Identification of the transcription factor ZEB1 as a central component of the adipogenic gene regulatory network.

Adipose tissue is a key determinant of whole body metabolism and energy homeostasis. Unraveling the regulatory mechanisms underlying adipogenesis is therefore highly relevant from a biomedical perspective. Our current understanding of fat cell differentiation is centered on the transcriptional cascades driven by the C/EBP protein family and the master regulator PPARγ. To elucidate further components of the adipogenic gene regulatory network, we performed a large-scale transcription factor (TF) screen overexpressing 734 TFs in mouse pre-adipocytes and probed their effect on differentiation. We identified 22 novel pro-adipogenic TFs and characterized the top ranking TF, ZEB1, as being essential for adipogenesis both in vitro and in vivo. Moreover, its expression levels correlate with fat cell differentiation potential in humans. Genomic profiling further revealed that this TF directly targets and controls the expression of most early and late adipogenic regulators, identifying ZEB1 as a central transcriptional component of fat cell differentiation.

A yeast one-hybrid and microfluidics-based pipeline to map mammalian gene regulatory networks.

The comprehensive mapping of gene promoters and enhancers has significantly improved our understanding of how the mammalian regulatory genome is organized. An important challenge is to elucidate how these regulatory elements contribute to gene expression by identifying their trans-regulatory inputs. Here, we present the generation of a mouse-specific transcription factor (TF) open-reading frame clone library and its implementation in yeast one-hybrid assays to enable large-scale protein-DNA interaction detection with mouse regulatory elements. Once specific interactions are identified, we then use a microfluidics-based method to validate and precisely map them within the respective DNA sequences. Using well-described regulatory elements as well as orphan enhancers, we show that this cross-platform pipeline characterizes known and uncovers many novel TF-DNA interactions. In addition, we provide evidence that several of these novel interactions are relevant in vivo and aid in elucidating the regulatory architecture of enhancers.

Genomic variation and its impact on gene expression in Drosophila melanogaster.

Understanding the relationship between genetic and phenotypic variation is one of the great outstanding challenges in biology. To meet this challenge, comprehensive genomic variation maps of human as well as of model organism populations are required. Here, we present a nucleotide resolution catalog of single-nucleotide, multi-nucleotide, and structural variants in 39 Drosophila melanogaster Genetic Reference Panel inbred lines. Using an integrative, local assembly-based approach for variant discovery, we identify more than 3.6 million distinct variants, among which were more than 800,000 unique insertions, deletions (indels), and complex variants (1 to 6,000 bp). While the SNP density is higher near other variants, we find that variants themselves are not mutagenic, nor are regions with high variant density particularly mutation-prone. Rather, our data suggest that the elevated SNP density around variants is mainly due to population-level processes. We also provide insights into the regulatory architecture of gene expression variation in adult flies by mapping cis-expression quantitative trait loci (cis-eQTLs) for more than 2,000 genes. Indels comprise around 10% of all cis-eQTLs and show larger effects than SNP cis-eQTLs. In addition, we identified two-fold more gene associations in males as compared to females and found that most cis-eQTLs are sex-specific, revealing a partial decoupling of the genomic architecture between the sexes as well as the importance of genetic factors in mediating sex-biased gene expression. Finally, we performed RNA-seq-based allelic expression imbalance analyses in the offspring of crosses between sequenced lines, which revealed that the majority of strong cis-eQTLs can be validated in heterozygous individuals.