Computational identification of the normal and perturbed genetic networks involved in myeloid differentiation and acute promyelocytic leukemia

Abstract

Background: Acute myeloid leukemia (AML) comprises a group of diseases characterized by the abnormal development of malignant myeloid cells. Recent studies have demonstrated an important role for aberrant transcriptional regulation in AML pathophysiology. Although several transcription factors (TFs) involved in myeloid development and leukemia have been studied extensively and independently, how these TFs coordinate with others and how their dysregulation perturbs the genetic circuitry underlying myeloid differentiation is not yet known. We propose an integrated approach for mammalian genetic network construction by combining the analysis of gene expression profiling data and the identification of TF binding sites.

Results: We utilized our approach to construct the genetic circuitries operating in normal myeloid differentiation versus acute promyelocytic leukemia (APL), a subtype of AML. In the normal and disease networks, we found that multiple transcriptional regulatory cascades converge on the TFs Rora and Rxra, respectively. Furthermore, the TFs dysregulated in APL participate in a common regulatory pathway and may perturb the normal network through Fos. Finally, a model of APL pathogenesis is proposed in which the chimeric TF PML-RARalpha activates the dysregulation in APL through six mediator TFs.

Conclusion: This report demonstrates the utility of our approach to construct mammalian genetic networks, and to obtain new insights regarding regulatory circuitries operating in complex diseases in humans.

Figure 1

Workflow of genetic networks construction. This workflow contains four major stages. (a) TF binding site identification. Genomic sequences of annotated genes are retrieved and aligned, and conserved TF binding sites in genomic sequences are identified. Binding probability scores are calculated using the identified binding sites. (b) TF target identification. The P value for observing a given binding probability score or higher by chance is calculated using permutation of TF binding sites. Using a P value cutoff, regulatory targets of each TF are identified. (c) Coexpressed gene cluster identification. Gene expression profiles are collected from experiments. Coherently expressed genes are identified and clustered. (d) Network construction. Genetic networks are identified for each coexpressed gene cluster using the target genes predicted for each TF within each gene cluster. The complete regulatory network is then constructed by consolidating individual networks. Hs, Homo sapiens; Mm, Mus musculus.

Figure 2

Coexpressed gene clusters identified during myeloid development. (a) The predominant cells in culture during the seven-day myeloid differentiation time course are promyelocytes, mid-myeloid cells, and terminally differentiated myeloid cells cultured at days 2 and 3, days 4 and 5, and days 6 and 7, respectively. (b) Coherently expressed gene clusters were identified for genes upregulated on just one day (UP0, UP1, UP2 and UP7) or over two consecutive days (UP01 and UP67) during in vitro myeloid differentiation.

Figure 3

Predicted regulatory targets of Egr1 in myeloid differentiation. (a) Seven genes were identified as direct regulatory targets of Egr1. Three of these genes encode TFs (circle nodes). (b) Evolutionarily conserved Egr1 binding sites (red bars) were identified in the ± 2 kb proximal promoter region of the predicted target genes. All the Egr1 binding sites were conserved in human, mouse and rat except for PRDM16, whose rat ortholog was not available. Gene annotation information is color coded: blue, repetitive elements; yellow, conserved sequence; dark green, coding region; light green, untranslated region.

Figure 4

Genetic networks operating in myeloid development and APL. In these networks, circle nodes represent TF genes. Genes that do not encode TFs are shown in rectangles. An arrow is drawn from TF-A to gene-B if TF-A regulates gene-B. (a) The predicted genetic network operating in myeloid differentiation. Multiple regulatory pathways in this network converge on one single TF, Rora. The expression profiles of the TF genes are color coded: blue, upregulated at day 0; yellow, upregulated at day 0 and day 1; purple, upregulated at day 7. (b) The seven TFs that are dysregulated in APL may be connected to form a common regulatory pathway. Aberrant expressions of these TFs are color coded: red, overexpression; green, underexpression. (c) The perturbation of the normal network by dysregulated TFs in APL. The normal and disease regulatory pathways converge on Rora and Rxra, respectively. The dysregulated pathway in APL may perturb the normal genetic network through Fos. Furthermore, many TFs in the normal network (shown in orange nodes) are predicted as direct targets of at least one TF dysregulated in APL (Table 4).

Figure 5

The proposed model of APL pathogenesis induced by PML-RARα. PML-RARα may activate the dysregulation of several TFs in the disease regulatory pathway in APL through six mediator TFs (dashed blue arrow). This regulatory circuitry ultimately converges on the overexpression of Rxra. Red circle, overexpressed TFs; green circle, underexpressed TFs; green box, underexpressed genes; orange circle, TFs in the normal network that are predicted as targets of dysregulated TFs in APL; gray circle, other TFs in the normal network; purple circle, PML-RARα.