Transcriptional networks in the liver: hepatocyte nuclear factor 6 function is largely independent of Foxa2.

A complex network of hepatocyte nuclear transcription factors, including HNF6 and Foxa2, regulates the expression of liver-specific genes. The current model, based on in vitro studies, suggests that HNF6 and Foxa2 interact physically. This interaction is thought to synergistically stimulate Foxa2-dependent transcription through the recruitment of p300/CBP by HNF6 and to inhibit HNF6-mediated transcription due to the interference of Foxa2 with DNA binding by HNF6. To test this model in vivo, we utilized hepatocyte-specific gene ablation to study the binding of HNF6 to its targets in the absence of Foxa2. Chromatin immunoprecipitation using anti-HNF6 antibodies was performed on chromatin isolated from Foxa2(loxP/loxP) Alfp.Cre and control mouse livers, and HNF6 binding to its target, Glut2, was determined by quantitative PCR. In contrast to the current model, we found no significant difference in HNF6 occupancy at the Glut2 promoter between Foxa2-deficient and control livers. In order to evaluate the Foxa2/HNF6 interaction model on a global scale, we performed a location analysis using a microarray with 7,000 mouse promoter fragments. Again, we found no evidence that HNF6 binding to its targets in chromatin is reduced in the presence of Foxa2. We also examined the mRNA levels of HNF6 targets in the liver using a cDNA array and found that their expression was similar in Foxa2-deficient and control mice. Overall, our studies demonstrate that HNF6 binds to and regulates its target promoters in vivo in the presence and absence of Foxa2.

Quantitative Features of the Choriocapillaris in Healthy Individuals Using Swept-Source Optical Coherence Tomography Angiography.

BACKGROUND AND OBJECTIVE: To quantify vessel density (VD) and grey value (GV) as a measure of flow in the choriocapillaris (CC) in healthy subjects with optical coherence tomography angiography (OCTA). PATIENTS AND METHODS: In this prospective, noncomparative case series, 3 mm × 3 mm OCTA images of 36 eyes of 22 healthy individuals were obtained using a swept-source instrument. VD and GV levels were calculated on CC en face slabs in the central 1-mm (subfoveal field) and surrounding 2.5-mm parafoveal ring. VD was calculated as a ratio of vessel area over nonvessel area following image binarization. GV was computed as the mean, un-normalized greyscale intensity value for all pixels in the region of interest. For each eye, the procedure was repeated 1 minute to 2 minutes later and intersession repeatability was analyzed. The choroidal thickness (CT) was automatically measured in the subfoveal and parafoveal regions and compared to VD and GV values. RESULTS: The VD ratio and GV was lower in the subfoveal field than in the parafoveal four sectors. The intersession intraclass correlation coefficients were high for both VD and GV measurements. There was no correlation observed between CT and VD or GV. CONCLUSIONS: Quantitative metrics can be obtained from CC OCTA en face images. These values show moderate to good intersession repeatability. These normative data may be of value as a reference of comparison in future studies of eyes with disease. [Ophthalmic Surg Lasers Imaging Retina. 2017;48:623-631.].

Using prior knowledge to improve genetic network reconstruction from microarray data.

The use of Bayesian Network methods to recover transcriptional regulatory networks from static microarray data is an active area of bioinformatics research. However, early work in this area lacked realistic analysis of the effects of data set size on learning performance and ignored the potentially immense benefits of using prior biological knowledge. More recent work which has utilized such information has tended to focus on qualitative descriptions of the results. In this paper, we construct a detailed, realistic model for glucose homeostasis and use this model to generate static, synthetic gene expression data. We then use a Bayesian Network method to reconstruct this genetic network from the synthetic microarray data utilizing various amounts and types of prior knowledge. By quantitatively analyzing the effects of data set size and the incorporation of different types of prior biological knowledge on our ability to reconstruct the original network, we show that characteristic portions of genetic networks can be reconstructed from microarray data. Incorporating prior knowledge into the learning scheme greatly reduces the data required, allowing these reverse engineering techniques to be used to learn regulatory interactions from microarray data sets of realistic size.

Orthogonal analysis of C/EBPbeta targets in vivo during liver proliferation.

CCAAT enhancer-binding protein beta (C/EBPbeta), a basic-leucine zipper transcription factor, is an important effector of signals in physiologic growth and cancer. The identification of direct C/EBPbeta targets in vivo has been limited by functional compensation by other C/EBP family proteins and the low stringency of the consensus sequence. Here we use the combined power of expression profiling and high-throughput chromatin immunoprecipitation to identify direct and biologically relevant targets of C/EBPbeta. We identified 25 potential C/EBPbeta targets, of which 88% of those tested were confirmed as in vivo C/EBPbeta-binding sites. Six of these genes also displayed differential expression in C/EBPbeta-/- livers. Computational analysis revealed that bona fide C/EBPbeta target genes can be distinguished by the presence of binding motifs for specific additional transcription factors in the vicinity of the C/EBPbeta site. This approach is generally applicable to the discovery of direct, biologically relevant targets of mammalian transcription factors.