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Record W4402705001 · doi:10.1101/2024.09.11.612489

PROS1 released by human lung basal cells upon SARS-CoV-2 infection facilitates epithelial cell repair and limits inflammation

2024· preprint· en· W4402705001 on OpenAlexaff
Theodoros Simakou, Agnieszka M. Szemiel, Lucy MacDonald, Karen Kerr, Jack Frew, Marcus Doohan, Katy Diallo, Domenico Somma, Olympia M Hardy, Aziza Elmesmari, Charles McSharry, Thomas D. Otto, Arvind H. Patel, Mariola Kurowska‐Stolarska

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2024
Typepreprint
Languageen
FieldMedicine
TopicNeonatal Respiratory Health Research
Canadian institutionsInstitute of Infection and Immunity
FundersMedical Research CouncilLifeArc
KeywordsBiologyProinflammatory cytokineImmunologyInflammation

Abstract

fetched live from OpenAlex

Abstract Factors governing the coagulopathy and pneumonitis associated with severe viral infections remain unresolved. We previously found that the expression of protein S (PROS1) is increased in lung epithelium of patients with mild COVID-19 as compared to severe COVID-19. We hypothesised that PROS1 may exert a local effect that protects the upper airway against severe inflammation by modulating epithelial and myeloid cell responses. To test this, in vitro air-interface cultures, seeded from primary healthy human lung epithelial cells, were infected with different SARS-CoV-2 clades. This model, validated by single-cell RNAseq analysis, recapitulated the dynamic cell-profile and pathogenic changes of COVID-19. We showed that PROS1 was located in the basal cells of healthy pseudostratified epithelium. During SARS-Cov-2 infection, PROS1 was released by basal cells, which was partially mediated by interferon. Transcriptome analysis showed that SARS-CoV-2 infection induced proinflammatory phenotypes (CXCL10/11 high , PTGS2 pos F3 high , S100A8/A9 high ) of basal and transitional cells. PROS1 strongly downregulated these cells and transformed the proinflammatory CXCL10/11 high basal cells into the regenerative S100A2 pos KRT high basal cell phenotype. In addition, SARS-CoV-2 infection elevated M-CSF secretion from epithelium, which induced MERTK, a receptor for PROS1, on monocytes added into 3D lung epithelial culture. We demonstrated that SARS-CoV-2 drives monocyte phenotypes expressing coagulation (F13A1) and complement (C1Ǫ) genes. PROS1 significantly downregulated these phenotypes and induced higher expression of MHC class II. Overall, this study demonstrated that the epithelium-derived PROS1 during SARS-CoV-2 infection inhibits the proinflammatory epithelial phenotypes, favours basal cell regeneration, and inhibits myeloid inflammation while enhancing antigen presentation. These findings highlight the importance of basal epithelial cells and PROS1 protection from viral infection induced severe lung pathology. Abstract Figure 1) SARS-CoV2 infection of the epithelium results in release of IFN. 2) IFN secretion has an autocrine effect on epithelial cells 3) Infection and IFN cause release of PROS1 from the basal cells, as well as M-CSF from the epithelium 4) PROS1 acts on basal cells which express MERTK, a PROS1 receptor 5) PROS1 downregulated the proinflammatory phenotypes expanded by viral infection, while upregulating KRT high basal cells with repair phenotypes 6) The secreted M-CSF drives MERTK expression on monocytes in cocultures with epithelium. 7) PROS1 induces downregulation of monocyte clusters characteristic of viral infection that express pro-coagulation and complement genes, while upregulating clusters with higher MHC class II. 8) In summary, PROS1 mediates phenotypic switch of SARS-Cov2 induced pathogenic myeloid clusters with complement and coagulation phenotypes into phenotype with efficient antigen presentation, reduces proinflammatory activation of epithelium and induces epithelial barrier repair, resulting in mild COVID-19.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.026
GPT teacher head0.301
Teacher spread0.275 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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