RESUMO
The t(8;21)(q22;q22) rearrangement represents the most common chromosomal translocation in acute myeloid leukemia (AML). It results in a transcript encoding for the fusion protein AML1-ETO (AE) with transcription factor activity. AE is considered to be an attractive target for treating t(8;21) leukemia. However, AE expression alone is insufficient to cause transformation, and thus the potential of such therapy remains unclear. Several genes are deregulated in AML cells, including KIT that encodes a tyrosine kinase receptor. Here, we show that AML cells transduced with short hairpin RNA vector targeting AE mRNAs have a dramatic decrease in growth rate that is caused by induction of apoptosis and deregulation of the cell cycle. A reduction in KIT mRNA levels was also observed in AE-silenced cells, but silencing KIT expression reduced cell growth but did not induce apoptosis. Transcription profiling of cells that escape cell death revealed activation of a number of signaling pathways involved in cell survival and proliferation. In particular, we find that the extracellular signal-regulated kinase 2 (ERK2; also known as mitogen-activated protein kinase 1 (MAPK1)) protein could mediate activation of 23 out of 29 (79%) of these upregulated pathways and thus may be regarded as the key player in establishing the t(8;21)-positive leukemic cells resistant to AE suppression.
Assuntos
Apoptose/genética , Subunidade alfa 2 de Fator de Ligação ao Core/genética , Regulação Leucêmica da Expressão Gênica , Leucemia Mieloide Aguda/genética , Proteínas de Fusão Oncogênica/genética , Proteínas Proto-Oncogênicas c-kit/genética , Transdução de Sinais/genética , Ciclo Celular/genética , Linhagem Celular Tumoral , Proliferação de Células , Regulação para Baixo/genética , Células HEK293 , Humanos , Leucemia Mieloide Aguda/patologia , Modelos Genéticos , RNA Interferente Pequeno/genética , Proteína 1 Parceira de Translocação de RUNX1RESUMO
The coupling of membrane-bound receptors to transcriptional regulators and other effector functions is mediated by multi-domain proteins that form complex assemblies. The modularity of protein interactions lends itself to a rule-based description, in which species and reactions are generated by rules that encode the necessary context for an interaction to occur, but also can produce a combinatorial explosion in the number of chemical species that make up the signalling network. The authors have shown previously that exact network reduction can be achieved using hierarchical control relationships between sites/domains on proteins to dissect multi-domain proteins into sets of non-interacting sites, allowing the replacement of each 'full' (progenitor) protein with a set of derived auxiliary (offspring) proteins. The description of a network in terms of auxiliary proteins that have fewer sites than progenitor proteins often greatly reduces network size. The authors describe here a method for automating domain-oriented model reduction and its implementation as a module in the BioNetGen modelling package. It takes as input a standard BioNetGen model and automatically performs the following steps: 1) detecting the hierarchical control relationships between sites; 2) building up the auxiliary proteins; 3) generating a raw reduced model and 4) cleaning up the raw model to provide the correct mass balance for each chemical species in the reduced network. The authors tested the performance of this module on models representing portions of growth factor receptor and immunoreceptor-mediated signalling networks and confirmed its ability to reduce the model size and simulation cost by at least one or two orders of magnitude. Limitations of the current algorithm include the inability to reduce models based on implicit site dependencies or heterodimerisation and loss of accuracy when dynamics are computed stochastically.
