C/EBP Bypasses Granulocyte Colony-Stimulating Factor Signals to Rapidly Induce PU.1 Gene Expression, Stimulate Granulocytic Differentiation, and Limit Proliferation in 32D cl3 Myeloblasts

The complex landscape of haematopoietic lineage commitments is encoded in the coarse-grained endogenous network

Haematopoietic lineage commitments are presented by a canonical roadmap in which haematopoietic stem cells or multipotent progenitors (MPPs) bifurcate into progenitors of more restricted lineages and ultimately mature to terminally differentiated cells. Although transcription factors playing significant roles in cell-fate commitments have been extensively studied, integrating such knowledge into the dynamic models to understand the underlying biological mechanism remains challenging. The hypothesis and modelling approach of the endogenous network has been developed previously and tested in various biological processes and is used in the present study of haematopoietic lineage commitments. The endogenous network is constructed based on the key transcription factors and their interactions that determine haematopoietic cell-fate decisions at each lineage branchpoint. We demonstrate that the process of haematopoietic lineage commitments can be reproduced from the landscape which orchestrates robust states of network dynamics and their transitions. Furthermore, some non-trivial characteristics are unveiled in the dynamical model. Our model also predicted previously under-represented regulatory interactions and heterogeneous MPP states by which distinct differentiation routes are intermediated. Moreover, network perturbations resulting in state transitions indicate the effects of ectopic gene expression on cellular reprogrammes. This study provides a predictive model to integrate experimental data and uncover the possible regulatory mechanism of haematopoietic lineage commitments.

Whole Transcriptome Data Analysis Reveals Prognostic Signature Genes for Overall Survival Prediction in Diffuse Large B Cell Lymphoma

Background

With the improvement of clinical treatment outcomes in diffuse large B cell lymphoma (DLBCL), the high rate of relapse in DLBCL patients is still an established barrier, as the therapeutic strategy selection based on potential targets remains unsatisfactory. Therefore, there is an urgent need in further exploration of prognostic biomarkers so as to improve the prognosis of DLBCL.

Methods

The univariable and multivariable Cox regression models were employed to screen out gene signatures for DLBCL overall survival (OS) prediction. The differential expression analysis was used to identify representative genes in high-risk and low-risk groups, respectively, where student t test and fold change were implemented. The functional difference between the high-risk and low-risk groups was identified by the gene set enrichment analysis.

Results

We conducted a systematic data analysis to screen the candidate genes significantly associated with OS of DLBCL in three NCBI Gene Expression Omnibus (GEO) datasets. To construct a prognostic model, five genes (CEBPA, CYP27A1, LST1, MREG, and TARP) were then screened and tested using the multivariable Cox model and the stepwise regression method. Kaplan–Meier curve confirmed the good predictive performance of this five-gene Cox model. Thereafter, the prognostic model and the expression levels of the five genes were validated by means of an independent dataset. High expression levels of these five genes were significantly associated with favorable prognosis in DLBCL, both in training and validation datasets. Additionally, further analysis revealed the independent value and superiority of this prognostic model in risk prediction. Functional enrichment analysis revealed some vital pathways responsible for unfavorable outcome and potential therapeutic targets in DLBCL.

Conclusion

We developed a five-gene Cox model for the clinical outcome prediction of DLBCL patients. Meanwhile, potential drug selection using this model can help clinicians to improve the clinical practice for the benefit of patients.

Exogenous cdk4 overcomes reduced cdk4 RNA and inhibition of G1 progression in hematopoietic cells expressing a dominant-negative CBF - a model for overcoming inhibition of proliferation by CBF oncoproteins

Core Binding Factor (CBF) is required for the development of definitive hematopoiesis, and the CBF oncoproteins AML1-ETO, TEL-AML1, and CBFbeta-SMMHC are commonly expressed in subsets of acute leukemia. CBFbeta-SMMHC slows the G1 to S cell cycle transition in hematopoietic cells, but the mechanism of this effect is uncertain. We have sought to determine whether inhibition of CBF-mediated trans-activation is sufficient to slow proliferation. We demonstrate that activation of KRAB-AML1-ER, a protein containing the AML1 DNA-binding domain, the KRAB repression domain, and the Estrogen receptor ligand binding domain, also slows G1, if its DNA-binding domain is intact. Also, exogenous AML1 overcame CBFbeta-SMMHC-induced inhibition of proliferation. Representational difference analysis (RDA) identified cdk4 RNA expression as an early target of KRAB-AML1 activation. Inhibition of CBF activities by KRAB-AML1-ER or CBFbeta-SMMHC rapidly reduced endogenous cdk4 mRNA levels, even in cells proliferating at or near control rates as a result of exogenous cdk4 expression. Over-expression of cdk4, especially a variant which cannot bind p16INK4a, overcame cell cycle inhibition resulting from activation of KRAB-AML1-ER, although cdk4 did not accelerate proliferation when expressed alone. These findings indicate that mutations which alter the expression of G1 regulatory proteins can overcome inhibition of proliferation by CBF oncoproteins. Oncogene (2000).