Limited redundancy in phosphorylation of retinoblastoma tumor suppressor protein by cyclin-dependent kinases in acute lymphoblastic leukemia

Cyclin-dependent kinases (CDKs) successively phosphorylate the retinoblastoma protein (RB) at the restriction point in G1 phase. Hyperphosphorylation results in functional inactivation of RB, activation of the E2F transcriptional program, and entry of cells into S phase. RB unphosphorylated at serine 608 has growth suppressive activity. Phosphorylation of serines 608/612 inhibits binding of E2F-1 to RB. In Nalm-6 acute lymphoblastic leukemia extracts, serine 608 is phosphorylated by CDK4/6 complexes but not by CDK2. We reasoned that phosphorylation of serines 608/612 by redundant CDKs could accelerate phospho group formation and determined which G1 CDK contributes to serine 612 phosphorylation. Here, we report that CDK4 complexes from Nalm-6 extracts phosphorylated in vitro the CDK2-preferred serine 612, which was inhibited by p16INK4a, and fascaplysin. In contrast, serine 780 and serine 795 were efficiently phosphorylated by CDK4 but not by CDK2. The data suggest that the redundancy in phosphorylation of RB by CDK2 and CDK4 in Nalm-6 extracts is limited. Serine 612 phosphorylation by CDK4 also occurred in extracts of childhood acute lymphoblastic leukemia cells but not in extracts of mobilized CD34+ hemopoietic progenitor cells. This phenomenon could contribute to the commitment of childhood acute lymphocytic leukemia cells to proliferate and explain their refractoriness to differentiation-inducing agents.

Statistical analysis of multichannel scalp field data

High density spatial and temporal sampling of EEG data enhances the quality of results of electrophysiological experiments. Because EEG sources typically produce widespread electric fields (see Chapter 3) and operate at frequencies well below the sampling rate, increasing the number of electrodes and time samples will not necessarily increase the number of observed processes, but mainly increase the accuracy of the representation of these processes. This is namely the case when inverse solutions are computed. As a consequence, increasing the sampling in space and time increases the redundancy of the data (in space, because electrodes are correlated due to volume conduction, and time, because neighboring time points are correlated), while the degrees of freedom of the data change only little. This has to be taken into account when statistical inferences are to be made from the data. However, in many ERP studies, the intrinsic correlation structure of the data has been disregarded. Often, some electrodes or groups of electrodes are a priori selected as the analysis entity and considered as repeated (within subject) measures that are analyzed using standard univariate statistics. The increased spatial resolution obtained with more electrodes is thus poorly represented by the resulting statistics. In addition, the assumptions made (e.g. in terms of what constitutes a repeated measure) are not supported by what we know about the properties of EEG data. From the point of view of physics (see Chapter 3), the natural “atomic” analysis entity of EEG and ERP data is the scalp electric field