Identifying Genomic Regions Targeted During Eggplant Domestication Using Transcriptome Data.

Identifying genes and traits that have diverged during domestication provides key information of importance for maintaining and even increasing yield and nutrients in existing crops. A "bottom-up" population genetics approach was used to identify signatures of selection across the eggplant genome, to better understand the process of domestication. RNA-seq data were obtained for 4 wild eggplants (Solanum insanum L.) and 16 domesticated eggplants (S. melongena L.) and mapped to the eggplant genome. Single-nucleotide polymorphism (SNPs) exhibiting signatures of selection in domesticates were identified as those exhibiting high FST between the 2 populations (evidence of significant divergence) and low π for the domesticated population (indicative of a selective sweep). Some of these regions appear to overlap with previously identified quantitative trait loci for domestication traits. Genes in regions of linkage disequilibrium surrounding these SNPs were searched against the Arabidopsis thaliana and tomato genomes to find orthologs. Subsequent gene ontology (GO) enrichment analysis identified over-representation of GO terms related to photosynthesis and response to the environment. This work reveals genomic changes involved in eggplant domestication and improvement, and how this compares to observed changes in the tomato genome, revealing shared chromosomal regions involved in the domestication of both species.

Reconstructing promoter activity from Lux bioluminescent reporters.

The bacterial Lux system is used as a gene expression reporter. It is fast, sensitive and non-destructive, enabling high frequency measurements. Originally developed for bacterial cells, it has also been adapted for eukaryotic cells, and can be used for whole cell biosensors, or in real time with live animals without the need for euthanasia. However, correct interpretation of bioluminescent data is limited: the bioluminescence is different from gene expression because of nonlinear molecular and enzyme dynamics of the Lux system. We have developed a computational approach that, for the first time, allows users of Lux assays to infer gene transcription levels from the light output. This approach is based upon a new mathematical model for Lux activity, that includes the actions of LuxAB, LuxEC and Fre, with improved mechanisms for all reactions, as well as synthesis and turn-over of Lux proteins. The model is calibrated with new experimental data for the LuxAB and Fre reactions from Photorhabdus luminescens-the source of modern Lux reporters-while literature data has been used for LuxEC. Importantly, the data show clear evidence for previously unreported product inhibition for the LuxAB reaction. Model simulations show that predicted bioluminescent profiles can be very different from changes in gene expression, with transient peaks of light output, very similar to light output seen in some experimental data sets. By incorporating the calibrated model into a Bayesian inference scheme, we can reverse engineer promoter activity from the bioluminescence. We show examples where a decrease in bioluminescence would be better interpreted as a switching off of the promoter, or where an increase in bioluminescence would be better interpreted as a longer period of gene expression. This approach could benefit all users of Lux technology.