Evolutionary Rewiring of Human Regulatory Networks by Waves of Genome Expansion

Why does a human “look different” than an elephant, which “appears different” than a frog? As these GEITP pages have often described, whereas the genotype is the genetic makeup of an organism, the phenotype (trait) is how genetic and environmental influences come together to create an organism’s physical appearance and behavior. A trait includes such things as: height, body mass index, blood pressure, serum cholesterol level, shape of ears, or manifestation of any Mendelian or complex disease (Marfan syndrome, type-2 diabetes, cancer). Evolution of regulatory networks is believed to be the cause of a substantial fraction of phenotypic divergence among vertebrates (animals having a spinal column).

Genetic events affecting gene regulation can be classified into two classes: [a] exaptation (i.e. a trait that has been co-opted for a use other than the one for which natural selection has built it) of existing DNA sequence (and epigenetic effects) through accumulation of small-scale mutations; and [b] de novo appearance of regulatory DNA through genome expansion driven (for example) by transposable elements (TEs; i.e. “jumping genes” or DNA segments that move around and re-insert elsewhere in chromosomes). Both mechanisms have been shown to be relevant in the evolution of human regulatory DNA.

In particular, information-rich binding sites (BSs) –– such as the one recognized by CTCF (a single protein that is a TF; CCCTC-binding factor) –– are much less likely to arise through accumulation of random point mutations than simpler binding motifs. In fact, it has been shown that the expansion of lineage-specific TEs efficiently remodeled the CTCF regulome. In a study of the activity of TEs in generating transcription-factor-binding sites (TFBSs), it was observed that ~20% of BSs are embedded within TEs, thus revealing the latent regulatory potential of these elements. It was shown that recent enhancer evolution in mammals can be largely explained by exaptation of existing ancestral sequences rather than by the expansion of lineage-specific repeated elements. A systematic investigation of the role of genomic sequence expansion in rewiring regulatory networks is, however, still missing; authors [see attached] have therefore tried to fill this gap –– by attempting to reconstruct a much longer evolutionary history, focusing on regulatory evolution through genome expansion since the evolutionary time of the common ancestor of all vertebrates.

To investigate the role of newly arising sequences in rewiring regulatory networks, authors [see attached] estimated the age of each region of the human genome by applying maximum parsimony (in phylogenetic biology, maximum parsimony is an optimality criterion –– under which the phylogenetic tree that minimizes the total number of character-state changes is the preferred condition). Authors carried out genome-wide alignments with 100 vertebrate species. They then studied the age distribution of several types of functional regions –– with a focus on regulatory elements. The age distribution of regulatory elements reveals the extensive use of newly formed genomic sequence in the evolution of regulatory interactions. Intriguingly, many TFs have expanded their repertoire of targets through waves of genomic expansions that can be traced to specific evolutionary times.

Repeated elements contributed a major part of such expansion: many classes of such elements are enriched in BSs of one or a few specific TFs, whose binding sites are localized in specific portions of DNA modules and characterized by distinctive motif signatures; these features suggest that the BSs were available as soon as the new sequence entered the genome –– rather than being created later by accumulation of point mutations. By comparing the age of regulatory regions to the evolutionary shift in expression of nearby genes, authors show that rewiring through genome expansion played an important role in shaping human regulatory networks. This is a really cool, but very complicated, study to appreciate. 🙂

Am J Hum Genet 1 Feb 218; 102: 207–218

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