The DOI matches its Crossref record; 87 of the 87 references it cites were found in a registry or an index.
IFN signaling and neutrophil degranulation transcriptional signatures are induced during SARS-CoV-2 infection
Open accessPublished version of a preprint
Abstract
SARS-CoV-2 virus has infected more than 92 million people worldwide resulting in the Coronavirus disease 2019 (COVID-19). Using a rhesus macaque model of SARS-CoV-2 infection, we have characterized the transcriptional signatures induced in the lungs of juvenile and old macaques following infection. Genes associated with Interferon (IFN) signaling, neutrophil degranulation and innate immune pathways are significantly induced in macaque infected lungs, while pathways associated with collagen formation are downregulated, as also seen in lungs of macaques with tuberculosis. In COVID-19, increasing age is a significant risk factor for poor prognosis and increased mortality. Type I IFN and Notch signaling pathways are significantly upregulated in lungs of juvenile infected macaques when compared with old infected macaques. These results are corroborated with increased peripheral neutrophil counts and neutrophil lymphocyte ratio in older individuals with COVID-19 disease. Together, our transcriptomic studies have delineated disease pathways that improve our understanding of the immunopathogenesis of COVID-19.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Figures










Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“a study of SARS-CoV-2 infection in old and juvenile rhesus macaques showed lower levels of ACE2 in old versus young macaques after SARS-CoV-2 infection, supporting the hypothesis that ACE2 is dysregulated in the elderly, even across species67.”
From SARS-CoV-2, COVID-19 and the aging immune system (2021) · cites this paper as [67]
In the citing paper
“In a rhesus macaque model of SARS-CoV-2 infection, old macaques also had reduced type I IFN and Notch signaling pathways in their lungs when compared to juvenile counterparts67.”
From SARS-CoV-2, COVID-19 and the aging immune system (2021) · cites this paper as [67]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
Trimmomatic: a flexible trimmer for Illumina sequence data
Bolger, A. M., Lohse, M., & Usadel, B. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu170 · Cited by 65,548
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709
Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China
Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., Fan, G., Xu, J., Gu, X., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30183-5 · Cited by 48,884
featureCounts: an efficient general purpose program for assigning sequence reads to genomic features
Liao, Y., Smyth, G. K., & Shi, W. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btt656 · Cited by 27,219
lmerTest Package: Tests in Linear Mixed Effects Models
Kuznetsova, A., Brockhoff, P. B., & Christensen, R. H. B. (2017) · Journal of Statistical Software · doi:10.18637/jss.v082.i13 · Cited by 24,346
Differential expression analysis for sequence count data
Anders, S., & Huber, W. (2010) · Genome Biology · doi:10.1186/gb-2010-11-10-r106 · Cited by 15,581
Pathological findings of COVID-19 associated with acute respiratory distress syndrome
Xu, Z., Shi, L., Wang, Y., Zhang, J., Huang, L., Zhang, C., Liu, S., Zhao, P., Liu, H., Zhu, L., et al. (2020) · The Lancet Respiratory Medicine · doi:10.1016/s2213-2600(20)30076-x · Cited by 8,545
Wu, C., Chen, X., Cai, Y., Xia, J., Zhou, X., Xu, S., Huang, H., Zhang, L., Zhou, X., Du, C., et al. (2020) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2020.0994 · Cited by 8,249
Pulmonary Vascular Endothelialitis, Thrombosis, and Angiogenesis in Covid-19
Ackermann, M., Verleden, S. E., Kuehnel, M., Haverich, A., Welte, T., Laenger, F., Vanstapel, A., Werlein, C., Stark, H., Tzankov, A., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2015432 · Cited by 5,613
Cited by
Sorted by citations
Items 101–106 of 106
Additional file 1 of Morphologically intact airways in lung fibrosis have an abnormal proteome
Herrera, J., Dingle, L., Monetero, M. A., Venkateswaran, R., Blaikley, J., Granato, F., Pearson, S. T., Lawless, C., & Thornton, D. J. (2023) · Figshare · doi:10.6084/m9.figshare.22624820
Sun, F., Shi, M., Li, X., Liu, X., & Wang, X. (2025) · Shock · doi:10.1097/shk.0000000000002768
Bay, P., Boizeau, L., Préau, S., Favory, R., Guigon, A., Heming, N., Gault, E., Pham, T. O., Chaghouri, A., Turpin, M., et al. (2026) · Communications Medicine · doi:10.1038/s43856-026-01474-0
FOLR3+neutrophils contribute to sepsis by exacerbating hyper-inflammation
Chen, F., Tan, H., Wang, D., Su, R., Liao, J., Ge, H., & Wang, W. (2026) · Genes and Immunity · doi:10.1038/s41435-026-00396-0
Pesenti, L. (2024) · theses.fr (ABES) · doi:10.70675/04a80eeazdd63z42e4zbd7fz230cf5dbc199
de Almeida, L. F., Furtado, F. B., de Moraes, L. N., Fortaleza, C. M. C. B., & Grotto, R. M. T. (2026) · Research Square · doi:10.21203/rs.3.rs-10348565/v1
Metadata
- Authors
- Rosa, B. A., Ahmed, M., Singh, D. K., Choreño-Parra, J. A., Cole, J., Jiménez-Álvarez, L. A., Rodríguez-Reyna, T. S., Singh, B., Gonzalez, O., Carrion, R., Schlesinger, L. S., Martin, J., Zúñiga, J., Mitreva, M., Kaushal, D., & Khader, S. A.
- Affiliations
- Washington University in St. Louis (US)
- Texas Biomedical Research Institute (US)
- Instituto Nacional de Enfermedades Respiratorias (MX)
- Corresponding author
- Mitreva, Makedonka
- Kaushal, Deepak
- Khader, Shabaana A.
- ORCID
- Venue
- Communications Biology
- Published
- 2021-03-05
- Volume
- 4
- Issue
- 1
- Pages
- 290-290
- ISSN
- 2399-3642 (ISSN-L)
- PMC
- PMC7935909
- OpenAlex
- W3135408003
- Cited by
- 106
- References
- 87
- Citations by year
- 2026: 3 · 2025: 16 · 2024: 19 · 2023: 18 · 2022: 25 · 2021: 21
- FWCI
- 6.57
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Communications Biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (submittedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Greater South Information System
- Funders
- U.S. Department of Health and Human Services (HHSO100201700020C)
- Washington University in St. Louis
- Regeneron Pharmaceuticals
- Texas Biomedical Research Institute
- Consejo Nacional de Ciencia y Tecnología (FORDECYT/10SE/2020/05/14-06, FORDECYT/10SE/2020/05/14-07, CVU-737347)
- Institute of Nuclear Energy Research
- Secretaría de Estado de Ciencia, Tecnología e Innovación
- National Institutes of Health (P51OD011103, P51 OD111033, COVID-19, U42OD010442, P51OD011133)
- DHHS Office of the Secretary
- Biomedical Advanced Research and Development Authority (HHSO100201700020C)
- National Institute of Allergy and Infectious Diseases (R01AI134236, R01AI123780)
- Topics
- Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
- Health Sciences › Medicine › Infectious Diseases › COVID-19 Clinical Research Studies
- Life Sciences › Immunology and Microbiology › Immunology › interferon and immune responses
- Keywords
- SARS-CoV-2, COVID-19 pandemic, interferon signaling, neutrophil degranulation, lung transcriptome, rhesus macaque model, age-related susceptibility, type I interferon, Notch signaling, innate immunity, collagen synthesis, neutrophil-to-lymphocyte ratio, immunopathogenesis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review