Aging-related cell type-specific pathophysiologic immune responses that exacerbate disease severity in aged COVID-19 patients

doi:10.1111/acel.13544

. 2022 Feb;21(2):e13544.

doi: 10.1111/acel.13544. Epub 2022 Jan 12.

Aging-related cell type-specific pathophysiologic immune responses that exacerbate disease severity in aged COVID-19 patients

Yuan Hou¹, Yadi Zhou¹, Lara Jehi^{2

3}, Yuan Luo⁴, Michaela U Gack⁵, Timothy A Chan⁶, Haiyuan Yu^{7

8}, Charis Eng^{1

9

10

11}, Andrew A Pieper^{12

13

14

15

16

17}, Feixiong Cheng^{1

9

11}

Affiliations

¹ Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA.
² Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA.
³ Neurological Institute, Cleveland Clinic, Cleveland, Ohio, USA.
⁴ Division of Health and Biomedical Informatics, Department of Preventive Medicine, Clinical and Translational Sciences Institute and Center for Health Information Partnerships, Northwestern University, Evanston, Illinois, USA.
⁵ Florida Research and Innovation Center, Cleveland Clinic, Port Saint Lucie, Florida, USA.
⁶ Center for Immunotherapy and Precision Immuno-Oncology, Cleveland Clinic, Cleveland, Ohio, USA.
⁷ Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA.
⁸ Department of Computational Biology, Cornell University, Ithaca, New York, USA.
⁹ Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio, USA.
¹⁰ Department of Genetics and Genome Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA.
¹¹ Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA.
¹² Harrington Discovery Institute, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA.
¹³ Department of Psychiatry, Case Western Reserve University, Cleveland, Ohio, USA.
¹⁴ Geriatric Psychiatry, GRECC, Louis Stokes Cleveland VA Medical Center, Cleveland, Ohio, USA.
¹⁵ Institute for Transformative Molecular Medicine, School of Medicine, Case Western Reserve University, Cleveland, Ohio, USA.
¹⁶ Weill Cornell Autism Research Program, Weill Cornell Medicine of Cornell University, New York, New York, USA.
¹⁷ Department of Neuroscience, School of Medicine, Case Western Reserve University, Cleveland, Ohio, USA.

PMID: 35023286
PMCID: PMC8844132
DOI: 10.1111/acel.13544

Aging-related cell type-specific pathophysiologic immune responses that exacerbate disease severity in aged COVID-19 patients

Yuan Hou et al. Aging Cell. 2022 Feb.

. 2022 Feb;21(2):e13544.

doi: 10.1111/acel.13544. Epub 2022 Jan 12.

Authors

Affiliations

¹ Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA.
² Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA.
³ Neurological Institute, Cleveland Clinic, Cleveland, Ohio, USA.
⁴ Division of Health and Biomedical Informatics, Department of Preventive Medicine, Clinical and Translational Sciences Institute and Center for Health Information Partnerships, Northwestern University, Evanston, Illinois, USA.
⁵ Florida Research and Innovation Center, Cleveland Clinic, Port Saint Lucie, Florida, USA.
⁶ Center for Immunotherapy and Precision Immuno-Oncology, Cleveland Clinic, Cleveland, Ohio, USA.
⁷ Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA.
⁸ Department of Computational Biology, Cornell University, Ithaca, New York, USA.
⁹ Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio, USA.
¹⁰ Department of Genetics and Genome Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA.
¹¹ Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA.
¹² Harrington Discovery Institute, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA.
¹³ Department of Psychiatry, Case Western Reserve University, Cleveland, Ohio, USA.
¹⁴ Geriatric Psychiatry, GRECC, Louis Stokes Cleveland VA Medical Center, Cleveland, Ohio, USA.
¹⁵ Institute for Transformative Molecular Medicine, School of Medicine, Case Western Reserve University, Cleveland, Ohio, USA.
¹⁶ Weill Cornell Autism Research Program, Weill Cornell Medicine of Cornell University, New York, New York, USA.
¹⁷ Department of Neuroscience, School of Medicine, Case Western Reserve University, Cleveland, Ohio, USA.

PMID: 35023286
PMCID: PMC8844132
DOI: 10.1111/acel.13544

Abstract

Coronavirus disease 2019 (COVID-19) is especially severe in aged patients, defined as 65 years or older, for reasons that are currently unknown. To investigate the underlying basis for this vulnerability, we performed multimodal data analyses on immunity, inflammation, and COVID-19 incidence and severity as a function of age. Our analysis leveraged age-specific COVID-19 mortality and laboratory testing from a large COVID-19 registry, along with epidemiological data of ~3.4 million individuals, large-scale deep immune cell profiling data, and single-cell RNA-sequencing data from aged COVID-19 patients across diverse populations. We found that decreased lymphocyte count and elevated inflammatory markers (C-reactive protein, D-dimer, and neutrophil-lymphocyte ratio) are significantly associated with age-specific COVID-19 severities. We identified the reduced abundance of naïve CD8 T cells with decreased expression of antiviral defense genes (i.e., IFITM3 and TRIM22) in aged severe COVID-19 patients. Older individuals with severe COVID-19 displayed type I and II interferon deficiencies, which is correlated with SARS-CoV-2 viral load. Elevated expression of SARS-CoV-2 entry factors and reduced expression of antiviral defense genes (LY6E and IFNAR1) in the secretory cells are associated with critical COVID-19 in aged individuals. Mechanistically, we identified strong TGF-beta-mediated immune-epithelial cell interactions (i.e., secretory-non-resident macrophages) in aged individuals with critical COVID-19. Taken together, our findings point to immuno-inflammatory factors that could be targeted therapeutically to reduce morbidity and mortality in aged COVID-19 patients.

Keywords: COVID-19; SARS-CoV-2; aging; cellular immunology; molecular biology of aging.

PubMed Disclaimer

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

**FIGURE 1**
Epidemiological data analysis between aged and younger COVID‐19 patients. (a) The percentage of fatal cases of COVID‐19 and flu across three age groups. Data source from U.S. CDC. The upper panel shows the percentage of fatal cases of COVID‐19 in the United States. Each dot in the boxplot represents one state. The lower panel shows the percentage of fatal cases of flu from 2010 to 2020. Each dot in the boxplot represents one flu season. Statistical p‐value was computed by two‐tailed paired t test. For details about CDC dataset, see Tables S1 and S2. (b) Sex differences in the percentage of fatal cases of COVID‐19 across three age groups. (c) Odds ratio (OR) analysis of U.S. CDC and COVID‐19 registry datasets. U.S. CDC dataset, “Younger” is defined as 20 to 49 years of age (n = 2,369,919), and ‘aged’ is defined as >60 years old (n = 1,048,011); COVID‐19 registry dataset, “Younger” is defined as 18 to 55 years of age (n = 12,651), and ‘aged’ is defined as ≥65 years old (n = 32,426). OR >1 indicates aged COVID‐19 patients with increased likelihood of hospitalization, ICU admission, and death. Two colors denote OR models with different adjusted confounders. Features of the COVID‐19 registry dataset are shown in Table S3. (d) and (e) Boxplot show the lab testing values of five inflammatory markers between aged (>65 years, n = 1405) and younger (18 to 55 years, n = 970) individuals. Adjusted p‐value [q] was computed by Mann–Whitney U test with Benjamini–Hochberg (BH) multiple testing correction

**FIGURE 2**
Deep immune‐profiling of aged and younger patients with COVID‐19. (a) and (f) Scatterplots show the differential immune cell type (a) and cytokines (f) between ICU (n = 39 samples, aged n = 26, younger n = 13) versus non‐ICU (105 samples, aged n = 68, younger n = 37) COVID‐19 patients. The cell flow and cytokine profiling datasets were collected from a recent study (Takahashi et.al, 2020) (see Method). Y‐axis and X‐axis show the log2(Fold Change [FC]) in younger and aged subpopulations. The pairwise comparison group is ICU vs. non‐ICU patients with COVID‐19. Solid green dots denote significantly different cell types or cytokines in both younger and aged patients. Solid blue and orange dots denote significantly different cell types or cytokines in younger and aged patients, respectively. (b) and (g) Scatterplots show the differential immune cell type (b) and cytokines (g) in aged (n = 94 samples) versus younger (50 samples) COVID‐19 patients. Y‐axis and X‐axis show the log2FC in ICU and non‐ICU subpopulations. The pairwise comparison group is aged vs. younger patients with COVID‐19. Solid red dots denote significantly different cell types or cytokines in both ICU and non‐ICU patients. Solid purple dots denote significantly different cell types or cytokines in non‐ICU patients. (c) The abundance of major immune cell types in PBMC and (d) subtypes of CD8+ T cells in all CD3‐positive cells. Statistical adjusted p‐value (q) was computed by Mann–Whitney U test with BH multiple testing correction (e) Heatmap showing the ratio of naïve vs memory lymphocytes. Gradient color indicated the log2 fold change in average ratio between aged and younger in non‐ICU or ICU subgroup, respectively. Black circle indicates q < 0.05. (h) The abundance of four cytokines changes between younger and aged COVID‐19 patients in hospital, ICU, and non‐ICU groups

**FIGURE 3**
Single‐cell transcriptome of CD8 T cells in aged COVID‐19 patients. (a) UMAP plot displays five identified CD8 T‐cell subpopulations. The single‐cell transcriptomic dataset (25 of Severe\Critical COVID‐19 patients, aged n = 12, younger n = 13) was collected from a recent study (Stephenson et al., 2021) (Table S1 and Method). (b) Pathway enrichment analysis across five CD8 T‐cell subtypes. Black circle indicates q < 0.05. (c) A highlighted protein–protein interaction subnetwork for age‐biased differentially expressed genes in CD8 naïve T cells from patients with critical COVID‐19. The colors for nodes and edges represent different immune pathways

**FIGURE 4**
Analysis of SARS‐CoV‐2 viral load and related entry gene expression in nasal tissues. (a) Volcano plot showing the differential genes of bulk RNA‐sequencing data in aged versus younger patients in high viral load nasal tissues. A publicly available bulk RNA‐seq dataset of 147 nasal samples (Lieberman et al., 2020) was used, including 61 aged patients (high [n = 27] and low [n = 34] viral load) and 86 younger patients (high [n = 46] and low [n = 40] viral load). (b) Gene‐set enrichment analysis (GSEA) of 22 immune pathways for differential genes of aged vs. younger in high or low viral load subgroups. The gradient color bar shows the NES score (see Method). NES score >0 and q < 0.05 indicate that up‐regulated differential expressed genes (DEGs) in aged vs. young are significantly enriched in immune pathways, while NES score <0 and q < 0.05 indicate down‐regulated DEGs in aged vs. young are significantly enriched in immune pathways. Black dots denote q < 0.05. (c) Boxplot showing the lab testing data changes in aged and younger COVID‐19 patients with high (>4.5 log10 RNA copies/ml) and low (<4.5 log10 RNA copies/ml) viral load(Pekosz et al., ; Yang, Jiang, et al., 2020). (d) SARS‐CoV‐2‐related entry gene expression profile across 15 cell types of nasal tissue between aged and younger patients. The size of dot denotes the percentage of the positive cell which expressed the tested genes. The gradient color bar represents the z‐score scaled average expression of genes in each cell type

**FIGURE 5**
Distinct epithelial‐immune cell interaction profile in aged and younger patients with critical COVID‐19. (a) Heatmap showing the total log‐scaled interaction number between epithelial–immune cells in critical COVID‐19 disease. Aged group, n = 3 patients, younger group, n = 5 patients. The cell–cell interaction network depicted all significant cell pairs in which the number of ligand–receptor interaction >50 (permutation test with BH multiple testing correction, q < 0.05). Edge size denotes the number of significant ligand–receptor interactions between two cell types. Different colors indicate the immune or epithelial cell types. (b) Dot plot showing significant ligand–receptor interactions between epithelial–immune cell interaction in critical COVID‐19 disease. The circle size indicates ‐log₁₀(q), and gradient color bar shows the log2‐scaled means of average expression of interacted cell pair

**FIGURE 6**
Proposed mechanistic models for age‐biased COVID‐19 severity in aged individuals. Several age‐related pathophysiologic immune responses are associated with disease susceptibility and severity in COVID‐19: a) decreased lymphocyte count and elevated inflammatory markers (C‐reactive protein [CRP], D‐dimer, and neutrophil–lymphocyte ratio); b) elevated pro‐inflammation cytokines IL‐8, IL‐27, and IL‐6 in aged COVID‐19 patients; c) reduced abundance of naïve CD8 T cells with decreased expression of antiviral defense genes (i.e., *IFITM3* and *TRIM22*) in aged individuals with severe COVID‐19; d) type I interferon deficiency is associated with SARS‐CoV‐2 viral load in aged individuals; e) elevated expression of SARS‐CoV‐2 entry factors (*BSG* and *FURIN*) and reduced expression of antiviral defense genes (*IFNAR1*, *OAS1*, *IFIT1*) in the secretory cells of critical COVID‐19 in aged individuals; f) strong TGF‐beta‐mediated immune–epithelial cell interactions (i.e., secretory—nrMa) in aged individuals with critical COVID‐19

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References

1. Acharya, D. , Liu, G. , & Gack, M. U. (2020). Dysregulation of type I interferon responses in COVID‐19. Nature Reviews Immunology, 20(7), 397–398. 10.1038/s41577-020-0346-x - DOI - PMC - PubMed
1. Aman, M. J. , Rudolf, G. , Goldschmitt, J. , Aulitzky, W. E. , Lam, C. , Huber, C. , & Peschel, C. (1993). Type‐I interferons are potent inhibitors of interleukin‐8 production in hematopoietic and bone marrow stromal cells. Blood, 82, 2371–2378. 10.1182/blood.V82.8.2371.2371 - DOI - PubMed
1. Bastard, P. , Rosen, L. B. , Zhang, Q. , Michailidis, E. , Hoffmann, H.‐H. , Zhang, Y. U. , Dorgham, K. , Philippot, Q. , Rosain, J. , Béziat, V. , Manry, J. , Shaw, E. , Haljasmägi, L. , Peterson, P. , Lorenzo, L. , Bizien, L. , Trouillet‐Assant, S. , Dobbs, K. , de Jesus, A. A. , … Casanova, J.‐L. (2020). Autoantibodies against type I IFNs in patients with life‐threatening COVID‐19. Science, 370(6515), 10.1126/science.abd4585 - DOI - PMC - PubMed
1. Becht, E. , McInnes, L. , Healy, J. , Dutertre, C.‐A. , Kwok, I. W. H. , Ng, L. G. , Ginhoux, F. , & Newell, E. W. (2019). Dimensionality reduction for visualizing single‐cell data using UMAP. Nature Biotechnology, 37, 38–44. 10.1038/nbt.4314 - DOI - PubMed
1. Benjamini, Y. , & Hochberg, Y . (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B (Methodological), 57(1), 289–300. 10.1111/j.2517-6161.1995.tb02031.x - DOI

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[1] Acharya, D. , Liu, G. , & Gack, M. U. (2020). Dysregulation of type I interferon responses in COVID‐19. Nature Reviews Immunology, 20(7), 397–398. 10.1038/s41577-020-0346-x - DOI - PMC - PubMed

[2] Acharya, D. , Liu, G. , & Gack, M. U. (2020). Dysregulation of type I interferon responses in COVID‐19. Nature Reviews Immunology, 20(7), 397–398. 10.1038/s41577-020-0346-x - DOI - PMC - PubMed

[3] Aman, M. J. , Rudolf, G. , Goldschmitt, J. , Aulitzky, W. E. , Lam, C. , Huber, C. , & Peschel, C. (1993). Type‐I interferons are potent inhibitors of interleukin‐8 production in hematopoietic and bone marrow stromal cells. Blood, 82, 2371–2378. 10.1182/blood.V82.8.2371.2371 - DOI - PubMed

[4] Aman, M. J. , Rudolf, G. , Goldschmitt, J. , Aulitzky, W. E. , Lam, C. , Huber, C. , & Peschel, C. (1993). Type‐I interferons are potent inhibitors of interleukin‐8 production in hematopoietic and bone marrow stromal cells. Blood, 82, 2371–2378. 10.1182/blood.V82.8.2371.2371 - DOI - PubMed

[5] Bastard, P. , Rosen, L. B. , Zhang, Q. , Michailidis, E. , Hoffmann, H.‐H. , Zhang, Y. U. , Dorgham, K. , Philippot, Q. , Rosain, J. , Béziat, V. , Manry, J. , Shaw, E. , Haljasmägi, L. , Peterson, P. , Lorenzo, L. , Bizien, L. , Trouillet‐Assant, S. , Dobbs, K. , de Jesus, A. A. , … Casanova, J.‐L. (2020). Autoantibodies against type I IFNs in patients with life‐threatening COVID‐19. Science, 370(6515), 10.1126/science.abd4585 - DOI - PMC - PubMed

[6] Bastard, P. , Rosen, L. B. , Zhang, Q. , Michailidis, E. , Hoffmann, H.‐H. , Zhang, Y. U. , Dorgham, K. , Philippot, Q. , Rosain, J. , Béziat, V. , Manry, J. , Shaw, E. , Haljasmägi, L. , Peterson, P. , Lorenzo, L. , Bizien, L. , Trouillet‐Assant, S. , Dobbs, K. , de Jesus, A. A. , … Casanova, J.‐L. (2020). Autoantibodies against type I IFNs in patients with life‐threatening COVID‐19. Science, 370(6515), 10.1126/science.abd4585 - DOI - PMC - PubMed

[7] Becht, E. , McInnes, L. , Healy, J. , Dutertre, C.‐A. , Kwok, I. W. H. , Ng, L. G. , Ginhoux, F. , & Newell, E. W. (2019). Dimensionality reduction for visualizing single‐cell data using UMAP. Nature Biotechnology, 37, 38–44. 10.1038/nbt.4314 - DOI - PubMed

[8] Becht, E. , McInnes, L. , Healy, J. , Dutertre, C.‐A. , Kwok, I. W. H. , Ng, L. G. , Ginhoux, F. , & Newell, E. W. (2019). Dimensionality reduction for visualizing single‐cell data using UMAP. Nature Biotechnology, 37, 38–44. 10.1038/nbt.4314 - DOI - PubMed

[9] Benjamini, Y. , & Hochberg, Y . (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B (Methodological), 57(1), 289–300. 10.1111/j.2517-6161.1995.tb02031.x - DOI

[10] Benjamini, Y. , & Hochberg, Y . (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B (Methodological), 57(1), 289–300. 10.1111/j.2517-6161.1995.tb02031.x - DOI

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Aging-related cell type-specific pathophysiologic immune responses that exacerbate disease severity in aged COVID-19 patients

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Aging-related cell type-specific pathophysiologic immune responses that exacerbate disease severity in aged COVID-19 patients

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