Transcriptional profiling of embryos lacking the lipoprotein receptor SR-B1 reveals a regulatory circuit governing a neurodevelopmental or metabolic decision during neural tube closure.

BACKGROUND: The high-density lipoprotein receptor SR-B1 mediates cellular uptake of several lipid species, including cholesterol and vitamin E. During early mouse development, SR-B1 is located in the maternal-fetal interface, where it facilitates vitamin E transport towards the embryo. Consequently, mouse embryos lacking SR-B1 are vitamin E-deficient, and around half of them fail to close the neural tube and show cephalic neural tube defects (NTD). Here, we used transcriptomic profiling to identify the molecular determinants of this phenotypic difference between SR-B1 deficient embryos with normal morphology or with NTD. RESULTS: We used RNA-Seq to compare the transcriptomic profile of three groups of embryos retrieved from SR-B1 heterozygous intercrosses: wild-type E9.5 embryos (WT), embryos lacking SR-B1 that are morphologically normal, without NTD (KO-N) and SR-B1 deficient embryos with this defect (KO-NTD). We identified over 1000 differentially expressed genes: down-regulated genes in KO-NTD embryos were enriched for functions associated to neural development, while up-regulated genes in KO-NTD embryos were enriched for functions related to lipid metabolism. Feeding pregnant dams a vitamin E-enriched diet, which prevents NTD in SR-B1 KO embryos, resulted in mRNA levels for those differentially expressed genes that were more similar to KO-N than to KO-NTD embryos. We used gene regulatory network analysis to identify putative transcriptional regulators driving the different embryonic expression profiles, and identified a regulatory circuit controlled by the androgen receptor that may contribute to this dichotomous expression profile in SR-B1 embryos. Supporting this possibility, the expression level of the androgen receptor correlated strongly with the expression of several genes involved in neural development and lipid metabolism. CONCLUSIONS: Our analysis shows that normal and defective embryos lacking SR-B1 have divergent expression profiles, explained by a defined set of transcription factors that may explain their divergent phenotype. We propose that distinct expression profiles may be relevant during early development to support embryonic nutrition and neural tube closure.

Automated generation of context-specific gene regulatory networks with a weighted approach in Drosophila melanogaster.

The regulation of gene expression is a key factor in the development and maintenance of life in all organisms. Even so, little is known at whole genome scale for most genes and contexts. We propose a method, Tool for Weighted Epigenomic Networks in Drosophila melanogaster (Fly T-WEoN), to generate context-specific gene regulatory networks starting from a reference network that contains all known gene regulations in the fly. Unlikely regulations are removed by applying a series of knowledge-based filters. Each of these filters is implemented as an independent module that considers a type of experimental evidence, including DNA methylation, chromatin accessibility, histone modifications and gene expression. Fly T-WEoN is based on heuristic rules that reflect current knowledge on gene regulation in D. melanogaster obtained from the literature. Experimental data files can be generated with several standard procedures and used solely when and if available. Fly T-WEoN is available as a Cytoscape application that permits integration with other tools and facilitates downstream network analysis. In this work, we first demonstrate the reliability of our method to then provide a relevant application case of our tool: early development of D. melanogaster. Fly T-WEoN together with its step-by-step guide is available at https://weon.readthedocs.io.

Filtering of Data-Driven Gene Regulatory Networks Using Drosophila melanogaster as a Case Study.

Gene Regulatory Networks (GRNs) allow the study of regulation of gene expression of whole genomes. Among the most relevant advantages of using networks to depict this key process, there is the visual representation of large amounts of information and the application of graph theory to generate new knowledge. Nonetheless, despite the many uses of GRNs, it is still difficult and expensive to assign Transcription Factors (TFs) to the regulation of specific genes. ChIP-Seq allows the determination of TF Binding Sites (TFBSs) over whole genomes, but it is still an expensive technique that can only be applied one TF at a time and requires replicates to reduce its noise. Once TFBSs are determined, the assignment of each TF and its binding sites to the regulation of specific genes is not trivial, and it is often performed by carrying out site-specific experiments that are unfeasible to perform in all possible binding sites. Here, we addressed these relevant issues with a two-step methodology using Drosophila melanogaster as a case study. First, our protocol starts by gathering all transcription factor binding sites (TFBSs) determined with ChIP-Seq experiments available at ENCODE and FlyBase. Then each TFBS is used to assign TFs to the regulation of likely target genes based on the TFBS proximity to the transcription start site of all genes. In the final step, to try to select the most likely regulatory TF from those previously assigned to each gene, we employ GENIE3, a random forest-based method, and more than 9,000 RNA-seq experiments from D. melanogaster. Following, we employed known TF protein-protein interactions to estimate the feasibility of regulatory events in our filtered networks. Finally, we show how known interactions between co-regulatory TFs of each gene increase after the second step of our approach, and thus, the consistency of the TF-gene assignment. Also, we employed our methodology to create a network centered on the Drosophila melanogaster gene Hr96 to demonstrate the role of this transcription factor on mitochondrial gene regulation.