Networks In Evolutionary Systems
Principal Investigator: Anton CROMBACH
Evolution shapes biological systems and those systems in turn shape the evolutionary process. As a result a system's structural, functional, and evolutionary properties are intertwined. The aim of the group is to study the contribution of each of them in genomes and gene regulatory networks using mathematical models, computer simulations, and bioinformatic data analysis. The questions that particularly drive us are:
How does genome content shape 3D structure and vice versa?
Genomes form looping domains called TADs (Fig. 1), which involves transposable elements (TEs). While the function of such chromatin loops is unclear, their folding structure appears conserved between cell types and across species. In apparent contradiction, its building blocks, TEs, are well-known mutagens. So why do we find genomes folded into looping domains and how do these loops evolve? We focus on two topics. On the one hand, we assess the impact of spatial proximity on genomic rearrangements. On the other, we are interested in TE mutational dynamics and their influence on looping domains. In both cases, we employ evolutionary simulations to evolve minimal polymer genomes and focus on the interplay between mutations, genome content, and genome structure.
How constrained and/or contingent is evolution from one (intermediate) phenotype to another?
An important, general patterning process in development is to make stripes. Striped domains of gene expression (pre-) pattern the limb, the hindbrain, and color across the animal body. In dipterans (flies, midges, and mosquitoes), the initial segmented body plan is laid down as stripes of gap gene expression. We take advantage of the recent elucidation of the gap gene network in three fly species (Fig. 1) to investigate the evolution of patterning. The same set of genes may generate a simple pattern (C. albipunctata) or a more complex one (D. melanogaster and M. abdita). Thus our aim is to understand the paths that can be taken by regulatory evolution both to increase pattern complexity and to maintain it as upstream factors of the gap gene network change.
Figure 1. Spatial genome structure and data-driven gene networks.
A. Sketch of a nucleus with chromosomal territories and folded chromatin. Zooming in, chromatin loops are anchored by two CTCF proteins and a proposed Cohesin ring. Some TEs contain binding sites for CTCF. B. Gap gene system in dipterans. Maternal inputs (Bcd: Bicoid, Cad: Caudal) feed into the gap gene network, resulting in a striped phenotypic outcome. Cad is not maternal and late-expressed in M. abdita (white wave pattern). The anterior determinant is unknown in C. albipunctata, indicated with a question mark (?). Trunk gap genes: hunchback (hb), Krüppel (Kr), knirps (kni), and giant (gt). Horizontal axes indicate anterior-posterior (A P) position along the embryo, with the head at 0%; the trunk region is shown (~30–90%). Vertical axes indicate gene expression levels in arbitrary units. Expression dynamics are in 3D to show development over time. C. Gap gene network of D. melanogaster. Boxes indicate the position of expression domains in the trunk region, along the A-P axis. Background gradients represent activating maternal inputs. T-bars represent repression; dashed lines indicate net repressive interactions between overlapping domains. Terminal gap genes: tailless (tll), huckebein (hkb).
Crombach, A., Wotton, K.R., Jiménez-Guri, E. & Jaeger, J. (2016), Gap Gene Regulatory Dynamics Evolve along a Genotype Network. Mol. Biol. Evol. 33, 1293–1307.
Wotton, K.R., Jiménez-Guri, E., Crombach, A., Janssens, H., Alcaine-Colet, A., Lemke, S., Schmidt-Ott, U. & Jaeger, J. (2015), Quantitative system drift compensates for altered maternal inputs to the gap gene network of the scuttle fly Megaselia abdita. Elife 4. eLife.04785
Crombach, A., García-Solache & M.A., Jaeger, J. (2014), Evolution of early development in dipterans: reverse-engineering the gap gene network in the moth midge Clogmia albipunctata (Psychodidae). BioSystems 123, 74–85.
Jaeger, J. & Crombach, A. (2012), Life’s attractors : understanding developmental systems through reverse engineering and in silico evolution. Adv. Exp. Med. Biol. 751, 93–119.
Crombach, A. & Hogeweg, P. (2008), Evolution of evolvability in gene regulatory networks. PLoS Comput. Biol. 4, e1000112.
Crombach, A. & Hogeweg, P. (2007), Chromosome rearrangements and the evolution of genome structuring and adaptability. Mol. Biol. Evol. 24, 1130–1139.