Interactive collection of cis-regulatory modules from Drosophila
Lifanov A., Makeev V., Nazina A. and Papatsenko D.,
appendix to papers:
"
Homotypic regulatory clusters in Drosophila"
"Distance preferences in arrangement of binding motifs and hierarchical levels in organization of transcription regulatory information"
"Statistical extraction of eukaryotic cis-regulatory modules using exhaustive assessment of local word frequency"
Introduction
Regulation of gene expression at the early stages of the fly embryo is probably the most well understood among the other known examples of transcriptional regulation of eukaryotic genes and, therefore, holds a special place in many investigations. Regulatory sequences of the Drosophila developmental genes represent independent sequence elements, cis-regulatory modules (CRM) containing combinations of binding motifs for upstream regulators, transcription factors from the same developmental group. The combinations of binding motifs in the CRM sequences provide gradient-dependent response of the developmental genes to the upstream regulators. Therefore the formation of precisely positioned gene expression patterns can be explored at the level of transcriptional regulation.
Figure 1 demonstrates the approximate position of the developmental genes in the network according to the time of their expression (panel A) and according to their relative spatial distribution in the embryo (panel B). At the initial stages of the temporal cascade, the spatial information is distributed in the form of broad expression domains of the maternal and gap product gradients. Later on in, this information is finalized into much more specific patterns (pair-rule genes and segment polarity genes) that correspond to future compartments of the Drosophila body.
Figure 1. Developmental cascade and spatial distribution of gene expression patterns in the fly embryo.
How does such spatial distribution of transcriptional signals regulate expression of the developmental genes? One of the most studied examples of the gradient-dependent transcriptional response is regulation of the pair-rule geneeven-skipped. This gene has several regulatory regions (CRMs) scattered in a range of approximately 15Kb around coding sequence of eve. Five of these regions (see annotation below) are responsible for early expression of eve in the form of seven regularly spaced stripes in the embryo (Figure 2, panel A). Stripe 2, stripe 3+7, stripe 4+6, stripe 1 and stripe 5 enhancers (CRMs) drive expression of the corresponding stripes and contain all necessary transcriptional information for their correct spatial distribution in the embryo (Figure 2, panel A).
Example on Figure 2, panel B, demonstrates the transcriptional response of the eve stripe 2 enhancer: The maternal activator Bicoid binds to its recognition motif in the enhancer and activates the expression, while the two repressor gap gene products, Giant and Kruppel limit anterior and posterior borders of this expression. The eve stripe 2 enhancer contains multiple recognition motifs for all these transcription factors and sometimes recognition motifs for the activator and repressors overlap.
Figure 2 Regulation of even-skipped by maternal and gap gene gradients.
Expression driven by the other eve stripes is regulated in a similar way. Repressors Knirps and Hunchback delimit expression borders of the even-skipped stripes 3,4, 6 and 7 (see Figure 2, panel C). Two corresponding regulatory regions, 4+6 and 3+7 enhancers encode all sufficient transcriptional information for these four stripes. The eve stripes 4 and 6 are formed in the embryo zones with lower concentration of hunchback and higher concentration of Knirps, conversely the eve stripes 3 and 7 are formed where hunchback concentration is greater than that of Knirps. This model suggests that not only the specific combination of recognition motifs, but also the relative affinity and the number of binding sites in the regulatory region is critical. Presence of the larger number of sites with higher affinity results in higher sensitivity of the enhancer to the transcriptional regulator. According to this, 3+7 enhancer should have more high-affinity Knirps sites than the stripe 4+6 enhancer. And, stripe 4+6 enhancer should have more high affinity sites for hunchback than the stripe 3+7 does.
Interactive CRM sequences
Each file contains interactive functional map, based on experimental data and sequence of the locus for corresponding gene. All maps show relative size of the functional elements. Proximal promoters are marked by deep red color, early enhancers are in red, enhancers that regulate later stages of development are in orange, exons are in yellow. Tables below the the maps show relative positions of the functional elements in locus and transcription factors, involved in the regulation. Beside interactive maps, D.melanogaster sequences and the data for upstream regulators, the annotation also contains sequences from related species. We recommend IE 4.0 and higher to explore the annotation.
Buttonhead (btd) [1-6]
Giant (gt) [8-13]
Hunchback (hb) [14-19]
Knirps (kni) [20-22]
Kruppel (kr) [8, 23-25]
Orthodenticle (otd) [26-30]
Spalt (sal) [31-35]
Tailless (tll) [36-39]
Even-skipped (eve) [40-54]
Fushi-Tarazu (ftz) [55-72]
Gooseberry (gsb) [73-76]
Hairy (h) [77-80]
Paired (prd) [81-85]
Runt (run) [86-90]
Engrailed (en) [91-98]
Abdominal-A (abd-A) [99-100]
Distalless (dll) [101-103]
Empty spiracles (ems) [104-105]
Ultrabithorax (ubx) [106-121]
maps.zip - all experimental maps for annotated regulatory regions.
Binding motifs for upstream regulators
motifs.zip - alignments for all motifs.
Software and support (for windows):
Download programs and required accessory tools, in case you are using MATLAB® you may also download graphical interface for the program. Create a root directory and unpack all files into this directory.
1 Building distribution of site probability for PWM score in a reference sequence (genome) using GENOME_HIST:
2 Scanning your sequence using chosen motif.
3 Calculating statistical significance and correlation of identified clusters with known regulatory regions.
4 Generating distribution of potential regulatory regions for defined parameter combination.
5 Find maximum of agreement and draw distribution of clusters in a test sequence (currently requires MATLAB® 6.1).
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[*] - computational data only.
