The aging transcriptome and cellular landscape of the human lung in relation to SARS-CoV-2
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Abstract
Age is a major risk factor for severe coronavirus disease-2019 (COVID-19). Here, we interrogate the transcriptional features and cellular landscape of the aging human lung. By intersecting these age-associated changes with experimental data on SARS-CoV-2, we identify several factors that may contribute to the heightened severity of COVID-19 in older populations. The aging lung is transcriptionally characterized by increased cell adhesion and stress responses, with reduced mitochondria and cellular replication. Deconvolution analysis reveals that the proportions of alveolar type 2 cells, proliferating basal cells, goblet cells, and proliferating natural killer/T cells decrease with age, whereas alveolar fibroblasts, pericytes, airway smooth muscle cells, endothelial cells and IGSF21 + dendritic cells increase with age. Several age-associated genes directly interact with the SARS-CoV-2 proteome. Age-associated genes are also dysregulated by SARS-CoV-2 infection in vitro and in patients with severe COVID-19. These analyses illuminate avenues for further studies on the relationship between age and COVID-19.
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SciScore for 10.1101/2020.04.07.030684: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources Single cell transcriptomes of human lungs were obtained from the Tissue Stability Cell Atlas (https://www.tissuestabilitycellatlas.org/) 19 and from the Human Lung Cell Atlas (https://github.com/krasnowlab/HLCA and https://www.synapse.org/#!Synapse:syn21041850/) 42. https://www.synapse.org/#suggested: (Multiple Myeloma survival predictor, RRID:SCR_017651)Identification of age-associated genes in human lung: To identify age-associated genes, the raw counts values were analyzed by … SciScore for 10.1101/2020.04.07.030684: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources Single cell transcriptomes of human lungs were obtained from the Tissue Stability Cell Atlas (https://www.tissuestabilitycellatlas.org/) 19 and from the Human Lung Cell Atlas (https://github.com/krasnowlab/HLCA and https://www.synapse.org/#!Synapse:syn21041850/) 42. https://www.synapse.org/#suggested: (Multiple Myeloma survival predictor, RRID:SCR_017651)Identification of age-associated genes in human lung: To identify age-associated genes, the raw counts values were analyzed by DESeq2 (v1.24.0) 22, using the likelihood ratio test (LRT). DESeq2suggested: (DESeq, RRID:SCR_000154)Gene ontology and pathway analysis of lung age-associated genes: Gene ontology and pathway enrichment analysis was performed using DAVID (v6.8) 70 (https://david.ncifcrf.gov/), separating the age-associated genes into the two clusters (increasing or decreasing with age), as described above. DAVIDsuggested: (DAVID, RRID:SCR_001881)Age-associated genes that are transcriptionally regulated upon SARS-CoV-2 infection: To assess whether the expression of lung age-associated genes is influenced by SARS-CoV-2 infection, we utilized the data from a preprint manuscript detailing the transcriptional response to SARS-CoV-2 infection 68, from the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE147507) (accessed on April 13, 2020). Gene Expression Omnibussuggested: (Gene Expression Omnibus (GEO, RRID:SCR_005012)Results from OddPub: Thank you for sharing your data.
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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SciScore for 10.1101/2020.04.07.030684: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable A total of 578 lung RNA-seq profiles were compiled, of which 31.66% were from women. Cell Line Authentication not detected. Table 2: Resources
Experimental Models: Cell Lines Sentences Resources AT2 cells also frequently expressed ATP1B1 , ALG5 , NEU1 , and ATP6V1A ( 70.21 % , 50.64 % , 43.24 % , and 27.78 % ) . AT2suggested: NoneVolcano plots of differentially expressed genes in A549 cells ( a) , A549 cells transduced with an ACE2 vector ( A549-ACE2 ) ( b) , or Calu-3 cells ( c) . … SciScore for 10.1101/2020.04.07.030684: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable A total of 578 lung RNA-seq profiles were compiled, of which 31.66% were from women. Cell Line Authentication not detected. Table 2: Resources
Experimental Models: Cell Lines Sentences Resources AT2 cells also frequently expressed ATP1B1 , ALG5 , NEU1 , and ATP6V1A ( 70.21 % , 50.64 % , 43.24 % , and 27.78 % ) . AT2suggested: NoneVolcano plots of differentially expressed genes in A549 cells ( a) , A549 cells transduced with an ACE2 vector ( A549-ACE2 ) ( b) , or Calu-3 cells ( c) . A549suggested: NoneVenn diagram of shared SARS-CoV-2 induced genes ( a ) or SARS-CoV-2 repressed genes ( b ) in A549 cells , A549-ACE2 cells , and Calu-3 cells . A549-ACE2suggested: NoneTable S24: Differential expression analysis in Calu-3 cells, infected with SARS-CoV-2 vs mock control, with age-association annotations. Calu-3suggested: BCRJ Cat# 0264, CVCL_0609Software and Algorithms Sentences Resources Since functional screening data with SARS-CoV-2 has not yet been described ( as of March 30 , 2020) , we instead searched for data on SARS-CoV . SARS-CoV-2suggested: (Sino Biological Cat# 40143-R019, AB_2827973)Single cell transcriptomes of human lungs were obtained from the Tissue Stability Cell Atlas ( https://www.tissuestabilitycellatlas.org/ ) 19 and from the Human Lung Cell Atlas ( https://github.com/krasnowlab/HLCA and https://www.synapse.org/# ! Synapse:syn21041850/ ) 42 . https://www.synapse.org/#suggested: (Multiple Myeloma survival predictor, SCR_017651)Identification of age-associated genes in human lung To identify age-associated genes , the raw counts values were analyzed by DESeq2 ( v1.24.0 ) 22 , using the likelihood ratio test ( LRT) . DESeq2suggested: (DESeq, SCR_000154)Age-associated genes that are transcriptionally regulated upon SARS-CoV-2 infection To assess whether the expression of lung age-associated genes is influenced by SARSCoV-2 infection , we utilized the data from a preprint manuscript detailing the transcriptional response to SARS-CoV-2 infection 68 , from the Gene Expression Omnibus ( https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi ? acc=GSE147507 ) ( accessed on April 13 , 2020) . Gene Expression Omnibussuggested: (Gene Expression Omnibus (GEO), SCR_005012)Gene ontology and pathway enrichment analysis was performed using DAVID ( v6.8 ) 70 ( https://david.ncifcrf.gov/). DAVIDsuggested: (DAVID, SCR_001881)Results from OddPub: Thank you for sharing your data.
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