Open Access

Computational identification of developmental enhancers: conservation and function of transcription factor binding-site clusters in Drosophila melanogaster and Drosophila pseudoobscura

  • Benjamin P Berman1,
  • Barret D Pfeiffer2,
  • Todd R Laverty3,
  • Steven L Salzberg4,
  • Gerald M Rubin1, 2, 3,
  • Michael B Eisen1, 5, 6Email author and
  • Susan E Celniker2
Contributed equally
Genome Biology20045:R61

DOI: 10.1186/gb-2004-5-9-r61

Received: 14 July 2004

Accepted: 6 August 2004

Published: 20 August 2004

Abstract

Background

The identification of sequences that control transcription in metazoans is a major goal of genome analysis. In a previous study, we demonstrated that searching for clusters of predicted transcription factor binding sites could discover active regulatory sequences, and identified 37 regions of the Drosophila melanogaster genome with high densities of predicted binding sites for five transcription factors involved in anterior-posterior embryonic patterning. Nine of these clusters overlapped known enhancers. Here, we report the results of in vivo functional analysis of 27 remaining clusters.

Results

We generated transgenic flies carrying each cluster attached to a basal promoter and reporter gene, and assayed embryos for reporter gene expression. Six clusters are enhancers of adjacent genes: giant, fushi tarazu, odd-skipped, nubbin, squeeze and pdm2; three drive expression in patterns unrelated to those of neighboring genes; the remaining 18 do not appear to have enhancer activity. We used the Drosophila pseudoobscura genome to compare patterns of evolution in and around the 15 positive and 18 false-positive predictions. Although conservation of primary sequence cannot distinguish true from false positives, conservation of binding-site clustering accurately discriminates functional binding-site clusters from those with no function. We incorporated conservation of binding-site clustering into a new genome-wide enhancer screen, and predict several hundred new regulatory sequences, including 85 adjacent to genes with embryonic patterns.

Conclusions

Measuring conservation of sequence features closely linked to function - such as binding-site clustering - makes better use of comparative sequence data than commonly used methods that examine only sequence identity.

Background

The transcription of protein-coding genes in distinct temporal and spatial patterns plays a central role in the differentiation and development of animal embryos. Decoding how the unique expression pattern of every transcript is encoded in DNA is essential to understanding how genome sequences specify organismal form and function.

Understanding gene regulation requires discovering the cis-acting sequences that control transcription, identifying which trans-acting factors act on each regulatory sequence, and determining how these interactions affect the timing and organization of transcription. The first step in this process is by no means straightforward. Regulatory regions are often large and complex. Functional cis-acting sequences are found 5' and 3' of transcripts and in introns, and can act over short or long distances. Most of the described animal regulatory sequences were identified by experimental dissection of a locus, and astonishingly few of these are well characterized.

Despite the paucity of good examples, as multiple regulatory sequences from different organisms were identified and characterized, some common features became apparent [1, 2]. Most animal regulatory sequences act as compact modular units, with regions of roughly a kilobase (kb) in size controlling specific aspects of a gene's transcription. These regulatory units - referred to here as cis-regulatory modules (CRMs) - tend to contain functional binding sites for several different transcription factors, often with multiple sites for each factor.

As the first animal genome sequences were completed [36], researchers began to tackle the challenge of identifying regulatory sequences on a genomic scale. We and several other groups began to ask whether common characteristics of regulatory sequences - modularity and high binding-site density - might be distinguishing characteristics that would permit the computational identification of new regulatory sequences. A number of in silico methods to identify regulatory sequences on the basis of binding-site clustering have been developed and applied to animal genomes [710]. Some of the predictions have the expected in vivo regulatory activity [1117], yet few of these predictions have been systematically evaluated.

The transcriptional regulatory network governing early Drosophila development is perhaps the best system in which to apply and evaluate these methods. Development of the Drosophila embryo is arguably better understood than that of any other animal. Sophisticated genetic screens [18, 19] have identified most of the key regulators of early development, and the molecular biology and biochemistry of these factors and their target sequences have received a great deal of attention. The spatial and temporal embryonic expression patterns of a large number of genes are known from microarray [20] and in situ expression studies [21]. Transcriptional regulation plays a uniquely important role in pre-gastrula patterning, as most of the key events occur in the absence of cell membranes and the cell-cell signaling systems that play a crucial role later in fly development and throughout the development of most other animals.

In a previous study [11], we identified 37 regions of the Drosophila melanogaster genome with unusually high densities of predicted binding sites for the early-acting transcription factors Bicoid (BCD), Hunchback (HB), Krüppel (KR), Knirps (KNI) and Caudal (CAD). As nine of these regions overlapped previously known CRMs, we proposed the remaining 28 as predicted CRMs (pCRMs). We tested one of the previously untested pCRMs for enhancer activity in a standard reporter gene assay [22, 23] and showed that it is responsible for directing a portion of the embryonic expression pattern of the gap transcription factor gene giant (gt) in a posterior stripe. Here, we report the systematic testing of the remaining 27 untested pCRMs for enhancer activity, resulting in collections of both bona fide positive and false-positive predictions, allowing us to develop and evaluate methods to improve the accuracy of methods for identifying functional cis-regulatory sequences.

We were particularly interested in methods based on the comparison of genome sequences of related species. The genome sequence of D. pseudoobscura (which diverged from D. melanogaster approximately 46 million years ago [24]) was recently completed by the Baylor Human Genome Sequencing Center, and several other Drosophila species are currently being sequenced. The morphological and molecular events in early embryonic development are highly conserved among drosophilids, and we expect the activity of the transcriptional regulators and the architecture of regulatory networks to be highly conserved as well. Most D. melanogaster regulatory sequences should have functional orthologs in other Drosophila species [25, 26], and a major rationale for sequencing other Drosophila species is the expectation that regulatory sequences have characteristic patterns of evolution that can be used to identify them and to better understand their function.

Most methods used to identify regulatory sequences from interspecies sequence comparison are fairly simple. They identify 'conserved' non-coding sequences (CNSs), operationally defined as islands of non-coding sequence with relatively high conservation flanked by regions of low conservation, and assume that this conservation reflects regulatory function. Although crude, these methods have been remarkably effective in identifying mammalian regulatory sequences [27, 28], and preliminary studies in Drosophila suggest that similar methods will be valuable in insects as well [29]. However, despite such successes, the extent of the efficacy of comparative sequence analysis in regulatory sequence discovery remains unclear. A systematic comparison of human-mouse sequence conservation in known regulatory regions and ancestral repeats (which provide a model for neutral evolution) suggests that regulatory regions cannot generally be distinguished on the basis of simple sequence conservation measures alone [30, 31]. Similarly, a recent analysis of D. melanogaster and D. pseudoobscura showed that known regulatory regions are only slightly more conserved than the rest of the non-coding genome [32], highlighting the need for further study and the development of comparative methods that go beyond measures of sequence identity.

Results

Expression patterns of pCRM containing transgenes

The 37 pCRMs are shown in Table 1. Each has been assigned an identifier (of the form PCEXXXX). The first nine overlap previously known enhancers of runt (run), even-skipped (eve), hairy (h), knirps (kni) and hunchback (hb). To determine whether any of the remaining 28 pCRMs also function as enhancers, we generated P-element constructs containing the pCRM sequence with minimal flanking sequence on both sides fused to the eve basal promoter and a lacZ reporter gene (see Materials and methods). As the margins of the tested sequences do not precisely correspond to the margins of the clusters, we assigned a unique identifier (of the form CEXXXX) to each tested fragment (identical CE and PCE numbers correspond to the same pCRM).
Table 1

Genomic location of pCRMs and neighboring genes

 

pCRM

ID*

Name

CRM activity

Arm

pCRM start

pCRM end

pCRM length

5' gene

pCRM relative position

3' gene

pCRM relative position

1

PCE7001

 

runt stripe 3

+

X

20,357,206

20,358,294

1,089

CG1338

-9,550

run

-8,561

2

PCE7002

 

eve stripes 3/7

+

2R

5,035,494

5,036,771

1,278

CG12134

3,713

eve

-2,952

3

PCE7003

 

eve stripe 2

+

2R

5,038,454

5,039,040

587

CG12134

6,673

eve

-683

4

PCE7004

 

eve stripes 4/6

+

2R

5,044,597

5,045,395

799

eve

4,874

TER94

-4,398

5

PCE7005

 

hairy stripe 7

+

3L

8,624,351

8,625,245

895

CG6486

16,118

h

-9,423

6

PCE7006

 

hairy stripe 6

+

3L

8,625,452

8,626,319

868

CG6486

17,219

h

-8,349

7

PCE7007

 

hairy stripes 1,5

+

3L

8,629,180

8,629,966

787

CG6486

20,947

h

-4,702

8

PCE7008

 

kni upstream

+

3L

20,615,070

20,616,425

1,356

kni

-1,169

CG13253

21,311

9

PCE7009

 

hb HZ1.4

+

3R

4,526,315

4,527,521

1,207

hb

-2,760

CG8112

403

10

PCE8001

1

gt posterior domain

+

X

2,187,439

2,188,382

944

gt

-1,704

tko

12,366

11

PCE8010

2

odd stripes 3/6

+

2L

3,601,750

3,602,509

760

odd

-2,433

Dot

-9,351

12

PCE8011

3

nub blastoderm

+

2L

12,605,345

12,606,039

695

CG15488

2,687

nub

-1,178

13

PCE8024

4

ftz stripes 1/5

+

3R

2,693,713

2,694,405

693

ftz

3,667

Antp

131,873

14

PCE8012

5

pdm2 neurogenic

+

2L

12,663,878

12,664,600

723

pdm2

2,875

pdm2

2,875

15

PCE8027

6

sqz neurogenic

+

3R

15,000,096

15,000,905

810

sqz

10,137

CG14282

-1,833

16

PCE8005

7

cluster_at_7A

amb.

X

6,996,209

6,996,756

548

CG32725

-17,671

CG1958

-10,524

17

PCE8016

8

cluster_at_55C

amb.

2R

13,354,407

13,355,109

703

CG14502

957

CG14502

957

18

PCE8020

9

cluster_at_70F

amb.

3L

14,665,967

14,666,676

710

ome

10,334

ome

10,334

19

PCE8006

13

cluster_at_7B

-

X

7,239,486

7,240,124

639

CG11368

46,902

CG32719

13,096

20

PCE8008

15

cluster_at_8F

-

X

9,457,631

9,458,375

745

btd

24,460

Sp1

-33,567

21

PCE8013

17

cluster_at_34E

-

2L

13,989,283

13,990,132

850

rk

-5,879

bgm

-5,767

22

PCE8014

18

cluster_at_36F

-

2L

18,400,758

18,401,458

701

CG31749

36,362

RpS26

19,862

23

PCE8015

19

cluster_at_47A

-

2R

5,664,440

5,665,094

655

psq

45,904

psq

45,904

24

PCE8017

20

cluster_at_56B

-

2R

14,266,629

14,267,261

633

CG7097

24,156

CG7097

24,156

25

PCE8018

21

cluster_at_59B

-

2R

17,995,894

17,996,609

716

CG32835

759

CG32835

759

26

PCE8019

22

cluster_at_67B

-

3L

9,529,913

9,530,579

667

CG32048

10,499

CG32048

10,499

27

PCE8021

23

cluster_at_75C

-

3L

18,339,914

18,340,665

752

grim

-86,621

rpr

6,617

28

PCE8022

24

cluster_at_76C

-

3L

19,594,180

19,594,883

704

CG8786

-1,409

CG8782

4,923

29

PCE8023

25

cluster_at_84A

-

3R

2,595,162

2,595,926

765

Ama

6,847

Dfd

-21,632

30

PCE8025

26

cluster_at_85C

-

3R

4,944,607

4,945,444

838

pum

117,315

pum

117,315

31

PCE8026

27

cluster_at_88F

-

3R

11,424,315

11,424,996

682

CG18516

-45,803

CG5302

-33,626

32

PCE8028

28

cluster_at_95C

-

3R

19,757,908

19,758,531

624

Gdh

950

Gdh

950

33

PCE8003

11

cluster_at_5C.1

-

X

5,658,504

5,659,131

628

CG3726

952

CG3726

952

34

PCE8004

12

cluster_at_5C.2

-

X

5,674,913

5,675,606

694

CG3726

17,361

CG3726

17,361

35

PCE8009

16

cluster_at_12E

-

X

14,146,556

14,147,218

663

CG32600

93,317

CG32600

93,317

36

PCE8002

10

cluster_at_4B

-

X

4,124,119

4,125,459

1,341

CG12688

2,032

CG32773

3,408

37

PCE8007

14

cluster_at_7F

Unknown

X

8,350,658

8,351,315

658

Caf1-180

-5,486

oc

38,281

*IDs in this column are taken from [11]. Genomic locations of the 37 pCRMs identified in our previous genome search. All coordinates are from D. melanogaster Release 3 [68]. pCRMs 1-9 were reported prior to our original search, and we attempted to characterize 10-37 in the current study (we reported PCE8001 in our previous publication). pCRMs10-15 recapitulate endogenous expression patterns of embryonic genes, and 16-18 drive ambiguous (amb.) expression patterns, as described in the text. pCRMs 19-36 drove no detectable expression in the embryo, and pCRM 37 was not tested. Orthologous regions were identified in D. pseudoobscura for all but pCRMs 33-37. The 5' and 3' gene columns correspond to the closest transcription (or annotation) start 5' and 3' of the pCRM. If a pCRM is within an intron, only the intron-containing gene is reported and its name is given in italics. The names of genes with early anterior-posterior patterns are in bold.

We successfully generated multiple independent transgenic fly lines for 27 of the 28 pCRMs. We repeatedly failed to generate transgenes containing CE8007. This sequence contains five copies of an approximately 358 base-pair (bp) degenerate repeat. One additional pCRM (CE8002) also contains tandem repeats. While we were able to generate transgenes for CE8002 and assay its expression, these two tandem repeat-containing pCRMs (CE8007 and CE8002) were excluded from subsequent analyses.

We examined the expression of these constructs by in situ RNA hybridization to the lacZ transcript in embryos at different stages in at least three independent transformant lines. Nine of the 27 transgenes showed mRNA expression during embryogenesis (Figure 1), while the remaining 18 assayed transgenes showed no detectable expression at any stage during embryogenesis.
https://static-content.springer.com/image/art%3A10.1186%2Fgb-2004-5-9-r61/MediaObjects/13059_2004_Article_865_Fig1_HTML.jpg
Figure 1

Expression patterns of active pCRMs. Embryonic whole-mount in situ RNA hybridizations using lacZ probe of transgenes with positive expression in independent lines (see Materials and methods). The first column (wild type) shows the endogenous gene expression; the second column (lacZ) shows transgene expression patterns; the third column shows double-labeled embryos with the endogenous (red) and transgene (blue) expression patterns. To the right of the images are maps of the gene regions centered on each pCRM.

To identify the genes regulated by the nine pCRMs with embryonic expression, we examined the expression patterns of genes containing the pCRM in an intron and genes with promoters within 20 kb of the CRM (see Figure 1). We used the embryonic microrarray and whole-mount in situ expression data available in the Berkeley Gene Expression Database [21], supplemented with additional whole-mount in situ experiments where necessary (data not shown; these new in situ's will be included in the public expression database [33] at its next release).

Six of the active pCRMs drive lacZ expression in patterns that recapitulate portions of the expression of a gene adjacent to or containing the pCRM. Four of these new enhancers act in the blastoderm and two during germ-band elongation.

CE8001 is 5' of the gene for the gap transcription factor giant and recapitulates the posterior domain (65-85% egg length measuring from the anterior end of the embryo) of gt expression in the blastoderm as previously described [11].

CE8011 is 5' of the gene for the POU-homeobox transcription factor nubbin (nub). The CRM recapitulates the endogenous blastoderm expression pattern of nub, first detected as a broad band extending from 50 to 75% egg length. Although nub expression continues in later embryonic stages, CE8011 expression is limited to the blastoderm stage.

CE8010 is 5' of the pair-rule gene odd-skipped (odd) and drives expression of two of its seven stripes: stripe 3 at 55% and stripe 6 at 75% egg length. This CRM also has the ability to drive later, more complex, patterns of expression. During stages 6 and 7, expression is detected in the procephalic ectoderm anlage and in the primordium of the posterior midgut. By stage 13, expression is also detected in the anterior cells of the midgut which will give rise to the proventriulus, the first midgut constriction, the posterior midgut and microtubule primordial as well as cells in the hindgut, all similar to portions of the pattern of wildtype odd protein expression previously described [34].

CE8024 is 3' of the pair-rule gene fushi-tarazu (ftz) and drives expression of two of its stripes: stripe 1 at 35% and stripe 5 at 65% egg length. Using a similar CRM reporter assay, this pattern of expression was also detected by [35].

CE8012 is in the third intron of POU domain protein 2 (pdm2) and appears to completely recapitulate its stage-12 expression pattern, which is limited to a subset of the developing neuroblasts and ganglion mother cells of the developing central nervous system. A similar pattern of expression was previously described for the protein product of pdm2 [36]. It is worth noting that we do not detect expression of CE8012 in the blastoderm stage, whereas the endogenous gene exhibits a blastoderm expression pattern similar to nub.

CE8027 is 3' of the gene for the Zn-finger transcription factor squeeze (sqz) and recapitulates the wild-type expression pattern of sqz RNA in a subset of cells in the neuroectoderm at stage 12. The wild-type sqz expression pattern was previously described [37].

The remaining three active pCRMs cannot be easily associated with a specific gene. CE8005 drives expression in the ventral region of the embryo. It is 3' of a gene encoding a ubiquitously expressed Zn-finger containing protein (CG9650) that is maternally expressed and deposited in the embryo. This strong maternal expression potentially obscures a zygotic expression pattern. Two additional adjacent genes, CG32725 and CG1958, showed no expression in whole-mount in situ hybridization of embryos.

CE8016 drives a seven-stripe expression pattern in the blastoderm. It is in the first intron of CG14502 which shows very low level expression by microarrays in the blastoderm, and has no obvious detectable pattern of expression in whole-mount in situ hybridization of embryos. This pCRM is approximately 2 kb 5' of scribbler (sbb), which is expressed maternally, possibly obscuring an early zygotic expression pattern (a few in situ images show a hint of striping). sbb is also expressed later in development in the ventral nervous system. An additional potential target, Otefin (Ote), is also expressed maternally and relatively ubiquitously through germ-band extension. All other nearby genes displayed in Figure 1 showed no embryonic expression in whole-mount in situ hybridization or by microarray.

CE8020 drives an atypical four-stripe pattern in the blastoderm - two stripes at 7% and 26% that are anterior to the first ftz stripe and two stripes at 39% and 87%. It is in the first intron of ome (CG32145), which is not expressed maternally and has no blastoderm expression, but is expressed late in salivary gland, trachea, hindgut and a subset of the epidermis. All other nearby genes displayed in Figure 1 showed no embryonic expression in whole-mount in situ hybridization or by microarray.

With these results, and the nine previously known enhancers, at least 15 of the 37 highest density clusters of the five transcription factors used in our initial screen have early-embryonic enhancer activity. The remainder of this paper examines 35 of the original 37 clusters, with the two tandem repeat-containing clusters excluded. We divide these 35 into three categories - 15 positives (the nine overlapping previously known enhancers plus the six new enhancers identified here), three ambiguous (the three positives without a clear regulated gene), and 17 negatives (see Table 2). We largely focus on differences between the positives and negatives.
Table 2

Sequence and binding-site conservation in pCRMs between D. melanogaster and D. pseudoobscura

 

pCRM

Name

CRM activity

pCRM length (D. melanogaster)

pCRM length (D. pseudoobscura)

Percent identity

D. melanogaster sites

D. pseudoobscura sites

Conserved sites

Fraction conserved

         

A

A+P

A

A+P

1

PCE7001

runt stripe 3

+

1,089

1,504

71%

27

20

11

20

41%

74%

2

PCE7002

eve stripes 3/7

+

1,278

1,114

61%

28

25

21

25

75%

89%

3

PCE7003

eve stripe 2

+

587

771

67%

14

10

9

10

64%

71%

4

PCE7004

eve stripes 4/6

+

799

1,003

70%

20

18

13

17

65%

85%

5

PCE7005

hairy stripe 7

+

895

869

66%

20

16

12

16

60%

80%

6

PCE7006

hairy stripe 6

+

868

952

62%

23

19

11

19

48%

83%

7

PCE7007

hairy stripes 1,5

+

787

723

56%

16

15

9

13

56%

81%

8

PCE7008

kni upstream

+

1,356

1,654

68%

33

31

24

30

73%

91%

9

PCE7009

hb HZ1.4

+

1,207

1,383

69%

24

23

17

21

71%

88%

10

PCE8001

gt posterior domain

+

944

1,092

64%

23

19

15

18

65%

78%

11

PCE8010

odd stripes 3/6

+

760

825

70%

17

19

12

16

71%

94%

12

PCE8011

nub blastoderm

+

695

894

70%

18

13

10

12

56%

67%

13

PCE8024

ftz stripes 1/5

+

693

744

77%

14

10

10

10

71%

71%

14

PCE8012

pdm2 neurogenic

+

723

723

72%

14

8

4

8

29%

57%

15

PCE8027

sqz neurogenic

+

810

818

69%

16

17

11

14

69%

88%

16

PCE8005

cluster_at_7A

amb.

548

819

54%

13

4

2

2

15%

15%

17

PCE8016

cluster_at_55C

amb.

703

1,617

55%

16

6

3

6

19%

38%

18

PCE8020

cluster_at_70F

amb.

710

538

47%

14

2

2

2

14%

14%

19

PCE8006

cluster_at_7B

-

639

663

69%

15

9

8

8

53%

53%

20

PCE8008

cluster_at_8F

-

745

716

58%

14

2

1

2

7%

14%

21

PCE8013

cluster_at_34E

-

850

919

61%

17

8

6

8

35%

47%

22

PCE8014

cluster_at_36F

-

701

596

53%

15

6

5

6

33%

40%

23

PCE8015

cluster_at_47A

-

655

652

66%

16

3

3

3

19%

19%

24

PCE8017

cluster_at_56B

-

633

331

33%

15

9

4

8

27%

53%

25

PCE8018

cluster_at_59B

-

716

960

59%

16

4

3

4

19%

25%

26

PCE8019

cluster_at_67B

-

667

675

62%

15

7

5

6

33%

40%

27

PCE8021

cluster_at_75C

-

752

640

59%

19

13

10

12

53%

63%

28

PCE8022

cluster_at_76C

-

704

725

67%

15

9

7

9

47%

60%

29

PCE8023

cluster_at_84A

-

765

1,001

55%

16

7

5

7

31%

44%

30

PCE8025

cluster_at_85C

-

838

827

54%

16

6

1

5

6%

31%

31

PCE8026

cluster_at_88F

-

682

1,096

62%

16

6

5

5

31%

31%

32

PCE8028

cluster_at_95C

-

624

723

60%

15

6

4

6

27%

40%

33

PCE8003

cluster_at_5C.1

-

628

None

 

15

     

34

PCE8004

cluster_at_5C.2

-

694

None

 

15

     

35

PCE8009

cluster_at_12E

-

663

None

 

15

     

36

PCE8002

cluster_at_4B

-

1,341

None

 

28

     

37

PCE8007

cluster_at_7F

Unknown

658

None

 

15

     
 

Mean (pCRMs 1-15)

 

899

1,005

67%

20

18

13

17

61%

80%

 

Mean (pCRMs 19-32)

 

712

752

58%

16

7

5

6

30%

40%

Conservation properties are listed for the pCRMs described in Table 1. The number and fraction of conserved sites are shown under two conditions - aligned sites only (A), or aligned + preserved sites (A+P) (see Materials and methods). D. pseudoobscura sequences used to determine these properties are available as supplemental material at [42].

Distinguishing active and inactive clusters

All 15 positives are within 20 kb of the transcription start site (or, where the transcription start site is unknown, the start of the gene annotation) of transcripts expressed in spatiotemporal patterns consistent with regulation by the maternal and gap transcription factors used in our screen (that is, in anterior-posterior patterns in the blastoderm or in the developing neuroblasts of the central nervous system). Only one of the 17 negatives was located within 20 kb of a plausible target (PCE8021 is 7 kb upstream of reaper), so out of 16 pCRMs located within 20 kb of a gene with appropriate expression, 15 (94%) are active enhancers.

The positives are, on average, larger than the negatives (average cluster size of positive = 900 bp, while average cluster size of negatives was 711 bp), a difference that is significant by the Komogorov-Smirnov (KS) test (p = 0.017). The positives have a slightly higher density of binding sites, but this difference was not significant. The binding site composition of the positives and negatives are similar (the positives contain more KR, and fewer BCD binding sites, but again these differences are not highly significant). Although others have reported that some factors have characteristic spacings with respect to themselves and other factors [38], we could not find evidence for such spacing or identify other differences that could distinguish positive pCRMs from negative (Figure 2).
https://static-content.springer.com/image/art%3A10.1186%2Fgb-2004-5-9-r61/MediaObjects/13059_2004_Article_865_Fig2_HTML.jpg
Figure 2

Predicted and aligned binding sites in pCRMs. Predicted binding sites and aligned binding sites (see Materials and methods) in positive, ambiguous and negative pCRMs (the positions of overlapping sites were adjusted slightly so that all sites could be seen).

Use of D. pseudoobscura

We assembled the D. pseudoobscura genome from traces deposited in the NCBI's TraceDB using the Celera assembler [39, 40]. These assemblies were used to examine the conservation of our pCRMs and to assess whether conservation could be used instead of or in addition to binding site clustering as a way to identify CRMs.

We first assessed whether positive pCRMs could be distinguished from their flanking sequences based on degree of conservation. In vertebrate comparative genomics, relatively simple methods (such as VISTA [41]) are commonly used to identify CNSs that are a surprisingly rich source of new cis-regulatory sequences. We evaluated the potential of using such methods with D. melanogaster and D. pseudoobscura in two ways. First, we constructed percent-identity plots for the regions containing all of the 37 pCRMs (Figure 3; similar plots for all pCRMs are available in the online supplement at [42]) with the location of pCRMs and other known regulatory sequences clearly indicated. Although it appears that some CRMs (that is, eve stripe 3/7) would have been successfully identified by such simple comparative methods, positive pCRMs do not collectively appear distinguishable from flanking sequence on the basis of conservation alone. Although positive pCRMs are almost all in highly conserved blocks, there is a surprisingly high amount of non-coding sequence conservation throughout these regions, and most negative pCRMs are also contained in highly conserved blocks. It remains to be seen whether this difference in the conservation landscape of Drosophila non-coding sequences compared to vertebrates reflects a significant difference in the functional organization of non-coding sequences, or simply indicates that there is too little divergence between D. melanogaster and D. pseudoobscura to detect useful differences in the rates of evolution (see Discussion).
https://static-content.springer.com/image/art%3A10.1186%2Fgb-2004-5-9-r61/MediaObjects/13059_2004_Article_865_Fig3_HTML.jpg
Figure 3

Binding-site conservation, but not sequence conservation, correlates with pCRM activity. Three 25-kb regions were chosen to illustrate patterns of sequence conservation and binding-site conservation. (a) even-skipped (eve) contains five previously known segmentation enhancers (labeled eve3/7, eve2, eve4/6, eve1, and eve5); (b) odd-skipped (odd) contains a single functional (positive) pCRM (CE8010); and (c) pipsqueak (psq) contains a non-functional (negative) pCRM (CE8015). Annotated genes are shown in blue, and the direction of transcription is indicated by the arrow. Gray ovals indicate experimentally tested fragments, and shaded gray boxes show the extent of pCRMs as defined by CIS-ANALYST (minimum of 13 sites within a 700 bp window). The green graphs show average percent identity (in 100-bp windows). Below the percent identity plots are shown insertions (gray boxes) and deletions (orange boxes) of 80 or more bp in the D. melanogaster sequence relative to their D. pseudoobscura ortholog. The location of binding sites in D. melanogaster, binding sites in D. pseudoobscura and aligned binding sites along with the average density of sites (700-bp windows) are shown in the bottom three panels for each region. * in (a) indicates a new prediction (PCE8100).

We next assessed whether positive pCRMs can be distinguished from negative pCRMs on the basis of their degree of similarity between D. melanogaster and D. pseudoobscura. For each pCRM-containing region, we identified orthologous contigs from the D. pseudoobscura assembly and aligned them using the alignment program LAGAN [43]. We were able to find orthologous regions for 32 pCRMs (see Table 2). Using the simple measure of percent identity, we find that positive pCRMs are, on average, more highly conserved than negative pCRMs (see Table 2). Although this difference is significant (p = 0.002 by KS test), the distribution of conservation scores for positive and negative pCRMs overlap considerably, and thus conservation alone is not a useful way of distinguishing positive and negative pCRMs (see Figure 4b).
https://static-content.springer.com/image/art%3A10.1186%2Fgb-2004-5-9-r61/MediaObjects/13059_2004_Article_865_Fig4_HTML.jpg
Figure 4

Conservation of clustering distinguishes positive and negative pCRMs. Each panel compares positive, negative and ambiguous pCRMs and random 1,000-bp non-coding regions based on (a) binding site density in D. melanogaster, (b) percent identity, (c) density of aligned sites, and (d) density of aligned plus preserved sites. The top portion of each panel contains a histogram of the values for randomly chosen 1,000-bp regions of the D. melanogaster genome. The blue line plots the cumulative distribution. The colored asterisks show the average values for each class of pCRM. The unshaded panel below the histogram shows the values for each pCRM (each dot represents one pCRM, with positives in blue, negatives in red, ambiguous in green). The shaded panel at the bottom shows the average value for 1,000-bp non-coding sequences within 20 kb of each pCRM.

To get a genome-wide perspective on the degree of conservation in positive pCRMs, we analyzed the conservation of CRM-sized (1 kb) regions in randomly chosen sections of the genome (Figure 4b). Positive pCRMs are, generally, more conserved than average CRM-sized sequences, and some positive pCRMs are among the most highly conserved non-coding sequences in the genome. However, a conservation cut-off necessary to select the majority of positive pCRMs would select roughly one third of the non-coding regions of the genome, and thus is not a practical method for prioritizing sequences for functional analysis.

Conservation of binding sites and conservation of clustering

We expect that most genes will have similar expression patterns in D. melanogaster and D. pseudoobscura, and that most D. melanogaster enhancers should have functional orthologs in D. pseudoobscura. For those enhancers we seek to identify here - namely those where binding site clustering reflects their function - we expect clustering to be found in both D. melanogaster and D. pseudoobscura. Conversely, clusters that simply occur by chance in either genome but do not reflect the function of the sequence (as, we believe, is the case for many of our false-positive predictions) should not be conserved. Thus, looking for conservation of binding-site clustering should provide a valuable way of distinguishing functional and non-functional binding-site clusters in the D. melanogaster genome.

We used the alignments described above to examine the conservation of individual predicted binding sites in all of the pCRMs (Table 2). We refer to a predicted D. melanogaster binding site that overlaps a predicted D. pseudoobscura binding site for the same factor in an alignment as an 'aligned' site. We require overlap and not perfect alignment to compensate for alignment ambiguity; the overwhelming majority (85%) of aligned sites are perfectly aligned. Although there is only a subtle difference in the binding-site density in the positive and negative pCRMs in D. melanogaster (22.7 sites/kb compared to 22.2), the density of aligned binding sites in positive pCRMs (13.8 sites/kb) is nearly twice that in negative pCRMs (6.8 sites/kb). This is a highly significant difference (p < 0.001 by KS test) and aligned site density better discriminates positive and negative pCRMs than sequence conservation (compare Figure 4c and 4b).

Sixty-one percent of the predicted binding sites in positive pCRMs are aligned, while only 30% of the sites in negative pCRMs are aligned. Across the genome, 22.3% of predicted binding sites are aligned meaning that there is a roughly fourfold increase over background in the probability that a binding site in a positive pCRM is conserved in place compared to a binding site in a negative pCRM. Sixty-one percent is almost certainly an underestimate of the fraction of pCRM sites that are functionally conserved. The D. melanogaster-D. pseudoobscura alignments were not always unambiguous (using simulations we have assessed the role of alignment algorithms in identifying conserved transcription factor binding sites, see [44]), and some orthologous binding sites may not have been properly aligned. More important, studies of the evolution of various Drosophila enhancers suggest that the positions of binding sites within an enhancer are somewhat plastic, and the functional conservation of a binding site does not necessarily require positional conservation [25, 26].

To characterize the extent of binding site conservation independent of positional conservation, we computed a second measure of binding-site conservation. We consider an unaligned binding site in D. melanogaster to be 'preserved' if it can be matched to a corresponding site in the D. pseudoobscura pCRM (allowing each D. pseudoobscura site to match only one D. melanogaster site). If we consider both aligned and preserved sites to be conserved, then roughly 80% of the sites in positive pCRMs are conserved compared with 40% in negative pCRMs.

The density of preserved but not aligned sites in positive pCRMs (4.3/kb) is considerably higher than in negative pCRMs (2.2/kb) or random sequences (1.8/kb). Thus, in the D. pseudoobscura orthologs of active D. melanogaster CRMs we observe an increase in binding-site density that cannot be explained by the positional conservation of sites found in D. melanogaster or the random occurrence of sites in the genome. Several of the 15 positive CRMs have high densities of these preserved but unaligned sites, but two in particular, runt stripe 3 and hairy stripe 6, stand out from the rest. These two have almost as many preserved sites as strictly aligned sites.

Aligned plus preserved (conserved) site density (Figure 4d) almost perfectly separates positive from negative pCRMs. Only one of the positive pCRMs (PCE8012) has a conserved site density below 14 sites/kb, while only one of the negative pCRMs (PCE8021) has a conserved site density above 14 sites/kb.

eCIS-ANALYST: a comparative enhancer finder

As the conservation of binding sites and binding-site clusters between D. melanogaster and D. pseudoobscura successfully distinguishes positive and negative predictions made using the D. melanogaster sequence alone, we incorporated comparative sequence data into our enhancer-prediction algorithm CIS-ANALYST [11]. Instead of searching for clusters of predicted binding sites in a single genome, eCIS-ANALYST (the 'e' is for evolutionary) searches for conserved clusters of sites between the two genomes (see Materials and methods). eCIS-ANALYST is available at [45].

Using 17 negative pCRMs and an expanded set of 25 positive pCRMs (which included the 15 positive predictions discussed above and 10 functional enhancers known to respond to the five factors; these 10 additional enhancers were discussed and analyzed in [11] but had binding-site densities below the threshold used there), we compared the ability of CIS-ANALYST and eCIS-ANALYST to identify positive pCRMs and to distinguish positive and negative pCRMs at different binding-site density cutoffs (Figure 5). The incorporation of the conservation criteria greatly improves the algorithm's apparent performance. The expected fraction of false positives is markedly reduced, and it is possible to lower the binding site threshold to recover six of the ten previously missed positive enhancers without increasing the number of expected false-positive predictions.
https://static-content.springer.com/image/art%3A10.1186%2Fgb-2004-5-9-r61/MediaObjects/13059_2004_Article_865_Fig5_HTML.jpg
Figure 5

Inclusion of evolutionary information greatly increases the specificity and selectivity of CRM searches based on binding-site clustering. The effects of integrating comparative data into searches for binding site clusters were assessed by counting the number of (a) true positive, (b) negative and (c) novel CRMs recovered at the different site density cutoffs plotted on the x-axis. The positives used here include the 15 positive pCRMs from Table 2 and 10 additional positive CRMs from the literature (see text), all of which have identifiably orthologous sequence in D. pseudoobscura, while the negatives included only the 14 non-functional pCRMs for which orthologous sequence in D. pseudoobscura could be found. The solid line in each panel shows the results without the use of D. pseudoobscura; the dashed line shows the results with D. pseudoobscura. Searches displayed were performed using the aligned sites constraint (see Materials and methods). Comparable results were obtained for the aligned + preserved sites constraint. The number of false positives is not strictly monotonically decreasing with an increasing binding site cutoff. This stems from the cluster merging behavior of CIS-ANALYST - sometimes a decrease in the minimum number of sites leads CIS-ANALYST to tack on a lower-density cluster that is adjacent to a higher-density one, resulting in a single cluster with more sites but lower site density. This can actually increase the number of conserved sites necessary to reach the conservation threshold (see Materials and methods).

New predictions

As eCIS-ANALYST has markedly better specificity than CIS-ANALYST, we sought to identify BCD, HB, KR, KNI and CAD targets that were missed with the relatively stringent criteria used in our previous analysis. Rather than use a stringent cutoff (15 binding sites per 700 bp) as we did in [11], we performed three separate runs with lower cutoffs (for example, 10 sites per 700 bp in one run) and applied a conservation threshold (see Materials and methods and Additional data file 3) to select 929 conserved binding-site clusters. There were 842 new pCRMs within 20 kb or in an intron of an annotated transcript (Additional data file 7) and 87 more than 20 kb (Additional data file 8). We ranked these new pCRMs by a simple scoring scheme that measures both the density and the total number of sites conserved (we evaluated several different scoring schemes, and selected one that optimally identified regions near genes with blastoderm expression patterns; see Materials and methods). The 75 highest-scoring pCRMs within 20 kb of an annotated transcript are shown in Table 3. Thirteen of the 15 positive pCRMs described above are in the top 75 (ftz stripe 1/5 is number 107 and the pdm2 neurogenic enhancer is number 418) as are five other known enhancers. One of our negative pCRMs, CE8021, is ranked number 12.
Table 3

New pCRMs from genome-wide eCIS-ANALYST (75 highest scoring predictions)

 

CRM

Known element overlap

Arm

pCRM start

pCRM end

pCRM length

5' gene

pCRM relative position

3' gene

pCRM relative position

Conserved sites

Conserved site density

z score

Additional gap/pair-rule gene within 20 kb

pCRM relative position

           

A

A+P

A

A+P

   

1

PCE8050

h stripes 3/4,6,7 [73]

3L

8,622,879

8,626,839

3,961

CG6486

+14646

h

-7829

36

62

9

16

20.1

  

2

PCE8051

kni upstream [74]

3L

20,614,714

20,617,020

2,307

kni

-813

CG13253

+20716

25

31

11

13

13.2

  

3

PCE8052

nub blastoderm

2L

12,604,311

12,606,913

2,603

CG15488

+1653

nub

-304

20

33

8

13

11.6

  

4

PCE8053

eve stripes 3/7 [75]

2R

5,035,493

5,037,290

1,798

CG12134

+3712

eve

-2433

21

24

12

13

11.5

Adam

+5901

5

PCE8054

hairy stripes 1,5 [73]

3L

8,628,846

8,631,011

2,166

CG6486

+20613

h

-3657

17

29

8

13

10.5

  

6

PCE8055

runt stripe 3 [76]

X

20,356,848

20,360,054

3,207

CG1338

-9192

run

-6801

17

34

5

11

10.3

  

7

PCE8056

 

X

20,323,964

20,326,397

2,434

CG11692

-12536

Cyp6v1

-4186

16

28

7

12

9.6

  

8

PCE8057

hb HZ1.4 [77]

3R

4,526,225

4,527,991

1,767

hb

-2670

CG8112

+1273

17

21

10

12

9.5

  

9

PCE8059

eve stripes 4/6 [78]

2R

5,044,597

5,046,030

1,434

eve

+4874

TER94

-3763

15

18

10

13

9.0

Adam

+15005

10

PCE8060

gt posterior [11]

X

2,186,709

2,189,069

2,361

gt

-974

tko

+11679

18

21

8

9

8.9

  

11

PCE8061

 

X

3,169,806

3,172,348

2,543

CG12535

-17954

CG14269

+21857

13

29

5

11

8.8

  

12

PCE8063

CE8021

3L

18,339,914

18,341,941

2,028

grim

-86621

rpr

+5341

16

20

8

10

8.5

  

13

PCE8064

 

3R

6,255,663

6,256,945

1,283

CG6345

-13879

Cyp12e1

-3594

13

17

10

13

8.4

  

14

PCE8065

 

3R

4,026,032

4,027,816

1,785

grn

-18853

CG7800

-15898

15

19

8

11

8.4

  

15

PCE8066

 

X

20,348,460

20,352,624

4,165

CG1338

-804

run

-14231

16

28

4

7

8.3

  

16

PCE8067

ftz upstream [23]

3R

2,682,314

2,684,591

2,278

Scr

-7972

ftz

-5455

15

22

7

10

8.3

  

17

PCE8068

 

X

18,701,007

18,702,700

1,694

CG32541

+39691

CG32541

+39691

12

22

7

13

8.2

  

18

PCE8069

 

2R

17,274,311

17,276,017

1,707

CG3380

-2521

dve

-11496

14

19

8

11

8.2

  

19

PCE8070

 

2L

7,616,050

7,618,366

2,317

CG6739

+15430

CG13792

+19862

14

23

6

10

8.1

  

20

PCE8071

sqz neurogenic

3R

14,999,463

15,001,552

2,090

sqz

+9504

CG14282

-1186

12

24

6

11

8.0

nos

+16485

21

PCE8072

 

X

5,674,422

5,676,386

1,965

CG3726

+16870

CG12728

-6597

11

24

6

12

7.8

  

22

PCE8073

 

2R

14,903,099

14,903,925

827

Toll-7

+12482

Obp56i

-27903

11

11

13

13

7.8

  

23

PCE8074

 

3R

23,192,304

23,192,750

447

CG13980

+8073

side

+40862

7

8

16

18

7.7

  

24

PCE8075

 

3R

10,762,920

10,764,750

1,831

CG3837

+18501

CG14861

-75759

13

19

7

10

7.6

  

25

PCE8076

eve stripe 2 [75]

2R

5,038,454

5,039,041

588

CG12134

+6673

eve

-682

8

10

14

17

7.6

Adam

+8862

26

PCE8077

 

2L

13,541,662

13,542,651

990

kuz

+9371

kuz

+9371

11

13

11

13

7.6

  

27

PCE8078

 

2L

14,424,056

14,425,158

1,103

BG:DS06238.4

-16773

BG:DS08340.1

+7810

12

13

11

12

7.6

  

28

PCE8080

odd stripes 3/6

2L

3,601,045

3,602,748

1,704

odd

-1728

Dot

-9112

12

19

7

11

7.5

  

29

PCE8081

 

3L

17,412,324

17,413,414

1,091

CG18265

+24035

CG7603

-1413

11

14

10

13

7.5

  

30

PCE8083

 

3L

14,121,556

14,123,127

1,572

Sox21b

-41352

D

+4373

12

17

8

11

7.3

  

31

PCE8084

 

2L

4,098,489

4,099,006

518

ed

+74542

ed

+74542

7

9

14

17

7.3

  

32

PCE8085

 

2R

12,253,766

12,255,302

1,537

CG10953

-23540

CG10950

-3625

13

15

8

10

7.2

  

33

PCE8086

 

3L

20,612,647

20,614,073

1,427

kni

+1254

CG13253

+23663

11

17

8

12

7.2

  

34

PCE8087

 

2R

3,391,037

3,391,561

525

CG30358

+10444

CG14755

-16724

7

9

13

17

7.2

  

35

PCE8088

 

3L

16,418,107

16,418,469

363

CG33158

+49435

argos

+14111

6

6

17

17

7.2

  

36

PCE8089

 

3R

12,368,159

12,368,687

529

CG11769

+28970

CG31448

-670

7

9

13

17

7.2

CG14889

-13735

37

PCE8091

 

3L

11,213,064

11,213,664

601

scylla

+3224

CG32083

+24695

8

9

13

15

7.1

  

38

PCE8092

 

2L

1,233,357

1,235,228

1,872

CG5156

+3715

CG5397

-6475

9

23

5

12

7.1

  

39

PCE8093

 

3L

15,688,222

15,691,204

2,983

comm

-10920

CG13445

-67172

13

22

4

7

7.0

  

40

PCE8094

 

2R

10,492,861

10,493,546

686

CG30472

-5321

CG12959

-26488

9

9

13

13

7.0

  

41

PCE8095

 

3R

23,894,562

23,895,459

898

CG12870

+31901

CG12870

+31901

10

11

11

12

7.0

  

42

PCE8096

 

3L

6,762,543

6,765,157

2,615

vvl

+12855

Prat2

+108336

13

20

5

8

6.9

  

43

PCE8097

 

3R

10,238,130

10,238,652

523

CG14846

-1983

CG14847

+4557

7

8

13

15

6.8

  

44

PCE8099

 

2L

18,305,051

18,306,251

1,201

Fas3

+6868

Fas3

+6868

10

14

8

12

6.7

  

45

PCE8100

eve early APR [79]

2R

5,042,174

5,042,884

711

eve

+2451

TER94

-6909

8

10

11

14

6.7

Adam

+12582

46

PCE8102

tll posterior [80]

3R

26,663,942

26,665,204

1,263

CG15544

+21005

tll

-2251

11

13

9

10

6.6

  

47

PCE8104

ems neurogenic [81]

3R

9,723,602

9,724,936

1,335

E5

-23682

ems

-2663

12

12

9

9

6.6

  

48

PCE8105

 

3R

17,817,909

17,818,791

883

Eip93F

+25598

Eip93F

+25598

9

11

10

12

6.6

  

49

PCE8106

 

3L

10,499,018

10,501,551

2,534

CG32062

+25485

CG32062

+25485

11

21

4

8

6.6

  

50

PCE8107

 

3L

4,612,891

4,614,005

1,115

CG13716

-161

CG13715

+1681

11

11

10

10

6.6

  

51

PCE8108

 

2L

14,403,771

14,404,937

1,167

CG15284

-4301

BG:DS06238.4

+2346

10

13

9

11

6.5

  

52

PCE8109

 

3R

7,941,601

7,942,426

826

CG31361

+17775

CG4702

+11512

9

10

11

12

6.5

  

53

PCE8110

 

2L

8,804,166

8,805,336

1,171

CG9468

-30684

SoxN

-12519

10

13

9

11

6.5

  

54

PCE8111

 

3L

8,612,337

8,613,016

680

CG6486

+4104

h

-21652

8

9

12

13

6.5

  

55

PCE8112

 

3L

4,377,989

4,379,208

1,220

CG7447

+13842

Syx17

-3984

11

12

9

10

6.5

  

56

PCE8113

 

2L

14,113,291

14,113,893

603

CG15292

-3974

CG31768

-6693

7

9

12

15

6.5

  

57

PCE8114

 

3L

3,997,600

3,998,923

1,324

CG14985

+13500

fd64A

-799

11

13

8

10

6.5

  

58

PCE8115

eve stripe 1 [79]

2R

5,046,559

5,047,297

739

eve

+6836

TER94

-2496

8

10

11

14

6.5

Adam

+16967

59

PCE8116

 

2R

16,921,501

16,922,240

740

CG13493

-11091

PpN58A

+4194

8

10

11

14

6.5

  

60

PCE8118

 

3R

14,822,848

14,823,484

637

gukh

+13085

gukh

+13085

8

8

13

13

6.4

  

61

PCE8119

 

3R

12,671,525

12,672,987

1,463

abd-A

-15737

CG10349

-32477

11

14

8

10

6.4

  

62

PCE8120

 

3L

10,492,688

10,495,539

2,852

CG32062

+19155

CG32062

+19155

10

23

4

8

6.4

  

63

PCE8121

 

2L

16,841,696

16,842,392

697

CG6012

-2193

CG31781

-5178

8

9

11

13

6.4

  

64

PCE8122

 

3L

6,885,832

6,887,436

1,605

Prat2

-11445

CG14820

-5022

11

15

7

9

6.4

  

65

PCE8123

 

2L

15,162,778

15,164,524

1,747

BG:DS03192.2

-6373

BG:DS07295.1

+59479

11

16

6

9

6.4

  

66

PCE8124

 

2R

6,888,483

6,889,700

1,218

CG12443

+13963

CG13192

-428

10

13

8

11

6.4

  

67

PCE8125

 

2L

20,466,022

20,467,708

1,687

CG2493

-32831

CG15476

+4184

10

17

6

10

6.4

  

68

PCE8126

 

3L

2,779,198

2,779,658

461

CG2083

+1101

CG2083

+1101

6

7

13

15

6.3

  

69

PCE8127

 

X

4,630,473

4,632,106

1,634

CG12681

+14179

CG15470

-3196

9

18

6

11

6.3

  

70

PCE8128

 

3R

27,713,381

27,715,087

1,707

heph

+35171

heph

+35171

10

17

6

10

6.3

  

71

PCE8130

 

3R

12,383,752

12,385,269

1,518

CG14889

+1858

CG14889

+1858

11

14

7

9

6.3

  

72

PCE8131

 

3R

21,329,716

21,331,058

1,343

CG5111

+8355

msi

-2351

8

17

6

13

6.3

  

73

PCE8132

 

3R

16,242,660

16,243,128

469

CG10881

+8657

CG17208

+20535

6

7

13

15

6.3

  

74

PCE8133

 

3R

24,120,296

24,122,240

1,945

CG12516

-668

larp

+19112

12

15

6

8

6.2

  

75

PCE8134

 

3L

8,733,754

8,734,394

641

CG32030

+8601

CG32030

+8601

7

9

11

14

6.2

  

Seventy-five top pCRMs, ranked by a z-score based on the number and density of conserved binding sites (see text for details). Site density columns list the number of conserved sites per kilobase (relative to the D. melanogaster sequence). The number and density of conserved sites are shown under two conditions - aligned sites only (A), or aligned + preserved sites (A+P) (see Materials and methods). The 5' and 3' gene columns correspond to the closest transcription (or annotation) start 5' and 3' of the pCRM. If a pCRM is within an intron, only the intron-containing gene is reported and its name is italicized. The names of genes with early anterior-posterior patterns are in bold. Early anterior-posterior genes that start within 20 kb of the pCRM (but are not the immediate annotation in the 5' or 3' direction) are also listed. Named enhancers without a reference are from this study.

To focus our search for new enhancers on genes likely to be regulated by BCD, HB, KR, KNI and/or CAD, we searched FlyBase [46] and a database of Drosophila embryonic expression patterns [21] and identified 278 genes with anterior-posterior patterns in the blastoderm (AP genes; Figure 6 and see also Additional data files 2 and 9). Thirty-one of the 75 highest-scoring new predictions are adjacent to or within 20 kb of one or more of these genes, including 11 pCRMs that do not overlap previously described enhancers. The 75 highest-scoring predictions within 20 kb of an AP gene but not in Table 3, are shown in Table 4. In Tables 3 and 4 together, there are 106 high-scoring conserved binding-site clusters near AP genes, 90 of which do not overlap known enhancers.
https://static-content.springer.com/image/art%3A10.1186%2Fgb-2004-5-9-r61/MediaObjects/13059_2004_Article_865_Fig6_HTML.jpg
Figure 6

Expression patterns of genes adjacent to high-scoring pCRMs. Wild-type embryonic expression patterns of 36 genes adjacent to 53 pCRMs identified by eCIS-ANALYST (see Tables 3 and 4). The images were obtained from the BDGP Embryonic Expression Pattern Database [33], and include all pCRMs from Tables 3 and 4 for which an adjacent gene had an early segmentation pattern.

Table 4

Additional new pCRMs within 20 kb of genes with anterior-posterior patterns

 

CRM

Known element overlap

Arm

pCRM start

pCRM end

pCRM length

5' gene

pCRM relative position

3' gene

pCRM relative position

Conserved sites

Conserved site density

z score

Additional Gap/pair-rule gene within 20 kb

pCRM relative position

           

A

A+P

A

A+P

   

1

PCE8137

 

3R

12,053,627

12,055,472

1,846

tara

+2239

tara

+2239

10

17

5

9

6.1

  

2

PCE8139

 

2R

6,573,169

6,574,383

1,215

inv

+32752

CG30034

+12378

10

12

8

10

6.1

en

+19407

3

PCE8140

 

2R

15,167,055

15,168,270

1,216

CG16898

-98356

18w

-6952

10

12

8

10

6.1

  

4

PCE8144

 

3L

3,503,831

3,504,156

326

Eip63E

+7518

Eip63E

+7518

4

6

12

18

6.1

ImpE2

-10525

5

PCE8145

 

3R

4,536,237

4,536,936

700

CG8112

+1795

CG8112

+1795

8

8

11

11

6.0

hb

-12682

6

PCE8150

 

3R

6,379,567

6,380,474

908

hth

+50936

hth

+50936

8

11

9

12

6.0

  

7

PCE8165

 

X

8,390,109

8,392,075

1,967

oc

-513

CG12772

-23984

10

16

5

8

5.8

  

8

PCE8166

 

3R

12,570,467

12,571,123

657

Ubx

-10101

CG31275

+5951

7

8

11

12

5.7

  

9

PCE8167

Ubx S1 [82]

3R

12,589,099

12,589,755

657

CG31275 (Ubx adjacent)

-11970

Glut3

-24295

7

8

11

12

5.7

  

10

PCE8169

ftz stripes 1/5 [51]

3R

2,693,336

2,694,915

1,580

ftz

+3290

Antp

+63624

11

12

7

8

5.7

  

11

PCE8170

 

3R

2,670,658

2,672,242

1,585

Scr

+2100

Scr

+2100

9

15

6

9

5.7

ftz

-19388

12

PCE8177

 

2R

5,634,520

5,635,604

1,085

psq

+4661

psq

+4661

8

12

7

11

5.7

  

13

PCE8183

 

2L

7,305,525

7,305,940

416

wg

+4205

wg

+4205

5

6

12

14

5.6

  

14

PCE8187

 

2L

8,286,022

8,287,399

1,378

Btk29A

+5904

Btk29A

+5904

9

13

7

9

5.6

  

15

PCE8190

 

3L

6,589,453

6,590,721

1,269

Glu-RI

+5891

Glu-RI

+5891

9

12

7

9

5.6

  

16

PCE8193

Kr CD2 [83]

2R

20,268,656

20,269,940

1,285

CG9380

-36249

Kr

-244

7

15

5

12

5.5

  

17

PCE8195

 

3L

5,126,445

5,126,805

361

CG32423

+17297

CG32423

+17297

4

6

11

17

5.5

  

18

PCE8198

 

2L

3,767,311

3,769,396

2,086

bowl

+2110

bowl

+2110

9

17

4

8

5.5

  

19

PCE8210

 

3L

7,925,371

7,926,049

679

exex

+17651

RNaseX25

-4074

6

9

9

13

5.4

  

20

PCE8214

 

2L

12,601,146

12,602,225

1,080

ref2

-895

CG15488

-433

8

11

7

10

5.4

nub

-6071

21

PCE8218

 

2L

10,545,226

10,547,197

1,972

CG31721

+7937

CG31721

+7937

10

14

5

7

5.3

  

22

PCE8226

 

2L

12,541,433

12,542,145

713

bun

-11992

CG15489

-40512

6

9

8

13

5.2

  

23

PCE8235

 

X

2,190,216

2,191,697

1,482

gt

-4481

tko

+9051

9

12

6

8

5.2

  

24

PCE8237

 

2L

12,670,755

12,671,417

663

pdm2

+3280

pdm2

+3280

6

8

9

12

5.2

  

25

PCE8258

 

3L

15,491,385

15,492,925

1,541

CrebA

+7093

CrebA

+7093

7

15

5

10

5.1

  

26

PCE8270

 

3L

16,421,730

16,422,846

1,117

argos

+9734

argos

+9734

8

10

7

9

5.0

  

27

PCE8275

 

3L

18,329,419

18,330,261

843

grim

-76126

rpr

+17021

6

10

7

12

5.0

  

28

PCE8277

 

3R

6,448,750

6,449,993

1,244

hth

+8759

hth

+8759

6

14

5

11

5.0

  

29

PCE8297

 

2R

20,280,374

20,281,018

645

Kr

+10190

CG30429

-9080

6

7

9

11

4.9

  

30

PCE8306

 

3L

12,278,550

12,279,346

797

CG4328

-28041

CG32105

-7436

6

9

8

11

4.9

  

31

PCE8307

 

3L

5,580,997

5,581,649

653

CG12756

-13449

CG5249

-8641

6

7

9

11

4.9

  

32

PCE8309

 

2L

3,825,809

3,827,419

1,611

slp1

+7561

slp2

-1991

8

13

5

8

4.9

  

33

PCE8314

 

2L

3,842,537

3,843,621

1,085

slp2

+13127

CG3964

-11628

6

12

6

11

4.8

  

34

PCE8328

 

2L

16,418,533

16,419,580

1,048

BG:DS02780.1

+8016

Idgf1

-3783

7

10

7

10

4.8

  

35

PCE8331

 

3L

5,582,709

5,583,340

632

CG12756

-15161

CG5249

-6950

5

8

8

13

4.8

  

36

PCE8332

 

3R

2,725,376

2,726,195

820

Antp

+32344

Antp

+32344

6

9

7

11

4.8

  

37

PCE8338

 

3R

3,987,824

3,989,532

1,709

grn

+17647

grn

+17647

8

13

5

8

4.7

  

38

PCE8348

 

3L

18,966,181

18,967,380

1,200

nkd

+26830

nkd

+26830

7

11

6

9

4.7

  

39

PCE8355

 

3R

6,421,647

6,422,583

937

hth

+8827

hth

+8827

6

10

6

11

4.7

  

40

PCE8356

 

3L

22,244,275

22,244,894

620

Ten-m

+80890

CG32450

-2161

6

6

10

10

4.7

  

41

PCE8358

 

3R

26,740,914

26,742,495

1,582

Ptx1

+2496

Ptx1

+2496

8

12

5

8

4.7

  

42

PCE8361

Ubx BRE [84]

3R

12,526,665

12,527,949

1,285

Ubx

+32417

Ubx

+32417

6

13

5

10

4.6

  

43

PCE8367

 

2R

4,771,288

4,771,881

594

CG10459

+3018

dap

-1074

5

7

8

12

4.6

  

44

PCE8369

 

3L

14,540,753

14,541,382

630

HGTX

+7066

HGTX

+7066

6

6

10

10

4.6

  

45

PCE8370

 

3L

2,395,158

2,396,393

1,236

CG13800

+12412

CG32306

-13538

5

14

4

11

4.6

  

46

PCE8391

 

3L

5,254,002

5,254,895

894

CG32423

-16750

lama

+55892

6

9

7

10

4.5

  

47

PCE8394

Kr 730 [83]

2R

20,266,323

20,267,047

725

CG9380

-33916

Kr

-3137

6

7

8

10

4.5

  

48

PCE8398

 

3R

2,770,846

2,771,901

1,056

Antp

+12307

Antp

+12307

7

9

7

9

4.5

  

49

PCE8401

 

2L

12,660,502

12,661,614

1,113

CG15485

-2463

pdm2

+5861

6

11

5

10

4.5

  

50

PCE8408

 

X

8,379,690

8,381,014

1,325

oc

+8582

oc

+8582

5

14

4

11

4.4

  

51

PCE8415

 

3R

13,867,601

13,868,164

564

CG7794

+18158

htl

+6934

5

6

9

11

4.4

  

52

PCE8417

 

2L

587,804

588,638

835

Gsc

+7714

Gsc

+7714

6

8

7

10

4.4

  

53

PCE8418

 

3R

18,950,000

18,950,634

635

CG31457

-5638

hh

+7739

5

7

8

11

4.4

cenB1A

12397

54

PCE8425

 

2R

18,693,096

18,694,318

1,223

retn

+16917

CG5411

-6825

7

10

6

8

4.4

  

55

PCE8439

 

X

4,770,587

4,771,859

1,273

CG12680

+32240

ovo

-17051

7

10

5

8

4.3

  

56

PCE8444

 

3L

18,330,763

18,332,045

1,283

grim

-77470

rpr

+15237

7

10

5

8

4.3

  

57

PCE8450

 

3L

5,141,131

5,141,793

663

CG32423

+2971

CG10677

-438

5

7

8

11

4.3

  

58

PCE8458

 

3L

19,101,833

19,102,666

834

fz2

+6194

fz2

+6194

5

9

6

11

4.2

  

59

PCE8464

 

3L

17,314,105

17,314,815

711

tap

+5577

Cad74A

+13577

6

6

8

8

4.2

  

60

PCE8483

 

2L

8,265,854

8,267,283

1,430

Btk29A

+2646

Btk29A

+2646

4

15

3

10

4.1

  

61

PCE8493

 

3R

6,403,852

6,405,604

1,753

hth

+25806

hth

+25806

7

12

4

7

4.1

  

62

PCE8494

 

3R

7,931,641

7,932,680

1,040

CG31361

+7815

CG31361

+7815

6

9

6

9

4.1

  

63

PCE8495

 

2L

5,214,677

5,215,845

1,169

CG6514

+3847

tkv

+14084

6

10

5

9

4.1

  

64

PCE8501

 

2L

5,247,719

5,248,767

1,049

tkv

+10898

Cyp4ac1

-7804

6

9

6

9

4.1

  

65

PCE8511

 

3R

6,469,170

6,470,599

1,430

hth

-4766

CG6465

+32311

7

10

5

7

4.0

  

66

PCE8512

pdm2 neurogenic

2L

12,663,453

12,664,721

1,269

pdm2

+2754

pdm2

+2754

5

12

4

9

4.0

  

67

PCE8513

 

3L

14,550,945

14,551,746

802

HGTX

-2497

Cyp314a1

-16963

5

8

6

10

4.0

  

68

PCE8515

 

2L

16,390,610

16,392,235

1,626

BG:DS02780.1

+34314

BG:DS02780.1

+34314

7

11

4

7

4.0

  

69

PCE8519

 

3L

8,975,309

8,975,873

565

Doc2

+2077

Doc2

+2077

5

5

9

9

4.0

Doc3

11402

70

PCE8520

 

2L

12,080,772

12,081,448

677

prd

-5445

CG5325

-1193

4

8

6

12

4.0

  

71

PCE8521

 

2L

7,252,370

7,253,008

639

CG31909

+2569

Wnt4

+16391

5

6

8

9

4.0

Ndae1

-19639

72

PCE8528

 

X

14,366,706

14,367,311

606

NetA

+17535

NetA

+17535

4

7

7

12

4.0

  

73

PCE8531

 

3R

6,363,866

6,364,968

1,103

CG31394

-8970

hth

+66442

6

9

5

8

4.0

  

74

PCE8533

 

3R

24,402,963

24,403,946

984

fkh

-2792

Noa36

+10421

6

8

6

8

3.9

  

75

PCE8536

 

3R

12,764,472

12,765,970

1,499

Abd-B

+4036

Abd-B

+4036

7

10

5

7

3.9

  

Seventy-five top pCRMs within 20 kb of a gene with early anterior-posterior expression, excluding those already listed in Table 3, are ranked by a z-score based on the number and density of conserved binding sites (see text for details). Site density columns list the number of conserved sites per kilobase (relative to the D. melanogaster sequence). The number and density of conserved sites are shown under two conditions - aligned sites only (A), or aligned + preserved sites (A+P) (see Materials and methods). The 5' and 3' gene columns correspond to the closest transcription (or annotation) start 5' and 3' of the pCRM. If a pCRM is within an intron, only the intron-containing gene is reported and its name is italicized. The names of genes with early anterior-posterior patterns are in bold. Early anterior-posterior genes that start within 20 kb of the pCRM (but are not the immediate annotation in the 5' or 3' direction) are also listed. Named enhancers without a reference are from this study.

Discussion

We performed a large and comprehensive evaluation of the efficacy of computational methods for the identification of functional cis-regulatory modules in Drosophila. Analysis of the in vivo activity of 36 high-density clusters of predicted BCD, HB, KR, KNI and CAD binding sites identified in our previous study [11] offers compelling support for the use of transcription factor binding-site clustering as a method to identify regulatory sequences, as at least 15 of these sequences function as early developmental enhancers in vivo. An evolutionary analysis of these sequences - based on comparisons of the D. melanogaster and D. pseudoobscura genomes - shows that sequence conservation alone can not reliably discriminate cluster-containing regions that function in vivo from those that do not. However, a new method that combines binding-site clustering and comparative sequence analysis to search for binding-site clusters that are present in multiple species does reliably discriminate active and inactive clusters. Using this method, we make several hundred predictions of new CRMs, a large number of which are located near likely target genes.

Binding-site clustering

The success of relatively simple binding-site clustering methods here and in other work is remarkable given the crudeness of these methods. As our negative predictions demonstrate, the mere presence of a cluster of binding sites is not sufficient to make an active embryonically expressed CRM. Although these 17 sequences have binding-site densities and compositions indistinguishable from their functional cousins, they do not function as enhancers in a simple transgene assay.

It is possible that some of these negative pCRMs may be functional enhancers that respond to the factors used in our screen, perhaps requiring a different promoter or other flanking sequences not used in the transgene. While further experiments could address this possibility, we felt these were a low priority, as few of the D. pseudoobscura orthologs of these negative pCRMs have binding-site clusters, and few are near genes with appropriate expression patterns. Thus it is unlikely that many function in their endogenous locations in vivo.

Both the general activity and, more important, the specific regulatory output of a CRM are a complex, and still poorly understood, function of the specific architecture of its sites. The emerging picture of the ordered multiprotein complexes that mediate enhancer activity suggests constraints on enhancer composition and architecture [1, 2, 47] whose elucidation will form a critical part of the future dissection of the function of cis-regulatory sequences.

It is intriguing that three of the clusters we tested direct expression patterns that bear no obvious relationship to the expression of a neighboring gene despite our extensive efforts to identify such genes. We cannot yet exclude the possibility that these pCRMs have an in vivo function related to their observed expression patterns. However, the poor conservation of these elements in D. pseudoobscura suggest that they do not have a regulatory function, and raises the possibility that some 'random' clusters of binding sites (that occur by chance or perhaps through selection on some functionally unrelated sequence feature) have the necessary characteristics to be active enhancers in the proper genomic environment (that is, near a promoter and not silenced by trans-acting chromatin mechanisms). That any such sequences exist suggests that the compositional and architectural constraints on binding sites in enhancers may be fairly weak.

Whatever the nature of these constraints, it is clear that binding-site density is not the sole defining characteristic of functional enhancers. However, it is a surprisingly effective distinguishing one, and the usefulness of this and related methods [48] suggests that the broader application of such methods to different collections of transcription factors will be extremely valuable in annotating the regulatory content of animal genomes.

New enhancers

We identified double-stripe enhancers for ftz and odd. ftz and odd are generally classified as 'secondary' pair-rule genes whose expression is governed by other pair-rule genes rather than by the maternal and gap transcription factors that govern the so-called 'primary' pair-rule genes (eve, h and runt) ([49]; also reviewed in [50]). However, the ftz and odd enhancers described here were identified on the basis of binding sites for maternal and gap transcription factors, and function like the enhancers of primary pair-rule genes in directing expression in specific stripes.

It has been suggested that the ftz enhancer is an evolutionary relic of the homeotic role played by ftz in primitive insects [51], a view supported by the apparently normal expression and activity of ftz when this element is missing. However, given our observation that non-functional binding sites clusters are not conserved, even over the relatively short evolutionary distance separating D. melanogaster and D. pseudoobscura, it seems unlikely that this element is purely vestigial. In fact, Yu and Pick [52] examined the expression pattern of the endogenous ftz gene and show that stripes 1 and 5 appear before other ftz stripes and they postulate the existence of stripe-specific regulatory elements that may exist outside of the characterized zebra and upstream elements such as the one identified and characterized in this study. The conservation of binding sites in both the ftz and odd enhancers suggest that they play an important role in development, and further call into question the distinction between primary and secondary pair-rule genes.

Two of the new enhancers (CE8011 and CE8012) are adjacent to and apparently regulate two linked genes with very similar patterns of embryonic expression. Both nub (also known as pdm1) and pdm2 are expressed in the anterior and posterior midgut primordium and in neuroblasts. CE8011, found immediately upstream of nub, regulates its early expression, and not its later neuroblast expression. In contrast, CE8012, found in an intron of pdm2 regulates its expression only in neuroblasts and not earlier. While we did not detect a neuroblast enhancer for nub or a blastoderm enhancer for pdm2 in our single-species binding-site cluster search, a number of interesting pdm2 regions were discovered in our eCIS-ANALYST search (two are listed in Table 4).

Regulatory models and improving the accuracy of CRM prediction

The accuracy of our enhancer predictions would almost certainly be improved if we restricted our search space to genomic regions adjacent to genes known to be regulated by particular transcription factors. Drosophila enhancers have been known to work at distances of up to 100 kb, but most are within 10 kb of their target gene. All of our true-positive predictions were within 10 kb of the known or predicted transcription start site of a gene with a pattern that was known, or plausibly could have been, regulated by the five regulators used in our screen (anterior-posterior patterns in the blastoderm; expression in neuroblasts). In contrast, only one of the negative predictions was this close to such a gene - an additional four were within 50 kb. As the comprehensive atlas of embryonic expression patterns is completed [21, 53] it will be possible to restrict searches for CRMs to regions of the genome near genes with expression patterns that could arise from the regulators being considered, or to prioritize the results of whole-genome screens on the basis of whether they are near plausible targets.

Comprehensive methods for inferring regulatory interactions where they are not already known will be critical for the widespread application of binding-site clustering methods. In addition to allowing less stringent focused screens, they will also help overcome the combinatorial challenge raised by the existence of up to 700 sequence-specific transcription factors in Drosophila. Even assuming the availability of binding data for all of these factors, it will not be possible to search for targets of all combinations of these factors - there are too many possibilities. This is not just a practical problem - it is a fundamental statistical problem. While the false-positive rate for a single combination of factors is low, if we tried even all pairs of factors, it is likely that every region of the genome would have a high binding-site density for some collection of factors. Sequence data from other Drosophila species may allow us to determine which of these collections are conserved and therefore likely to be functional, but it is unlikely that all aspects of regulation can be inferred from comparative analyses and therefore it is essential that we continue to dissect the regulatory network by traditional means.

A greater current limitation in the widespread application of binding-site clustering methods is the absence of high-quality binding data for most Drosophila transcription factors. The initial success of methods that use in vitro binding data to predict regulatory targets has prompted the characterization of binding specificities for many additional factors. However, the heterogeneity of approaches used makes it difficult to combine these data in an optimal manner. In addition, most of the available transcription factor binding data consists of a few to several dozen high-affinity sites. While these data are very useful, they do not fully represent the binding capacity of a factor and thus do not permit the identification of intermediate or low-affinity sites which are known to be important in some regulatory systems [54]. We have begun to apply high-throughput methods [55] to characterize a broad spectrum of target sites for all of the transcription factors involved in early embryogenesis. The results will ultimately allow us to estimate the binding affinity of each factor for any target sequence.

Comparative genomics in CRM predictions

The extent of non-coding sequence conservation between D. melanogaster and D. pseudoobscura was surprising. A major motivation for the National Human Genome Research Institute (NHGRI) support of the D. pseudoobscura genome sequencing was the identification of conserved regions that would guide the annotation of functional sequences in D. melanogaster. D. pseudoobscura was chosen as the second member of this genus to be sequenced in part because it was felt that it had separated from D. melanogaster sufficiently long ago that non-functional sequences would exhibit substantial divergence. However, despite an evolutionary separation that is greater than human and mouse (an average synonymous substitution rate of 1.8-2.6 substitutions/site [29] compared to 0.6 substitutions/site [30]), and despite some variation in conservation in non-coding sequences, we were not able to use standard measures of sequence conservation to differentiate active pCRMs from their flanking sequence or from inactive pCRMs, reinforcing other recent observations [32].

One reason for the limited efficacy of these methods is that they do not recognize the specific patterns of conservation characteristic of different classes of functional sequences. For example, coding sequences can be easily recognized from the characteristic triplet pattern in evolutionary rates where the third (and often synonymous) position of codons tends to evolve at a greater rate than the first two positions [56, 57]. Similarly, RNAs that form conserved secondary structures can be recognized by patterns of co-substitution ([58] and references cited within). The early developmental enhancers we are studying here are made up of large collections of transcription factor-binding sites, and it is expected that both individual functional binding sites and the overall composition of functional CRMs will be conserved [25, 26]. Conservation of binding-site clustering is a specific evolutionary signature of this class of functional regulatory sequences, and, like the evolutionary signatures of protein-coding and RNA genes, can be used to specifically identify these sequences from comparative sequence data.

Contrast PCE8010 (the odd stripe enhancer) and PCE8015 (Figure 3). Both have the same overall amount of sequence conservation, indicating that they are under some functional constraint. However, 80% of the predicted binding sites in PCE8001 are conserved, compared to 20% for PCE8015. The conservation of binding sites (both number and location) in PCE8001 makes it highly unlikely that the cluster was found by chance in D. melanogaster, and suggests (correctly) that this sequence is actively responding to the presence of these binding sites. The poor conservation of binding sites in PCE8015 (no greater than is found in random regions of genome) suggests either that the BCD, HB, KR, KNI and CAD sites in this region are not functional or that the region is undergoing rapid functional diversification. Of course the absence of binding site conservation does not suggest that the sequence is non-functional, merely that these sequences are unlikely to have the particular function we are studying here.

From the data shown in Figure 4, we expect the incorporation of binding-site conservation into the CRM search process to greatly reduce the number of false-positive predictions. We anticipate that a significant number of the new predictions from our genome-wide screen and screen targeted at genes with early anterior-posterior patterns to be active CRMs, and we have begun testing these predictions.

The pattern of binding-site conservation in positive pCRMs sheds additional light on the processes that govern CRM evolution. We find that predicted binding sites in positive D. melanogaster pCRMs are roughly three times more likely to be aligned to predicted sites in the D. pseudoobscura compared to predicted binding sites in negative pCRMs, in the sequences flanking pCRMs, or in random regions of the genome. The demonstration that this strictest form of binding-site conservation is strengthened in functional CRMs contrasts with an earlier study that concluded that binding sites in functional CRMs had only a slightly elevated probability of falling in conserved sequence [32]. Their methodology differed from ours in that they used randomly shuffled binding-site positions within functional CRMs as the background, while we used actual predicted binding-site positions in randomly picked regions of the genome.

In addition to this colinear conservation, we also observe that there is an overall enrichment for binding sites in positive pCRMs independent of the conservation of individual sites. Specifically, the presence of a binding site for a factor in a positive D. melanogaster pCRM increases (relative to negative pCRMs and random genomic fragments) the probability of finding a site for the same factor in the orthologous region of D. pseudoobscura, even if the site is not in the same (aligned) position. Thus, in this set of positive pCRMs, there appears to be selection to maintain binding site composition, but not always the specific order and orientation of sites. This is consistent with models of enhancer plasticity that have been proposed and discussed elsewhere [25, 5961].

The relative importance of binding-site architecture and binding-site composition to maintaining the function of an enhancer over evolutionary time remains unclear. Over relatively short evolutionary distances (as between D. melanogaster and D. pseudoobscura) most binding sites are conserved and found in the same place. Over longer evolutionary distances, individual binding sites are often poorly conserved even as the overall composition and function of a CRM is conserved.

From a practical perspective, this requires adjusting how conservation is incorporated into searches for clusters of binding sites that are likely to be CRMs. For relatively short evolutionary distances, searches for clusters of aligned sites will be less sensitive to noise and will focus on functional binding sites. For longer distances, where binding site turnover will likely preclude searching for clusters of conserved sites, searches for conserved binding site clusters should still work well. In fact, this latter method can work - with some modification - among species whose sequences can no longer be aligned. Anopheles gambiae diverged from its common ancestor with D. melanogaster roughly 220 million years ago, and there is little or no detectable non-coding sequence similarity between these two species. Nonetheless, we find clusters of HB, KR and KNI binding sites in the vicinity of gap and pair-rule genes and suggest that many of these are functional orthologs of D. melanogaster CRMs. Despite strong selection to maintain function, enough binding-site turnover has occurred in these CRM during their 220 million years of independent evolution to eliminate detectable sequence similarity. But they remain functionally similar and we can detect this functional similarity through its evolutionary signature.

With methods like the one we have presented here, aided by new and better binding data on Drosophila transcription factors and an impending wealth of comparative sequence data, we anticipate rapid progress on the identification and functional characterization of regulatory sequences. We will then be able to turn our attention to the next great challenge - understanding the precise relationship between the binding-site composition and architecture of regulatory sequences and the expression patterns they specify.

Materials and methods

Collection of CRMs

The collection of CRM sequences was previously described [11]

Transgenics

DNA fragments identified as candidate CRMs were amplified from either bacterial artifical chromosome (BAC) or y; cn bw sp fly genomic DNA by PCR using two primers containing unique sequence and synthetic AscI and NotI restriction sites (Additional data file 5). The PCR product was digested with AscI and NotI, and inserted in its native orientation into the AscI-NotI site of a modified CaSpeR-AUG-bgal transformation vector [62] containing the eve basal promoter, starting at -42 bp and continuing through codon 22 fused in-frame with lacZ [63]. The P-element transformation vectors were injected into w1118 embryos, as described previously [63, 64]. Transgenic fly lines containing CRMs CE8005 (7A), CE8016 (55C) and CE8020 (70EF) were verified by generating genomic DNA [65] from each line for PCR. PCR products were amplified using primers designed from the CaSpeR-AUG-bgal vector - forward primer 5' CGCTTGGAGCTTCGTCAC and reverse primer 5' GAGTAACAACCCGTCGGATTC and 35 cycles (Gene Amp 9700, Perkin-Elmer). The resulting PCR products were sequenced using standard conditions with BigDye version 3.0 and electrophoresed on a 3730 capillary sequencer (ABI).

Whole-mount in situhybridizations

Embryonic whole-mount in situ RNA hybridizations were performed as previously described [21]. RNA probes were generated using cDNA clones RE29225 (gt), RE14252 (odd), RE34782 (nub), RE49429 (pdm2), and RE47384 (sqz). Exon 1 of the ftz gene was amplified from genomic DNA using forward primer 5' GCGTTGCGTGCACATC and reverse primer 5' ATTCTTCAGCTTCTGCGTCTG. The PCR product was cloned into the TA vector (Invitrogen) and used to generate ftz RNA probe.

Double-labeling

RNA probes, using cDNAs or genomic DNA as templates, were labeled with fluorescein-12-UTP while lacZ RNA probes were labeled with digoxigenin-11-UTP (Roche). Hybridizations were performed as described above with the following modifications: (1) 2 μl of each probe were added to give a final concentration of 1:50; (2) sequential alkaline phosphatase staining was performed first with Sigma Fast red to detect endogenous transcripts, stopped by washing for 30 min in 0.1 M glycine-HCl pH 2.2, 0.1% Tween-20 at room temperature, and then continued as described to detect lacZ expression.

Assembly

The input to the genome assembly was the set of whole-genome shotgun reads from the Baylor Genome Sequencing Center retrieved from the National Center for Biotechnology Information (NCBI) Trace Archive, consisting of 2,607,525 total sequences. After trimming the sequences to remove vector and low-quality regions, the average read length was 607 bp. Approximately 75% of the reads were from short insert (approximately 2.5-3.0 kb) libraries, with another 25% from longer (6-7 kb) libraries. Another 46,040 reads came from the ends of 40-kb fosmids.

We ran the Celera Assembler several times, and found that by adjusting one parameter in particular we could produce considerably better assemblies. In particular, the assembler has an arrival rate statistic j, which measures the probability that a contig is repetitive on the basis of its depth of coverage. The default setting is very conservative: if a contig has more than 50% likelihood of being repetitive, it is marked as such and is set aside during most of the assembly process. For large highly repetitive mammalian genomes this setting may be appropriate, but for D. pseudoobscura we found that setting it to 90% or higher produced considerably better contigs, while apparently causing few if any misassemblies.

The overall assembly contained 10,089 scaffolds and 10,329 contigs, containing 165,864,212 bp. The estimated span of the scaffolds, using the gap sizes estimated from clone insert sizes, is 172,362,884. The largest scaffold was 3.05 million base-pairs (Mbp) and the scaffold N50 size was 418,046. (The N50 size is the size of the smallest scaffold such that the total length of all scaffolds greater than this size is at least one half the total genome size, where genome size here is 172 Mbp.) There are 308 scaffolds larger than 100,000 bp, whose total span is 129.5 Mbp. The N50 contig size, using 166 Mbp as the genome size (not counting gaps), was 43,555. Another measure of assembly quality is the number of large contigs: if we define 'large' as 10 kbp, then the assembly contains 3177 large contigs whose total length is 131,067,828 bp. (For reference, the assembly produced by the Baylor Human Genome Sequencing Center contains 129.4 Mbp in all contigs, including small ones, and the span of all scaffolds is 139.3 Mbp.) All of our contigs and scaffolds are freely available by anonymous ftp at [66].

Alignment and conservation of pCRMs

The extent and pattern of conservation between D. melanogaster and D. pseudoobscura in regions containing pCRMs were determined as follows. The D. melanogaster genomic sequence of the region of interest (with known repetitive elements masked) was extracted from a BioPerl genome database [67] containing Release 3.1 sequence and annotations from the Berkeley Drosophila Genome Project [68]. Potentially orthologous D. pseudoobscura contigs/scaffolds were identified using WU-BLAST 2.0 [69] using default parameters except for (-span1 -spsepqmax = 5000 -hspsepsmax = 5000 -gapsepmax = 5000 -gapsepsmax = 5000). High-scoring pairs (HSPs) with E-values less than 1e-20 were flagged as potential homologous regions. HSPs located more than 5,000 bp from each other in the D. melanogaster sequence were treated as separate hits. After examining dot-plots of the hits, we noticed a large number of small, local inversions that were found in both our assembly and the assemblies released by the Baylor Human Genome Sequencing Center. We used BLASTZ [70]) to automatically identify inversions, and when necessary inverted the corresponding D. pseudoobscura sequence. Each D. pseudoobscura sequence was aligned to the D. melanogaster corresponding sequence using LAGAN 1.2 [43] with default settings. A total of 31 genomic loci of approximately 50 kb were examined; these regions contain 36 pCRMs (the eve and h loci contain three pCRMs each, and PCE8003 and PCE8004 are within 20 kb of each other). Twenty-eight regions had aligned D. pseudoobscura sequence that spanned all or most of the region. For three regions (PCE8002, PCE8003/8004 and PCE8009) we were not able to identify large regions of orthologous sequence; these were excluded from subsequent comparative analyses. Dot-plots of the alignments from all 30 regions are available at [42].

Scoring gross conservation of pCRMs

The conservation of a specific genomic segment was scored as the fraction of D. melanogaster bases aligned to the identical base in aligned regions (percent identity).

Scoring binding-site conservation of pCRMs

We used two definitions of binding-site conservation. A binding site was considered 'aligned' if it overlaps a predicted D. pseudoobscura binding site for the same factor in the LAGAN alignment. Only overlap, and not strict alignment, was required to compensate for small errors in the alignment. A non-aligned binding site was considered 'preserved' if it could be matched to a D. pseudoobscura site for the same factor within the bounds of the pCRM, allowing each D. pseudoobscura site to be the match for only a single D. melanogaster site. The number of aligned plus preserved sites for each factor in a region is thus equal to the minimum number of sites for that factor in the two species.

Generating an orthology map for genome searches

To develop an orthology map for genome-wide searches, we used NUCmer [71] to align the Release 3 D. melanogaster genome (with annotated repetitive elements and transposable elements masked) and the D. pseudoobscura scaffolds described above. NUCmer was run with the command line parameters (-c 36 -g 10 --mum -d 0.3 -l 9). NUCmer generated a collection of short, highly conserved regions of homology ('anchors') spaced on average every 1 kb throughout the D. melanogaster genome. Anchors flanking either side of a D. melanogaster region of interest were used to pull out the corresponding D. pseudoobscura region, and additional flanking anchors were examined to ensure that the region was unambiguously orthologous. The region identified was re-aligned to the melanogaster region with LAGAN 1.2 using default settings.

Random sampling of non-coding genome

To characterize properties of non-coding sequences across the genome, we picked 4,000 1-kb segments of the D. melanogaster genome, sampled uniformly from all non-coding sequence. For 3,300 of these, we could find orthologous regions in D. pseudoobscura, and these were used to calculate the properties of random non-coding sequence shown in Figure 4 and discussed in the text. Properties determined using this data are considered properties of only the portion of the genome that is detectably orthologous under our conditions. The regions themselves are available as supplemental material at [42].

eCIS-ANALYST genome searches

Binding-site clusters in the D. melanogaster genome were determined as described in [11], where the minimum number of sites (min_sites) and the window size (wind_size) are variable. Release 3 genomic sequence with exons masked was searched with PATSER [72] using the following command line options: -c -d2 -l4. An 'alphabet' file (specified with the command line parameter '-a') was used to provide the following background frequencies: A/T = 0.297, G/C = 0.203. Position weight matrix (PWM) models were identical to those used in [11]. In the online version of eCIS-ANALYST, the minimum PWM match threshold site_p is also variable, but in the current study it was held constant at 0.0003 for all factors. Tests using alternate values for this variable did not lead to significant improvement in prediction efficacy.

For each potential D. melanogaster cluster, we identified the corresponding D. pseudoobscura region using the homology anchors described above. A pairwise alignment was made using LAGAN 1.2 (default parameters), and the number of aligned and preserved binding sites were determined as described above. The 2-kb flanking either side of the pCRM was included in the alignment to avoid edge effects, and was subsequently removed when calculating pCRM properties.

We examined our functional (positive) and non-functional (negative) pCRMs and noticed that in the positives, the lower bound for the number of conserved sites as a function of D. melanogaster sites followed an approximately logarithmic curve (Additional data file 3). From this observation, we classified a D. melanogaster binding site cluster as conserved if:

https://static-content.springer.com/image/art%3A10.1186%2Fgb-2004-5-9-r61/MediaObjects/13059_2004_Article_865_Equa_HTML.gif

where NS m is the number of binding sites in the D. melanogaster pCRM and NS c is the number of conserved binding sites. Different values of the logarithmic base b give different behavior. The data shown in Additional data file 3 support values of b between 1.15 and 1.4. We defined a more intuitive parameter, CF (conservation factor), which can range from 0 to 1 where 0 is the least stringent threshold (b = 1.4) and 1 is the most stringent (b = 1.15)

b = 1.4 - (CF * (1.4 - 1.15))     (2)

We performed genome searches with CF values of 0.25, 0.5, 0.55 and 0.75 and manually inspected the results with respect to false-negative and false-positive rates based on our 15 positive and 17 negative pCRMs (Additional data file 3). While we did not strictly optimize a single metric, we picked the values that gave a reasonable balance between false positives and false negatives, b = 0.25 for aligned sites alone, and b = 0.55 for aligned plus preserved sits.

Genome-wide predictions

eCIS-ANALYST genome searches were run with the following parameters: min_sites = 10, wind_size = 700 (run #1), and min_sites = 13, wind_size = 1,100 (run #2). All conserved clusters (with conservation defined as described in Equations 1 and 2 above) were combined. In order to capture weaker clusters, we performed an additional run (run number 3) using min_sites = 9, wind_size = 700. For this low stringency run, we used a non-standard conservation threshold different from the one described above, accepting all clusters with at least four aligned plus preserved sites, independent of the number of sites in D. melanogaster. We merged overlapping clusters from runs 1-3, yielding 929 non-overlapping clusters as described in Results.

Four metrics were then used to rank these 929 pCRMs: the number of aligned binding sites; the density of aligned binding sites; the number of aligned plus preserved binding sites; and the density of aligned plus preserved binding sites. All values were normalized according to background distribution of random non-coding sequences. The four normalized values were then summed to compute an overall score, which was then renormalized to arrive at a final z-score used to rank pCRMs in Tables 3 and 4 and Additional data files 7, 8, 10, and 11.

Additional data files

The following additional data files are available with the online version of this article.

Additional data file 1 shows the binding site densities (column 1), aligned site densities (column 2), and aligned plus preserved site densities (column 3) for individual transcription factors. The top portion of each panel contains a histogram of the values for randomly chosen 1,000 bp regions of the D. melanogaster genome. The blue line plots the cumulative distribution. The colored asterisks show the average values for each class of pCRM. The panel below the histogram shows the values for each pCRM (each dot represents one pCRM, with positives in blue, negatives in red, ambiguous in green).

Additional data file 2 shows expression patterns of 65 genes adjacent to 122 pCRMs identified by eCIS-ANALYST. The images were obtained from the BDGP Embryonic Expression Pattern Database [33], and include all pCRMs from Additional data files 7,8,10,11 for which an adjacent gene had an early segmentation pattern.

Additional data file 3 shows discrimination of positive and negative pCRMs. Comparisons of the number of predicted binding sites in D. melanogaster pCRMs to the number of aligned sites (top panel) and aligned plus preserved sites (bottom panel). Blue dots represent the 15 positive pCRMs from the text; green dots the ten known CRMs that were below the threshold used in [11]; red dots negative pCRMs; pink dots ambiguous pCRMs. Gray boxes represent the distribution of values for random 1,000 bp non-coding regions. The blue line shows the discrimination function (see Materials and methods).

Additional data file 4 shows new pCRMs. Three 30 kb regions were chosen to illustrate new predictions: (A) the argos locus, (B) the CG4702 locus (note that CG31361 is not expressed in blastoderm embryos and PCE8494 is a low-scoring pCRM), and (C) the SoxN locus. Exons are shows as blue boxes, introns are represented with horizontal lines, and the direction of transcription is indicated by the arrow. New pCRMs are shown as gray ovals. The green graphs show average (in 300 bp windows) percent identity and fraction of bases in conserved blocks. Below the percent identity plots are shown insertions (gray boxes) and deletions (orange boxes) in the D. melanogaster sequence relative to their D. pseudoobscura ortholog. The location of binding sites in D. melanogaster, binding sites in D. pseudoobscura and aligned binding sites along with the density of sites averaged over 700 bp are shown in the bottom three panels for each region.

Additional data file 5 gives the primers used to amplify pCRMs for transgenics. Additional data file 6 gives additional information from Table 2. Additional data file 7 gives all new pCRMs from genome-wide eCIS-ANALYST located within 20 kb of annotated transcript. Additional data file 8 gives all new pCRMs from genome-wide eCIS-ANALYST located more than 20 kb from annotated transcript. Additional data file 9 lists genes with anterior-posterior patterns and the source of the information. Additional data file 10 gives all new pCRMs from genome-wide eCIS-ANALYST located within 20 kb of gene with anterior-posterior pattern. And, finally, Additional data file 11 gives all new pCRMs from genome-wide eCIS-ANALYST located between 20 kb and 50 kb from gene with anterior-posterior pattern.

Notes

Declarations

Acknowledgements

We thank Richard Weiszman, Naomi Win and Nipam Patel for assistance with RNA in situ hybridizations, Pavel Tomancak for generating the database to store images of stained transgenic embryos and Amy Beaton and members of the Hartenstein lab for discussions of embryonic patterns of expression, Casey Bergman and Joseph Carlson for generating the database to store CRM transgenic sequences and the members of the BDGP for clones and sequencing support. We also thank Arthur Delcher and Mihai Pop for help with running and fine-tuning the Celera Assembler. This work was supported by National Institutes of Health Grants HG00750 (to G.M.R.), and HL667201 (to M.B.E.), and LM06845 (to S.L.S.); Department of Energy contract DE-AC03-76SF00098 (to M.B.E.); and by the Howard Hughes Medical Institute. M.B.E. is a Pew Scholar in the Biomedical Sciences. Author contributions are as follows: B.D.P. made P-element constructs containing the 28 candidate CRMs. T.R.L. injected these constructs into Drosophila embryos, screened for transformants and generated the lines for analysis. B.D.P. collected embryos, generated probes and performed whole-mount in situ hybridization. B.D.P. and S.E.C. imaged and analyzed transgenic embryos. S.L.S. assembled the D. pseudoobscura genomic sequence. B.P.B. and M.B.E. performed all computational analyses. S.E.C., M.B.E. and G.M.R. provided guidance and direction for the project. S.E.C. supervised experimental aspects of the project. M.B.E. supervised computational aspects of the project. M.B.E. wrote the paper. B.P.B. prepared the tables and figures. B.D.P. and S.E.C. contributed to the content and edited the paper.

Authors’ Affiliations

(1)
Department of Molecular and Cell Biology, University of California
(2)
Berkeley Drosophila Genome Project, Genome Sciences Department, Life Sciences Division, Lawrence Orlando Berkeley National Laboratory
(3)
Howard Hughes Medical Institute, Department of Molecular and Cell Biology, University of California
(4)
The Institute for Genomic Research
(5)
Genome Sciences Department, Genomics Division, Lawrence Orlando Berkeley National Laboratory
(6)
Center for Integrative Genomics, University of California

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