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Record W4399292325 · doi:10.1101/2024.05.31.595679

Low abundant intestinal commensals modulate immune control of chronic myeloid leukemia stem cells

2024· preprint· en· W4399292325 on OpenAlexaff
Magdalena Hinterbrandner, Francesca Ronchi, Viviana Rubino, Michaela Römmele, Tanja Chiorazzo, Catherine Mooser, Stephanie C. Ganal‐Vonarburg, Kathy D. McCoy, Andrew J. Macpherson, Adrian F. Ochsenbein, Carsten Riether

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2024
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDigestive system and related health
Canadian institutionsUniversity of Calgary
FundersSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsCommensalismMyeloid leukemiaImmune systemStem cellImmunologyMyeloid cellsMyeloidBiologyMicrobiologyCancer researchBacteriaCell biologyGenetics

Abstract

fetched live from OpenAlex

Abstract Leukemia stem cells (LSCs) are resistant to therapy and immune control. The reason for their resistance to elimination by cytotoxic T cells (CTLs) remains unclear. This study shows that specific low abundant Gram-negative intestinal commensals of the genus Sutterella suppress the anti-leukemia immune response in chronic myeloid leukemia (CML). We found that germ-free and specific opportunistic pathogen-free (SOPF) mice are protected from CML development and that colonization of SOPF mice with Sutterella wadsworthensis , but not other related and unrelated bacterial strains, rescues CML development. A higher prevalence of this microbe resulted in Myd88/TRIF-mediated CTL exhaustion in SPF compared to SOPF CML mice as evidenced by higher surface expression of exhaustion markers on CTLs, a reduced capacity to produce interferon-gamma and granzyme B and to kill LSCs in vitro . These findings provide new insights into the immune control of LSCs and identify Sutterella species as regulators of anti-leukemic immunity in CML.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.008
GPT teacher head0.216
Teacher spread0.209 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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