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Record W2527059769 · doi:10.1182/blood.v112.11.193.193

Differentially Expressed and Novel Transcripts in Highly Purified Chronic Phase CML Stem Cells

2008· article· en· W2527059769 on OpenAlexaff
Yun Zhao, Allen Delaney, Afshin Raouf, Kamini Raghuram, Haiyan I. Li, Angelique Schnerch, Xiaoyan Jiang, Allen Eaves, Marco A. Marra, Connie J. Eaves

Bibliographic record

VenueBlood · 2008
Typearticle
Languageen
FieldMedicine
TopicChronic Myeloid Leukemia Treatments
Canadian institutionsCanada's Michael Smith Genome Sciences CentreBC Cancer Agency
Fundersnot available
KeywordsCD38Stem cellBiologyHaematopoiesisCD34Molecular biologyCancer researchImmunologyCell biology

Abstract

fetched live from OpenAlex

Abstract The chronic phase of CML is sustained by rare BCR-ABL+ stem cells. These cells share many properties with normal pluripotent hematopoietic stem cells, but also differ in critical ways that alter their growth, drug responsiveness and genome stability. Understanding the molecular mechanisms underlying the biological differences between normal and CML stem cells is key to the development of more effective CML therapies. To obtain new insights into these mechanisms, we generated Long Serial Analysis of Gene Expression (SAGE) libraries from paired isolates of highly purified lin-CD34+CD45RA-CD36- CD71-CD7-CD38+ and lin-CD34+CD45RA-CD36-CD71-CD7-CD38- cells from 3 chronic phase CML patients (all with predominantly Ph+/BCR-ABL+ cells in both subsets) and from 3 control samples: a pool of 10 normal bone marrows (BMs), a single normal BM and a pool of G-CSF-mobilized blood cells from 9 donors. In vitro bioassays showed the CD34+CD38+ cells were enriched in CFCs (CML: 3–20% pure; normal: 4–19% pure) and the CD34+CD38- cells were enriched in LTC-ICs (CML: 0.2–26% pure; normal: 12–52% pure). Each of the 12 libraries was then sequenced to a depth of ~200,000 tags and tags from libraries prepared from like phenotypes were compared between genotypes using DiscoverySpace software and hierarchical clustering. 1687 (355 with clustering) and 1258 (316 with clustering) transcripts were thus identified as differentially expressed in the CML vs control CD34+CD38− and CD34+CD38+ subsets, respectively. 266 of these transcripts (11 with clustering) were differentially expressed in both subsets. The differential expression of 5 genes (GAS2, IGF2BP2, IL1R1, DUSP1 & SELL) was confirmed by real-time PCR analysis of lin-CD34+ cells isolated from an additional 5 normal BMs and 11 CMLs, and lin-CD34+CD38− cells from an additional 2 normal BMs and 2 CMLs (with dominant Ph+ cells). GAS2 and IL1R1 transcript levels were correlated with BCR-ABL transcript levels in both primitive subsets, and predicted differences in expression of IL1R1 and SELL were apparent within 3 days in CD34+ cord blood cells transduced with a lenti-BCR-ABL-IRES-GFP vs a control lenti-GFP vector (n=3). These findings support a direct role of BCR-ABL in perturbing the expression of these 3 genes. Further comparison of the meta CD34+CD38− and CD34+CD38+ CML cell libraries with most publicly accessible SAGE data revealed 69 novel tags in the CD34+ CML cells that correspond to unique but conserved genomic sequences. Nine of these were recovered by 5′- and 3′- RACE applied to cDNAs pooled from several human leukemic cell lines. These results illustrate the power of SAGE to reveal key components of the transcriptomes of rare human CML stem cell populations including transcripts of genes not previously known to exist. Continuing investigation of their biological roles in primary CML cells and primitive BCR-ABL-transduced human cells offer important strategies for delineating their potential as therapeutic targets.

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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.028
GPT teacher head0.249
Teacher spread0.221 · 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

Citations3
Published2008
Admission routes1
Has abstractyes

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