Science Spotlight

Immune signatures of severe COVID disease

From the Lund lab, Vaccine and Infectious Disease Division
March 15, 2021 • By B Traxinger

Among people infected with SARS-CoV-2, disease outcomes range from no symptoms to death, but the immune correlates of protection and host factors that influence the severity of COVID-19 disease are not fully understood. The identification of immune correlates of protection from severe COVID-19 disease would inform vaccine design and provide the ability to predict which patients will go on to experience severe symptoms, allowing for preemptive therapeutic intervention. However, studies aimed at resolving correlates of protection are difficult to perform in humans, as paired pre-infection baseline immune profiling and post-infection disease screening must be performed on the same person. Therefore, some SARS-coronavirus studies have resorted to mouse models.

While inbred lab mice are useful for eliminating unwanted variables during controlled experiments, genetically identical mice fail to model the natural inter-person human diversity that accounts for much of the COVID-19 disease variability. To overcome this challenge, Dr. Jessica Graham from the Lund lab in the Vaccine and Infectious Disease Division, along with colleagues from the University of North Carolina at Chapel Hill, used an innovative mouse model to mimic human genetic diversity and collect pre- and post-infection data. This work, recently published in PLoS Pathogens, takes advantage of a previously established mouse resource called the Collaborative Cross (CC), a population of recombinant inbred mouse strains derived from the breeding of eight founder strains of both inbred and wild background followed by inbreeding. The resulting mice are genetically diverse, and have been previously shown to replicate the interpersonal genetic variance in humans. Subsequent progeny derived from CC crosses (CC recombinant intercross, CC-RIX) can be used to introduce further, controlled genetic heterogeneity and function as a suitable model of human genetic diversity.

To apply the CC model to SARS-coronavirus research, Dr. Graham and colleagues performed an initial screen by infecting mice across 100 distinct CC-RIX lines with SARS-CoV MA15—a mouse-adapted SARS-CoV strain— and monitored each for weight loss and survival for 28 days, and measured lung viral load on days 2 and 4 post-infection in a parallel group of mice. From this screen, CC-RIX lines that displayed “extreme” disease phenotypes –those with either low viral titers and mild disease (LID) or high viral titers and severe disease (HID)— were selected for further analysis.

SARS-CoV MA15 infection of genetically diverse mice results in a variety of viral load trajectories. (A) Six-to-eight week old F1 hybrid female CC mice (N = 18–28) were transferred internally within UNC to an ABSL-3 facility for SARS-CoV infection. Mice were intranasally infected with SARS-CoV MA15. 3–6 CC-RIX were euthanized at d2 and at d4 for lung viral load assessment, while other cohorts were monitored daily for weight loss up to d28 post-infection. Concurrently, CC-RIX male mice were transferred from UNC to the University of Washington and housed directly in a BSL-2+ laboratory within an SPF barrier facility. 8–10 week old mice were used for all baseline immune flow cytometry experiments, with 3–6 mice per experimental group. (B) Average weight loss at day 6 post infection (pi) is shown for each CC-RIX line. (C) Average viral loads in the lung at day 2 pi are shown for each CC-RIX line. Red dotted line indicates titers above 107 PFU, and blue dotted line indicates viral titers below 105 PFU. (D) Average viral loads in the lung at day 2 pi and at day 4 pi for each CC-RIX line. (E) The day 2 post-infection average lung viral loads are shown for selected CC-RIX lines are with extreme phenotypes: low or high viral titers. Lines with an average lung viral load of less than 105 at day 2 post-infection (N = 8) were considered to be “low titer”, and lines with an average lung viral load of greater than 107 at day 2 post-infection (N = 24) were considered to be “high titer” for further analysis. 3–6 mice per group were used for each viral load time point, and weight loss/clinical score data was collected for each mouse in the study up to experimental endpoint.
SARS-CoV MA15 infection of genetically diverse mice results in a variety of viral load trajectories. (A) Six-to-eight week old F1 hybrid female CC mice (N = 18–28) were transferred internally within UNC to an ABSL-3 facility for SARS-CoV infection. Mice were intranasally infected with SARS-CoV MA15. 3–6 CC-RIX were euthanized at d2 and at d4 for lung viral load assessment, while other cohorts were monitored daily for weight loss up to d28 post-infection. Concurrently, CC-RIX male mice were transferred from UNC to the University of Washington and housed directly in a BSL-2+ laboratory within an SPF barrier facility. 8–10 week old mice were used for all baseline immune flow cytometry experiments, with 3–6 mice per experimental group. (B) Average weight loss at day 6 post infection (pi) is shown for each CC-RIX line. (C) Average viral loads in the lung at day 2 pi are shown for each CC-RIX line. Red dotted line indicates titers above 107 PFU, and blue dotted line indicates viral titers below 105 PFU. (D) Average viral loads in the lung at day 2 pi and at day 4 pi for each CC-RIX line. (E) The day 2 post-infection average lung viral loads are shown for selected CC-RIX lines are with extreme phenotypes: low or high viral titers. Lines with an average lung viral load of less than 105 at day 2 post-infection (N = 8) were considered to be “low titer”, and lines with an average lung viral load of greater than 107 at day 2 post-infection (N = 24) were considered to be “high titer” for further analysis. 3–6 mice per group were used for each viral load time point, and weight loss/clinical score data was collected for each mouse in the study up to experimental endpoint. Figure from publication.

Once establishing which mice progressed to either mild or severe disease, the authors interrogated the baseline immune signatures that correlate with either outcome. Immune cells from a second cohort of uninfected mice from each of the relevant CC-RIX lines were profiled with flow cytometry, revealing that that LID mouse lines had higher frequencies of activated CD4 and CD8 T cells compared to HID mice. Additionally, LID mice had increased frequencies of regulatory T cells (Tregs), a subset of CD4 T cells that restrain conventional T cells and facilitate the balance of allowing for sufficient aggressive immune responses to clear virus but not so much that local tissues become secondarily damaged. Furthermore, T cells in LID mice expressed more cytokines interferon-gamma and interleukin-17, rather than increased tumor necrosis factor cytokine, as found in the HID mice. These findings suggest that mice with higher baseline frequencies of both conventional and regulatory T cells are better able to control SARS-CoV infection.

To further understand why certain individuals experience varied disease outcomes in response to the same viral infection, the authors selected CC-RIX lines that did not clear SARS-CoV virus effectively but did not experience severe weight loss or death (no disease high titer; NDHT). The baseline immune profiles were examined in these mice and compared to those that also had high viral titer but experienced severe disease or death (disease high titer; DHT). In the context of high lung viral titer, mice which did not experience disease, compared to those that did, contained lower frequencies of activated Tregs and higher frequencies of CD8+T cells that could produce pro-inflammatory cytokines. These findings suggest that a dysregulated, less-inflammatory circulating immune profile response is correlated with severe disease, a result that is supported by previous mouse and human SARS-CoV research.

“Overall, the results from our study demonstrated that baseline T cell phenotypes can predict early virologic and clinical outcomes upon infection with SARS-coronaviruses,” summarized Dr. Graham. This work also “highlights the complexity of inflammation, which can be both protective and detrimental to the host, and some immune signatures might promote rapid immunity upon infection while also limiting collateral damage,” Dr. Graham said, explaining why Tregs, in different contexts, are correlated with both mild and severe disease. Going forward, the Lund lab and their collaborators at the University of North Carolina Chapel Hill are interested in using a mouse model that allows for inducible Treg ablation to “transiently deplete Tregs prior to infection to directly test the role of Tregs in SARS-CoV virologic and clinical outcomes,” Dr. Graham explained. “Additionally, further study in the CC model using lines with naturally low or high frequency of Tregs or other extreme immune phenotypes could be used to validate our findings with SARS-CoV MA15 as well as mouse-adapted SARS-CoV-2,” Dr. Graham said.

Graham JB, Swarts JL, Leist SR, Schafer A, Menachery VD, Gralinski LE, Miller DR, Mooney MA, McWeeney SK, Ferris MT, Pardo-Manuel de Villena F, Heise MT, Baric RS, Lund JM. Baseline T cell immune phenotypes predict virologic and disease control upon SARS-CoV infection in Collaborative Cross mice. PLoS Pathog. 2021 Jan 29;17(1):e1009287. doi: 10.1371/journal.ppat.1009287. eCollection 2021 Jan.

This work was supported by the National Institutes of Health.

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