Characterization of the leukemogenic potential of distal cytoplasmic CSF3R truncation and missense mutations.

An increasing number of variants of unknown significance are being identified in leukemia patients with the application of deep sequencing and these include CSF3R cytoplasmic mutations. Previous studies have demonstrated oncogenic potential of certain CSF3R truncation mutations prior to internalization motifs. However, the oncogenic potential of truncating the more distal region of CSF3R cytoplasmic domain as well as cytoplasmic missense mutations remains uncharacterized. Here we identified that CSF3R distal cytoplasmic truncation mutations (Q793-Q823) also harbored leukemogenic potential. Mechanistically, these distal cytoplasmic truncation mutations demonstrated markedly decreased receptor degradation, probably owing to loss of the de-phosphorylation domain (residues N818-F836). Furthermore, all truncations prior to Q823 demonstrated increased expression of the higher molecular weight CSF3R band, which is shown to be essential for the receptor surface expression and the oncogenic potential. We further demonstrated that sufficient STAT5 activation is essential for oncogenic potential. In addition, CSF3R K704A demonstrated transforming capacity due to interruption of receptor ubiquitination and degradation. In summary, we have expanded the region of the CSF3R cytoplasmic domain in which truncation or missense mutations exhibit leukemogenic capacity, which will be useful for evaluating the relevance of CSF3R mutations in patients and helpful in defining targeted therapy strategies.

Genes, behavior and next-generation RNA sequencing.

Advances in next-generation sequencing suggest that RNA-Seq is poised to supplant microarray-based approaches for transcriptome analysis. This article briefly reviews the use of microarrays in the brain-behavior context and then illustrates why RNA-Seq is a superior strategy. Compared with microarrays, RNA-Seq has a greater dynamic range, detects both coding and noncoding RNAs, is superior for gene network construction, detects alternative spliced transcripts, detects allele specific expression and can be used to extract genotype information, e.g. nonsynonymous coding single nucleotide polymorphisms. Examples of where RNA-Seq has been used to assess brain gene expression are provided. Despite the advantages of RNA-Seq, some disadvantages remain. These include the high cost of RNA-Seq and the computational complexities associated with data analysis. RNA-Seq embraces the complexity of the transcriptome and provides a mechanism to understand the underlying regulatory code; the potential to inform the brain-behavior relationship is substantial.