Human TFAP2B (Transcription factor AP-2-beta) ELISA Kit

Here, Hu et al. show that AP-2 transcription factors have diverged to take on bidirectional control of sleep in mammals. This is the first instance where a sleep gene is shown to have diversified in evolution ..... Sleep is a conserved behavioral state. Invertebrates typically show quiet sleep, whereas in mammals, sleep consists of periods of nonrapid-eye-movement sleep (NREMS) and REM sleep (REMS). We previously found that the transcription factor AP-2 promotes sleep in Caenorhabditis elegans and Drosophila. In mammals, several paralogous AP-2 transcription factors exist. Sleep-controlling genes are often conserved. However, little is known about how sleep genes evolved from controlling simpler types of sleep to govern complex mammalian sleep. Here, we studied the roles of Tfap2a and Tfap2b in sleep control in mice. Consistent with our results from C. elegans and Drosophila, the AP-2 transcription factors Tfap2a and Tfap2b also control sleep in mice. Surprisingly, however, the two AP-2 paralogs play contrary roles in sleep control. Tfap2a reduction of function causes stronger delta and theta power in both baseline and homeostasis analysis, thus indicating increased sleep quality, but did not affect sleep quantity. By contrast, Tfap2b reduction of function decreased NREM sleep time specifically during the dark phase, reduced NREMS and REMS power, and caused a weaker response to sleep deprivation. Consistent with the observed signatures of decreased sleep quality, stress resistance and memory were impaired in Tfap2b mutant animals. Also, the circadian period was slightly shortened. Taken together, AP-2 transcription factors control sleep behavior also in mice, but the role of the AP-2 genes functionally diversified to allow for a bidirectional control of sleep quality. Divergence of AP-2 transcription factors might perhaps have supported the evolution of more complex types of sleep.

Objective: The objective of this study was the description of a valid genetic risk score (GRS) to predict individuals with high susceptibility to childhood overweight by their genetic profiles. Design and methods: Case-control study including a group of children with high-risk familial predisposition to morbid obesity. Birth cohort from general population constituted the validation sample. For the discovery sample, 218 children with non-syndromic obesity and 190 control individuals were included. The validation sample was 653 children from two birth cohorts belonging to the INMA (Infancia y Medio Ambiente [Environment and Childhood]) project. 109 SNPs located in the genes FTO, SEC16B, BDNF, ETV5, SH2B1, GNPDA2, LYPLAL1, MSRA, TFAP2, KCTD15, MTCH2 and NEGR1, previously reported in association to body mass index (BMI) were analysed. For the validation sample, association between genome-wide data and BMI measurements between 3.5 and 5 years of age, were evaluated. Results: The GRS includes six SNPs in the genes FTO, TFAP2B, SEC16B, ETV5 and SH2B1. The score distribution differs among cases and controls (P = 9.2 x 10(-14)) showing a significant linear association with obesity (odds ratio [OR] per allele = 1.69; confidence interval [CI] 95% = 1.46-1.97; P = 4.3 x 10(-1) and area under the receiver operating characteristic curve [AUC] = 0.727; CI 95% = 0.676-0.778). The results were validated by the INMA cohort (OR per allele = 1.23 CI 95% = 1.03-1.48 and AUC = 0.601 CI 95% = 0.522-0.680). Conclusions: The use of our proposed genetic score provides useful information to determine those children who are susceptible to obesity. To improve the efficiency of clinical prevention and treatment of obesity, it is essential to design individualized based protocols in advance knowledge of the molecular basis of inherited susceptibility.