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Stable Suppression of a Novel Oncogene, AHI-1, in Human Cutaneous T-Cell Leukemia Cells Normalizes Its Transforming Activity In Vitro and In Vivo and Aberrant Expression of AHI-1 Is Also Present in Leukemic Sezary Cells from Patients with Sezary Syndrome.

2005· article· en· W2550348382 on OpenAlexaff
Ashley Ringrose, Youwen Zhou, Emily M. Pang, Ann Lin, Xiaojiang Li, Irene Li, Andrew P. Weng, Xiaoyan Jiang

Bibliographic record

VenueBlood · 2005
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Degradation and Inhibitors
Canadian institutionsCanada's Michael Smith Genome Sciences CentreBC Cancer Agency
Fundersnot available
KeywordsBiologyAutocrine signallingCell cultureLeukemiaProvirusGene knockdownMolecular biologyRNA interferenceSmall hairpin RNACancer researchImmunologyGeneRNAGenetics

Abstract

fetched live from OpenAlex

Abstract Ahi-1 (Abelson helper integration site 1) is a novel gene that is commonly activated by provirus insertional mutagenesis in v-abl and myc-induced murine leukemias and lymphomas. It encodes a unique protein with SH3 and WD40-repeat domains suggesting novel signaling activities. Involvement of Ahi-1 in leukemogenesis is suggested by the high frequency of Ahi-1 mutations seen in certain virus-induced murine leukemias and lymphomas and by the gross perturbations seen in the expression of human AHI-1 and its isoforms in several human leukemia cell lines, particularly in the cutaneous T-cell leukemia cell lines, Hut 78 and Hut 102, where increases in AHI-1 transcripts of 40-fold are seen. To test directly whether the deregulated expression of AHI-1 in leukemic cells contributes to their transformed properties, knockdown of AHI-1 expression in Hut 78 cells, a cell line derived from peripheral blood of a patient with Sezary syndrome, was performed using retroviral-mediated RNA interference (RNAi). In a screen of 9 constructs that produce specific short hairpin AHI-1 transcripts, one was found to specifically inhibit AHI-1 expression in transduced Hut 78 cells by 80%, as evaluated by quantitative real-time RT-PCR, Northern and Western blot analyses. Retroviral-mediated suppression of AHI-1 also reduced the autocrine production of IL-2, IL-4 and TNFalpha in Hut 78 cells by up to 85% and caused a significant reduction in their growth factor independence in semi-solid cultures (up to 10-fold) and in single cell cultures (4-fold) by comparison to cells transduced with a control vector. Interestingly, although addition of IL-4, TNFalpha or a combination of 3 growth factors restored colony formation from the shRNA-transduced Hut 78 cells in semi-solid cultures, this was not achieved if only IL-2 was added, even though AHI-1 expression was inhibited. The ability of Hut 78 cells to produce tumors in NOD/SCID-β2microglobulin−/− mice within 3 weeks was also lost when AHI-1 expression was suppressed. Microarray analysis on RNA from Hut 78 cells with the suppression of AHI-1, using the Affymetrix Human Genome U133 plus 2.0 Arrays, identified differentially expressed molecules critical in T-cell activation, signal transduction, as well as cell proliferation and differentiation. Q-RT-PCR analysis revealed that the transcript levels of AHI-1 and its isoforms were significantly increased in CD4+CD7− Sezary cells, in which more than 85% of these cells are leukemic cells, in 5 of 6 blood samples obtained from patients with Sezary syndrome as compared to T cells similarly isolated from 8 healthy individuals. Elevated AHI-1 transcript levels were not found in 3 patient samples containing less than 35% leukemic Sezary cells. Taken together, these findings provide strong evidence of the oncogenic activity of AHI-1 in human T-cell leukemic cells and its deregulation can contribute to the development of human cutaneous T-cell lymphomas, including Sezary syndrome.

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 score0.007

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.006
GPT teacher head0.205
Teacher spread0.199 · 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

Citations188
Published2005
Admission routes1
Has abstractyes

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