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Record W2951324699 · doi:10.1101/083246

Genome-wide Analysis of Differential Transcriptional and Epigenetic Variability Across Human Immune Cell Types

2016· preprint· en· W2951324699 on OpenAlexaff
Simone Ecker, Lu Chen, Véra Pancaldi, Frederik Otzen Bagger, José M. Fernández, Enrique Carrillo de Santa Pau, David Juan, Alice Mann, Stephen Watt, Francesco Paolo Casale, Nicolas Rapin, Angelika Merkel, Henk G. Stunnenberg, Oliver Stegle, Mattia Frontini, Kate Downes, Tomi Pastinen, Taco W. Kuijpers, Daniel Rico, Alfonso Valencia, Stephan Beck, Nicole Soranzo, Dirk S. Paul

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2016
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsMcGill University
Fundersnot available
KeywordsEpigeneticsBiologyImmune systemChromatinDNA methylationCD14PhenotypeGeneGeneticsGene expressionImmunologyComputational biology

Abstract

fetched live from OpenAlex

Abstract Background A healthy immune system requires immune cells that adapt rapidly to environmental challenges. This phenotypic plasticity can be mediated by transcriptional and epigenetic variability. Results We applied a novel analytical approach to measure and compare transcriptional and epigenetic variability genome-wide across CD14 + CD16 − monocytes, CD66b + CD16 + neutrophils, and CD4 + CD45RA + naïve T cells, from the same 125 healthy individuals. We discovered substantially increased variability in neutrophils compared to monocytes and T cells. In neutrophils, genes with hypervariable expression were found to be implicated in key immune pathways and to associate with cellular properties and environmental exposure. We also observed increased sex-specific gene expression differences in neutrophils. Neutrophil-specific DNA methylation hypervariable sites were enriched at dynamic chromatin regions and active enhancers. Conclusions Our data highlight the importance of transcriptional and epigenetic variability for the neutrophils’ key role as the first responders to inflammatory stimuli. We provide a resource to enable further functional studies into the plasticity of immune cells, which can be accessed from: http://blueprint-dev.bioinfo.cnio.es/WP10/hypervariability .

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.447
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.009
GPT teacher head0.233
Teacher spread0.223 · 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 teacher head, not a consensus.

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

Citations23
Published2016
Admission routes1
Has abstractyes

Explore more

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