Connections between ETV6-modulated genes: identification of shared features

Expression and function of NF-Y subunits in cancer

NF-Y is a Transcription Factor (TF) targeting the CCAAT box regulatory element. It consists of the NF-YB/NF-YC heterodimer, each containing an Histone Fold Domain (HFD), and the sequence-specific subunit NF-YA. NF-YA expression is associated with cell proliferation and absent in some post-mitotic cells. The review summarizes recent findings impacting on cancer development. The logic of the NF-Y regulome points to pro-growth, oncogenic genes in the cell-cycle, metabolism and transcriptional regulation routes. NF-YA is involved in growth/differentiation decisions upon cell-cycle re-entry after mitosis and it is widely overexpressed in tumors, the HFD subunits in some tumor types or subtypes. Overexpression of NF-Y -mostly NF-YA- is oncogenic and decreases sensitivity to anti-neoplastic drugs. The specific roles of NF-YA and NF-YC isoforms generated by alternative splicing -AS- are discussed, including the prognostic value of their levels, although the specific molecular mechanisms of activity are still to be deciphered.

Recalculation of 23 mouse HDL QTL datasets improves accuracy and allows for better candidate gene analysis

In the past 15 years, the quantitative trait locus (QTL) mapping approach has been applied to crosses between different inbred mouse strains to identify genetic loci associated with plasma HDL cholesterol levels. Although successful, a disadvantage of this method is low mapping resolution, as often several hundred candidate genes fall within the confidence interval for each locus. Methods have been developed to narrow these loci by combining the data from the different crosses, but they rely on the accurate mapping of the QTL and the treatment of the data in a consistent manner. We collected 23 raw datasets used for the mapping of previously published HDL QTL and reanalyzed the data from each cross using a consistent method and the latest mouse genetic map. By utilizing this approach, we identified novel QTL and QTL that were mapped to the wrong part of chromosomes. Our new HDL QTL map allows for reliable combining of QTL data and candidate gene analysis, which we demonstrate by identifying Grin3a and Etv6, as candidate genes for QTL on chromosomes 4 and 6, respectively. In addition, we were able to narrow a QTL on Chr 19 to five candidates.