sabato, Luglio 24, 2021
HomeHealthCareCOVID-19: How childhood infections could shape pandemics

COVID-19: How childhood infections could shape pandemics

A child’s first influenza infection shapes their immunity to future airborne flu viruses – including emerging pandemic strains. But not all flu strains spur the same initial immune defense, according to new findings published today by University of Pittsburgh School of Medicine virologists in the journal PLOS Pathogens.

These results are relevant right now to the COVID-19 pandemic,” said senior author Seema Lakdawala, Ph.D., assistant professor of microbiology and molecular genetics at Pitt.

“They may explain age-based distributions of SARS-CoV-2 disease severity and susceptibility.

“Having flu once does not make you immune to all future influenza viruses,” she said. “Nor does having had the original SARS virus in 2003 or any of the ‘common cold’ coronaviruses in circulation necessarily mean you can’t get infected with SARS-CoV-2. But your susceptibility to infection might be different than someone who has never encountered a coronavirus.”

Lakdawala and her colleagues devised an experiment using ferrets—which previous studies have shown have a similar susceptibility and immune response to flu as humans – and mimicked real-world, human conditions. The experiment was designed to test the concept of “The Original Antigenic Sin,” which is when a person’s first exposure to a pathogen imprints on their immunity to all future infections.

This phenomenon is seen in the populations affected by previous flu epidemics and pandemics. For example, the 2009 H1N1 flu pandemic disproportionately affected people ages 5 to 24, suggesting that older people had been exposed to a previous strain of flu that gave them lasting immunity, protecting them from the newer strain.

In the ferret experiment, the scientists infected different groups of ferrets who had never had the flu with one of two different strains of influenza—seasonal H3N2 flu or the 2009 pandemic H1N1 flu—and waited three months to allow the immune system to calm down and develop a more mature immunity to whichever strain they were exposed to.

Next, the ferrets with H3N2 immunity were exposed to ferrets contagious with H1N1 virus, and the ferrets with H1N1 immunity were exposed to ferrets contagious with H3N2 virus.

The scientists mimicked human workdays and weekends, comingling the contagious ferrets with their peers for 8 hours per day over five-day periods—much the way humans who work in cubicles would mix—or continuously over two days, similar to a family weekend.

The ferrets with previous H1N1 infection had protection against airborne transmission of H3N2 flu from a contagious peer. But ferrets with previous H3N2 infection didn’t have the same level of protection against H1N1 and got infected at the same rate as an animal without prior immunity.

“This was really surprising,” said Lakdawala. “Our immunity can shape how susceptible we are to subsequent infections, but that is not uniform. We have long ignored that not every strain of a virus is going to transmit through a population in the same way. That’s important to understand when preparing for future pandemics.”

The experiment did not reveal why the ferrets with previous H1N1 infection were protected against H3N2, nor why prior H3N2 infection didn’t block H1N1. But the scientists did find that the immunity was not due to neutralizing antibodies, which are antibodies acquired following vaccination or infection that specifically target and neutralize a defined pathogen.

This finding indicates the immunity was likely driven by the adaptive immune response—meaning that the previous H1N1 infection primed the immune system to be on the lookout for H3N2 and quickly eliminate it.

Future study is needed to reveal the precise immunological mechanism underlying such an immune response, but Lakdawala said that doesn’t mean public health authorities should wait to put the findings into action, especially in the midst of the COVID-19 pandemic. Understanding the different ways that infections affect people based on prior exposure could be leveraged to target age-based interventions or vaccination programs.


As data start to accumulate on the detection and characterization of SARS-CoV-2 T cell responses in humans, a surprising finding has been reported: lymphocytes from 20–50% of unexposed donors display significant reactivity to SARS-CoV-2 antigen peptide pools1–4.

In a study by Grifoni et al.1, reactivity was detected in 50% of donor blood samples obtained in the USA between 2015 and 2018, before SARS-CoV-2 appeared in the human population. T cell reactivity was highest against proteins other than the coronavirus spike protein, but T cell reactivity was also detected against spike.

The SARS-CoV-2 T cell reactivity was mostly associated with CD4+ T cells, with a smaller contribution by CD8+ T cells1. Similarly, in a study of blood donors in the Netherlands, Weiskopf et al.2 detected CD4+ T cell reactivity against SARS-CoV-2 spike peptides in 1 of 10 unexposed subjects and against SARS-CoV-2 non-spike peptides in 2 of 10 unexposed subjects. CD8+ T cell reactivity was observed in 1 of 10 unexposed donors.

In a third study, from Germany, Braun et al.3 reported positive T cell responses against spike peptides in 34% of SARS-CoV-2 seronegative healthy donors (CD4+ and CD8+ T cells were not distinguished). Finally, a study of individuals in Singapore, by Le Bert et al.4, reported T cell responses to nucleocapsid protein nsp7 or nsp13 in 50% of subjects with no history of SARS, COVID-19, or contact with patients with SARS or COVID-19.

A study by Meckiff using samples from the UK also detected reactivity in unexposed subjects5. Taken together, five studies report evidence of pre-existing T cells that recognize SARS-CoV-2 in a significant fraction of people from diverse geographical locations.

These early reports demonstrate that substantial T cell reactivity exists in many unexposed people; nevertheless, data have not yet demonstrated the source of the T cells or whether they are memory T cells. It has been speculated that the SARS-CoV-2-specific T cells in unexposed individuals might originate from memory T cells derived from exposure to ‘common cold’ coronaviruses (CCCs), such as HCoV-OC43, HCoV-HKU1, HCoV-NL63 and HCoV-229E, which widely circulate in the human population and are responsible for mild self-limiting respiratory symptoms.

More than 90% of the human population is seropositive for at least three of the CCCs6. Thiel and colleagues3 reported that the T cell reactivity was highest against a pool of SARS-CoV-2 spike peptides that had higher homology to CCCs, but the difference was not significant.

What are the implications of these observations? The potential for pre-existing crossreactivity against COVID-19 in a fraction of the human population has led to extensive speculation. Pre-existing T cell immunity to SARS-CoV-2 could be relevant because it could influence COVID-19 disease severity.

It is plausible that people with a high level of pre-existing memory CD4+ T cells that recognize SARS-CoV-2 could mount a faster and stronger immune response upon exposure to SARS-CoV-2 and thereby limit disease severity. Memory T follicular helper (TFH) CD4+ T cells could potentially facilitate an increased and more rapid neutralizing antibody response against SARS-CoV-2.

Memory CD4+ and CD8+ T cells might also facilitate direct antiviral immunity in the lungs and nasopharynx early after exposure, in keeping with our understanding of antiviral CD4+ T cells in lungs against the related SARS-CoV7 and our general understanding of the value of memory CD8+ T cells in protection from viral infections.

Large studies in which pre-existing immunity is measured and correlated with prospective infection and disease severity could address the possible role of pre-existing T cell memory against SARS-CoV-2.

If the pre-existing T cell immunity is related to CCC exposure, it will become important to better understand the patterns of CCC exposure in space and time. It is well established that the four main CCCs are cyclical in their prevalence, following multiyear cycles, which can differ across geographical locations8.

This leads to the speculative hypothesis that differences in CCC geo-distribution might correlate with burden of COVID-19 disease severity. Furthermore, highly speculative hypotheses related to pre-existing memory T cells can be proposed regarding COVID-19 and age. Children are less susceptible to COVID-19 clinical symptoms. Older people are much more susceptible to fatal COVID-19. The reasons for both are unclear.

The age distribution of CCC infections is not well established and CCC immunity should be examined in greater detail. These considerations underline how multiple variables may be involved in potential pre-existing partial immunity to COVID-19 and multiple hypotheses are worthy of further exploration, but caution should be exercised to avoid overgeneralizations or conclusions in the absence of data.

Pre-existing CD4+ T cell memory could also influence vaccination outcomes, leading to a faster or better immune response, particularly the development of neutralizing antibodies, which generally depend on T cell help. At the same time, pre-existing T cell memory could also act as a confounding factor, especially in relatively small phase I vaccine trials.

For example, if subjects with pre-existing reactivity were assorted unevenly in different vaccine dose groups, this might lead to erroneous conclusions. Obviously, this could be avoided by considering pre-existing immunity as a variable to be considered in trial design.

Thus, we recommend measuring pre-existing immunity in all COVID-19 vaccine phase I clinical trials. Of note, such experiments would also offer an exciting opportunity to ascertain the potential biological significance of pre-existing SARS-CoV-2-reactive T cells.

It is frequently assumed that pre-existing T cell memory against SARS-CoV-2 might be either beneficial or irrelevant. However, there is also the possibility that pre-existing immunity might actually be detrimental, through mechanisms such as ‘original antigenic sin’ (the propensity to elicit potentially inferior immune responses owing to pre-existing immune memory to a related pathogen), or through antibody-mediated disease enhancement.

While there is no direct evidence to support these outcomes, they must be considered. A detrimental effect linked to pre-existing immunity is eminently testable and would be revealed by the same COVID-19 cohort and vaccine studies proposed above.

There is substantial data from the influenza literature indicating that pre-existing cross-reactive T cell immunity can be beneficial. In the case of the H1N1 influenza pandemic of 2009, it was noted that an unusual ‘V’-shaped age distribution curve existed for disease severity, with older people faring better than younger adults.

This correlated with the circulation of a different H1N1 strain in the human population decades earlier, which presumably generated pre-existing immunity in people old enough to have been exposed to it. This was verified by showing that pre-existing immunity against H1N1 existed in the general human population9,10.

It should be noted that if some degree of pre-existing immunity against SARS-CoV-2 exists in the general population, this could also influence epidemiological modelling, and suggests that a sliding scale model of COVID-19 susceptibility may be considered.

In conclusion, it is now established that SARS-CoV-2 pre-existing immune reactivity exists to some degree in the general population. It is hypothesized, but not yet proven, that this might be due to immunity to CCCs. This might have implications for COVID-19 disease severity, herd immunity and vaccine development, which still await to be addressed with actual data.

reference link : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7339790/


More information: Le Sage V, Jones JE, Kormuth KA, Fitzsimmons WJ, Nturibi E, Padovani GH, et al. (2021) Pre-existing heterosubtypic immunity provides a barrier to airborne transmission of influenza viruses. PLOS Pathogens (2021). DOI: 10.1371/journal.ppat.1009273

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