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Record W3187746373 · doi:10.21203/rs.3.rs-764250/v1

Risk-focused Differences in Molecular Processes Implicated in SARS-CoV-2 Infection: Corollaries in DNA Methylation and Gene Expression

2021· preprint· en· W3187746373 on OpenAlexafffund
Chaini Konwar, Rebecca Asiimwe, Amy M. Inkster, Sarah M. Merrill, Gian Luca Negri, Maria J. Aristizabal, Christopher F. Rider, Julie L. MacIsaac, Christopher Carlsten, Michael S. Kobor

Bibliographic record

VenueResearch Square · 2021
Typepreprint
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsOntario Brain InstituteQueen's UniversityCanada's Michael Smith Genome Sciences CentreBC Cancer AgencyUniversity of TorontoBC Children's HospitaliCo Therapeutics (Canada)University of British Columbia
FundersCanadian Institutes of Health ResearchWorkSafe VictoriaBC Children's Hospital
KeywordsDNA methylationGeneBiologyMethylationGeneticsGene expressionDNAExpression (computer science)VirologyComputer science

Abstract

fetched live from OpenAlex

Abstract Background Understanding the molecular basis of susceptibility factors to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is a global health imperative. It is well-established that males are more likely to acquire SARS-CoV-2 infection and exhibit more severe outcomes. Similarly, exposure to air pollutants and pre-existing respiratory chronic conditions like asthma and chronic obstructive respiratory disease (COPD) confer an increased risk to coronavirus disease 2019 (COVID-19). Methods We investigated molecular patterns associated with risk factors in 398 candidate genes relevant to COVID-19 biology. To accomplish this, we downloaded DNA methylation and gene expression datasets from publicly available repositories (GEO and GTEx portal) and utilized data from our unpublished controlled human exposure study. Results First, we observed sex-biased DNA methylation patterns in autosomal immune genes such as NLRP2, TLE1, GPX1, and ARRB2 (FDR <0.05, magnitude of DNA methylation difference Δβ >0.05). Second, our analysis on the X-linked genes identified sex associated DNA methylation profiles in genes such as ACE2, CA5B, and HS6ST2 (FDR <0.05, Δβ >0.05). These associations were observed across multiple respiratory tissues (lung, nasal epithelia, airway epithelia, and bronchoalveolar lavage) and in whole blood. Some of these genes, like NLRP2 and CA5B, also exhibited sex-biased expression patterns. Third, we identified modest DNA methylation changes in CpGs associated with PRIM2 and TATDN1 (FDR <0.1, Δβ >0.05) in response to particle-depleted diesel exhaust in bronchoalveolar lavage. Finally, we captured a DNA methylation signature associated with COPD diagnosis in a gene involved in nicotine dependence (COMT) (FDR <0.1, Δβ >0.05). Conclusion Our findings on sex differences are of clinical relevance given they potentially point to an exaggerated immune response in males. We also found tissue-specific DNA methylation differences in response to particulate exposure potentially capturing an NO2 effect – a contributor to COVID-19 susceptibility. While we identified a molecular signature associated with COPD, all COPD-affected individuals were smokers, which may either reflect an association with the disease, smoking, or may highlight a compounded effect of these two risk factors in COVID-19. Overall, the findings point towards the molecular basis of variation in susceptibility factors that may partly explain disparities in SARS-CoV-2 infection.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.094
GPT teacher head0.418
Teacher spread0.323 · 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
Published2021
Admission routes2
Has abstractyes

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