So you’ve had COVID and have now recovered. You don’t have ongoing symptoms and luckily, you don’t seem to have developed long COVID.
But what impacts has COVID had on your overall immune system?
It’s early days yet. But growing evidence suggests there are changes to your immune system that may put you at risk of other infectious diseases.
Here’s what we know so far.
A round of viral infections
Over this past winter, many of us have had what seemed like a continual round of viral illness. This may have included COVID, influenza or infection with respiratory syncytial virus. We may have recovered from one infection, only to get another.
Then there is the re-emergence of infectious diseases globally such as monkeypox or polio.
Could these all be connected? Does COVID somehow weaken the immune system to make us more prone to other infectious diseases?
There are many reasons for infectious diseases to emerge in new locations, after many decades, or in new populations. So we cannot jump to the conclusion COVID infections have given rise to these and other viral infections.
But evidence is building of the negative impact of COVID on a healthy individual’s immune system, several weeks after symptoms have subsided.
What happens when you catch a virus?
There are three possible outcomes after a viral infection:
your immune system clears the infection and you recover (for instance, with rhinovirus which causes the common cold)
your immune system fights the virus into “latency” and you recover with a virus dormant in our bodies (for instance, varicella zoster virus, which causes chickenpox)
your immune system fights, and despite best efforts the virus remains “chronic,” replicating at very low levels (this can occur for hepatitis C virus).
Ideally we all want option 1, to clear the virus. In fact, most of us clear SARS-CoV-2, the virus that causes COVID. That’s through a complex process, using many different parts of our immune system.
But international evidence suggests changes to our immune cells after SARS-CoV-2 infection may have other impacts. It may affect our ability to fight other viruses, as well as other pathogens, such as bacteria or fungi.
How much do we know?
An Australian study has found SARS-CoV-2 alters the balance of immune cells up to 24 weeks after clearing the infection.
There were changes to the relative numbers and types of immune cells between people who had recovered from COVID compared with healthy people who had not been infected.
Researchers found fewer of these cells circulating after people recovered from COVID. The ones that remained were less able to activate white blood cells known as T-cells, a critical step in activating anti-viral immunity.
Other studies have found different impacts on T-cells, and other types of white blood cells known as B-cells (cells involved in producing antibodies).
After SARS-CoV-2 infection, one study found evidence many of these cells had been activated and “exhausted.” This suggests the cells are dysfunctional, and might not be able to adequately fight a subsequent infection. In other words, sustained activation of these immune cells after a SARS-CoV-2 infection may have an impact on other inflammatory diseases.
One study found people who had recovered from COVID have changes in different types of B-cells. This included changes in the cells’ metabolism, which may impact how these cells function. Given B-cells are critical for producing antibodies, we’re not quite sure of the precise implications.
Could this influence how our bodies produce antibodies against SARS-CoV-2 should we encounter it again? Or could this impact our ability to produce antibodies against pathogens more broadly—against other viruses, bacteria or fungi? The study did not say.
What impact will these changes have?
One of the main concerns is whether such changes may impact how the immune system responds to other infections, or whether these changes might worsen or cause other chronic conditions.
So more work needs to be done to understand the long-term impact of SARS-CoV-2 infection on a person’s immune system.
For instance, we still don’t know how long these changes to the immune system last, and if the immune system recovers. We also don’t know if SARS-CoV-2 triggers other chronic illnesses, such as chronic fatigue syndrome (myalgic encephalomyelitis). Research into this is ongoing.
What we do know is that having a healthy immune system and being vaccinated (when a vaccine has been developed) is critically important to have the best chance of fighting any infection.
Authors: Nurshad Ali*Department of Biochemistry and Molecular Biology, Shahjalal University of Science and Technology, Sylhet, Bangladesh Optom Open Access 2022, Vol 7(3): 3DOI: 10.4172/2476-213X.1000152
Vitamin D is a steroid hormone, produced endogenously with the effect of ultraviolet radiation on the skin or available from exogenous food sources or dietary supplements. Vitamin D insufficiency is a public health problem affecting over a billion people across all life stages worldwide. In the past decade, several studies demonstrated a potential link between vitamin D deficiency and various diseases, including systemic infection. Vitamin D insufficiency affects the immune functions as vitamin D exerts an immunomodulation role, increasing innate immunity by secretion of antiviral peptides, which improves mucosal defenses . In clinical studies, low levels of serum vitamin D were associated with acute respiratory tract infections including epidemic influenza.
The outbreak and fast spreading of SARS-CoV-2 are a global health threat with an unstable outcome worldwide. A recent data reported the antiviral effects of vitamin D, which can hinder viral replication directly, and also be effective in an anti-inflammatory and immunomodulatory way. It seems that SARS-CoV-2 primarily uses the immune evasion process during infection, which is followed by hyper reaction and cytokine storm in some patients, as a known pathogenic process of acute respiratory disease syndrome (ARDS) development . SARS-CoV-2 uses angiotensin-converting enzyme 2 as the host receptor to enter into alveolar and intestinal epithelial cells. Subsequent dysregulation of the renin–angiotensin system may lead to excess cytokine production resulting in prospective fatal ARDS.
Considering the differences in the severity and fatality of COVID-19 in the globe, it is important to understand the reasons behind it. Improvement of immunity through better nutrition might be a considerable factor. The nutrient such as vitamin D shows significant roles in immune function. However, little is known about the role of vitamin D in preventing COVID-19 infection and fatality. This study evaluated the correlation of vitamin D concentrations with COVID-19 cases and deaths per one million of the population in 20 European countries using data from the COVID-19 pandemic data portal for 20 May 2020 (most countries after peak). This review also discussed the possible preventing role of vitamin D in acute respiratory tract infections . Furthermore, the available studies that determined the role of vitamin D in COVID-19 severity and mortality have been discussed. PubMed, Google Scholar, Web of Science, Scopus, Cochrane Central Register of Controlled Trials, and medRXiv were searched for relevant literature about the role of vitamin D in COVID-19 infections, severity, and mortality.
Vitamin D and mechanisms to decrease viral infections
Some recent reviews demonstrated some pathways by which vitamin D decreases the risk of microbial infections. Vitamin D follows different mechanisms in reducing the risk of viral infection and mortality. To reduce the risk of common cold, vitamin D uses three pathways: physical barrier, cellular natural immunity, and adaptive immunity . A recent review also supported the possible role of vitamin D in decreasing the risk of COVID-19 infections and mortality. These comprise maintaining of cell junctions, and gap junctions, increasing cellular immunity by decreasing the cytokine storm with influence on interferon γ and tumor necrosis factor α and regulating adaptive immunity through inhibiting T helper cell type 1 responses and stimulating of T cells induction. Vitamin D supplementation was also found to enhance CD4+ T cell count in HIV infection.
One of the major manifestations of severe SARS-CoV-2 infection is lymphopenia. In both the mouse models and in human cell lines, vitamin D exerted activity in lung tissue and played protective effects on experimental interstitial pneumonitis . Several in vitro studies demonstrated that vitamin D plays a significant role in local “respiratory homeostasis” either by stimulating the exhibition of antimicrobial peptides or by directly interfering with the replication of respiratory viruses. Vitamin D insufficiency can, therefore, be involved in ARDS and heart failure and these are the manifestations of severely ill COVID-19 subjects . Therefore, vitamin D deficiency promotes the renin-angiotensin system (RAS), which may lead to chronic cardiovascular disease (CVD) and reduced lung function. Although, many studies supported the immunomodulatory characteristics of vitamin D and its significant role in the maintenance of immune homeostasis; well-designed randomized controlled trials are required to elucidate the plausible role of vitamin D in protective immune responses against respiratory microbes and in preventing various types of acute respiratory tract infections.
The relevance of vitamin D to COVID-19
Yet, it is important to fully elucidate the virulence mechanisms of COVID-19, several cellular mechanisms including Papainlike protease (PLpro)-mediated replication, dipeptidyl peptidase-4 receptor (DPP-4/CD26) binding, disruption of M-protein mediated type-1 IFN induction and MDA5 and RIG-I host-recognition evasion have been recognized in the closely-related COVID-MERS virus. Of the above processal, human DPP-4/CD26 has been exhibited to connect with the S1 domain of the COVID-19 spike glycoprotein, suggesting that it could also be a salient virulence factor in Covid-19 infection. The expression of the DPP-4/CD26 receptor is reduced significantly in vivo upon the correctness of vitamin D insufficiency . There is also an indication that maintaining of vitamin D may reduce some of the unfavorable downstream immunological sequelae thought to extract poorer clinical outcome in Covid-19 infection, such as interleukin 6 elevation, delayed interferon-gamma response, and, a negative prognostic marker in subjects with acutely-ill pneumonia, including those having Covid-19.
Epidemiological and clinical observations regarding COVID-19
Some clinical and epidemiological studies support to outline the hypothesis regarding COVID-19 and its relationship with vitamin D status. Recent studies indicated that COVID-19 is associated with the increased generation of pro-inflammatory cytokines, C-reactive protein (CRP), ARDS, pneumonia, and heart failure. In China, chronic fatality rates were 6-10% for people with chronic respiratory tract disease, cardiovascular disease, hypertension, and diabetes . In other studies, serum concentrations of 25(OH) D were inversely associated with pro-inflammatory cytokines, IL-6, increased CRP, and increased risk of pneumonia, ARDS, diabetes and heart failure. In randomized control trials, vitamin D supplementation has been shown to reduce the risk of respiratory diseases. A placebo-controlled trial with 5660 subjects showed that vitamin D supplementation significantly reduces the risk of respiratory tract infections. A review included five clinical studies reported that respiratory tract infections were significantly lower in the vitamin D supplementation group than the control group . Another study included 25 randomized controlled trials, with 10,933 participants in total from 14 different countries indicated the beneficial effects of vitamin D supplementation in reducing the risk of at least one acute respiratory tract infection.
A proportion of patients surviving acute coronavirus disease 2019 (COVID-19) infection develop post-acute COVID syndrome (long COVID (LC)) lasting longer than 12 weeks. Here, we studied individuals with LC compared to age- and gender-matched recovered individuals without LC, unexposed donors and individuals infected with other coronaviruses. Patients with LC had highly activated innate immune cells, lacked naive T and B cells and showed elevated expression of type I IFN (IFN-β) and type III IFN (IFN-λ1) that remained persistently high at 8 months after infection. Using a log-linear classification model, we defined an optimal set of analytes that had the strongest association with LC among the 28 analytes measured. Combinations of the inflammatory mediators IFN-β, PTX3, IFN-γ, IFN-λ2/3 and IL-6 associated with LC with 78.5–81.6% accuracy. This work defines immunological parameters associated with LC and suggests future opportunities for prevention and treatment.
Acute COVID-19, caused by infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is characterized by a broad spectrum of clinical severity, from asymptomatic to fatal1,2. The immune response during acute illness contributes to both host defense and pathogenesis of severe COVID-19 (ref. 3). Pronounced immune dysregulation with lymphopenia and increased expression of inflammatory mediators3,4 have been described in the acute phase. Following acute COVID-19 infection, a proportion of patients develop physical and neuropsychiatric symptoms lasting longer than 12 weeks (known as Long COVID, chronic COVID syndrome or post-acute sequelae of COVID-19 (ref. 5)), henceforth denoted as LC. Although similar syndromes have been described following infection with SARS-CoV-1 (ref. 6) and Middle East respiratory syndrome–related coronavirus7, LC often develops after mild-to-moderate COVID-19 (refs. 8,9). Symptoms persisting 6 months were observed in 76% of hospitalized patients, with muscle weakness and fatigue being most frequently reported10,11. LC affects between 10% and 30% of community-managed COVID-19 cases 2 to 3 months after infection12,13 and can persist >8 months after infection14. LC symptoms include severe relapsing fatigue, dyspnea, chest tightness, cough, brain fog and headache15. The underlying pathophysiology of LC is poorly understood.
Here, we analyzed a cohort of individuals followed systematically for 8 months after COVID-19 infection according to a predefined schedule, comparing them to healthy donors unexposed to SARS-CoV-2 (unexposed healthy controls (UHCs)) before December 2019, and individuals who had been infected with prevalent human coronaviruses (HCoVs; HCoV-NL63, O229E, OC43 or HKU1), but not SARS-CoV-2. The ADAPT study9 enrolled adults with SARS-CoV-2 infections confirmed by PCR at St Vincent’s Hospital community-based testing clinics in Sydney (Australia). For the majority of participants, their first visit occurred between months 2 and 3 after infection (median of 79 days after the date of initial diagnosis)9,14, with 93.6% and 84.5% of participants completing subsequent month 4 (median, 128 days) and month 8 (median, 232 days) visits (Table 1). Of the 147 patients recruited (70.5% through ADAPT sites and 29.5% externally), 31 participants (21.08%) were designated as LC based on the occurrence of one of three major symptoms (fatigue, dyspnea or chest pain) at month 4 (Supplementary Table 1). These participants were age and gender matched with 31 asymptomatic matched controls (MCs) from the same cohort who did not report symptoms at month 4 after infection but were symptomatic during the acute phase of the infection (Supplementary Table 2). There was a 10% trend toward some improvement of symptoms over time in LC, but this trend was not statistically significant (Fisher’s exact P = 0.44).Table 1 Patient characteristics
To examine biomarkers associated with LC, we assessed 28 analytes in the serum of patients from the LC, MC, HCoV and UHC groups at month 4 after infection using a bead-based assay. Six proinflammatory cytokines (interferon β (IFN-β), IFN-λ1, IFN-γ, CXCL9, CXCL10, interleukin-8 (IL-8) and soluble T cell immunoglobulin mucin domain 3 (sTIM-3)) were elevated in the LC and MC groups compared to the HCoV and UHC groups (Fig. 1), with no difference observed in the 22 other analytes, including IL-6 and IL-33 (Extended Data Fig. 1). There was no difference between LC and MCs for any individual analyte at this time point (Extended Data Fig. 1a, b). IFN-β was 7.92-fold and 7.39-fold higher in the LC and MC groups compared to the HCoV group and 7.32- and 6.83-fold higher compared to UHCs (Fig. 1a). IFN-λ1 was increased 2.44-fold and 3.24-fold in the LC and MC groups compared to the HCoV group and 2.42- and 3.21-fold compared to UHCs. IL-8 was higher in the LC (3.43-fold) and MC (3.56-fold) groups compared to the HCoV and UHC groups (Fig. 1a). CXCL10 was elevated in the LC group compared to the HCoV (2.15-fold) and UHC (3.2-fold) groups and in the MC group compared to the HCoV (1.7-fold) and UHC (3.06-fold) groups. CXCL9 was 1.69-fold higher in the LC group than in the UHC group, and sTIM-3 was elevated in the LC group, but not the MC group, when compared to the HCoV group (1.46-fold) (Fig. 1a and Extended Data Fig. 1c).
IFN-β and IFN-λ1 decreased 4.4-fold and 1.8-fold, respectively, in the MC group at month 8 compared to month 4 (Fig. 1b). In the LC group, IFN-β decreased by 1.5-fold, and IFN-λ1 increased by 1.05-fold at month 8 compared to month 4, which was not statistically significant (Fig. 1b). At month 8, IFN-β and IFN-λ1 remained significantly elevated in the LC group compared to the MC, HCoV and UHC groups (Extended Data Fig. 2a). Reductions in CXCL9, CXCL10, IL-8 and sTIM-3 were observed in the LC and MC groups at month 8 compared to month 4 (Fig. 1b). At month 8, there was also decreased expression of some of the 22 analytes that were not significantly different among the four groups at month 4 (Extended Data Fig. 2b,c).
Because plasma ACE2 activity has been reported to be elevated 114 days after SARS-CoV-2 infection16, we investigated whether this occurred in our cohort at months 3, 4 and 8 after infection. Median plasma ACE2 activity was significantly higher in both LC and MC groups compared to the HCoV group at month 3 (LC, 1.92-fold; MC, 2.47-fold) and month 4 (LC, 1.75-fold; MC, 2.62-fold) after infection (Fig. 1c). At month 8, plasma ACE2 activity in the LC and MC groups decreased to levels observed in the HCoV and UHC groups (Fig. 1c). No difference was observed within LC and MC groups at months 3, 4 or 8, but both groups had higher activity compared to the HCoV group, suggesting that this parameter is specific to SARS-CoV-2 infection and is not a common feature of other coronaviruses.
Next, we used a classification model to determine an optimal set of analytes most strongly associated with LC. This linear classifier was trained on log-transformed analyte data to reduce the bias observed in each of the analytes and improve model accuracy. This log-linear classification model was used to develop a metric for feature importance17. To identify analytes that were associated with LC and not MC, we used the analyte data at month 8, the time point with the greatest difference between the LC and MC groups. The performance of each of the log-linear models was quantified by an accuracy estimate and an F1 score evaluated by taking averages after bootstrapping, which randomly sampled from the original population to create a new population. By considering every possible pair of the 28 serum analytes and plasma ACE2 activity, a classification model including two analytes (IFN-β and pentraxin 3 (PTX3)) had an LC prognostic accuracy of 78.54% and an F1 score of 0.77. Three analytes (IFN-β, PTX3 and IFN-γ) achieved an accuracy of 79.68%, with an F1 score of 0.79. Four analytes (IFN-β, PTX3, IFN-λ2/3 and IL-6) achieved an accuracy of 81.59% and an F1 score of 0.81. When all 29 analytes were featured, the calculated accuracy was 77.4%, with an F1 score of 0.76 (Table 2).Table 2 Accuracy and F1 score (with confidence intervals) for the top two, three and four features and all features identified by machine learning utilizing a log-linear classification model
After generating 1,000 randomly sampled populations, we counted the number of times each feature appeared in the best performing set of features, combining sets if several sets achieved the same accuracy. This revealed that IFN-β was the most important feature, appearing in 89%, 93% and 94% of the best sets of two, three and four features, respectively (Fig. 2a). Linear classifiers defined a decision boundary. Each patient analyte concentration at month 8 lied on either side of the boundary, and its positioning relative to the boundary determined whether the patient was predicted to experience LC or asymptomatic COVID (Fig. 2b). Although the decision boundary of the four featured analytes at month 8 is four dimensional, the boundary can be visualized with two-dimensional projections of IFN-β against the other highly associated analytes (PTX3, IFN-γ, IFN-λ2/3 and IL-6 (Fig. 2b). Longitudinal levels of these key feature cytokines indicate the advantage of log-linear models in differentiating LC from MCs (Fig. 2c).
To investigate differences in immune cell profiles between LC and MCs, we developed a 19-parameter flow cytometry panel and phenotyped peripheral blood mononuclear cells (PBMCs) from LC and MC donors at months 3 and 8 after infection. Dimensional reduction via TriMap coupled with Phenograph clustering (n = 14; LC = 7, MC = 7) identified 24 distinct cell clusters at month 3 and 21 clusters at month 8 (Extended Data Fig. 3a) including T, B, NK and myeloid cell clusters (Extended Data Fig. 3b,c). Concatenated phenotype data from each of the 7 LC or MC and 7 UHC contributed to every population cluster (Extended Data Fig. 4a–d). Of the 24 subsets identified at month 3, five were absent in LC donors: naive CD127lowGzmB−CCR7+CD45RA+CD27+CD8+ T cells, CD57+GPR56+GzmB+CD8+ T cells, naive CD127loTIM-3−CCR7+CD45RA+CD27+CD4+ T cells, innate-like CD3+CD4−CD8− T cells (may comprise natural killer T cells and γδ-T cells), and naive CD127loTIM-3−CD38lowCD27−IgD+ B cells (Fig. 3a). Three clusters remained absent at month 8 in LC donors (naive CD127lowGzmB−CCR7+CD45RA+CD27+CD8+ T cells, naive CD127lowTIM-3−CCR7+CD45 RA+CD27+CD4+ T cells, and naive CD127lowTIM-3−CD38lowCD27−IgD+ B cells) (Fig. 3b), indicating perturbations at month 8 in LC donors. Naive T and B cells expressing low levels of CD127 and TIM-3 were detected in the MC and UHC groups but were absent in the LC group at months 3 and 8 (Extended Data Fig. 4e,f).
The frequency of highly activated CD38+HLA-DR+ myeloid cells was elevated at month 8 in the LC group compared to MCs (Fig. 3c). Frequencies of activated CD14+CD16+ monocytes were higher in the LC group compared to MCs at months 3 and 8. The percentages of plasmacytoid dendritic cells (pDCs) expressing the activation markers CD86 and CD38 were also higher in the LC group at both time points compared to MCs (Fig. 3c). There was no difference in the frequencies of activated CD11c+ myeloid dendritic cells between month 3 and month 8 (Extended Data Fig. 5a). The T cell activation and exhaustion markers PD-1 and TIM-3 were more highly expressed on CD8+ T cells in the LC group compared to MCs at month 3 (PD-1, 3.04-fold; TIM-3, 1.6-fold) and month 8 (PD-1 2.86-fold) (Fig. 3d). However, PD-1 and TIM-3 coexpression was similar on CD4+ and CD8+ T cells in the LC and MC groups (Extended Data Fig. 5b).
Here, we show that convalescent immune profiles after COVID-19 are different from those following infection with other coronaviruses. Several cytokines (mostly type I and III IFN, but also chemokines downstream of IFN-γ) were highly elevated in individuals following the resolution of active SARS-CoV-2 infection compared to HCoVs and UHCs at month 4 after infection. IFN-β and IFN-λ1 remained elevated in the LC group at month 8 after initial infection, while their levels began to resolve in MCs. Elevated plasma ACE2 activity was noted in the LC and MC groups at month 4 but trended toward normal by month 8 after infection. We identified a set of analytes (IFN-β, PTX3, IFN-γ, IFN-λ2/3 and IL-6) that highly associated with LC at month 8, indicating that components of the acute inflammatory response and activation of fibroblast or epithelial cells, T cells and myeloid cells are associated with LC. Immune cell phenotyping indicated chronic activation of a subset of CD8+ T cells, with expansion of PD-1+ and TIM-3+ subsets and pDCs and monocytes persisting from month 3 to month 8 in the LC group. These changes were accompanied by an absence of naive T and B cell subsets expressing low levels of CD127 and TIM-3 in peripheral blood of patients with LC. These findings suggest that SARS-CoV-2 infection exerts unique prolonged residual effects on the innate and adaptive immune systems and that this may be driving the symptomology known as LC.
IFN-β and IFN-λ1 were highly elevated in convalescent COVID-19 samples compared to HCoV and UHC samples. Although these levels decreased over time in patients who recovered, they remained high in patients with LC. The morbidity of acute COVID-19 infection appears to correlate with high expression of type I and III IFN in the lungs of patients18. IFN-λ produced by murine lung dendritic cells in response to synthetic viral RNA is associated with damage to lung epithelium19, and IFN-λ signaling hampers lung repair during influenza infection in mice20. Severe acute COVID-19 has been associated with diminished type I IFN and enhanced IL-6 and tumor necrosis factor (TNF) responses19. Although our cohort of individuals with LC consisted mostly of patients with mild or moderate initial illness, elevated type I and III IFN levels were maintained to month 8 after infection and are consistent with the observed prolonged activation of pDCs, indicating a chronic inflammatory response.
Patients with COVID-19 who are admitted to the intensive care unit have high plasma levels of sTIM-3 (ref. 21). We found elevated levels of sTIM-3 in the LC group, but not in the MC or HCoV groups, which is consistent with the expanded subsets of memory CD8+ T cells expressing TIM-3 and PD-1 and indicates chronic T cell activation and potentially exhaustion. Similarly, shedding of membrane-bound protein ACE-2 during acute infection22 resulting in increased activity in plasma16 continues into convalescence, regardless of symptom severity at month 4, and normalizes at month 8 in most patients.
We employed a log-linear classification model to assess all combinations of analytes to determine the subset of analytes most strongly associated with LC. IFN-β, together with PTX3, IFN-λ2/3, IFN-γ and IL-6, differentiated LC from MCs with high accuracy at month 8. IFN-λ2/3 are secreted by pDCs following viral RNA sensing by TLR7, TLR9 and RIG-123,24. PTX3 increased in lung epithelia and plasma of patients with severe COVID-19 and can serve as an independent strong prognostic indicator of short-term mortality25,26,27. IL-6 is a pleiotropic mediator that drives inflammation and immune activation28. A high IL-6/IFN-γ ratio is associated with severe acute COVID-19 infection29. The observation that the best correlate for LC is an eclectic combination of biomarkers reinforces the breadth of host response pathways that are activated during LC.
T cell activation (indicated by CD38 and HLA-DR), T cell exhaustion and increases in B cell plasmablasts occur during severe COVID-19 (refs. 30,31,32). These markers identified highly activated monocytes and pDCs, the frequencies of which decreased over time in MCs, but not in patients with LC. Type I and type III IFN upregulate major histocompatibility complex expression, including HLA-DR33. An unbiased large-scale dimensional reduction approach identified the depletion of three clusters of naive B and T cell subsets present in the LC group at month 8 after infection. Altogether, these observations suggest persistent conversion of naive T cells into activated states, potentially due to bystander activation secondary to underlying inflammation and/or antigen presentation by activated pDCs or monocytes. The ultimate result of this chronic stimulation may be expansion of PD-1+ or TIM-3+ CD8+ memory T cells. Bystander activation of unactivated naive subsets into more activated phenotypes is consistent with observations in acute severe COVID-19 (refs. 34,35).
Although individuals with LC and MCs were matched for age and gender, it is possible that the differences observed reflect differences in unrecognized factors between these groups. Although more LC donors had severe acute disease (eight LC donors and two MCs), sensitivity analyses excluding these patients did not alter the statistical significance of the major associations described here. Because of the timing of ethics approval and cohort setup, samples were not collected during acute infection. We were therefore unable to determine whether elevations in biomarkers during convalescence correlate with levels during acute infection. Although some perturbations observed here are potentially consistent with a hypothesis that the major drivers of the expression of biomarkers in convalescence are those in the acute infection, others are not. Our results require validation in other LC cohorts. Finally, our definition of LC was set internally given the lack of international consensus. Nevertheless, the inclusion of three of the most common persisting symptoms and blinding of cases and controls helped ensure the validity of our findings.
In summary, our data indicate an ongoing, sustained inflammatory response following even mild-to-moderate acute COVID-19, which is not found following prevalent coronavirus infection. The drivers of this activation require further investigation, but possibilities include persistence of antigen, autoimmunity driven by antigenic cross-reactivity or a reflection of damage repair. These observations describe an abnormal immune profile in patients with COVID-19 at extended time points after infection and provide clear support for the existence of a syndrome of LC. Our observations provide an important foundation for understanding the pathophysiology of this syndrome and potential therapeutic avenues for intervention.
The ADAPT study is a prospective cohort study of post–COVID-19 recovery established in April 2020 (ref. 14). A total of 147 participants with confirmed SARS-CoV-2 infection were enrolled, the majority following testing in community-based clinics run by St Vincent’s Hospital Sydney, with some patients also enrolled with confirmed infection at external sites. Initial study follow-up was planned for 12 months after COVID-19 and subsequently extended to 2 years. Extensive clinical data and a biorepository was systematically collected prospectively. The aims of ADAPT are to evaluate a number of outcomes after COVID-19 relating to pathophysiology, immunology and clinical sequalae. Laboratory testing for SARS-CoV-2 was performed using nucleic acid detection from respiratory specimens with the EasyScreen Respiratory Detection kit (Genetic Signatures) and the EasyScreen SARS-CoV-2 detection kit. Two ADAPT cohort subpopulations were defined based on initial severity of COVID-19 illness: (1) patients managed in the community and (2) patients admitted to the hospital for acute infection (including those requiring intensive care support for acute respiratory distress syndrome). Patients were defined as having LC at 4 months based on the presence of one or more of the following symptoms: fatigue, dyspnea or chest pain14. These patients were gender and age (±10 years) matched with ADAPT participants without LC (matched ADAPT controls) (Table 1). Samples for these analyses were collected at the 3-, 4- and 8-month assessments. Our cohort consisted of 62 participants (31 with LC and 31 MCs); enrollment visits were performed at a median of 76 (IQR, 64–93) days after initial infection. Their 4-month assessments were performed at a median of 128 (IQR, 115–142) days after initial infection (4.2 months). Their 8-month assessments were performed at a median of 232 (IQR, 226–253) days after initial infection (7.7 months). The dropout rate has been very low to date (approximately 9.4% at 12 months). Four participants did not complete the 8-month assessment after the 4-month assessment. The reasons for this include ‘did not attend’ (n = 2) and ‘lost to follow-up’ (n = 2). A further population of patients presenting to St Vincent’s Hospital clinics for COVID-19 testing on the multiplex respiratory panel who were PCR positive for any of the four human common cold coronaviruses (HCoV-NL63, O229E, OC43 or HKU1) and PCR negative for SARS-CoV-2 were recruited into the ADAPT-C substudy and used as a comparator group.
The ADAPT study was approved by the St Vincent’s Hospital Research Ethics Committee (2020/ETH00964) and is a registered trial (ACTRN12620000554965). The ADAPT-C substudy was approved by the same committee (2020/ETH01429). All data were stored using REDCap (v11.0.3) electronic data capture tools. Unexposed healthy donors were recruited through St Vincent’s Hospital and approved by St Vincent’s Hospital Research Ethics Committee (HREC/13/SVH/145). The University of Melbourne unexposed donors were approved by Medicine and Dentistry HESC study ID 2056689. All participants gave written informed consent, and patients were not compensated.
Sample processing and flow cytometry
Blood was collected for biomarker analysis (serum separating tube (SST) 8.5 ml x1 (serum) and EDTA 10 ml x1 (plasma)), and 36 ml was collected for PBMCs (ACD (citric acid, trisodium citrate and dextrose) 9 ml x4). Phenotyping of PBMCs was performed as described previously36. Briefly, cryopreserved PBMCs were thawed using RPMI (+L-glutamine) medium (ThermoFisher Scientific) supplemented with penicillin/streptomycin (Sigma-Aldrich) and subsequently stained with antibodies binding to extracellular markers for 20 min. Extracellular panel included Live/Dead dye Near InfraRed, CXCR5 (MU5UBEE) and CD38 (HIT2) (ThermoFisher Scientific); CD3 (UCHT1), CD8 (HIL-72021), PD-1 (EH12.1), TIM-3 (TD3), CD27 (L128), CD45RA (HI100), CD86 (BU63), CD14 (HCD14), CD16 (GB11), IgD (IA6-2), CD25 (2A3) and CD19 (HIB19) (BioLegend); and CD4 (OKT4), CD127 (A019D5), HLA-DR (L234), GRP56 (191B8), CCR7 (G043H7) and CD57 (QA17A04) (BD Biosciences). Perm Buffer II (BD Pharmingen) was used for intracellular staining of granzyme B (GB11, BD Biosciences). Samples were acquired on an Cytek Aurora (BioLegend) using Spectroflo software. Before each run, all samples were fixed in 0.5% paraformaldehyde.
The LEGENDplex Human Anti-Virus Response Panel (IL-1β, IL-6, IL-8, IL-10, IL-12p70, IFN-α2, IFN-β, IFN-λ1, IFN-λ2/3, IFN-γ, TNF-α, IP-10 and GM-CSF) and a custom-made panel (IL-5, IL-9, IL-13, IL-33, PD-1, sTIM-3, sCD25, CCL2 (MCP-1), PTX3, transforming growth factor β1, CXCL9 (MIG-1), myeloperoxidase, PECAM-1, ICAM-1 and VCAM-1) were purchased from BioLegend, and assays were performed as per the manufacturer’s instructions. Beads were acquired and analyzed on a BD Fortessa X20 SORP (BD Biosciences). Samples were run in duplicate, and 4,000 beads were acquired per sample. Data analysis was performed using Qognit LEGENDplex software (BioLegend). Lower limit of detection values were used for all analytes at the lower limit.
Catalytic ACE2 detection in plasma
Plasma ACE2 activity was measured using a validated, sensitive quenched fluorescent substrate-based assay as previously described37. Briefly, plasma (0.25 ml) was diluted into low-ionic-strength buffer (20 mmol l−1 Tris-HCl, pH 6.5) and added to 200 ml ANXSepharose 4 Fast-Flow resin (Amersham Biosciences, GE Healthcare) that removed a previously characterized endogenous inhibitor of ACE2 activity. After binding and washing, the resulting eluate was assayed for ACE2 catalytic activity. Duplicate samples were incubated with the ACE2-specific quenched fluorescent substrate, with or without 100 mM ethylenediaminetetraacetic acid. The rate of substrate cleavage was determined by comparison to a standard curve of the free fluorophore 4-amino-methoxycoumarin (Sigma-Aldrich) and expressed as picomoles of substrate cleaved per milliliter of plasma per minute. The intra- and interassay coefficients of variation were 5.6% and 11.8%, respectively. Samples below the limit of detection were designated 0.02 (half the lower limit of detection; i.e., 50% × 0.04).
The analytes most associated with LC were identified via log-linear classification. For an arbitrary set of four analytes, let the concentration of the ith analyte at 8 months be denoted wi. Log-linear classification assigns a weight ai to the logarithm of each analyte concentration. A linear function of these logged concentrations and weights takes the form f(a⃗ )f(a→) is a threshold parameter. The weights wi and the intercept w0 are selected to maximize the predictive power of the linear classifier by training on the analyte data, where f(a⃗ )>0f(a→)>0 results in the classifier predicting that the participant with analyte concentration a⃗ a→ has LC and does not have LC otherwise.
Because of the modest small sample size of 58 participants at month 8, we performed bootstrapping to randomly sample new populations of size 58 from our population with replacement. The sampled population was then split 29:29 into test and train datasets. The training dataset was used to train a log-linear classifier using Python3 v3.8.10 and the Scikit-learn machine learning package v0.24.1. From the test set, the number of true positives (TPs; both the classifier and data indicate the participant had LC), true negatives (TNs; both the classifier and data indicate the participant had asymptomatic COVID), false positives (FPs; classifier predicts the participant will have LC, but the data disagree) and false negatives (FNs; classifier predicts the participant will have asymptomatic COVID, but the data disagree) were identified. Then, two subsequent scores were calculated. The accuracy is defined as (TP + TN)/(TP + TN + FP + FN) and measures the proportion of test participants that had their COVID status correctly predicted. The second measure is the F1 score and is defined as TP/(TP + 0.5 × (FP + FN)), which is a measure that combines recall, how many LC cases were correctly predicted and precision (of all the participants predicted to have LC, how many were correct). This process is repeated for 1,000 different bootstrapped sample populations. The average accuracy of a model of N analytes is then calculated and used to assess which combination of N analytes performs the best.
Dimensional reduction and clustering analysis
FCS 3.0 files were compensated manually using acquisition-defined matrix as a guide, and the gating strategy was based on unstained or endogenous controls. Live singlets were gated from patients with LC and asymptomatic MCs using FlowJo v.10.7.2, samples were decoded and statistical analysis between groups and unsupervised analysis was performed, with matched asymptomatic controls as the primary comparator group. For unsupervised analysis, the following FlowJo plugins were used: DownSample (v.3), TriMap (v.0.2), Phenograph (v.3.0) and ClusterExplorer (v.1.5.9) (all FlowJo LLC). First, 100,000 events per sample were downsampled from the total live singlet gate (Extended Data Fig. 6). The newly generated FCS files were labeled according to control or patient group (LC or MCs) and concatenated per group. Subsequently, 20,000 events were taken from each grouped sample by downsampling. The two new FCS files corresponding to LC and MCs were then concatenated for dimensionality reduction analysis using TriMap (40,000 events in total). TriMap was conducted using the following parameters to include the markers CD25, CD38, CCR7, CD19, IgD, CD45RA, PD-1, TIM-3, CD4, CD57, CD127, CD27, HLA-DR, CD8, CXCR5, GPR56 and granzyme B and using the following conditions: metric = Euclidean, nearest neighbors = 15 and minimum distance = 0.5. The phenograph plugin was then used to determine clusters of phenotypically related cells. The same markers as TriMap and parameters k = 152 and Run ID = auto were used for analysis. Finally, the ClusterExplorer plugin was used to identify the phenotype of the clusters generated by phenograph.
All column graphs are presented as medians with IQRs. One-way analysis of variance with Kruskal–Wallis and Dunn’s correction for multiple comparisons was used for serum analyte analysis. A Wilcoxon paired t test was used to analyze statistical data with Prism v9.0 (GraphPad) software. For unpaired samples, a Mann–Whitney U test was used. Two-tailed P values less than 0.05 were considered significant (*P < 0.05, **P < 0.01, ***P < 0.001 and ****P < 0.0001).
To protect patient privacy, underlying electronic health records may be accessed via a remote server pending a material transfer agreement and approval from study steering committee. As data within this manuscript are from an ongoing clinical trial, further data will be provide by the corresponding author upon request and will require approval from study steering committee.
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. JAMA324, 782–793 (2020).CASArticlePubMedGoogle Scholar
Lam, M. H. et al. Mental morbidities and chronic fatigue in severe acute respiratory syndrome survivors: long-term follow-up. Arch. Intern. Med.169, 2142–2147 (2009).ArticlePubMedGoogle Scholar
Ahmed, H. et al. Long-term clinical outcomes in survivors of severe acute respiratory syndrome and Middle East respiratory syndrome coronavirus outbreaks after hospitalisation or ICU admission: a systematic review and meta-analysis. J. Rehabil. Med52, jrm00063 (2020).PubMedGoogle Scholar
Halpin, S. J. et al. Postdischarge symptoms and rehabilitation needs in survivors of COVID-19 infection: a cross-sectional evaluation. J. Med Virol.93, 1013–1022 (2021).CASArticlePubMedGoogle Scholar
Darley, D. R. et al. Persistent symptoms up to four months after community and hospital-managed SARS-CoV-2 infection. Med J. Aust.214, 279–280 (2021).ArticlePubMedGoogle Scholar
Sudre, C. H., et al. Anosmia and other SARS-CoV-2 positive test-associated symptoms, across three national, digital surveillance platforms as the COVID-19 pandemic and response unfolded: an observation study. Preprint at medRxivhttps://doi.org/10.1101/2020.12.15.20248096 (2020).
Tenforde, M. W. et al. Characteristics of adult outpatients and inpatients with COVID-19 – 11 academic medical centers, United States, March-May 2020. MMWR Morb. Mortal. Wkly Rep.69, 841–846 (2020).CASArticlePubMedPubMed CentralGoogle Scholar
Patel, S. K. Plasma ACE2 activity is persistently elevated following SARS-CoV-2 infection: implications for COVID-19 pathogenesis and consequences.Eur. Respir. J.57, 2003730 (2021).CASArticlePubMedPubMed CentralGoogle Scholar
Dunn, P. K. Log-linear models. In Encyclopedia of Social Measurement (Ed. Kempf-Leonard, K.) 585–589 (Elsevier, 2005).
Kelly, A. et al. Immune cell profiling of IFN-lambda response shows pDCs express highest level of IFN-lambdaR1 and are directly responsive via the JAK-STAT pathway. J. Interferon Cytokine Res.36, 671–680 (2016).CASArticlePubMedPubMed CentralGoogle Scholar
Norata, G. D. et al. Deficiency of the long pentraxin PTX3 promotes vascular inflammation and atherosclerosis. Circulation120, 699–708 (2009).CASArticlePubMedGoogle Scholar
Kimura, S. et al. Relationships of elevated systemic pentraxin-3 levels with high-risk coronary plaque components and impaired myocardial perfusion after percutaneous coronary intervention in patients with ST-elevation acute myocardial infarction. Circ. J.78, 159–169 (2014).CASArticlePubMedGoogle Scholar
Brunetta, E. et al. Macrophage expression and prognostic significance of the long pentraxin PTX3 in COVID-19. Nat. Immunol.22, 19–24 (2021).ArticlePubMedGoogle Scholar
Mathew, D. et al. Deep immune profiling of COVID-19 patients reveals patient heterogeneity and distinct immunotypes with implications for therapeutic interventions. Science369, 6508 (2020).ArticleGoogle Scholar
Keskinen, P., Ronni, T., Matikainen, S., Lehtonen, A. & Julkunen, I. Regulation of HLA class I and II expression by interferons and influenza A virus in human peripheral blood mononuclear cells. Immunology91, 421–429 (1997).CASArticlePubMedPubMed CentralGoogle Scholar
Gregorova, M. et al. Post-acute COVID-19 associated with evidence of bystander T-cell activation and a recurring antibiotic-resistant bacterial pneumonia. Elife9, e63430 (2020).CASArticlePubMedPubMed CentralGoogle Scholar
New blood assay provides critical information for revaccination strategies in vulnerable individuals Peer-Reviewed Publication THE MOUNT SINAI HOSPITAL / MOUNT SINAI SCHOOL OF MEDICINE
Mount Sinai researchers have developed a rapid blood assay that measures the magnitude and duration of someone’s immunity to SARS-CoV-2, the virus that causes COVID-19. This test will allow large-scale monitoring of the population’s immunity and the effectiveness of current vaccines to help design revaccination strategies for vulnerable immunosuppressed individuals, according to a study published in Nature Biotechnology in June.
The test takes less than 24 hours to perform and is scalable to use broadly in the population. It measures the activation of T cells, which are part of our adaptive immune response to SARS-CoV-2 infection or vaccination and help protect against severe disease outcomes or death.
“The assay we have created has the ability to measure the population’s cellular immunity and broadly test the efficacy of novel vaccines,” said one of the study’s senior authors, Ernesto Guccione, PhD, Professor of Oncological Sciences, and Pharmacological Sciences, at The Tisch Cancer Institute at Mount Sinai. “We know that vulnerable populations don’t always mount an antibody response, so measuring T cell activation is critical to assess the full extent of a person’s immunity. Additionally,the emergence of SARS-CoV-2 variants like Omicron, which evade most of the neutralizing ability of antibodies, points to the need for assays that can measure T cells, which are more effective against emerging variants of concern.”
Long-term protection from viral infection is mediated by both antibodies and T cell response. Many recent studies point to the importance of determining T cell function in individuals who have recovered from or been vaccinated against COVID-19 to help design vaccination campaigns. However, before this study, measurement of T cell responses has been rarely performed because of the associated technical challenges.
In conducting this study, Mount Sinai researchers and partners at Duke-NUS Medical School optimized qPCR-based assays that had the potential to be globally scalable, sensitive, and accurate tests. Researchers narrowed their focus to the two assays that offered the most scalability. One, the qTACT assay, was accurate and sensitive but had a relatively longer processing time of 24 hours per 200 blood samples, a moderate price, and a medium level of technical skill. The other, the dqTACT assay, was accurate and had a reduced processing time and cost, and required minimal lab experience, making it easy to implement.
The dqTACT assay has recently been granted the European CE-IVD (in vitro diagnostics) certification, while U.S. Food and Drug Administration and European Medicines Agency clinical validation is ongoing.
“The assays presented here are based on the ability of SARS-CoV-2 T cells to respond to peptides covering different proteins of the virus,” said another senior author, Jordi Ochando, PhD, Assistant Professor of Oncological Sciences at the Tisch Cancer Institute at Mount Sinai and Assistant Professor of Medicine (Nephrology), and Pathology, Molecular, and Cell-Based Medicine at the Icahn School of Medicine at Mount Sinai. “With the possibility of using different peptide pools, our approach represents a flexible strategy that can be easily implemented to detect the presence of T cells responding to different viral proteins. These T cells have an important role in protection from emerging mutant strains, thus immediately gauging the impact that viral mutations might have on cellular immunity.”
Megan Schwarz, a graduate student at Icahn Mount Sinai and first author of the study, added, “Precise measurement of cellular responses underlying virus protection represents a crucial parameter of our levels of immune defense.”
This new study was conducted using laboratory diagnostic services of Synlab and Hyris SystemTM, Hyris’ signature qPCR technology.
As many countries are going through another wave of infections, including some where the vast majority of the population has been vaccinated, many are starting to despair that we’ll never see the end of the pandemic. In this post, I will argue that, on the contrary, not only is the pandemic already on its way out, but the virus will be relatively harmless after it has become endemic. This is going to happen not because the SARS-CoV-2 will become intrinsically less dangerous, although it might, but rather because what made the virus so dangerous was that nobody had immunity against it, so once it has become endemic it will infect fewer people and even those who end up infected will be much less at risk. Moreover, I will explain that, despite widespread anxiety about the emergence of new variants and the danger of immune evasion, the fact that SARS-CoV-2 is mutating will not prevent this outcome because of the way immunity works. Finally, I will argue that, although some people are calling to pursue the eradication of SARS-CoV-2 (as we have done with smallpox), we almost certainly couldn’t eradicate it even if we wanted to and that even if we could it wouldn’t be worth it.
SARS-CoV-2 is going to become mostly harmless
You may have heard that, as they evolve, viruses necessarily become less lethal because it makes no evolutionary sense for them to kill the hosts on which they depend for their survival and reproduction, but this is a myth and it’s not what I’m saying. The claim I’m making is based on a much sounder and more straightforward argument. But to understand why it’s true, you first have to understand that, as the virologist Dylan H. Morris explained in a great essay, what made SARS-CoV-2 so dangerous is not so much its intrinsic characteristics but the fact that it was novel, which means that nobody in the population had immunity against it.1 Indeed, while the debate about whether SARS-CoV-2 was “worse than the flu” or “just like the flu” dominated the early phase of the pandemic and to some extent is still ongoing, this question is not even well-posed because there is no such thing as the dangerousness of a virus simpliciter. The dangerousness of a virus is always relative to a particular context. This should be obvious if you consider the impact that the availability of effective treatments can have on how much damage a virus does. For instance, HIV was initially devastating because it invariably killed the people it had infected within a few years after symptoms onset, but thanks to the development of effective treatments infected people can now live a relatively normal life, at least in the developed world where people can afford such treatments. HIV has not become any less intrinsically dangerous, but it’s undoubtedly far less dangerous in societies where effective treatments are easily available.
In the case of SARS-CoV-2 though, the key contextual factor is what proportion of the population has immunity against it. Immediately after the emergence of the virus, the population was immunologically naive, which means that nobody had immunity against it beyond that conferred by the innate immune system against any pathogen.2 The amount of damage and disruption caused by a virus can differ wildly depending on whether the population in which it’s introduced is immunologically naive to it. This is because, when nobody in the population has immunity, 1) the virus spreads more easily and infects more people because everyone is susceptible to infection and 2) when people get infected they have a much higher chance of developing a severe form of the disease because their immune system does not yet have any weapons specifically tailored to fight this virus. So the same virus, with exactly the same intrinsic properties, can do vastly more damage in a population that is immunologically naive than in a population where everyone has immunity against it, either because they have previously been infected or because they have been vaccinated. That’s one of the reasons why entire indigenous communities in America were almost completely wiped out by pathogens brought by Europeans, even though people in Europe had been living with the same pathogens for centuries or even millennia and, while they were not by any means harmless to them, they didn’t threaten their existence.3
As more people get infected by SARS-CoV-2 or vaccinated against it, the virus will become endemic and continue to circulate following a seasonal pattern (because immunity whether acquired naturally or through vaccination is not 100% effective against infection and wanes over time), but the number of people who end up at the hospital or dead because of it will gradually decrease until we reach a sort of equilibrium.4 In some places, especially in developed countries where the vast majority of the population has already been vaccinated, this process is already well under way and you can see it on a simple chart:
This is probably also true in other regions of the world, where infections usually played a bigger role than vaccination, and eventually it will be true everywhere, including in places such as Australia and New Zealand that have mostly been able to keep the virus out so far but won’t be able to do it forever as the virus becomes endemic in the rest of the world. Obviously, it’s preferable to build up immunity through vaccination rather than infections, but eventually everyone will get to the same point. The virus will become endemic and virtually everyone will have some immunity against it, at which point it will be relatively harmless and no longer cause the kind of damage we have seen during the pandemic. The whole process will take a few years, but again it’s already well under way in some places and this is where everyone is headed, dreams of eradication notwithstanding.
In order to understand how this transition takes place and why the virus will be mostly harmless once it has become endemic and the population is no longer immunologically naive to it, I think it’s useful to work through a simple numerical example, which doesn’t purport to be a quantitatively accurate description of what is going to happen but can illustrate the process qualitatively and help people to grasp the underlying logic. Let’s consider a population of 10 million with 3 million people between 0 and 18 years old, 4 million people between 19 and 59 people and 3 million people 60 and over. Suppose that in that population a virus kills 0.05% of the people between 0 and 18 years old it infects, 0.2% of the people between 19 and 59 and 1% of the people 60 and over. Let’s also assume that, during the first year after it’s introduced in the population (which is initially immunologically naive to it), 25% of the population is infected and this doesn’t vary by age. In that case, we expect that during that year it will kill 25% * 3,000,000 * 0.05% = 375 people between 0 and 18 years old, 25% * 4,000,000 * 0.2% = 2,000 people between 19 and 59 years old and 25% * 3,000,000 * 1% = 7,500 people 60 and over died, for a total death toll of 9,875. That is a pretty sizable mortality, comparable to what many countries have seen during the first year of the COVID-19 pandemic, which given the assumptions I made should not come as a surprise to anyone.
Now let’s consider the same virus but in another population of 10 million or in the same population at a subsequent date where, because of vaccination and infections, the prevalence of immunity is only 25% among people between 0 and 18 years old, but 100% in the rest of the population.5 Let’s further assume that immunity is 80% effective against death and that effectiveness doesn’t vary with age, but that it’s not as effective against infection. Still, it offers some protection against infection, so the virus doesn’t spread as much as in a population where there is no immunity whatsoever. Let’s be more specific and assume that, over the course of a year, 15% of people between 0 and 18 years old, 10% of people between 19 and 59 years old and 5% of people 60 and over get infected.6 Finally, let’s assume that 75% of the children who get infected had no prior immunity, while 100% of the adults who get infected had some immunity since we have assumed that except for children everyone had immunity. In that case, we expect that 15% * 3,000,000 * (75% * 0.05% + 25% * (1 – 80%) * 0.05%) = 180 people between 0 and 18 years old, 10% * 4,000,000 * (1 – 80%) * 0.2% = 160 people between 19 and 59 years old and 5% * 3,000,000 * (1 – 80%) * 1% = 300 people 60 and over died, for a total death toll of 640. That’s only ~6.5% of the death toll in the immunologically naive population, yet by assumption the virus is exactly the same as before, but the population is no longer immunologically naive and this changes everything. For various reasons I won’t get into here, reality is far more complicated than this simplistic model, but it’s good enough to grasp the basic logic that governs the transition toward endemicity and get a pretty accurate idea of what is going to happen.7
Sooner or later, as a result of both infections and vaccination, virtually everyone will develop some immunity against SARS-CoV-2. This immunity will not always prevent infection, but even if someone who has been vaccinated or previously infected gets reinfected, they will typically develop only a mild form of the disease, because while still not perfect the protection against severe illness that immunity confers is better and doesn’t wane as quickly as protection against infection. Even the protection against severe illness will likely wane after a while, but this won’t really be a problem because, since immunity is much less effective against infection and new people are going to get born who are completely susceptible because they have never been infected yet and won’t be vaccinated, the virus will continue to circulate so most people will be reinfected every few years. Most people see that as a bug, but in a way, it may actually be a feature. Indeed, those reinfections will typically be mild because immunity protects well against severe illness, but they will update immunity and therefore ensure that, the next time someone is infected, this reinfection is also mild. As long as the virus is not eradicated, which as we have seen is not going to happen, we don’t want it to circulate too much, but we also don’t want it to circulate too little. Otherwise, too much time may elapse between two infections in the same person, in which case even the protection against severe illness conferred by immunity may have waned by the time they get reinfected.
Eventually most people will have a primary infection when they’re children, which is perfectly harmless and, together with subsequent infections, will protect them against severe illness later, when infection would be more dangerous if they didn’t have any immunity. Since once people have immunity, infections are generally mild, most people likely won’t even bother getting vaccinated because the probability of becoming seriously ill due to SARS-CoV-2 will be very small since 1) the risk of getting infected in the first place will be low because immunity still offers some protection against infection and the virus will circulate much less after it has become endemic and 2) even if they are infected they will typically be well protected against severe illness. Elderly people will be the exception because their immune system is compromised, so for them it will make sense to get a vaccine booster on a regular basis and I expect that it’s what most of them will do, as they already do against the flu. Once it has become endemic, which again will take a few years or even decades for the transition to be fully over, SARS-CoV-2 will become just another respiratory virus and will never cause the damages it has just wrought on us again. At last, it will have become “just like the flu”, except that it probably won’t be as bad as the flu if only because immunity will be more effective and longer-lasting.8 This may have already happened in the past with a coronavirus after the 1889-1891 “Russian flu” pandemic, which some now believe to have actually been caused by the emergence of HCoV-OC43, another human coronavirus that is now endemic and causes the common cold. It’s likely that SARS-CoV-2 will follow a similar path and end up being similarly harmless.
How I learned not to worry about variants and why you shouldn’t either
I have argued that, although SARS-CoV-2 is not going anywhere and that it wouldn’t be eradicated, things are looking up and that as the virus becomes endemic it would become mostly harmless. However, I know that presented with the optimistic picture I painted of what lays ahead of us, many people will react in disbelief because they think that emerging variants of the virus will get in the way of this quasi-idyllic scenario. Instead of seeing the wave of infections associated with the Delta variant as the last jolts of a pandemic on the way out as the transition toward endemicity takes place, they see it as a sign that, because new variants will keep emerging, we are going to be trapped in a never-ending cycle of waves of infections, each of them leaving scores of dead behind. Given that since the end of 2020 and the emergence of the Alpha variant in England, a wave of variantophobia has taken over the world, I can’t blame you if you worry that something like that might be true, but if that’s the case then I think you will feel much better after reading this section because the case against this variantophobia is very strong and we have every reason to believe that variants won’t prevent the scenario I described above from unfolding. First, before I say anything else, just taking another look at the chart about what just happened in England above should already assuage your worries somewhat, but there is more so please just bear with me for a little longer and I promise that you won’t regret it.
Variants are neither a new phenomenon nor something peculiar to SARS-CoV-2. Viruses constantly mutate and, as a result, variants of SARS-CoV-2 started to emerge long before the public became aware of that phenomenon a few months ago. While I do not doubt that mutations can result in different properties, as I have already explained previously, the picture is more complicated than what epidemiologists claim, especially when it comes to their claims about the advantage of transmissibility that, according to them, some variants enjoy. But the real concern people have about variants in the long-run is that they might evade pre-existing immunity, in which case we’d pretty much be back to square one. Indeed, the optimistic prediction I made about what is going to happen as the virus becomes endemic depends on the fact that, once everyone has acquired immunity against the virus, it will no longer kill a large number of people because immunity will ensure that it circulates less so fewer people will be infected and that even when someone is infected the infection will usually be mild. Obviously, if new variants emerge that can evade this immunity, this is not going to work and the pandemic will not end. But this is not going to happen and people who say otherwise are just talking nonsense.
In order to understand why, you must know a few things about how immunity works. Most people think of immunity as a black-or-white kind of thing: you either have it and you’re completely protected against both infection and severe illness or you don’t have it and you’re not protected against either. However, that is not how it works, the reality is more complicated. Immunity has several layers and comes in degrees. I have already noted that immunity against SARS-CoV-2 offered better protection against severe illness than against infection, but it’s even more complicated than that. For one thing, even if you have never been infected by SARS-CoV-2 and have not been vaccinated, it’s not true that you have no immunity against it. You have some immunity against it because your innate immune system is capable of fighting off even pathogens that you have never encountered. If this were not true, everyone who is exposed to SARS-CoV-2 would have died, but almost everyone survives and the overwhelming majority of people only have mild symptoms or no symptoms at all. It’s just that sometimes this innate immunity is not enough to clear the infection on its own before things get ugly, so it needs the adaptive immune system, which is responsible for mounting a more specific immune response to pathogens.
Unlike the innate immune system, which offers generic protection against pathogens, the adaptive immune system offers tailor-made protection against specific pathogens that it previously encountered. It relies mainly on two types of cells, B-cells and T-cells, that each play a different role, but in both cases they work by recognizing parts of proteins called epitopes expressed by the pathogen, which in the case of SARS-CoV-2 is a virus. B-cells have receptors that directly bind epitopes on the surface of the virus, then proliferate and create antibodies that can also bind those epitopes, which prevents the virus from infecting cells and helps other types of cells in the immune system to remove them. In the case of T-cells, on the other hand, recognition is a bit more indirect. Viral proteins are first broken up into short chains of amino acids called peptides inside cells that are called antigen-presenting cells (APCs).9 Those peptides are then bound to molecules known as the major histocompatibility complex (MHC) and the resulting MHC-peptides complexes are transported to the surface of the APCs where they are presented for recognition by T-cells.10 T-cells have receptors that bind different types of MHC-peptide complexes and, if they recognize one of them, they get activated and start going to work against the virus. This contributes to the immune response in various ways, but in particular sets in motion the process that will result in the destruction of the cells that have been infected by the virus.11 Here is a chart adapted from this paper that summarizes B-cell and T-cell epitope recognition:A key fact about both T-cells and B-cells is that, when they are activated, they don’t just set in motion a process that will help clear the infection currently ongoing, but also a process that will allow them to do that more quickly the next time they encounter the virus.
You’re probably wondering why I’m telling you about all that, but don’t worry, you’re about to find out. In the case of SARS-CoV-2, antibodies seem to be crucial to protect against infection, which makes sense because if there are still many antibodies that can neutralize the virus around when someone is exposed to the virus again, it won’t even have the opportunity to infect cells and replicate. However, several studies have found that the number of antibodies against SARS-CoV-2 wanes relatively quickly after vaccination or a natural infection, so often immunity can’t prevent infection. But as we have just seen, the immune response is not limited to antibodies, let alone to the antibodies against SARS-CoV-2 that are still around by the time someone is exposed to the virus again. Upon a second exposure with the virus, T-cells whose receptors bind peptides from SARS-CoV-2 will go to work again, but this time they’ll be able to do it more quickly. This will ensure that, even if infection couldn’t be prevented, it will be cleared before things take a turn for the worst. Thus, T-cells play a key role in preventing severe illness and, unlike antibodies, neither B-cells nor T-cells specific to SARS-CoV-2 seem to wane quickly. In fact, according to various studies (including one which found that T-cells specific to SARS-CoV-1 were still present in the blood of people who had been infected 17 years ago), they likely stick around for years. So even though protection against infection seems relatively short, immunity likely confers protection against severe illness for a long time. But won’t new variants find a way to evade this pre-existing immunity and make even the protection against severe illness it confers ineffective? No, they almost certainly won’t, and T-cells are the reason why.
Indeed, T-cells mount a particularly robust immune response because they target a much greater number of epitopes than antibodies, so even the virus mutates to prevent antibodies resulting from a previous infection to bind it, this is unlikely to work against T-cells because the entire viral proteome of the virus, i. e. the complete set of proteins expressed by the virus, would have to be different. But SARS-CoV-2 mutates pretty slowly, so although new variants regularly emerge and will continue to do so in the future, most peptides from the virus will remain the same and therefore T-cells will still be able to recognize them. Indeed, the peptides that are bound to MHC molecules and presented on the surface of antigen-presenting cells are very short chains of between 8 and 25 amino acids (depending on the class of MHC to which they are bound), so they are unlikely to change even as the virus mutates. Since it mutates slowly, it’s kind of as if the virus were trying to win the lottery by just buying a handful of tickets, each of them with a very low probability of winning the jackpot. If it bought 500 of them, the probability that one of them is a winning ticket may be reasonably high, but since it only buys 8 to 25 of them in each case it’s very low. Moreover, even if one amino acid changes, this is usually not enough to prevent T-cell receptors from binding, so in this case having a winning ticket does not even guarantee that the virus will actually pocket any money. Of course, it will sometimes happen, but T-cells target hundreds of epitopes from SARS-CoV-2, so it won’t really make a difference to the overall immune response they mount against the virus. T-cells just take the recommendation that you shouldn’t put all your eggs in the same basket very seriously.
This looks fine in theory, but reality has a way of frustrating our theoretical expectations, so does it also work in practice? Yes, it does, it works exactly as theory predicts. A recent study examined the impact of SARS-CoV-2 variants on T-cell reactivity and found that, depending on the type of receptor, between 93% and 97% of the hundreds of previously identified T-cell epitopes were not affected by mutations in the variants of concern. Now, all epitopes do not contribute equally to the immune response mounted by T-cells, so in theory it could be that while only a handful of them were affected by mutations in variants of concern, they happened to be epitopes that were disproportionately involved in the T-cell response. But the authors checked and found that fully conserved epitopes accounted for on average 91.5% of the response, so this isn’t the case. Again, keep in mind that even for the handful of epitopes that were affected by mutations, it doesn’t mean that receptors from a previous infection are no longer capable of recognizing them. In any case, the study also found there was no statistically difference in reactivity of T-cells from people who had acquired immunity against the virus, whether it was through vaccination or a natural infection. It doesn’t mean that, had the sample been larger, a statistically significant difference wouldn’t have been found, but it means that at worse the loss of reactivity was small and possibly non-existent, which again is exactly what we’d expect based on the theoretical considerations. It may be that, although T-cells target hundreds of epitopes and SARS-CoV-2 is mutating slowly, after a long enough period of time it will have mutated enough that T-cells won’t be able to mount a strong enough immune response to protect against severe illness. But remember that SARS-CoV-2 is going to continue to circulate and that people will likely get reinfected every few years, so their immunity will be updated when they are, ensuring that any subsequent infections will also be mild.
But there is another reason almost nobody is talking about why it’s unlikely that we’ll see substantial immune evasion with T-cells. As I explained above, T-cells don’t recognize epitopes directly on the surface of the virus, but rather bind complexes formed by MHC molecules and peptides on the surface of antigen-presenting cells. Now, different MHC molecules can bind different peptides, which are then presented for recognition to T-cell receptors. As it happens, the region of the human genome that is responsible for the production of MHC molecules is the most polymorphic in the entire human genome, which means that even in the same population different individuals usually have different MHC molecules that can bind different epitopes from the virus before presenting them to T-cell receptors on the surface of antigen-presenting cells. This fact has been confirmed in the case of SARS-CoV-2 by another study that identified potential T-cell epitopes from the virus and used computational methods to predict their binding affinity with the MHC molecules produced by the different variants of the genes that code for them in human populations. The authors found there was significant variation in the epitopes derived from SARS-CoV-2 involved in T-cell response both across individual within the same population and between populations, although this variation wasn’t predicted to affect the overall level of response across individuals or populations.12 This is very important because it means that, even if the virus acquired mutations that allowed it to evade T-cell immunity in one individual or population, it typically wouldn’t help it evade T-cell immunity in another individual or population, which makes T-cell immune evasion even more unlikely.
The bottom line is that, if you’re the virus, T-cells are your worst nightmare. Getting ahead of antibodies is pretty easy and some variants of concern already do it to some extent, but T-cells are a completely different story and will be a much tougher nut to crack for the virus. As we have seen, we have very good theoretical and empirical reasons to expect that, in the war between the virus and T-cell immunity, not only is the latter going to win but it won’t even break a sweat doing it. It’s important to understand that, in that respect, SARS-CoV-2 is no different than other viruses and other viruses also have a hard time dealing with T-cell immunity. Indeed, as the authors of the study that examined the impact of SARS-CoV-2 variants on T-cell reactivity note, immune evasion at the level of T-cell response has never been reported for acute respiratory infections. People worry about variants because they hear that antibody response is not as effective against them, so they imagine that eventually another variant will emerge against which immunity will be completely ineffective, but that’s because they don’t know that antibodies are just one part of the immune response against SARS-CoV-2. Immunity has another layer depending on T-cells and, not only has this layer remained unaffected by mutations of the virus so far, but as we have just seen we have very good reasons to think it will continue to be true in the future.
As I noted above, it’s likely that SARS-CoV-2 will follow a trajectory similar to that of the other human coronaviruses (which are already endemic), so it’s particularly interesting to know that what I’m predicting for SARS-CoV-2 is exactly what is already happening with those human coronaviruses. A recent study examined the recent evolution of HCoV-229E, one of the four human coronaviruses that are already endemic, and found that its spike, the protein that allows the virus to enter cells and infect them, had undergone several mutations between 1984 and 2020. They used sera collected on recovering patients at various points during that period to test how well the antibodies they contain were able to bind reconstructed spikes of the virus from 1984, 1992, 2001, 2008 and 2016. What they found is that antibodies in sera collected at one date were able to find effectively the spikes that were found on HCoV-229E before that date, but not or not very effectively the spikes that were found on the virus after that date, which shows that HCoV-229E had mutated to evade antibody binding, which is already what we’re seeing in SARS-CoV-2. But HCoV-229E remained mostly harmless during that period, which is presumably because while people’s antibody response against it became less efficient due to mutations in the spike, T-cell immunity remained largely unaffected. This is exactly what we’re seeing with SARS-CoV-2 so far and we have every reason to believe that it will continue to be true in the future. The only difference is that, in the case of HCoV-229E, nobody bothers naming the variants and people aren’t freaking out because they think immunity will stop working against them. Again, SARS-CoV-2 is just another respiratory virus, what made it so devastating is that it was novel.
SARS-CoV-2 is not going anywhere
Some people insist that we can’t “live with the virus” and that we must therefore pursue a policy of eradication. They often draw a parallel with smallpox and say that we should do the same thing with SARS-CoV-2 that we did with that virus, which after plaguing mankind for thousands of years was finally eradicated in 1980. This parallel is extremely misleading though, because smallpox differs from SARS-CoV-2 in very important ways, which made eradication possible though difficult in the case of the former but make it very unlikely in the case of the latter. Before I get into that, it’s worth noting that to date only two infectious diseases have ever been successfully eradicated (smallpox in humans and rinderpest in cattle), which speaks to how difficult this sort of enterprise is. This is not for lack of trying, as several other infectious diseases have been targeted for eradication, but those efforts have not succeeded yet. Polio seems on the verge of eradication and probably will be eradicated soon, but isn’t yet. Even in the case of smallpox, eradication took decades. You might take this to suggest that, while SARS-CoV-2 will not be eradicated overnight, we might pull it off eventually if we really commit to it. But I don’t think it’s going to happen because again SARS-CoV-2 is very different from the viruses that cause smallpox or polio.
First, while I think there is no doubt that vaccines against SARS-CoV-2 protect against infections and not just severe disease (as we have seen above), I think it’s equally clear that the protection it offers against infection is far from perfect and that people can get infected even if they have been vaccinated. There is also growing evidence that, while it does not disappear almost immediately as some people had initially suggested based on weak evidence, the protection against infection conferred by vaccination is waning relatively quickly. As this study showed, the same thing is true for the immunity against endemic human coronaviruses induced by natural infection, so this is not particularly surprising. According to the COVID-19 Infection Survey, based on a random sample of the population in the United Kingdom, more than 90% of people had antibodies against SARS-CoV-2 in June, but it didn’t prevent a gigantic third or fourth wave (depending on how you’re counting) from ripping through the country in July. The same thing just happened in Iceland, where more than 90% of the population over 16 has received at least one dose of vaccine. As we have seen, this is not really a problem because thanks to vaccination and naturally acquired immunity mortality remained low, but it suggests that even mass vaccination within a short period of time cannot stop the virus from circulating. The vaccine against smallpox, on the other hand, probably confers lifelong protection against infection and the same thing seems to be true about naturally acquired immunity. Basically, in order to get rid of smallpox, we “just” needed to vaccinate everyone in their childhood and that was it. The same thing is true with polio.
So this means that, in order to eradicate SARS-CoV-2, we’d have to vaccinate the entire population every year for several years in a row and even that would probably not be enough.13 That’s a much larger effort than what we had to do to get rid of smallpox, yet even that comparatively simple endeavor took decades. Who can seriously believe that we’ll be able to sustain that effort for the years or even decades that it would take to eradicate the virus, when we aren’t even able to do it in the middle of a pandemic that just killed millions of people? This is a pipe dream, it will never happen. Indeed, convincing or coercing people to get vaccinated is going to become even harder, because as I have explained the virus will be mostly harmless once it has become endemic. If you think it’s hard to convince people to get vaccinated or politically difficult to coerce them to do so while people are dropping dead by the thousands, which it most certainly is, wait until the mortality caused by SARS-CoV-2 is divided by a factor of 20 or something. It’s pointless and wasteful to pursue a policy that has no realistic chance of succeeding, but that’s exactly what people who are calling to eradicate SARS-CoV-2 are doing. Not that it will make any difference, to be clear, because the same reasons that make this project a fantasy will ensure that calls to carry it out will remain unanswered.
Again the comparison with smallpox or even polio is extremely misleading here. Smallpox is one of the most lethal pathogens in history and has probably killed hundreds of millions of people in the last 100 years of its existence alone. It’s painfully obvious that the incentives are completely different in the case of SARS-CoV-2. Even with polio, whose infection fatality rate is similar to SARS-CoV-2, the incentives are very different because it mostly kills or maims children. Does anyone really expect that people are going to be as motivated to eradicate a virus that mostly kills elderly people as they are to get rid of a virus that kills or paralyzes children? Moreover, as I already noted, in the case of polio, you just have to administer a few shots to people when they’re very young children and you’re done with it. The comparison of SARS-CoV-2 with other pathogens can be illuminating in some cases, but comparing it to smallpox or even polio to suggest that we could eradicate it and that it’s a realistic possibility is extremely misleading. Even if we granted for the sake of the argument that it could be done if we committed enough resources to the effort, it’s totally unrealistic to expect that we ever will, because the incentives aren’t right.14
There are other differences between SARS-CoV-2 and smallpox or even polio that make it far more difficult to eradicate the former. In particular, smallpox and polio only infect humans, but SARS-CoV-2 can also infect animals and frequently does. While the evidence of animal-to-human transmission is so far very limited, I think it’s mostly because the studies that have found evidence that animals could be infected by SARS-CoV-2 were not designed to answer that question. If the virus becomes endemic in some animal populations that are frequently in contact with humans, then even if we somehow managed to temporarily eradicate it from human populations, animals would just reintroduce it and we’d be back to square one. At least one animal reservoir has already been found in the white-tailed deer population in the US, so this isn’t a purely theoretical worry. What this means is that, in order to permanently eradicate SARS-CoV-2 from human populations, we’d probably have to vaccinate wild animals. This can be done and has been done in some countries such as France, where a program to vaccinate some wild animals against rabies was undertaken, but it just makes eradication even more difficult and costly, which in turn makes it even more unlikely that we’ll even try, let alone succeed.
The pandemic is on its way out, but SARS-CoV-2 is here to stay. Fortunately, as everyone develops immunity to it (whether through vaccination or natural infection), it will soon no longer be a major problem anymore. The virus will continue to circulate, but much less than during the pandemic and, even when people are infected, the infection will typically be mild. In the future, almost everyone will get infected for the first time during their childhood, which is harmless and will protect them against severe illness when they are reinfected.15 The virus will continue to mutate and some of those mutations will favor immune evasion, but while this will allow it to infect people who have already been infected or vaccinated more easily, immunity should continue to protect against severe forms of the disease, thanks in particular to the role played by T-cells. This is likely what happened with other human coronaviruses, which are already endemic and typically cause a cold in the people they infect. To the extent that immune evasion occurs, it will be very gradual and the fact that most people will be infected every few years will update their immunity, ensuring that subsequent reinfections will also be mild. The most vulnerable people, whose immune system doesn’t work very well and could use some help to be ready in case of infection, can get a vaccine booster from time to time. The virus will still kill people, as the flu does, but it will never cause the same amount of disruption again. The hardest part of what lays ahead may be to convince people who have been traumatized by the pandemic that it’s over and that restrictions are no longer necessary.
P. S. I realize that, while it doesn’t exactly say that, this post makes it sound as though the only reason why protection against infection appears to have been waning is that new variants with mutations in the spike that allow them to prevent antibodies from binding have emerged, so to be clear that’s not what I’m saying. I was focusing on immune evasion, because that’s what people seem most worried about, but another reason why protection against infection is probably waning is that antibody levels progressively fall after infection. Moreover, as someone pointed out to me, so does the number of T-cells specialized against SARS-CoV-2 and I’m sure the same thing is true with B-cells, so as time goes by it also takes longer for the adaptive immune system to mount a response upon exposure to the virus. I also didn’t mean to suggest that mutations in the spike make antibodies completely inefficient. The point I wanted to make is just that, even if a variant is able to evade humoral immunity to a large extent, T-cell immunity should still work just fine against it and eventually the immune system should be able to mount a very effective response to infection, even if the fact that T-cell levels also wane means that it will take longer as the time since the last infection increases.
1As some studies suggest, there was probably some cross-immunity due to prior exposure to seasonal human coronaviruses, so this claim is not exactly true, but clearly this immunity was very limited.
2Biologists make a distinction between the innate immune system and the adaptive immune system. The former offers generic protection against pathogens that invade the body and can effectively deal with most of them, while the latter offers protection against specific pathogens that have been previously encountered. As I noted above, there was probably some adaptive immunity against SARS-CoV-2 in the population due to the similarity of parts of the proteins expressed by the virus with those of endemic human coronaviruses, but again it was very limited.
3Another reason is that natural selection had probably favored alleles that protect against those pathogens in Europeans precisely because they had lived with them for so long, whereas this was not the case in America where indigenous populations had separated from other human populations before the emergence of those diseases, which probably occurred during and after the neolithic when animals were first domesticated.
4The notion of endemic equilibrium has a precise mathematical definition in epidemiological models, but while those models may be useful to describe some aspects of this process in a stylized manner, I think they bear little connection to reality and use the term in a more informal sense.
5This is the kind of situation you would expect in a population where the virus has become endemic, almost everyone is infected for the first time during their childhood, immunity wanes over time but people get reinfected or vaccinated every few years.
6This is the kind of situation you would expect if old people got vaccinated regularly because they know they are vulnerable. You would expect the virus to circulate more among children since, by assumption, more of them are susceptible to infection.
7If you want to see a more realistic attempt at modeling the transition to endemicity, which tries to predict how long it will take depending on factors such as how fast the protection against infection conferred by immunity wanes and the basic reproduction number of the virus, I encourage you to read Lavine et al. (2021). I wouldn’t take very seriously their quantitative estimates, because the model still ignores many complications and the specific results are sensitive to various semi-arbitrary assumptions they make, but there is every reason to think their qualitative conclusions, which are consistent with the prediction I make below about what is going to happen once SARS-CoV-2 has become endemic, are correct because they just rest on the basic logic I have just explained.
8Indeed, influenza mutates faster than SARS-CoV-2 due to the absence of a similar proofreading mechanism during replication and because it has a segmented genome that makes recombination between various strains easier, which makes it harder for immunity to clear infection and explains why vaccines against the flu quickly become obsolete.
9The terminology can be a bit confusing, so it may be useful to clarify it. Epitopes are the parts of viral proteins that are recognized by the adaptive immune system, whether they are still part of the protein when this recognition takes place or have been broken up and are no longer part of it. In the case of B-cells, they are recognized directly on the protein that is still intact on the surface of the virus, but in the case of T-cells this recognition takes place after the viral proteins have been broken up into peptides. So peptides can be epitopes when they are presented on the surface of APCs for recognition by T-cells, but epitopes need not be peptides and peptides need not be epitopes.
10There are different classes of MHC molecules that are found on different kinds of APCs and are recognized by different types of T-cells, but this is not important for what I’m trying to explain.
11B-cells are APCs and therefore present MHC-peptide complexes to T-cells, which in turn stimulate the proliferation of B-cells specific to the relevant peptides and the production of antibodies that can bind them directly on the surface of the virus, so T-cells and B-cells are not entirely distinct parts of the immune system but interact in complex ways to produce the immune response.
12This result still held when they looked at potential T-cell peptides derived from individual proteins expressed by the virus rather than the entire viral proteome, so even if peptides derived from specific proteins are more important to the T-cell response than others, this response will still rely on different epitopes in different individuals and different populations. In particular, this is true for epitopes derived from the spike protein, which is the one used by the currently available vaccines to induce immunity.
13Perhaps this will change as new, more effective vaccines are developed, but I wouldn’t hold my breath, especially since as I have argued SARS-CoV-2 is going to become far less dangerous, so pharmaceutical companies will have less incentives to invest money into research and development for better vaccines against it.
14You may think that, although eradicating SARS-CoV-2 would be extremely costly and difficult, it would still be cost-effective given the expected death toll of COVID-19 in the long-run and you may even be right despite the fact that it’s going to become far less dangerous once it’s endemic. But this wouldn’t change the fact that it’s almost certainly not going to happen because, as we have seen during the pandemic, decision-makers are hardly utility maximizers. Thus, when I claim that eradication of SARS-CoV-2 is not desirable, I’m not committing myself to the view that, even if people were perfectly rational, such a policy wouldn’t pass a cost-benefit test (although I think it probably wouldn’t), but only to the weaker claim that it wouldn’t in the actual world because the lack of incentives to pursue this policy lowers the probability of success and increases the cost.
15At the moment, many people want to vaccinate their kids, but I doubt it will still be the case in a few years when the panic induced by the pandemic has subsided and people have realized that SARS-CoV-2 is harmless to children.
Coronavirus disease 19 (COVID-19) presents with disease severities of varying degree. In its most severe form, infection may lead to respiratory failure and multi-organ dysfunction. Here we study the levels and evolution of the damage associated molecular patterns (DAMPS) cell free DNA (cfDNA), extracellular histone H3 (H3) and neutrophil elastase (NE), and the immune modulators GAS6 and AXL in relation to clinical parameters, ICU scoring systems and mortality in patients (n = 100) with severe COVID-19. cfDNA, H3, NE, GAS6 and AXL were increased in COVID-19 patients compared to controls. These measures associated with occurrence of clinical events and intensive care unit acquired weakness (ICUAW). cfDNA and GAS6 decreased in time in patients surviving to 30 days post ICU admission. A decrease of 27.2 ng/mL cfDNA during ICU stay associated with patient survival, whereas levels of GAS6 decreasing more than 4.0 ng/mL associated with survival. The presence of H3 in plasma was a common feature of COVID-19 patients, detected in 38% of the patients at ICU admission. NETosis markers cfDNA, H3 and NE correlated well with parameters of tissue damage and neutrophil counts. Furthermore, cfDNA correlated with lowest p/f ratio and a lowering in cfDNA was observed in patients with ventilator-free days.
In severe cases, COVID-19 disease develops into acute respiratory distress syndrome (ARDS), an acute lung injury causing patients to be dependent of ventilator support, which may be accompanied by development of multiple organ failure (MOF)1. Mortality is seen primarily in patients over the age of 652,3,4,5 and is highest for infected individuals with underlying comorbidities such as hypertension, cardiovascular disease or diabetes6,7,8. For patients who are taken into the intensive care unit (ICU), a high SOFA (sequential organ failure assessment) score and increased levels of fibrin D-dimers have been reported9 to associate with poor prognosis. Thromboembolic complications develop in 35–45% of COVID-19 patients10, including thrombotic microangiopathies and disseminated intravascular coagulation (DIC) reminiscent of bacterial sepsis. Yet, COVID-19 has distinct features11 that point at a somewhat different pathological mechanism. The involvement of immune regulatory and hemostatic pathways appears evident, and recent findings have confirmed that the innate immune system and more in particular neutrophil extracellular traps (NETs) play a role in COVID-19 disease pathogenesis. NETs, networks of DNA fibers that are decorated with proteins such as histones and elastase, are released from neutrophils to bind and neutralize viral proteins, bacteria and fungi12. While extracellular histones and NE serve a protective, antimicrobial function, they are potentially harmful to the host.
NETs are abundant in lung capillaries13 and are known to be pro-coagulant due to their intrinsic capacity to activate platelets14.
Excessive NET production, initiated by several pathways that also include complement activation13, results in collateral damage to lung tissues, a disturbed microcirculation of the lung15, loss of alveolar-capillary barrier function and further release of pro-inflammatory cytokines16.
During the preparation of this work it was reported that cellular components that are released upon cellular disruption, so-called damage associated molecular patterns (DAMPs) and NETosis are involved in COVID-19 disease1718. This is fully in line with the observation that in ARDS, NETs contribute to disease progress19. Extracellular histones are cytotoxic DAMPs irrespective of their origin. They may appear during NETosis12,14,20 or originate from damaged tissues21, while cell free DNA (cfDNA) and the protease neutrophil elastase (NE) are released concomitantly22. Cellular free deoxyribonucleic acid (cfDNA) and histones promote proinflammatory cytokine release23,24. Histones have been shown to activate and recruit leukocytes25, damage alveolar macrophages26, activate erythrocytes27, epithelial and endothelial cells, in particular pulmonary endothelial cells28,29,30. If not cleared from circulation, cfDNA as well as histones facilitate severe systemic inflammation and worsen the clinical condition31,32. Presence of NE in plasma is associated with exacerbations, lung function decline and disease severity in patients with chronic obstructive pulmonary disease (COPD), bronchiectasis and cystic fibrosis33,34,35 and decrease of NE levels in bronchiectasis patients improved lung function and airway inflammation36.
At the same time that it provides a first line of defense against infections, the innate immune system initiates self-control responses to prevent damage to the host. One mechanism involved in early immunomodulation is the growth arrest-specific 6 (GAS6)/TAM ligand/receptor system37,38. The GAS6/AXL axis regulates the immune response by modulating cytokine production, inducing a reparative cellular response and by mediating efferocytosis, removing irreversibly damaged cells. The system also provides a mechanism of regulating endothelial and platelet activation and interaction39. Plasma concentrations of GAS6 and AXL increase in a diverse spectrum of inflammatory conditions40, including sepsis and septic shock; but also systemic inflammatory response syndrome (SIRS) without infection41. In several studies, GAS6 at IC admission correlated with severity of organ damage (i.e. SOFA) or with damage of specific organs41,42,43,44,45. This is also the case in viral infections46. These studies illustrate the modulatory role of the innate response provided by GAS6 and suggest that the presence of these components in plasma could be an early event in the orchestration of the immune response to viral infections.
cfDNA, extracellular histones and GAS6 are implicated in regulation of inflammatory and hemostatic pathways in the context of severe viral infections and ARDS, all of which are implicated in COVID-19. While other studies have reported the presence of DAMPs and NETosis markers in smaller COVID-19 populations, here we study a group of 100 severely ill COVID-19 patients admitted to the intensive care unit (ICU). Our hypothesis was twofold:
First, cfDNA, NE, histones and GAS6/AXL are activated in severe COVID-19. Second, cfDNA, NE, histones and GAS6/AXL are related to the severity of illness and reflect organ dysfunction in severe COVID-19.