Evidence for Deep Regulatory Similarities in Early Developmental Programs across Highly Diverged Insects

Abstract

Many genes familiar from Drosophila development, such as the so-called gap, pair-rule, and segment polarity genes, play important roles in the development of other insects and in many cases appear to be deployed in a similar fashion, despite the fact that Drosophila-like “long germband” development is highly derived and confined to a subset of insect families. Whether or not these similarities extend to the regulatory level is unknown. Identification of regulatory regions beyond the well-studied Drosophila has been challenging as even within the Diptera (flies, including mosquitoes) regulatory sequences have diverged past the point of recognition by standard alignment methods. Here, we demonstrate that methods we previously developed for computational cis-regulatory module (CRM) discovery in Drosophila can be used effectively in highly diverged (250–350 Myr) insect species including Anopheles gambiae, Tribolium castaneum, Apis mellifera, and Nasonia vitripennis. In Drosophila, we have successfully used small sets of known CRMs as “training data” to guide the search for other CRMs with related function. We show here that although species-specific CRM training data do not exist, training sets from Drosophila can facilitate CRM discovery in diverged insects. We validate in vivo over a dozen new CRMs, roughly doubling the number of known CRMs in the four non-Drosophila species. Given the growing wealth of Drosophila CRM annotation, these results suggest that extensive regulatory sequence annotation will be possible in newly sequenced insects without recourse to costly and labor-intensive genome-scale experiments. We develop a new method, Regulus, which computes a probabilistic score of similarity based on binding site composition (despite the absence of nucleotide-level sequence alignment), and demonstrate similarity between functionally related CRMs from orthologous loci. Our work represents an important step toward being able to trace the evolutionary history of gene regulatory networks and defining the mechanisms underlying insect evolution.

Redeployment of a conserved gene regulatory network during Aedes aegypti development

“…We previously developed SCRMshaw, an effective method to guide CRM discovery in Drosophila (Kantorovitz et al, 2009; Kazemian et al, 2011b), and recently showed that this same method can be used in a cross-species manner to identify CRMs in more diverged insect species (Kazemian et al, 2014b). SCRMshaw uses a “training set” of known Drosophila CRMs, drawn from the REDfly database (Gallo et al, 2011), to develop a computational model that can be applied to CRM discovery in either D. melanogaster or another holometabolous insect.…”

“…SCRMshaw uses a “training set” of known Drosophila CRMs, drawn from the REDfly database (Gallo et al, 2011), to develop a computational model that can be applied to CRM discovery in either D. melanogaster or another holometabolous insect. Because we lacked a sufficient training set of late midline stage CRMs, we used a mixed mesectoderm/midline primordium/late midline training set consisting of just eight validated CRMs (“mapping1.mesectoderm,” (Kazemian et al, 2014b)) to search both the D. melanogaster and A. aegypti genomes. Despite the limitations of our training set, we were able to predict CRMs from both species over the full range of midline developmental stages (based on recovery of all eight training set members and an additional 21 known D. melanogaster CRMs; see Table S1).…”

“…We selected a total of 21 predicted CRMs — twelve in D. melanogaster and nine in A. aegypti – to test for activity using reporter gene assays in transgenic Drosophila (as described in (Kazemian et al, 2014b); Table S3). D. melanogaster candidates were selected from the top 200 ranked predictions, while A. aegypti candidates were further filtered for those which (1) mapped within 25 kb of a gene with a known D. melanogaster ortholog; (2) mapped within 25 kb of genes putatively in the midline GRN whose expression was judged to have shifted laterally in A. aegypti; and (3) for which we had a predicted CRM in both species (Table S2).…”

“…The trained model is then used to score overlapping 500 bp windows in the “target genome,” and the highest peaks in the resulting score profile are predicted to be enhancers. We showed in Kazemian et al (2014b) that the target genome could be not just the D. melanogaster genome but also that of another holometabolous insect. In this work, SCRMshaw predictions were generated for the D. melanogaster and A. aegypti genomes using the dm3/release 5 and AaegL3.3 genome releases, respectively.…”

Developmental neurogenetics of sexual dimorphism in Aedes aegypti

“…FAIRE-seq studies, as well as the use of other biochemical approaches (i.e. DNAse-seq) or computational approaches for the identification of insect CREs (Kazemian et al, 2014), will also facilitate analysis of gene regulatory networks in the developing nervous system. Moreover, since FAIRE assesses chromatin states, it is anticipated that FAIRE-seq might also be applied for epigenetic analysis of sexual dimorphism in A. aegypti, an exciting prospect.…”