Using synthetic biology to study gene regulatory evolution

Transcriptional enhancers specify the precise time, level, and location of gene expression. Disentangling and characterizing the components of enhancer activity in multicellular eukaryotic development has proven challenging because enhancers contain activator and repressor binding sites for multiple factors that each exert nuanced, context-dependent control of enhancer activity. Recent advances in synthetic biology provide an almost unlimited ability to create and modify regulatory elements and networks, offering unprecedented power to study gene regulation. Here we review several studies demonstrating the utility of synthetic biology for studying enhancer function during development and evolution. These studies clearly show that synthetic biology can provide a way to reverse-engineer and reengineer transcriptional regulation in animal genomes with enormous potential for understanding evolution

Finding cell-specific expression patterns in the early Ciona embryo with single-cell RNA-seq

Single-cell RNA-seq has been established as a reliable and accessible technique enabling new types of analyses, such as identifying cell types and studying spatial and temporal gene expression variation and change at single-cell resolution. Recently, single-cell RNA-seq has been applied to developing embryos, which offers great potential for finding and characterising genes controlling the course of development along with their expression patterns. In this study, we applied single-cell RNA-seq to the 16-cell stage of the Ciona embryo, a marine chordate and performed a computational search for cell-specific gene expression patterns. We recovered many known expression patterns from our single-cell RNA-seq data and despite extensive previous screens, we succeeded in finding new cell-specific patterns, which we validated by in situ and single-cell qPCR

Cellular resolution models for even skipped regulation in the entire Drosophila embryo

Transcriptional control ensures genes are expressed in the right amounts at the correct times and locations. Understanding quantitatively how regulatory systems convert input signals to appropriate outputs remains a challenge. For the first time, we successfully model even skipped (eve) stripes 2 and 3+7 across the entire fly embryo at cellular resolution. A straightforward statistical relationship explains how transcription factor (TF) concentrations define eve’s complex spatial expression, without the need for pairwise interactions or cross-regulatory dynamics. Simulating thousands of TF combinations, we recover known regulators and suggest new candidates. Finally, we accurately predict the intricate effects of perturbations including TF mutations and misexpression. Our approach imposes minimal assumptions about regulatory function; instead we infer underlying mechanisms from models that best fit the data, like the lack of TF-specific thresholds and the positional value of homotypic interactions. Our study provides a general and quantitative method for elucidating the regulation of diverse biological systems. DOI: http://dx.doi.org/10.7554/eLife.00522.00

Advancing fish breeding in aquaculture through genome functional annotation

Genomics is increasingly applied in breeding programmes for farmed fish and shellfish species around the world. However, current applications do not include information on genome functional activity, which can enhance opportunities to predict relationships between genotypes and phenotypes and hence increase the accuracy of selection. Here, we review prospects for improving aquaculture breeding practises through the uptake of functional genomics data in light of the EU Horizon 2020 project AQUA-FAANG: ‘Advancing European Aquaculture by Genome Functional Annotation’. This consortium targeted the six major farmed fish species in European aquaculture, producing thousands of functional genomic datasets from samples representing embryos to mature adults of both sexes, and following immunological stimulation. This data was used to catalogue functional activity across the genome of each species, revealing transcribed regions, distinct chromatin states and regulatory elements impacting gene expression. These functional annotations were shared as open data through the Ensembl genome browser using the latest reference genomes for each species. AQUA-FAANG data offers novel opportunities to identify and prioritize causative genetic variants responsible for diverse traits including disease resistance, which can be exploited to enhance selective breeding. Such knowledge and associated resources have the potential to improve sustainability and boost production in aquaculture by accelerating genetic gain for health and robustness to infection, whilst reducing the requirement for animal testing. We further outline directions to advance and leverage genome functional annotation beyond the AQUA-FAANG project. Given the diversity of aquaculture sectors and businesses, the incorporation of functional genomic information into breeding decisions will depend on technological readiness level and scale of operation, with cost-benefit analysis necessary to determine the most profitable approach for each species and production system