Artificial selection experiments are key experiments in evolutionary biology. The demonstration that application of selective pressure across multiple generations results in heritable phenotypic changes is a tangible and reproducible proof of the evolution by natural selection.
Artificial selection experiments are used to evaluate the joint effects of selection on multiple traits, their genetic covariances and differences in responses in different environments. Most studies on artificial selection experiments report and base their analyses on phenotypic changes . More recently, changes in allele frequency and other patterns of molecular genetic diversity have been used to identify genomic locations where selection has had an effect. However, so far the changes in gene expression have not been in the focus of artificial selection experiment studies (see  for an example though).
In plants, one of the most famous artificial selection experiments is the Illinois Corn Experiment where maize (Zea mays) is selected for oil and protein content , but in addition, similar experiments have been conducted also for other traits in maize. In Saclay divergent selection experiment  two maize inbred lines (F252 and MBS847) have been selected for early and late flowering for 13 generations, resulting in two week difference in flowering time.
In ”Transcriptomic response to divergent selection for flowering time in maize reveals convergence and key players of the underlying gene regulatory network ”  Maud Tenaillon and her coworkers study the gene expression differences among these two independently selected maize populations. Their experiments cover two years in field conditions and they use samples of shoot apical meristem at three different developmental stages: vegetative, transitioning and reproductive. They use RNA-seq transcriptome level differences and qRT-PCR for gene expression pattern investigation. The work is continuation to earlier genetic and phenotypic studies on the same material [4, 6].
The reviewers and I agree that dataset is unique and its major benefit is that it has been obtained from field conditions similar to those that species may face under natural setting during selection. Their tissue sampling is supported by flowering time phenotypic observations and covers the developmental transition stage, making a good effort to identify key transcriptional and phenotypic changes and their timing affected by selection.
Tenaillon et al.  identify more than 2000 genes that are differentially expressed among early and late flowering populations. Expectedly, they are enriched for known flowering time genes. As they point out, differential expression of thousands of genes does not mean that they all were independently affected by selection, but rather that the whole transcriptional network has shifted, possibly due to just few upstream or hub-genes. Also, the year-to-year variation had smaller effect in gene expression compared to developmental stage or genetic background, possibly indicating selection for stability across environmental fluctuation for such an important phenotype as flowering time.
Another noteworthy observation is that they find convergent patterns of transcriptional changes among the two selected lines. 115 genes expression patterns are shifted due to selection in both genetic backgrounds. This convergent pattern can be a result of either selection on standing variation or de novo mutations. The data does not allow testing which process is underlying the observed convergence. However, their results show that this is an interesting future question that can be addressed using genotype and gene expression data from the same ancestral and derived material and possibly their hybrids.
 Hill, W. G., & Caballero, A. (1992). Artificial selection experiments. Annual Review of Ecology and Systematics, 23(1), 287-310. doi: 10.1146/annurev.es.23.110192.001443
 Konczal, M., Babik, W., Radwan, J., Sadowska, E. T., & Koteja, P. (2015). Initial molecular-level response to artificial selection for increased aerobic metabolism occurs primarily through changes in gene expression. Molecular biology and evolution, 32(6), 1461-1473. doi: 10.1093/molbev/msv038
 Moose, S. P., Dudley, J. W., & Rocheford, T. R. (2004). Maize selection passes the century mark: a unique resource for 21st century genomics. Trends in plant science, 9(7), 358-364. doi: 10.1016/j.tplants.2004.05.005
 Durand, E., Tenaillon, M. I., Ridel, C., Coubriche, D., Jamin, P., Jouanne, S., Ressayre, A., Charcosset, A. and Dillmann, C. (2010). Standing variation and new mutations both contribute to a fast response to selection for flowering time in maize inbreds. BMC evolutionary biology, 10(1), 2. doi: 10.1186/1471-2148-10-2
 Tenaillon, M. I., Seddiki, K., Mollion, M., Le Guilloux, M., Marchadier, E., Ressayre, A. and Dillmann C. (2019). Transcriptomic response to divergent selection for flowering time in maize reveals convergence and key players of the underlying gene regulatory network. BioRxiv, 461947 ver. 5 peer-reviewed and recommended by PCI Evolutionary Biology. doi: 10.1101/461947
 Durand, E., Tenaillon, M. I., Raffoux, X., Thépot, S., Falque, M., Jamin, P., Bourgais A., Ressayre, A. and Dillmann, C. (2015). Dearth of polymorphism associated with a sustained response to selection for flowering time in maize. BMC evolutionary biology, 15(1), 103. doi: 10.1186/s12862-015-0382-5