This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Research article
Stephen W. Bickler
Professor of Surgery and Pediatrics Rady Children’s Hospital—University of California San Diego 3030 Children’s Way San Diego, CA 92123 USA
Corresponding Author
ORCiD: https://orcid.org/0000-0003-2626-2407
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Older aged adults and those with pre-existing conditions are at highest risk for severe COVID-19 associated outcomes.
Methods: Using a large dataset of genome-wide RNA-seq profiles derived from human dermal fibroblasts (GSE113957) we investigated whether age affects the expression of pattern recognition receptor (PRR) genes and ACE2, the receptor for SARS-CoV-2.
Results: Older age was associated with increased expression of PRR genes, ACE2 and four genes that encode proteins that have been shown to interact with SAR2-CoV-2 proteins.
Conclusions: Assessment of PRR expression might provide a strategy for stratifying the risk of severe COVID-19 disease at both the individual and population levels.
Keywords
SARS-CoV-2, Pattern recognition receptors, Toll-like receptor 4, Aging, Skin fibroblasts
Figure 1
Figure 2
This is a list of supplementary files associated with this preprint. Click to download.
- supp1.xlsx
Additional file 1: Supplementary Table 1. Gene expression analysis between the oldest (≥80 years) and youngest (≤10) age groups: a) Differentially expressed genes with an Adjusted P Value <0.05 and Absolute FC >1.0, b) ACE2 and PRR genes
- supp1.xlsx
Additional file 1: Supplementary Table 1. Gene expression analysis between the oldest (≥80 years) and youngest (≤10) age groups: a) Differentially expressed genes with an Adjusted P Value <0.05 and Absolute FC >1.0, b) ACE2 and PRR genes
- supp2.xlsx
Additional file 2: Supplementary Table 2. ToppGene enrichment results for differentially expressed genes between the oldest and youngest age groups (filtered to show KEGG pathway results)
- supp2.xlsx
Additional file 2: Supplementary Table 2. ToppGene enrichment results for differentially expressed genes between the oldest and youngest age groups (filtered to show KEGG pathway results)
- supp3.xlsx
Additional file 3: Supplementary Table 3. a) Pearson r, b) P values, and c) Confidence intervals of r for the correlation matrix shown in Fig. 2a
- supp3.xlsx
Additional file 3: Supplementary Table 3. a) Pearson r, b) P values, and c) Confidence intervals of r for the correlation matrix shown in Fig. 2a
- supp4.pdf
Additional file 4: Supplementary Figure 1. Normalized gene counts for the ten Toll-like receptors expressed as a function of age
- supp4.pdf
Additional file 4: Supplementary Figure 1. Normalized gene counts for the ten Toll-like receptors expressed as a function of age
- supp5.xlsx
Additional file 5: Supplementary Table 4. Differentially expressed genes between TLR4 high vs low expressors
- supp5.xlsx
Additional file 5: Supplementary Table 4. Differentially expressed genes between TLR4 high vs low expressors
- supp6.xlsx
Additional file 6: Supplementary Table 5. ToppGene enrichment results for differentially expressed genes between TLR4 high and low expressors (filtered to show KEGG pathway results)
- supp6.xlsx
Additional file 6: Supplementary Table 5. ToppGene enrichment results for differentially expressed genes between TLR4 high and low expressors (filtered to show KEGG pathway results)
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This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Research article
Stephen W. Bickler
Professor of Surgery and Pediatrics Rady Children’s Hospital—University of California San Diego 3030 Children’s Way San Diego, CA 92123 USA
Corresponding Author
ORCiD: https://orcid.org/0000-0003-2626-2407
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 21 Sep, 2020
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Epigenetics & Genomics
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Older aged adults and those with pre-existing conditions are at highest risk for severe COVID-19 associated outcomes.
Methods: Using a large dataset of genome-wide RNA-seq profiles derived from human dermal fibroblasts (GSE113957) we investigated whether age affects the expression of pattern recognition receptor (PRR) genes and ACE2, the receptor for SARS-CoV-2.
Results: Older age was associated with increased expression of PRR genes, ACE2 and four genes that encode proteins that have been shown to interact with SAR2-CoV-2 proteins.
Conclusions: Assessment of PRR expression might provide a strategy for stratifying the risk of severe COVID-19 disease at both the individual and population levels.
Figure 1
Figure 2
This is a list of supplementary files associated with this preprint. Click to download.
- supp1.xlsx
Additional file 1: Supplementary Table 1. Gene expression analysis between the oldest (≥80 years) and youngest (≤10) age groups: a) Differentially expressed genes with an Adjusted P Value <0.05 and Absolute FC >1.0, b) ACE2 and PRR genes
- supp1.xlsx
Additional file 1: Supplementary Table 1. Gene expression analysis between the oldest (≥80 years) and youngest (≤10) age groups: a) Differentially expressed genes with an Adjusted P Value <0.05 and Absolute FC >1.0, b) ACE2 and PRR genes
- supp2.xlsx
Additional file 2: Supplementary Table 2. ToppGene enrichment results for differentially expressed genes between the oldest and youngest age groups (filtered to show KEGG pathway results)
- supp2.xlsx
Additional file 2: Supplementary Table 2. ToppGene enrichment results for differentially expressed genes between the oldest and youngest age groups (filtered to show KEGG pathway results)
- supp3.xlsx
Additional file 3: Supplementary Table 3. a) Pearson r, b) P values, and c) Confidence intervals of r for the correlation matrix shown in Fig. 2a
- supp3.xlsx
Additional file 3: Supplementary Table 3. a) Pearson r, b) P values, and c) Confidence intervals of r for the correlation matrix shown in Fig. 2a
- supp4.pdf
Additional file 4: Supplementary Figure 1. Normalized gene counts for the ten Toll-like receptors expressed as a function of age
- supp4.pdf
Additional file 4: Supplementary Figure 1. Normalized gene counts for the ten Toll-like receptors expressed as a function of age
- supp5.xlsx
Additional file 5: Supplementary Table 4. Differentially expressed genes between TLR4 high vs low expressors
- supp5.xlsx
Additional file 5: Supplementary Table 4. Differentially expressed genes between TLR4 high vs low expressors
- supp6.xlsx
Additional file 6: Supplementary Table 5. ToppGene enrichment results for differentially expressed genes between TLR4 high and low expressors (filtered to show KEGG pathway results)
- supp6.xlsx
Additional file 6: Supplementary Table 5. ToppGene enrichment results for differentially expressed genes between TLR4 high and low expressors (filtered to show KEGG pathway results)
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