The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
Effects of overexpression of HBP1 upon growth and differentiation of leukemic myeloid cells
Abstract
HMG-box containing protein 1 (HBP1) is a member of the high mobility group (HMG) of chromosomal proteins. Since HBP1 exhibits tumor-suppressor activity in nonmyeloid tissues, we examined the effects of ectopic overexpression of HBP1 upon the growth and differentiation of myeloid cells. We prepared transient and stable transfectants of the myeloblast cell line K562, which overexpress HBP1 mRNA and protein. HBP1 transfectants displayed slower growth in cell culture and reduced colony formation in soft agar, retardation of S-phase progression, reduced expression of cyclin D1 and D3 mRNAs and increased expression of p21 mRNA. HBP1 transfectants also underwent increased apoptosis, as demonstrated by morphology and binding of Annexin V. Fas ligand mRNA levels were increased in HBP1 transfectants, suggesting involvement of the Fas/Fas ligand pathway. HBP1 overexpression enhanced differentiation of K562 cells towards erythroid and megakaryocyte lineages, as evidenced by increased hemoglobin and CD41a expression. Overexpression of HBP1 modulated mRNA levels for myeloid-specific transcription factors C/EBPalpha, c-Myb, c-Myc, and JunB, as well as lineage-specific transcription factors PU.1, GATA-1, and RUNX1. These findings suggest that in myeloid cells HBP1 may serve as a tumor suppressor and a general differentiation inducer and may synergize with chemical differentiating agents to enhance lineage-specific differentiation.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“HBP1 has been linked to differentiation of malignant myeloid cells and to the regulation of other important TFs including PU.1, RUNX1, JUNB, or CEBP (Yao et al., 2005).”
From Transcriptome-Based Network Analysis Reveals a Spectrum Model of Human Macrophage Activation (2014) · cites this paper as Yao et al., 2005
Citations
Cites
Transcriptional activation: A complex puzzle with few easy pieces
Tjian, R., & Maniatis, T. (1994) · Cell · doi:10.1016/0092-8674(94)90227-5 · Cited by 1,099
Clément, M.-V., Hirpara, J. L., Chawdhury, S.-H., & Pervaiz, S. (1998) · Blood · doi:10.1182/blood.v92.3.996 · Cited by 611
van de Wetering, M., Oosterwegel, M., Dooijes, D., & Clevers, H. (1991) · The EMBO Journal · doi:10.1002/j.1460-2075.1991.tb07928.x · Cited by 538
Radomska, H. S., Huettner, C. S., Zhang, P., Cheng, T., Scadden, D. T., & Tenen, D. G. (1998) · Molecular and Cellular Biology · doi:10.1128/mcb.18.7.4301 · Cited by 466
Bakiri, L. (2000) · The EMBO Journal · doi:10.1093/emboj/19.9.2056 · Cited by 398
Sugimoto, A., Iino, Y., Maeda, T., Watanabe, Y., & Yamamoto, M. (1991) · Genes & Development · doi:10.1101/gad.5.11.1990 · Cited by 359
RUNX1 and GATA-1 coexpression and cooperation in megakaryocytic differentiation.
Elagib, K. E., Racke, F. K., Mogass, M., Khetawat, R., Delehanty, L. L., & Goldfarb, A. N. (2003) · Blood · doi:10.1182/blood-2002-09-2708 · Cited by 332
A signature for the HMG‐1 box DNA‐binding proteins
Landsman, D., & Bustin, M. (1993) · BioEssays · doi:10.1002/bies.950150807 · Cited by 257
Architectural Transcription Factors
Wolffe, A. P. (1994) · Science · doi:10.1126/science.8178167 · Cited by 186
Efficient Cloning of Ascomycete Mating Type Genes by PCR Amplification of the ConservedMATHMG Box
Arie, T., Christiansen, S. K., Yoder, O. C., & Turgeon, B. G. (1997) · Fungal Genetics and Biology · doi:10.1006/fgbi.1997.0961 · Cited by 167
Cited by
Transcriptome-Based Network Analysis Reveals a Spectrum Model of Human Macrophage Activation
Xue, J., Schmidt, S. V., Sander, J., Draffehn, A., Krebs, W., Quester, I., De Nardo, D., Gohel, T. D., Emde, M., Schmidleithner, L., et al. (2014) · Immunity · doi:10.1016/j.immuni.2014.01.006 · Cited by 2,210
Bollaert, E., Johanns, M., Herinckx, G., de Rocca Serra, A., Vandewalle, V. A., Havelange, V., Rider, M. H., Vertommen, D., & Demoulin, J.-B. (2018) · Cellular Signalling · doi:10.1016/j.cellsig.2018.01.014 · Cited by 18
Metadata
- Authors
- Yao, C. J., Works, K., Romagnoli, P. A., & Austin, G. E.
- Venue
- Leukemia
- Published
- 2005-09-22
- Volume
- 19
- Issue
- 11
- Pages
- 1958-1968
- ISSN
- 0887-6924
- 1476-5551
- Cited by
- 40
- References
- 35
- Language
- en
- Licence
- www.springer.com/tdm (tdm)
- www.springer.com/tdm (vor)
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.