Vertebrate Paralogous MEF2 Genes: Origin, Conservation, and Evolution

BACKGROUND: The myocyte enhancer factor 2 (MEF2) gene family is broadly expressed during the development and maintenance of muscle cells. Although a great deal has been elucidated concerning MEF2 transcription factors' regulation of specific gene expression in diverse programs and adaptive responses, little is known about the origin and evolution of the four members of the MEF2 gene family in vertebrates. METHODOLOGY/PRINCIPAL FINDINGS: By phylogenetic analyses, we investigated the origin, conservation, and evolution of the four MEF2 genes. First, among the four MEF2 paralogous branches, MEF2B is clearly distant from the other three branches in vertebrates, mainly because it lacks the HJURP_C (Holliday junction recognition protein C-terminal) region. Second, three duplication events might have occurred to produce the four MEF2 paralogous genes and the latest duplication event occurred near the origin of vertebrates producing MEF2A and MEF2C. Third, the ratio (K(a)/K(s)) of non-synonymous to synonymous nucleotide substitution rates showed that MEF2B evolves faster than the other three MEF2 proteins despite purifying selection on all of the four MEF2 branches. Moreover, a pair model of M0 versus M3 showed that variable selection exists among MEF2 proteins, and branch-site analysis presented that sites 53 and 64 along the MEF2B branch are under positive selection. Finally, and interestingly, substitution rates showed that type II MADS genes (i.e., MEF2-like genes) evolve as slowly as type I MADS genes (i.e., SRF-like genes) in animals, which is inconsistent with the fact that type II MADS genes evolve much slower than type I MADS genes in plants. CONCLUSION: Our findings shed light on the relationship of MEF2A, B, C, and D with functional conservation and evolution in vertebrates. This study provides a rationale for future experimental design to investigate distinct but overlapping regulatory roles of the four MEF2 genes in various tissues

DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning.

Recent technological advances have enabled DNA methylation to be assayed at single-cell resolution. However, current protocols are limited by incomplete CpG coverage and hence methods to predict missing methylation states are critical to enable genome-wide analyses. We report DeepCpG, a computational approach based on deep neural networks to predict methylation states in single cells. We evaluate DeepCpG on single-cell methylation data from five cell types generated using alternative sequencing protocols. DeepCpG yields substantially more accurate predictions than previous methods. Additionally, we show that the model parameters can be interpreted, thereby providing insights into how sequence composition affects methylation variability

Targeting of the MYCN protein with small molecule c-MYC inhibitors

This study was funded by grants from the Swedish Research Council and the Swedish Cancer Society. IM and HZ were recipients of graduate student grants from KI (KID), MAH was recipient of a Senior Investigator Award from the Swedish Cancer Society, and NJW was a Royal Society University Research Fellow when this work began.Members of the MYC family are the most frequently deregulated oncogenes in human cancer and are often correlated with aggressive disease and/or poorly differentiated tumors. Since patients with MYCN-amplified neuroblastoma have a poor prognosis, targeting MYCN using small molecule inhibitors could represent a promising therapeutic approach. We have previously demonstrated that the small molecule 10058-F4, known to bind to the c-MYC bHLHZip dimerization domain and inhibiting the c-MYC/MAX interaction, also interferes with the MYCN/MAX dimerization in vitro and imparts anti-tumorigenic effects in neuroblastoma tumor models with MYCN overexpression. Our previous work also revealed that MYCN-inhibition leads to mitochondrial dysfunction resulting in accumulation of lipid droplets in neuroblastoma cells. To expand our understanding of how small molecules interfere with MYCN, we have now analyzed the direct binding of 10058-F4, as well as three of its analogs; #474, #764 and 10058-F4(7RH), one metabolite C-m/z 232, and a structurally unrelated c-MYC inhibitor 10074-G5, to the bHLHZip domain of MYCN. We also assessed their ability to induce apoptosis, neurite outgrowth and lipid accumulation in neuroblastoma cells. Interestingly, all c-MYC binding molecules tested also bind MYCN as assayed by surface plasmon resonance. Using a proximity ligation assay, we found reduced interaction between MYCN and MAX after treatment with all molecules except for the 10058-F4 metabolite C-m/z 232 and the non-binder 10058-F4(7RH). Importantly, 10074-G5 and 10058-F4 were the most efficient in inducing neuronal differentiation and lipid accumulation in MYCN-amplified neuroblastoma cells. Together our data demonstrate MYCN-binding properties for a selection of small molecules, and provide functional information that could be of importance for future development of targeted therapies against MYCN-amplified neuroblastoma.Publisher PDFPeer reviewe