar 
  22. 22.Hadjadj, J. et al. Impaired type I interferon activity and inflammatory responses in severe COVID-19 patients. Science 369, 718 (2020).ADS CAS Article Google Scholar 
  23. 23.Sprent, J. & King, C. COVID-19 vaccine side effects: the positives about feeling bad. Sci. Immunol. 6, eabj9256 (2021).Article Google Scholar 
  24. 24.Akdis, C. A. et al. Type 2 immunity in the skin and lungs. Allergy 75, 1582 (2020).CAS Article Google Scholar 
  25. 25.Hjelholt, A., Christiansen, G., Sørensen, U. S. & Birkelund, S. IgG subclass profiles in normal human sera of antibodies specific to five kinds of microbial antigens. Pathog. Dis. 67, 206 (2013).CAS Article Google Scholar 
  26. 26.Lemos, M. P. et al. In men at risk of HIV infection, IgM, IgG1, IgG3, and IgA reach the human foreskin epidermis. Mucosal Immunol. 9, 798 (2016).CAS Article Google Scholar 
  27. 27.Kedor, C. et al. Chronic COVID-19 Syndrome and Chronic Fatigue Syndrome (ME/CFS) following the first pandemic wave in Germany—a first analysis of a prospective observational study. Preprint at medRxiv https://doi.org/10.1101/2021.02.06.21249256 (2021).
  28. 28.Akbar, A. et al. Report: long-term immunological health consequences of COVID-19. Br. Soc. Immunol. www.immunology.org/sites/default/files/BSI_Briefing_Note_August_2020_FINAL.pdf (2020).
  29. 29.Arnold, D. et al. Are vaccines safe in patients with Long COVID? A prospective observational study. medRxiv (2021), https://doi.org/10.1101/2021.03.11.21253225.30.
  30. 30.WHO. COVID-19 Clinical Management: Interim Guidance (World Health Organization, 2021).
  31. 31.ARDS-Definition-Task-Force. et al. Acute respiratory distress syndrome: the Berlin Definition. JAMA 307, 2526 (2012).Google Scholar 
  32. 32.Shann, F. & Mackenzie, A. Comparison of rectal, axillary, and forehead temperatures. Arch. Pediatr. Adolesc. Med. 150, 74 (1996).CAS Article Google Scholar 
  33. 33.Quer, G. et al. Wearable sensor data and self-reported symptoms for COVID-19 detection. Nat. Med. 27, 73 (2021).CAS Article Google Scholar 
  34. 34.ISRCTN registry. Zurich Coronavirus Cohort: an Observational Study to Determine Long-term Clinical Outcomes and Immune Responses After Coronavirus Infection (COVID-19), Assess the Influence of Virus Genetics, and Examine the Spread of the Coronavirus in the Population of the Canton of Zurich, Switzerland https://doi.org/10.1186/ISRCTN14990068 (2020).
  35. 35.Harrel, F. E. Regression modeling strategies. hbiostat https://hbiostat.org/doc/rms.pdf (2021).
  36. 36.Riley, R. D. et al. Minimum sample size for developing a multivariable prediction model: PART II – binary and time-to-event outcomes. Stat. Med. 38, 1276 (2019).MathSciNet Article Google Scholar 
  37. 37.Riley, R. D. et al. Penalization and shrinkage methods produced unreliable clinical prediction models especially when sample size was small. J. Clin. Epidemiol. 132, 88 (2021).Article Google Scholar 
  38. 38.Blomberg, B. et al. Long COVID in a prospective cohort of home-isolated patients. Nat. Medhttps://doi.org/10.1038/s41591-021-01433-3 (2021).
  39. 39.Harrell, F. E., Lee, K. L., Califf, R. M., Pryor, D. B. & Rosati, R. A. Regression modelling strategies for improved prognostic prediction. Stat. Med. 3, 143 (1984).Article Google Scholar 
  40. 40.Harrell, F. E., Lee, K. L., Matchar, D. B. & Reichert, T. A. Regression models for prognostic prediction: advantages, problems, and suggested solutions. Cancer Treat. Rep. 69, 1071 (1985).PubMed Google Scholar 
  41. 41.Peduzzi, P., Concato, J., Feinstein, A. R. & Holford, T. R. Importance of events per independent variable in proportional hazards regression analysis II. Accuracy and precision of regression estimates. J. Clin. Epidemiol. 48, 1503 (1995).CAS Article Google Scholar 
  42. 42.Peduzzi, P., Concato, J., Kemper, E., Holford, T. R. & Feinstein, A. R. A simulation study of the number of events per variable in logistic regression analysis. J. Clin. Epidemiol. 49, 1373 (1996).CAS Article Google Scholar 
  43. 43.Vittinghoff, E. & McCulloch, C. E. Relaxing the Rule of Ten events per variable in logistic and Cox regression. Am. J. Epidemiol. 165, 710 (2007).Article Google Scholar 
  44. 44.Van Calster, B., McLernon, D. J., van Smeden, M., Wynants, L. & Steyerberg, E. W. Calibration: the Achilles heel of predictive analytics. BMC Med. 17, 230 (2019).Article Google Scholar 
  45. 45.Löbel, M. et al. Polymorphism in COMT is associated with IgG3 subclass level and susceptibility to infection in patients with chronic fatigue syndrome. J. Transl. Med. 13, 264 (2015).Article Google Scholar 
  46. 46.Dunkler, D., Sauerbrei, W. & Heinze, G. Global, parameterwise and joint shrinkage factor estimation. J. Stat. Softw. 69, 1 (2016).Article Google Scholar 
  47. 47.Held, U. et al. Prognostic function to estimate the probability of meaningful clinical improvement after surgery—results of a prospective multicenter observational cohort study on patients with lumbar spinal stenosis. PLoS ONE 13, e0207126 (2018).Article Google Scholar 
  48. 48.Robin, X. et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinforma. 12, 77 (2011).Article Google Scholar 
  49. 49.Spiegelhalter, D. J. Probabilistic prediction in patient management and clinical trials. Stat. Med. 5, 421 (1986).CAS Article Google Scholar 
  50. 50.Collins, G. S., Reitsma, J. B., Altman, D. G. & Moons, K. G. M. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ 350, g7594 (2015).Article Google Scholar 
  51. 51.Von Elm, E. et al. Strengthening the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies. BMJ 335, 806 (2007).Article Google Scholar 

Download references

Acknowledgements

We thank the diagnostic laboratories of the University Hospital Zurich, Alessandra Guaita, Claudia Meloni, Jennifer Jörger, Barbara Turi, Alberto Turi, and the members of the Boyman laboratory for helpful discussions and support. Swiss National Science Foundation (NRP 78 Implementation Program to C.C. and O.B.; #4078P0-198431 to O.B. and J.N.; and #310030-200669 to O.B.), Digitalization Initiative of the Zurich Higher Education Institutions (#2021.1_RAC_ID_34 to C.C.), Clinical Research Priority Program CYTIMM-Z of University of Zurich (UZH) (to O.B.), Pandemic Fund of UZH (to O.B.), Innovation grant of USZ (to O.B.), UZH Forschungskredit Candoc (#FK-20-022 to S.A.), Swiss Academy of Medical Sciences (SAMW) fellowships (#323530-191220 to C.C.; #323530-191230 to Y.Z.; #323530-177975 to S.A.), Young Talents in Clinical Research Fellowship (YTCR 32/18) by SAMW and Bangerter Foundation (to M.R.).

Author information

Affiliations

  1. Department of Immunology, University Hospital Zurich, University of Zurich, Zurich, SwitzerlandCarlo Cervia, Yves Zurbuchen, Patrick Taeschler, Sara Hasler, Sarah Adamo, Miro E. Raeber, Jakob Nilsson & Onur Boyman
  2. Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, SwitzerlandTala Ballouz, Dominik Menges, Ulrike Held & Milo A. Puhan
  3. Clinic for Internal Medicine, Uster Hospital, Uster, SwitzerlandEsther Bächli
  4. Department of Medicine, Limmattal Hospital, Schlieren, SwitzerlandAlain Rudiger
  5. Clinic for Internal Medicine, City Hospital Triemli Zurich, Zurich, SwitzerlandMelina Stüssi-Helbling & Lars C. Huber
  6. Faculty of Medicine, University of Zurich, Zurich, SwitzerlandOnur Boyman

Contributions

Conceptualization: O.B.; Methodology: C.C., J.N., U.H., O.B.; Investigation: C.C., Y.Z., P.T., D.M., T.B., S.H., E.B., A.R., M.S.H., L.C.H., U.H., M.A.P., O.B.; Visualization: C.C., O.B.; Funding acquisition: C.C., Y.Z., S.A., M.E.R., J.N., O.B.; Project administration: S.H., M.E.R.; Supervision: O.B.; Writing: C.C., O.B.

Corresponding author

Correspondence to Onur Boyman.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review information

Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Description of Additional Supplementary Files

Supplementary Software 1

Reporting Summary

Source data

Source Data

Necessity of COVID-19 Vaccination in Persons Who Have Already Had COVID-19

Authors: Nabin K ShresthaPatrick C BurkeAmy S NowackiPaul TerpelukSteven M Gordon

Clinical Infectious Diseases, ciac022, https://doi.org/10.1093/cid/ciac022

The purpose of this study was to evaluate the necessity of COVID-19 vaccination in persons with prior COVID-19.Methods

Employees of Cleveland Clinic working in Ohio on Dec 16, 2020, the day COVID-19 vaccination was started, were included. Anyone who tested positive for COVID-19 at least once before the study start date was considered previously infected. One was considered vaccinated 14 days after receiving the second dose of a COVID-19 mRNA vaccine. The cumulative incidence of COVID-19, symptomatic COVID-19, and hospitalizations for COVID-19, were examined over the next year.Results

Among 52238 employees, 4718 (9%) were previously infected, and 36922 (71%) were vaccinated by the study’s end. Cumulative incidence of COVID-19 was substantially higher throughout for those previously uninfected who remained unvaccinated than for all other groups, lower for the vaccinated than unvaccinated, and lower for those previously infected than those not. Incidence of COVID-19 increased dramatically in all groups after the Omicron variant emerged. In multivariable Cox proportional hazards regression, both prior COVID-19 and vaccination were independently associated with significantly lower risk of COVID-19. Among previously infected subjects, a lower risk of COVID-19 overall was not demonstrated, but vaccination was associated with a significantly lower risk of symptomatic COVID-19 in both the pre-Omicron (HR 0.60, 95% CI 0.40–0.90) and Omicron (HR 0.36, 95% CI 0.23–0.57) phases.Conclusions

Both previous infection and vaccination provide substantial protection against COVID-19. Vaccination of previously infected individuals does not provide additional protection against COVID-19 for several months, but after that provides significant protection at least against symptomatic COVID-19.SARS-CoV-2COVID-19IncidenceVaccinesImmunityTopic: 

Issue Section: Major Article PDF

New data show those who recovered from Covid-19 were less likely than vaccinated to get infected during Delta wave

New data released Wednesday showed that both vaccination and prior infection offered strong protection against infection and hospitalization from Covid-19 during the Delta wave — and that case and hospitalization rates were actually lower among people who had recovered from Covid-19 than among those who had been vaccinated.

The data, released by the Centers for Disease Control and Prevention and health agencies in California and New York, are sure to inflame arguments from those who insist they don’t need to be vaccinated if they can show they’ve recovered from Covid-19. But the data contain many caveats that health officials stressed pointed to the value of vaccination, even on top of prior infection.

For one, the new report was based on data only through November, before the U.S. booster campaign really took off. It also looked at data during the Delta wave and does not account for the surging Omicron variant.

And while research has shown that infection can train the immune system to guard against the coronavirus in different ways than vaccination, Covid-19 also has killed more than 850,000 people in this country, sickened — often severely — millions more, and caused untold cases of long Covid. Serious side effects from the Covid-19 vaccines are extremely rare.

“We know that vaccination remains the safest strategy for protecting against Covid-19,” Benjamin Silk, a CDC epidemiologist, told reporters Wednesday.

The data also confirmed something we’ve known for a long time: Those who weren’t vaccinated and also hadn’t been previously infected were far more likely to be infected and hospitalized than either group.

The new report examined Covid-19 trends among adults in New York and California from May 30 to Nov. 20, 2021.

In early October, after Delta became dominant, infection rates among vaccinated people who hadn’t had Covid were 6.2-fold lower than among unvaccinated people who hadn’t had Covid-19 in California, and 4.5-fold lower in New York. People who previously had Covid-19 but had not been vaccinated had 29-fold (California) and 14.7-fold (New York) lower case rates. Vaccinated people who had also had Covid-19 had the lowest rates, with a 32.5-fold (California) and 19.8-fold (New York) lower infection rate than people who had no protection.

Hospitalization rates in California followed a similar pattern, the report says. (There were no hospitalization data from New York.) In October, hospitalization rates for people who’d been vaccinated but hadn’t had Covid were 19.8-fold lower than among those who hadn’t had Covid-19 or been vaccinated. The rates were 55.3-fold lower among unvaccinated people who’d had Covid-19, and 57.5-fold lower among people who’d been vaccinated and had Covid-19.

Erica Pan, California’s state epidemiologist, said hospitalizations among those who were vaccinated were mostly among older people.

Incidences among people who’d been vaccinated were highest among people who received the Johnson & Johnson shot, followed by the Pfizer-BioNTech and then the Moderna shots, the report said.

“Infection-derived protection was higher after the Delta variant became predominant, a time when vaccine-induced immunity for many persons declined because of immune evasion and immunologic waning,” the report states. Immune evasion refers to how, as the virus evolved, it started to erode the protection elicited by vaccination or an infection from an earlier form of the virus; this happened to some degree with the Delta variant, and to a much larger extent with the Omicron variant.

The new CDC report notes that the analyzed data are from the period before most people had received additional shots. It was only in mid-October, for example, that the government authorized booster shots for people who had received the J&J vaccine, recommending that people get them two months after the original jab of the one-dose shot. Boosters weren’t given the green light for all adults until November.