Summary
Homologous chromosomes colocalize to regulate gene expression in processes including genomic imprinting, X-inactivation, and transvection. In Drosophila, homologous chromosomes pair throughout development, promoting transvection. The “button” model of pairing proposes that specific regions along chromosomes pair with high affinity. Here, we identify buttons interspersed across the fly genome that pair with their homologous sequences, even when relocated to multiple positions in the genome. A majority of transgenes that span a full TAD function as buttons, but not all buttons contain TADs. Additionally, buttons are enriched for insulator protein clusters. Fragments of buttons do not pair, suggesting that combinations of elements within a button are required for pairing. Pairing is necessary but not sufficient for transvection. Additionally, pairing and transvection are stronger in some cell types than in others, suggesting that pairing strength regulates transvection efficiency between cell types. Thus, buttons pair homologous chromosomes to facilitate cell-type-specific interchromosomal gene regulation.
Graphical Abstract
eTOC Blurb
In Drosophila button regions pair homologous chromosomes. Pairing in turn facilitates transvection, an interchromosomal gene regulatory process. Viets et al. identify buttons across the genome and examine their association with TADs and insulators. They find that pairing and transvection are mechanistically separable and cell-type-specific.
Introduction
Chromosomes are organized in a complex manner in the nucleus. In higher organisms, they localize to distinct territories (Cremer and Cremer, 2010). Regions of chromosomes interact to form compartments, which are segregated based on gene expression states (Eagen, 2018). Chromosomes are further organized into topologically associating domains (TADs), regions of self-association that are hypothesized to isolate genes into regulatory domains and ensure their activation by the correct cis-regulatory elements (Eagen, 2018). TADs vary in size from ~100 kb in Drosophila melanogaster to ~1 Mb in mammals (Eagen, 2018; Sexton et al., 2012). Disruptions of nuclear organization, such as alteration of TAD structure and localization of genes to incorrect nuclear compartments, can have major effects on gene expression at some loci in cis (Clowney et al., 2012; Guo et al., 2015; Lupianez et al., 2015; Reddy et al., 2008). However, it is unclear how elements interact between chromosomes to organize chromatin and regulate gene expression in trans.
One key aspect of nuclear architecture involves the tight colocalization, or “pairing,” of homologous chromosomes to facilitate regulatory interactions between different alleles of the same gene (Joyce et al., 2016). In Drosophila, homologous chromosomes are paired in somatic cells (Stevens, 1906). This stable pairing provides an excellent paradigm to study the mechanisms driving interactions between chromosomes.
It is poorly understood how homologous chromosomes come into close physical proximity. Classical studies proposed a “zipper” model, in which all regions of the genome have an equal ability to pair based on sequence homology, and chromosome pairing begins at the centromere and proceeds distally to the telomeres (Gelbart, 1982; Lewis, 1954). Studies of pairing initiation during development led to a shift towards the “button” model, which proposes that regions of high pairing affinity are interspersed along chromosome arms and come together through a random walk (Fig. 1A) (Fung et al., 1998; Gemkow et al., 1998; Hiraoka et al., 1993).
Figure 1. Homologous chromosomes button together to facilitate transvection.
A. Button model of chromosome pairing. Yellow squares: button loci.
B. During transvection, two mutant alleles interact between chromosomes to rescue gene expression. Green box: functional enhancer. Gray box with red X: mutated enhancer. Green arrow: functional promoter. Gray arrow with red X: mutated promoter.
The nature of the high-affinity “buttons” that bring homologous chromosomes together is unclear. Many elements, including insulators, Polycomb Response Elements (PREs), and heterochromatic repeats, drive looping interactions in cis along the same chromosome arm or clustering between non-homologous sequences on different chromosomes (Blanton et al., 2003; Dernburg et al., 1996; Fujioka et al., 2016; Fujioka et al., 2009; Li et al., 2011; Li et al., 2013). However, only a handful of small DNA elements, including the gypsy retrotransposon and the Fab-7, Mcp, and TMR regions of the Abd-B locus, are known to drive pairing between homologous sequences on different chromosomes (Bantignies et al., 2003; Gerasimova et al., 2000; Li et al., 2011; Li et al., 2013; Lim et al., 2018; Ronshaugen and Levine, 2004; Vazquez et al., 2006). As three of these elements are within the same locus, the sequence and structural features that contribute to genome-wide pairing are unknown. The scarcity of small DNA elements that are known to drive pairing suggests that combinations of elements and/or higher order chromatin structures are required to button homologous chromosomes together.
Pairing facilitates a gene-regulatory mechanism known as transvection, in which two different mutant alleles interact between chromosomes to rescue gene expression (Fig. 1B) (Lewis, 1954). Transvection has been described for a number of Drosophila genes (Duncan, 2002). With the exceptions of Abd-B and certain transgenes containing the Homie, gypsy, Mcp, TMR, and Fab-7 sequences, transvection requires homologous chromosome pairing and is disrupted by chromosome rearrangements (Bantignies et al., 2003; Duncan, 2002; Fujioka et al., 2016; Gemkow et al., 1998; Hendrickson and Sakonju, 1995; Kravchenko et al., 2005; Lewis, 1954; Li et al., 2011; Muller et al., 1999; Sigrist and Pirrotta, 1997; Sipos et al., 1998; Vazquez et al., 2006; Zhou et al., 1999). DNA elements such as insulators and PREs contribute to transvection and similar phenomena at many loci (Bantignies et al., 2003; Fauvarque and Dura, 1993; Fujioka et al., 1999; Fujioka et al., 2016; Fujioka et al., 2009; Gindhart and Kaufman, 1995; Kapoun and Kaufman, 1995; Kassis, 1994; Kassis et al., 1991; Kravchenko et al., 2005; Li et al., 2011; Muller et al., 1999; Shimell et al., 2000; Sigrist and Pirrotta, 1997; Vazquez et al., 2006; Zhou et al., 1999), but it is unclear if the same DNA elements are always involved in both pairing and transvection or if pairing and transvection are mechanistically separable.
Homologous chromosome pairing occurs more strongly in some cell types than in others. Pairing occurs in 15-30% of nuclei in the early embryo, gradually increases throughout embryonic development, and reaches a peak of 90-95% by the third instar larval stage (Dernburg et al., 1996; Fung et al., 1998; Gemkow et al., 1998; Hiraoka et al., 1993). Similarly, transvection efficiency varies widely between cell types (Bateman et al., 2012; Blick et al., 2016; Kassis et al., 1991; Mellert and Truman, 2012). However, a direct link between the level of pairing and the strength of transvection in a given cell type has not been established.
Here, we develop a method to screen for DNA elements that pair and identify multiple button sites across the Drosophila genome, allowing us to examine features that determine button activity. We conduct Hi-C on Drosophila larval eye discs and identify TADs. ~80% of transgenes encompassing an entire TAD function as buttons, but not all buttons contain TADs. Additionally, we find that buttons are enriched for insulator protein clusters. Moreover, buttons drive pairing with their endogenous loci when placed at different positions in the genome. By testing mutant alleles and transgenes of the spineless gene, we show that pairing and transvection are mechanistically separable and cell-type-specific.
Results
Identification of button loci
To look for elements that bring homologous chromosomes together, we selected transgenes from multiple locations in the genome, inserted them on heterologous chromosomes, and tested if they drove pairing with their endogenous loci. This assay provided a sensitized system for identifying regions that have an especially high affinity for homologous sequences, since it detected DNA sequences that pair outside of the context of chromosome-wide pairing.
We screened a set of ~80-110 kb transgenes tiling a ~1 Mb region on chromosome 3R (Fig. 2E). We inserted individual transgenes into a site on chromosome 2L (site 1; Fig. 2A) and visualized their nuclear position using Oligopaints DNA FISH (Fig. S1S-T)(Beliveau et al., 2012). As the endogenous and transgenic sequences were identical, we distinguished between them by labeling the sequences neighboring the endogenous locus and the transgene insertion site (“2 color strategy”)(Fig. 2A). We examined pairing in post-mitotic larval photoreceptors to avoid disruptions caused by cell division. The larval retina has previously been used to study pairing (Li et al., 2011; Vazquez et al., 2006). Control experiments examining several endogenous sites confirmed that homologous chromosomes pair in 86-92% of larval photoreceptors (Fig. S1A-D).
Figure 2. A screen for pairing elements identifies buttons along chromosome 3R.
A. Two-color DNA FISH strategy. In controls, red and green FISH punctae on heterologous chromosomes are far apart. If a transgene drives pairing, punctae are close together. If a transgene does not drive pairing, punctae are far apart, similar to a control. Flies were diploid, but a single chromosome copy is shown for simplicity.
B-D. Control, pairer, and non-pairer. Image for 2L-3R control is the same experiment as Fig. 3B, 5G, and S2C. Scale bars=1 μm. White: Lamin B, red: neighboring endogenous sequence, green: neighboring transgene insertion site.
E. Hi-C heat map, directionality index, and insulator plot showing TADs and insulators in the ~1 Mb region of chromosome 3R used for pairing screen. Orange boxes: major developmental genes. Black lines: pairing elements Mcp, Fab-7, TMR. Dotted lines: TAD boundaries.
F. Quantifications for Transgenes A-Q. Black: control, blue: pairers, gray: non-pairers. T: contains a TAD. Control data are the same as in Fig. 3E, 3I, 1E-F, S3C, S5J, and S6U. Transgene D and F data are the same as in Fig. 3I and S1F. Transgene E data are the same as in Fig. 3I. Transgene G, I-M, O data are the same as in Fig. S1F. ****=p<0.0001, **=p<0.005, ns=p>0.05, one-way ANOVA on ranks with Dunn’s multiple comparisons test. n=100 for all datasets.
See also Fig. S2.
To determine whether a transgene drove pairing, we compared the 3-D distance between the transgene and its endogenous site to a negative control. If the distance between the insertion site and the endogenous site was significantly lower than in the negative control, then the transgene drove pairing (Fig. 2A). The negative control measured the distance between the transgene insertion site on chromosome 2L and a site on chromosome 3R in a wild type background. We examined additional negative controls across multiple chromosomes and observed no differences (Fig. S1E). Thus, for all transgene pairing experiments, we used single negative controls for each chromosome arm tested.
To determine statistical significance, we first tested each transgene dataset for a Gaussian distribution. When transgene distance distributions were non-Gaussian, we tested for statistical significance by comparing the median of each distribution to the negative control. Gaussian distance distributions were tested for significance using a t-test to compare means. In all cases where more than one transgene was compared to a control, we corrected for multiple comparisons.
Only a subset (9/17) of transgenes (“pairers”) in our screen drove pairing, bringing the distances between FISH signals significantly closer than in the negative control (Fig. 2B-C, E-F; Fig. S2A). The signals did not completely overlap, likely because they did not directly label the paired sites (Fig. S1G-I). For the remaining 8/17 transgenes (“non-pairers”), the distances were not significantly different from the negative control, indicating that they did not drive pairing (Fig. 2B, D-F; Fig. S2B).
With the exception of Transgene N, all tested transgenes were homozygous viable. In all genotypes that were homozygous for a transgene, we observed a single fluorescent signal for the endogenous locus and a single signal for the transgene locus (Fig. S1J-M), indicating that homologous chromosome pairing was preserved between the two copies of chromosome 2L and between the two copies of chromosome 3R (Fig. S1K-M). To verify that homologous chromosome pairing occurred between transgene copies, we quantified the level of pairing between homozygous copies of Transgenes D and E. Similar to a control endogenous locus on chromosome 3R, which paired with itself in 92% of photoreceptors, Transgene D paired with itself in 88% of photoreceptors, and Transgene E paired with itself in 94% of photoreceptors (Fig. S1N). Thus, when a transgene drives pairing with its endogenous site, all four copies of the locus (2 endogenous and 2 transgene) come together at one site and pair simultaneously, preserving homologous chromosome pairing (Fig. S1J-L).
In the case of the heterozygous Transgene N, we observed a single signal for the transgene, likely because the small disruption in homology between the wild type copy of chromosome 2L and the copy of chromosome 2L with Transgene N was not sufficient to disrupt homologous chromosome pairing (Fig. S1O-R). Since Transgene N paired with its endogenous locus on chromosome 3R, the single signal for the transgene insertion site in paired nuclei indicated that the single copy of Transgene N was bringing the wild-type copy of chromosome 2L into proximity with chromosome 3R due to homologous chromosome pairing between the two copies of chromosome 2L (Fig. S1P-Q).
To confirm our assignment of pairers vs. non-pairers, we used maximum likelihood estimation to fit our transgene data to single or double Gaussian distributions. 6/9 pairers fit a double Gaussian distribution, indicating a split between paired and unpaired populations (Fig. 2F). The remaining pairers, Transgenes G, K, and O, fit a single Gaussian distribution, but the means of their distributions were significantly lower than the mean of the negative control distribution, indicating a high degree of pairing (Fig. S1F). Only 2/8 non-pairers (Transgenes H and Q) fit a double Gaussian distribution (Fig. 2F). Since the median distances for Transgenes H and Q were not significantly different from the negative control (Fig. 2F), these transgenes may drive pairing at a minimal level that is significantly lower than other pairers. The remaining 6/8 non-pairers fit a single Gaussian distribution, and their means did not significantly differ from the negative control, consistent with a single population of non-pairers (Fig. S1F). Thus, our screen identified multiple new button elements.
Pairing could be affected by the transgene insertion site. To test if buttons drive pairing from different sites, we inserted Transgenes A, E, and L onto chromosome 3L (site 3; Fig. S2F). As on chromosome 2L, Transgenes A and E drove pairing, while Transgene L did not (Fig. S2G-H), showing that button pairing is position-independent.
Thus, we identified multiple button loci that overcome endogenous nuclear architecture to drive pairing between non-homologous chromosomes.
A majority of transgenes spanning TADs display pairing activity
We next sought to determine common features of the new buttons, focusing first on chromatin structure. As topologically associated domains (TADs) are regions of self-association, we performed Hi-C on third instar larval eye discs to determine the relationship between buttons and TADs. We defined TADs using a Hidden Markov Model (HMM). The HMM was used to generate heat maps and directionality indices, which indicate the bias of a region towards upstream or downstream interactions (Dixon et al., 2012). TADs on a directionality index are read from the beginning of a positive peak, which indicates downstream interactions, to the end of a negative peak, which indicates upstream interactions (Fig. 2E).
From our screen, we found that 44% of pairers (4/9) encompassed a complete TAD, including both TAD boundaries, compared to only 12.5% of non-pairers (1/8)(Fig. 2E-F), suggesting that TADs may contribute to button function. 3/4 of the pairers that spanned a TAD fit a double Gaussian distribution, while the non-pairer that spanned a TAD fit a single Gaussian distribution. To test the hypothesis that TADs contribute to pairing, we selected four additional transgenes encompassing entire TADs on chromosomes X, 3L and 3R (Fig. 3E; Fig. S3A-B) and compared them to 12 additional transgenes that did not encompass entire TADs, taken from chromosomes X, 2L, 2R, 3R, and 4 (Fig. 3E; Fig. S3A-B). Excepting Transgenes X, BB, and JJ, which were homozygous lethal, all transgenes were tested as homozygotes. Based on the availability of probes, we used an alternative FISH strategy for a subset of these transgenes, in which the identical transgene and endogenous sequences were labeled with the same fluorescent probes (Fig. 3A). With this 1-color strategy, FISH punctae ≤0.4 μm apart could not be distinguished as separate and were assigned a distance of 0.4 μm apart (see STAR Methods).
Figure 3. Examining the relationship between TADs and button activity.
A. One-color DNA FISH strategy: In controls, two red FISH punctae on heterologous chromosomes are far apart. If a transgene drives pairing, the punctae are close together and indistinguishable. If a transgene does not drive pairing, the punctae are far apart, similar to a control. Flies were diploid, but a single chromosome copy is shown for simplicity.
B-D. Control, pairer, and non-pairer. Scale bars=1 μm. White: Lamin B, red: probes against endogenous sequence and transgene. Image for 2L-3R control is the same experiment as Fig. 2B, 5G, and S2C. Image for Transgene AA is the same experiment as Fig. S1H.
E. Quantifications for additional transgenes. T: contains a TAD. Black: controls, blue: pairers, gray: weak/non-pairers. ****=p<0.0001, ***=p<0.001, *=p<0.05, ns=p>0.05, one-way ANOVA on ranks with Dunn’s multiple comparisons test (Transgenes U-X, Z-FF) or Wilcoxon rank-sum test (Transgenes Y and GG). 3L-X control data are the same as in Fig. S1E, S2E, and S3C. 3L-2R control data, 3L-2L control data, and 2L-4 control data are the same as in Fig. S1E. 2L-3R control data are the same as in Fig. 2F, 3I, S1E-F, S3C, S5J, and S6U. Data for Transgenes U-X and Z-BB are the same as in Fig. S3C. Controls were imaged in two colors, then pseudocolored red and re-scored in one color. n=100 for all datasets.
F. Percent of regions with and without TADs that drive pairing. Blue: pairers. Gray: weak/non-pairers. *=p<0.05, one-tailed Fisher’s exact test.
G. Comparison of length for pairers vs. non-pairers tested in Fig. 2F, 3E, and S2E. Blue: pairers, gray: non-pairers. ns=p>0.05, Wilcoxon rank-sum test. Black lines: medians.
H. Hi-C heat map, directionality index, and insulator plot showing TADs and insulators in the region covered by Transgenes D-F. Dotted lines: TAD boundaries.
I. Quantifications for transgenes that split the TAD covered by Transgene E in Fig. 3H. Black: control, blue: pairers, gray: non-pairers. *=p<0.05, ns=p>0.05, one-way ANOVA on ranks with Dunn’s multiple comparisons test. Control data are the same as in Fig. 2F, 3E, S1E-F, S3C, S5J, and S6U. Transgene D and F data are the same as in Fig. 2F and S1F. Transgene E data are the same as in Fig. 2F. Transgene S data are the same as in Fig. S6U. n=50 for all datasets.
Compared to controls, the distances between these transgenes and their endogenous sites divided into three categories: strong difference (p<0.001; “pairers”), weak difference (p<0.05; “weak pairers”), and no difference (p>0.05; “non-pairers”) from controls. Other transgenes on the same chromosome arms were significantly stronger pairers (p<0.005) than the weak pairers, Transgenes X and AA (Fig. S3C). Thus, for all subsequent analyses, we classified weak pairers and non-pairers into a single non-pairing category.
75% of the additional transgenes that spanned entire TADs (3/4) drove pairing (Fig. 3B-E; Fig. S3A-B). For all transgenes tested, 78% of transgenes that encompassed a full TAD (7/9) drove pairing (Fig. 3F). Additionally, only 12.5% of non-pairers (2/16) spanned a full TAD (Fig. 2F; Fig. 3E; Fig. S2E). Interestingly, 42% of the transgenes that did not span a TAD (10/24) displayed pairing activity (Fig. 3F), suggesting that additional factors contribute to button function. Together, a majority of transgenes encompassing a TAD were buttons, but not all buttons contained a TAD.
Longer transgenes are more likely to span an entire TAD, suggesting that they might pair more strongly. However, transgene length was not associated with pairing ability (Fig. 3G), indicating that the content of a transgene, regardless of length, influences pairing.
The ~110 kb maximum size of publicly available transgenes prevented testing larger TADs for pairing. Transgenes that covered parts of a large TAD on chromosome 3R did not drive pairing (Fig. S2I). To test this large TAD for pairing, we utilized a 460-kb duplication of chromosome 3R onto chromosome 2R (Fig. S2J), which encompassed the entire TAD (Fig. S2I). We found that the duplication drove pairing with its homologous endogenous site (Fig. S2K-L). Including the duplication and all transgenes, 80% of tested regions that encompassed a TAD were buttons (Fig. 3F), supporting a role for TADs in pairing.
Buttons are enriched for insulator clusters
As most transgenes encompassing TADs were pairers, but not all pairers contained TADs, we next examined other features that could contribute to pairing. As insulators have been linked to long-distance interactions (Blanton et al., 2003; Dernburg et al., 1996; Fritsch et al., 2006; Fujioka et al., 2016; Fujioka et al., 2009; Li et al., 2011; Li et al., 2013), we assessed whether the number of binding sites for individual Drosophila insulator proteins was higher in pairers than in non-pairers. Using published ChIP data (Cuartero et al., 2014; Negre et al., 2010; Ong et al., 2013; Van Bortle et al., 2012; Wood et al., 2011), we found an association between pairing and the DNA-binding insulator proteins CTCF and GAF and insulator cofactors Cp190 and Mod(mdg4)(Fig. 4A-F). Additionally, the total number of bound insulators per transgene was greater in pairers than in non-pairers (Fig. 4G), suggesting that insulator proteins contribute to pairing.
Figure 4. Clusters of insulators are associated with pairing.
A-I. Quantifications for pairers and non-pairers tested in Fig. 2F, 3E, and S2E. **=p<0.01, *=p<0.05, ns=p>0.05, Wilcoxon rank-sum test (A, D, F-G, I, K) or unpaired t-test with Welch’s correction (B-C, E, H, J, L). The Wilcoxon rank-sum test compares medians, while the unpaired t-test with Welch’s correction compares means. Therefore, the black lines in A, D, F-G, I, and K indicate medians, and the black lines in B-C, E, H, J, and L indicate means.
A-L. Blue: pairers, gray: weak/non-pairers.
A-F. ChIP peaks for CTCF (A), GAF (B), BEAF (C), Su(Hw) (D), Mod(mdg4) (E), Cp190 (F).
G-K. Number of clusters of ≥1 (G), ≥2 (H), ≥3 (I), ≥4 (J), or ≥5 (K) insulator proteins.
G-L. Any region where ChIP peaks for CTCF, GAF, BEAF, Su(Hw), Mod(mdg4), or Cp190 were overlapping or directly adjacent to one another was considered an insulator cluster.
L. Number of clusters containing any combination of BEAF, CTCF, and Cp190.
It is unlikely that the binding of a single type of insulator protein could provide the specificity required for a button to find its unique homologous partner. We therefore tested whether combinations of insulator proteins were associated with pairing by examining areas where multiple unique insulator binding sites overlapped. We focused on clusters of ≥2, ≥3, ≥4, or ≥5 unique insulator proteins. In all four cases, insulator clusters were enriched in pairers (Fig. 4H-K), suggesting that complex combinations of insulators may bring buttons together.
We next examined the association between pairers and clusters of the insulator proteins CTCF, BEAF, and Cp190, which have been detected at TAD boundaries (Sexton et al., 2012),. Again, we observed a relationship with pairing (Fig. 4L), suggesting that each button has a unique “code” of insulators bound across a region that assist in pairing.
Because the small DNA insulator elements Mcp, Fab-7, and TMR drive pairing between chromosomes (Bantignies et al., 2003; Li et al., 2011; Li et al., 2013; Ronshaugen and Levine, 2004; Vazquez et al., 2006), we next examined whether these elements drove pairing in our assay. Transgene N, which contained Fab-7 and TMR, drove pairing (Fig. 2E-F), supporting a role for these elements in pairing. Transgene M, which contained Mcp, did not pair (Fig. 2E-F), suggesting that Mcp drives pairing in a context-specific manner or at a low level that cannot be distinguished by this assay. Thus, these short DNA elements may assist in driving pairing at gene-specific sites in the genome.
Intact buttons drive pairing
Larger order chromatin structures like TADs or combinations of insulators across a region could drive pairing, or smaller elements contained within buttons could bring homologous regions together. We therefore examined the effects of splitting a button, focusing on the button defined by a TAD within Transgene E. Transgenes D and F, which cover the 5’ and 3’ ends of Transgene E, respectively, did not drive pairing (Fig. 3H-I). To address if a pairing element in the middle of Transgene E was split and not intact in Transgene D or F, we examined Transgene R, which overlapped the 3’ end of Transgene D and the 5’ end of Transgene F (Fig. 3H). Transgene R did not drive pairing (Fig. 3H-I). Thus, on their own, the 5’, middle, or 3’ regions of the Transgene E TAD are not sufficient to drive pairing. These observations suggest that complete buttons containing higher order chromatin structures and/or combinations of elements are required for pairing.
Examining the relationship between pairing, gene expression state, and chromatin factors
Gene activity plays a critical role in nuclear architecture: loci within TADs interact with each other in compartments, which are partitioned by expression state (Eagen, 2018). Pairing between transgenes and their endogenous loci might be a result of segregation of the genome into active (A) and repressed (B) compartments. If compartmentalization alone drives pairing, the distance between any two active regions or any two repressed regions should be less than the distance between an active and repressed region. To test this hypothesis, we performed RNA-seq on larval eye discs to identify active or repressed regions. We selected two loci on different chromosomes that were highly expressed (A1 and A2; Fig. S4A, C), and two loci that were expressed at low levels (B1 and B2; Fig. S4B-C). The level of A1-A2 and B1-B2 interaction did not differ from a negative control or the level of A1-B2 interactions (Fig. S4D-H). Moreover, we found no association between pairing and active transcription (Fig. S4I-J). We also examined modENCODE ChIP data and found no association between pairing and Polycomb Group (PcG) binding sites, activating or repressing epigenetic marks, or non-coding RNAs (ncRNAs)(Fig. S4K-Q). Our data suggest that pairing is driven by specific interactions between homologous buttons, rather than general interactions based on expression state.
Pairing and transvection occur despite chromosomal rearrangements
We next interrogated the relationship between pairing and transvection. Chromosomal rearrangements have been shown to disrupt pairing of genes located near breakpoints (Duncan, 2002; Lewis, 1954). However, we observed pairing of transgenes with their endogenous loci, suggesting that intact chromosomes are not required for pairing and that pairing tolerates nearby breakpoints. Supporting our observations, the Abd-B locus pairs in the presence of rearrangements (Gemkow et al., 1998; Hendrickson and Sakonju, 1995; Sipos et al., 1998). We therefore reexamined how rearrangements affect pairing, focusing on a button containing a TAD spanning the spineless (ss) locus (“ss button”; Fig. 5A).
Figure 5. Pairing is necessary but not sufficient for transvection.
A. Hi-C heat map and directionality index showing the TAD that defines the ss button.
B. Spineless (Ss) activates Rh4 and represses Rh3.
C. Ss is expressed in ~70% of R7s. Green: Ss, red: Prospero (R7 marker), white circles: Ss-expressing R7s.
D. Rh3 (blue) and Rh4 (red) expression in wild type R7s.
E. ss alleles and transgenes. ins: insulator, sil 1: silencer 1, enh: enhancer, sil 2: silencer 2. Small black arrows: transcription start sites. Gray rectangles: exons. Dotted gray lines: region required for transvection.
F. Strategy to assess pairing and transvection from site 1 in Fig. 5G-L. Gray arrow with “?” indicates that Transgenes S and T were tested for transvection. Flies were diploid for chromosomes 2 and 3, but a single copy is shown for simplicity.
G-I, O-Q. Scale bars= 1 μm. White: Lamin B, red: neighboring endogenous sequence, green: neighboring transgene insertion site.
J-L, R-T. Red: Rh4, blue: Rh3.
G-I. Pairing assay images of 2L-3R control, Transgene S site 1, and Transgene T site 1. See Fig. S6U for quantifications. Image for 2L-3R control is the same experiment as Fig. 2B, 3B, and S2C.
J-L. Ss/Rh4 expression in wild type control (70%), Transgene S site 1 (57%), and Transgene T site 1 (55%). The slight decrease in Rh4 for Transgene S site 1 and Transgene T site 1 is likely due to background genetic effects.
M. Natural chromosome looping forces transgenes into close proximity with endogenous ss, mimicking pairing and facilitating transvection. Gray arrow with “?” indicates that Transgenes S and T were tested for transvection.
N. Strategy to assess pairing and transvection from site 2 in Fig. 5O-T. Flies were diploid for chromosome 3, but a single copy is shown for simplicity.
O-Q. Pairing assay images of site 2 control, Transgene S site 2, and Transgene T site 2. See Fig. S6U for quantifications.
R-T. Ss/Rh4 expression in wild type control (70%), Transgene S site 2 (98%) and Transgene T site 2 (78%).
See also Fig. S6.
To assess the effects of local rearrangements on ss button pairing, we examined a naturally occurring inversion with a breakpoint upstream of ss (ssinversion) and a duplication with a breakpoint downstream of ss (Fig. 5E). Both ssinversion and the duplication paired with endogenous ss (Fig. S2I-L; Fig. S5A-B), showing that ss button pairing occurs despite rearrangements. Pairing also occurred at the ss locus in flies with balancer chromosomes containing numerous large inversions and rearrangements (Fig. S5H-J). Thus, pairing of ss occurs despite chromosomal rearrangements, consistent with a model in which buttons find each other independent of chromosome-wide homology.
Pairing is required for transvection, in which DNA elements on a mutant allele of a gene act between chromosomes to rescue expression of a different mutant allele (Fig. 1B). In cases where chromosomal rearrangements perturb pairing, transvection is disrupted (Duncan, 2002; Lewis, 1954). Since rearrangements did not ablate pairing at the ss button, we hypothesized that transvection would occur at the ss locus in these genetic conditions.
In the fly eye, Ss is normally expressed in ~70% of R7 photoreceptors to activate expression of Rhodopsin 4 (Rh4) and repress Rhodopsin 3 (Rh3; Fig. 5B-D). Ss is absent in the remaining 30% of R7s, allowing Rh3 expression (Fig. 5B-D) (Wernet et al., 2006). ss regulatory mutations change the ratio of SsON: SsOFF cells. When two ss alleles with different ratios are heterozygous, transvection (also known as Interchromosomal Communication) determines the final ratio of SsON: SsOFF R7s (Johnston and Desplan, 2014). Thus, the SsON: SsOFF ratio is a phenotype that allows for quantitative assessment of transvection. For our ss transvection experiments, we evaluated Rh3 and Rh4 expression, as they faithfully report Ss expression in R7s (i.e. SsON = Rh4; SsOFF = Rh3). We previously observed transvection at the ss locus for the duplication and balancer chromosomes (Johnston and Desplan, 2014). We similarly observed transvection at the ss locus for the ssinversion (Fig. S5C-E). Together, these data suggested that buttons can drive pairing and transvection despite chromosomal rearrangements.
Pairing is necessary but not sufficient for transvection
As chromosomal rearrangements did not impair ss pairing or transvection, we further investigated the relationship between pairing and transvection using ss transgenes. Transgenes S and T are expressed in 100% of R7s because they lack a silencer DNA element, but do not produce functional Ss protein because they lack critical coding exons (Fig. 5E; Fig. S6A-D)(Johnston and Desplan, 2014). Transgene T differs from Transgene S in that it lacks 6 kb at its 5’ end (Fig. 5E). We predicted that if Transgenes S and T performed transvection, they would upregulate expression of endogenous ss.
When inserted onto chromosomes 2L or 3L (sites 1 and 3; Fig. 5F; Fig. S6E), Transgenes S and T did not drive pairing with endogenous ss on chromosome 3R (Fig. 5G-I; Fig. S6F-G, I, U). At these sites, Transgenes S and T did not upregulate ss expression, indicating that they could not perform transvection when unpaired (Fig. 5J-L; Fig. S6H, J).
We next wondered whether Transgenes S and T could perform transvection if we mimicked pairing by forcing them into close physical proximity with endogenous ss. Using a FISH screen, we identified three genomic sites that naturally loop to endogenous ss (Fig. 5M), located 4.8 Mb upstream of ss, 0.4 Mb upstream of ss, and 4.6 Mb downstream of ss (sites 2, 4, and 5; Fig. 5N-O; Fig. S6K-L, Q-R, U).
When inserted at these sites, Transgene S was forced into close proximity with endogenous ss (Fig. 5P; Fig. S6M, S, U) and upregulated Ss/Rh4 into nearly 100% of R7s (Fig. 5R-S; Fig. S6N, T)(Johnston and Desplan, 2014). Thus, natural chromosome looping can force loci into proximity and, like pairing, facilitate transvection. In contrast, when Transgene Twas forced into close proximity with endogenous ss (Fig. 5Q; Fig. S6O, U), it did not upregulate Ss/Rh4 (Fig. 5T; Fig. S6P), indicating that it could not perform transvection even when paired. Thus, pairing is necessary but not sufficient for transvection.
We compared the DNA sequence of Transgene T, which does not perform transvection, to Transgene S, the ssinversion, and the duplication, which perform transvection. An upstream region of ~1.6 kb is present in Transgene S, the ssinversion, and the duplication, but not Transgene T, suggesting that this region is critical for transvection (Fig. 5E). ModENCODE ChIP data showed that this region was bound by the insulator proteins CTCF, BEAF, Mod(Mdg4), and Cp190. Additionally, this sequence performed P-element homing (Johnston and Desplan, 2014), an indicator of insulator activity. Together, these data suggested that the DNA element required for transvection is an insulator.
We next investigated the effects of deleting the insulator in the endogenous ss locus. CRISPR-mediated deletion of the insulator produced no viable mutant progeny, preventing testing of the role of this insulator in its endogenous locus. However, an allele of ss in which all of silencer 1, including the insulator, is deleted is viable. This allele does not perform transvection (Johnston and Desplan, 2014), supporting a role for the insulator in transvection at the endogenous ss locus.
To further test the role of the insulator, we examined Transgene E, which drove pairing and contained the complete ss locus, except for the insulator element (Fig. 2F; Fig. 5E). We utilized genetic backgrounds in which Transgene E was the only source of Ss protein, so that any changes in Ss/Rh4 would indicate transvection effects on Transgene E. As a control, we examined Transgene E expression when the endogenous ss locus was hemizygous for a protein null allele (ssprotein null) that did not perform transvection (52% Ss/Rh4; Fig. S6V-W). We next tested Transgene E for transvection with a high-frequency protein null allele (sshigh freq null), which can perform transvection to increase ss expression (Johnston and Desplan, 2014). When the endogenous ss locus was hemizygous for the sshigh freq null, we observed no increase in Transgene E expression, indicating that it did not perform transvection (51% Ss/Rh4; Fig. S6X). Moreover, Transgene E did not perform transvection in other genetic conditions (Fig. S6Y-Z). Thus, Transgene E paired with the endogenous ss locus but failed to perform transvection. These data show that an insulator is required for transvection but not for pairing, indicating that these processes are mechanistically separable (Fig. 5E).
Pairing and transvection are cell-type-specific
It is poorly understood how pairing impacts transvection in a cell-type-specific manner. We propose three models. In the constitutive model, all buttons drive pairing in both cell types, and differences in transvection occur due to variation in transcription factor binding or chromatin state between cell types (Fig. 6A). In the cell-type-specific model, different buttons drive pairing in each cell type, bringing different regions into physical proximity to control transvection efficiency (Fig. 6A). In the strong versus weak model, all buttons pair more strongly in certain cell types than in others, leading to stronger transvection at all sites in strong pairing cell types (Fig. 6A). In a weak pairing cell type, chromosomes are more loosely associated, but the combined force of many buttons along the chromosome is sufficient to bring chromosomes together (Fig. 6H).
Figure 6. Transgenes drive pairing in the eye disc but not the antennal disc.
A. Proposed models for homologous chromosome pairing.
B. Third instar larval eye-antennal disc.
C. Quantifications for Transgenes A-Q in the antennal disc. 2L-3R control data are the same as in Fig. 6D. Black: control, gray: non-pairer. T: contains a TAD. ns=p>0.05, one-way ANOVA on ranks with Dunn’s multiple comparisons test. n=100 for all datasets.
D. Quantifications for Transgene AA in the antennal disc. 2L-3R control data are the same as in Fig. 6C. Black: control, blue: pairer. ***=p<0.001, Wilcoxon rank-sum test. Control was imaged in two colors, then pseudocolored red and re-scored in one color. n=100 for all datasets.
E. Quantifications for Transgenes HH and II in the antennal disc. Black: control, blue: pairer. ns=p>0.05, 1-way ANOVA on ranks with Dunn’s multiple comparisons test. n=100 for all datasets.
F. Antennal disc Hi-C heat maps, directionality indices, and insulator plots for the region spanned by the duplication. Dotted lines: TAD boundaries.
G. Quantifications for the duplication in the antennal disc. Black: control, blue: pairer. **=p<0.005, Wilcoxon rank-sum test. n=100 for all datasets.
H. Models of pairing in strong vs. weak pairing cell types. In a strong pairing cell type, a single button is able to drive pairing with its homolog, and whole chromosomes that contain many buttons also pair. In a weak pairing cell type, a single button alone is not sufficient to drive pairing, but the combined strength of many buttons interspersed along an entire chromosome brings homologous chromosomes together.
See also Fig. S7.
To test these models, we investigated differences in homologous chromosome pairing between the larval eye and antenna (Fig. 6B). As pairing is widespread across fly somatic tissues (Stevens, 1906), we hypothesized that endogenous loci would pair with each other in all three models. As we hypothesized, we observed pairing between endogenous copies of ss in both the eye and antennal discs (Fig. S5F-G, J; Fig. S7A, C).
Transgene-driven pairing offered a sensitized system to distinguish between models. We hypothesized that if pairing occurred through the constitutive model, all transgenes that paired in the eye would pair in the antenna. If pairing occurred through the cell-type-specific model, different transgenes would drive pairing in the eye and antenna. If pairing occurred through the strong vs. weak model, few or none of the transgenes that paired in the eye would pair in the antenna, because a single button alone would not be strong enough to pair in a weak pairing cell type (Fig. 6H). To test this hypothesis, we assessed whether transgenes, which we examined in the eye, paired in the antenna. With one exception (Transgene AA; Fig. 6D), transgenes did not drive pairing (Fig. 6C, E), supporting the strong vs. weak model.
A prediction of the strong vs. weak model is that several buttons combined across a longer region are strong enough to drive pairing in a weak pairing cell type. Supporting this idea, the duplication and chromosome rearrangements paired in the eye and the antenna (Fig. 6F-G; S7B-C, D-F), suggesting that larger regions encompassing several buttons drive pairing in all cell types.
As pairing is required for transvection and buttons pair more strongly in certain cell types, we hypothesized that transvection also occurs more strongly in these cell types. To test this hypothesis, we again used the ss locus as a paradigm. In addition to its role in R7 photoreceptors, ss is required for the development of the arista, a structure on the antenna (Fig. 7A-B) (Morata and Lawrence, 1979; Wernet et al., 2006). Disruption of ss in the aristae transforms the aristae into legs (aristapedia). Transgene E, which contains ss, does not pair in the antennal disc (Fig. 6C; Fig. S7G-J), which develops into the adult arista. Therefore, we hypothesized that ss would not perform transvection efficiently in the arista.
Figure 7. ss transvection is cell-type-specific.
A,C,E,G. Genotypes tested for transvection. Gray rectangles: exons. Small black arrows: transcription start sites. Red X indicates an uncharacterized mutation in the ssarista 1 sequence. Red X over gray arrow indicates an absence of transvection between alleles in the arista.
B,D,F,H. Arista phenotype. Scale bars=50 μm. White arrows indicate arista. Image for Fig. 7F is the same experiment as Fig. S8C. Image for Fig. 7H is the same experiment as in Fig. S8F.
I-K. Models for TAD- and insulator-driven homologous chromosome pairing.
I. Black ovals represent TAD boundaries.
J. Blue, purple, and green ovals, green hexagons, orange squares, and blue triangles represent insulator proteins.
K. Translucent pink and orange ovals represent microcompartments formed by TADs and insulators. See also Fig. S8.
To test this hypothesis, we examined an allele of ss that specifically affects arista development (ssarista 1)(Fig. 7E; Fig. S8A-C). Flies heterozygous for ssarista 1 and a ss deficiency (ssdef) displayed aristapedia (Fig. 7E-F; Fig. S8A, C), as did ssprotein null / ssdefflies (Fig. 7C-D). In the eye, ssprotein null performed transvection to rescue ss expression (Fig. S8M-P). However, the aristapedia mutant phenotype persisted in ssarista 1 / ssprotein null flies (Fig. 7G-H; Fig. S8D-F), suggesting that, unlike in the eye, transvection does not rescue ss expression in the arista. Cell-type-specific transvection of the ss gene in the eye but not the arista was also observed in other genetic conditions (Fig. S8G-L, Q-BB)
What factors contribute to stronger pairing and transvection in the eye than in the antenna? While earlier work suggested that TADs are preserved between cell types (Dixon et al., 2012; Smith et al., 2016), more recent studies using imaging- and single cell-based approaches show considerable cell-to-cell heterogeneity (Cattoni et al., 2017; Finn et al., 2019; Flyamer et al., 2017; Nagano et al., 2013). Therefore, we investigated whether the TADs we tested were present in both the eye and antenna. With the exception of the TADs spanned by Transgenes K and M, which were eye-disc-specific, all TADs were present in both the eye and antenna (Fig. S7K-R), suggesting that cell-type-specific differences in TADs do not contribute to cell-type-specific pairing.
As insulators are also associated with transgene pairing in the eye, we hypothesized that higher levels of insulator expression in the eye contribute to cell-type-specific pairing. To test this hypothesis, we performed RNA-seq on the larval eye and antenna. We compared the number of insulator transcripts between tissues using kallisto to perform RNA-seq quantification and sleuth to perform Likelihood Ratio tests. CTCF, Cp190, Mod(mdg4) and GAF all had significant p-values (Table S1), indicating slight enrichment of these insulators in the eye. However, none of the six insulator proteins had significant false-discovery-adjusted q-values (Table S1), suggesting that differences in insulator levels may contribute to cell-type-specific pairing but are not the main factor driving this process. Therefore, differences in levels of other protein factors, insulator protein occupancy, or mitotic state between the two tissues may contribute to cell-type-specific pairing.
As transgenes pair in the eye but not the antenna and ss transvection occurs in the eye but not the antenna, our data support the strong vs. weak model, in which buttons hold chromosomes together more tightly in certain cell types to facilitate efficient transvection.
Discussion
The mechanisms driving chromosome pairing and transvection have remained a mystery of fly genetics since their initial discoveries (Lewis, 1954; Stevens, 1906). Through our development of a FISH assay to test regions of the genome for pairing ability, we provide support for the button model of pairing initiation and offer evidence of general features, TADs and clusters of insulators, that contribute to pairing. Consistent with our findings, recent work examining steady-state interactions between paired chromosomes indicates that tightly associated regions are enriched for insulator proteins (Rowley et al., 2019). Additionally, homologous TADs have been observed to interact between paired chromosomes using super resolution microscopy (Szabo et al., 2018). Moreover, homologous chromosome pairing initiates at the same embryonic stage as TAD formation (Dernburg et al., 1996; Fung et al., 1998; Gemkow et al., 1998; Hiraoka et al., 1993; Hug et al., 2017). However, this relationship is complicated, as it is also the stage when zygotic genome activation occurs (Hug et al., 2017).
Our work provides an important complement to other recent work examining steady-state interactions between homologous chromosomes after pairing has already been established (AlHaj Abed et al., 2019; Erceg et al., 2019; Rowley et al., 2019). Whereas these studies allow genome-wide detection of tightly paired regions, our assay provides a direct functional examination of button regions for pairing ability, enabling testing of specific hypotheses about the factors that contribute to pairing. Our results reveal a link between the presence of TADs and the ability to pair.
Our work is also relevant to the study of nuclear architecture in general. The role of TADs in genome function remains unclear. While it is generally thought that TADs isolate genes into regulatory domains to ensure their activation by the correct cis-regulatory elements, only a few studies have identified a role for TADs in maintaining proper gene activity (Guo et al., 2015; Lupianez et al., 2015). Studies that dissolve or rearrange TADs (Ghavi-Helm et al., 2019; Nora et al., 2017; Rao et al., 2017; Schwarzer et al., 2017) find relatively minor effects on gene expression, suggesting that TADs may play additional roles beyond the maintenance of transcriptional activity. Our work identifies one such additional role for these poorly understood chromatin structures.
Our data suggest that intact buttons comprising complete TADs and combinations of insulators are sufficient to drive pairing, and that smaller fragments of buttons do not have strong pairing activity. We propose a model in which complex networks of insulator elements within TADs bring homologous chromosomes together. Pairing may initiate at insulator-enriched TAD boundaries, bringing the interiors of TADs into close physical proximity as a result (Fig. 7I). Alternatively, each TAD may bind a unique “code” of insulator proteins, allowing it to find its homologous partner (Fig. 7J). Unique insulator codes might also facilitate the formation of button-specific chromatin conformations, allowing each button to create its own microcompartment and pair with its homolog (Fig. 7K). Therefore, while TADs act as regions of association in cis, our results suggest that TADs may also act as regions of self-association in trans.
Furthermore, we find that pairing is necessary but not sufficient for transvection and that distinct elements are required for these processes: TADs and clusters of insulators play a role in pairing, while a single insulator element facilitates transvection to the endogenous spineless locus. Canonical studies of transvection indicate that disruptions of transvection are caused by disruptions in pairing. Our data clearly indicate that transvection can also be disrupted by the loss of small DNA elements, even when pairing is preserved. Consistent with our findings using endogenous alleles, insulators play roles in transvection between transgenes (Lim et al., 2018; Piwko et al., 2019).
Pairing and transvection are stronger in certain cell types than in others, suggesting that tighter pairing in a given cell type enables more efficient transvection. While a number of studies have noted differences in the level of pairing between cell types (Dernburg et al., 1996; Fung et al., 1998; Gemkow et al., 1998; Hiraoka et al., 1993) or in the efficiency of transvection between cell types (Bateman et al., 2012; Blick et al., 2016; Kassis et al., 1991; Mellert and Truman, 2012), our study provides a link between the level of pairing in a given tissue and the efficiency of transvection in that tissue. This cell-type-specific pairing may be partially facilitated by differences in insulator levels, or by tissue-specific factors that recruit insulator proteins. Additionally, larval photoreceptors are post-mitotic, while the antennal disc is still undergoing cell division. Frequent cell division in the antennal disc may disrupt pairing interactions between transgenes and their endogenous loci.
In addition to differences in pairing efficiency, differences in the function of cis-regulatory elements between cell types may contribute to cell-type-specific transvection. Certain enhancers have a stronger capacity to act between chromosomes than others (Blick et al., 2016). An eye-specific enhancer for a gene might have a stronger transvection capacity than the antenna-specific enhancer for the same gene, facilitating more efficient transvection in the eye. Additionally, enhancer action between chromosomes is often more efficient in the absence of a promoter in cis (Casares et al., 1997; Duncan, 2002; Gohl et al., 2008; Martinez-Laborda et al., 1992; Morris et al., 1999; Sipos et al., 1998). Therefore, certain enhancers, such as those in the antenna, may only perform transvection in specific promoter-mutant backgrounds. However, our data suggest that the strength of pairing also plays a major role in determining transvection efficiency between cell types. Variation in levels of pairing or transvection across cell types has been observed for a number of loci (Blick et al., 2016; Fung et al., 1998; Gemkow et al., 1998), suggesting that differences in pairing between cell types may be a general mechanism regulating gene expression.
Together, our findings suggest a general mechanism in which TADs and insulators contribute to homologous chromosome pairing and interchromosomal gene regulation across organisms to facilitate processes including X-inactivation and imprinting.
STAR Methods
LEAD CONTACT AND MATERIALS AVAILABILITY
Further information and requests for resources and reagents should be directed to and will be fulfilled by Robert J. Johnston Jr. (robertjohnston@jhu.edu). Fly lines, Oligopaints FISH probes, and other reagents are available upon request from the authors.
EXPERIMENTAL MODEL AND SUBJECT DETAILS
Drosophila lines
Flies were raised on standard cornmeal-molasses-agar medium and grown at 25° C. All experiments in this study included both male and female flies. See Table S2 for a full list of fly genotypes used in this study. Information on previously published fly lines is available in (Duncan et al., 1998; Johnston and Desplan, 2014; Lee et al., 2001; Lewis, 1978; Morata and Lawrence, 1979; Parks et al., 2004; Payne, 1918; Venken et al., 2009; Waddington, 1952)
METHOD DETAILS
Transgene constructs
Constructs were purchased from the CHORI Drosophila melanogaster BAC library collection (Venken et al., 2009) or generated in (Johnston and Desplan, 2014) and sent to BestGene Inc. (Chino Hills, CA) or Rainbow Transgenic Flies, Inc. (Camarillo, CA) for injection. Constructs were inserted via PhiC31 integration at the landing sites described in Table S3.
Oligopaints probe libraries
Information on all Oligopaints FISH probes generated for this study can be found in Table S4.
Pairing controls
Information on all controls used for pairing experiments in this study can be found in Table S5.
Compartments
Information on the A and B compartments examined in this study can be found in Table S6.
Antibodies
Antibodies and dilutions were as follows: mouse anti-Lamin B (DSHB ADL67.10 and ADL84.12), 1:100; rabbit anti-GFP (Invitrogen), 1:500; rabbit anti-Rh4 (gift from C. Zuker, Columbia University), 1:50; mouse anti-Rh3 (gift from S. Britt, University of Texas at Austin), 1:50; mouse anti-Prospero (DSHB MR1A), 1:10; rat anti-Elav (DSHB 7E8A10), 1:50; guinea pig anti-Ss (gift from Y.N. Jan, University of California, San Francisco), 1:500. All secondary antibodies (Molecular Probes) were Alexa Fluor-conjugated and used at a dilution of 1:400.
Antibody staining for pupal and adult eyes
Dissections were performed as described in references (Hsiao et al., 2012; Johnston et al., 2011; Jukam et al., 2016; Thanawala et al., 2013). Eyes were dissected and fixed at room temperature for 15 minutes in 4% formaldehyde diluted in 1X PBX (PBS+0.3% Triton-X), then washed three times in 1X PBX. Eyes were incubated overnight at room temperature in primary antibody diluted in 1X PBX, then washed three times in 1X PBX and incubated in PBX at room temperature for ≥3 hours. Secondary antibody diluted in 1X PBX was added and incubated overnight at room temperature. Eyes were then washed three times in 1X PBX and incubated in PBX at room temperature for ≥2 hours. Adult eyes were mounted in SlowFade Gold (Invitrogen), and pupal eyes were mounted in Vectashield (Vector Laboratories, Inc.). Images were acquired on a Zeiss LSM700 confocal microscope.
The adult eye dissection protocol was used for Fig. 5D, J-L, R-T; Fig. S5C-E; S6H, J, N, P, T, W-X, Z; and S8B, E, H, K, N, P, R, U, X, AA. The pupal dissection protocol was used for Fig. 5C and Fig. S6B, D.
Oligopaints probe design
Probes for DNA FISH were designed using the Oligopaints technique (Beliveau et al., 2015; Beliveau et al., 2012). Target sequences were run through the bioinformatics pipeline available at http://genetics.med.harvard.edu/oligopaints/ to identify sets of 42-bp (for old ss 90K probes) or 50-bp (for all other probes) optimized probe sequences (i.e. “libraries”) tiled across the DNA sequence of interest. Five 19-bp barcoding primers, gene F and R; universal (univ) F and R, and either sublibrary (sub) F or random (rando) R, were appended to the 5’ and 3’ ends of each probe sequence (Fig. S1S-T). To ensure that all probes were the same length, an additional 8-bp random sequence was added to the 3’ end of the old ss 90K probes. The gene F and R primers allowed PCR amplification of a probe library of interest out of the total oligo pool, and the univ F and R primers allowed conjugation of fluorophores, generation of single-stranded DNA probes, and PCR addition of secondary sequences to amplify probe signal. The ss 50-kb left and right extension libraries had a sub F primer between the gene and universal forward primers to allow PCR amplification of probes targeting a specific sub-region of the locus of interest (Fig. S1S). All other probe libraries had a rando R primer appended at the 3’ end to maintain a constant sequence length between all probes (Fig. S1T).
Barcoding primer sequences were taken from a set of 240,000 randomly generated, orthogonal 25-bp sequences (Qikai Xu, 2008) and run through a custom script (available at https://github.com/kviets0913/Oligopaints-Primers-Custom-Script) to select 19-bp sequences with ≤15-bp homology to the Drosophila genome. Primers were appended to probe sequences using the orderFile.py script available at http://genetics.med.harvard.edu/oligopaints/. Completed probe libraries were synthesized as custom oligo pools by Custom Array, Inc. (Bothell, WA), and fluorescent FISH probes were generated as described in references (Beliveau et al., 2015; Beliveau et al., 2012).
DNA FISH
DNA FISH was performed using modified versions of the protocols described in references (Beliveau et al., 2015; Beliveau et al., 2012). 20-50 eye-antennal discs attached to mouth hooks from third instar larvae were collected on ice and fixed in 129 μL ultrapure water, 20 μL 10X PBS, 1 μL Tergitol NP-40, 600 μL heptane, and 50 μL fresh 16% formaldehyde. Tubes containing the fixative and eye discs were shaken vigorously by hand, then fixed for 10 minutes at room temperature with nutation. Eye discs were then given three quick washes in 1X PBX, followed by three five-minute washes in PBX at room temperature with nutation. Eye discs were then removed from the mouth hooks and blocked for 1 hour in 1X PBX+1% BSA at room temperature with nutation. They were then incubated in primary antibody diluted in 1X PBX overnight at 4°C with nutation. Next, eye discs were washed three times in 1X PBX for 20 minutes and incubated in secondary antibody diluted in 1X PBX for two hours at room temperature with nutation. Eye discs were then washed two times for 20 minutes in 1X PBX, followed by a 20-minute wash in 1X PBS. Next, discs were given one 10-minute wash in 20% formamide+2X SSCT (2X SSC+.001% Tween-20), one 10-minute wash in 40% formamide+2X SSCT, and two 10-minute washes in 50% formamide+2X SSCT. Discs were then predenatured by incubating for four hours at 37°C, three minutes at 92°C, and 20 minutes at 60°C. Primary probes were added in 45 μL hybridization buffer consisting of 50% formamide+2X SSCT+2% dextran sulfate (w/v), + 1 μL RNAse A. All probes were added at a concentration of >5 pmol fluorophore/μL. For FISH experiments in which a single probe was used, 4 μL of probe was added. For FISH experiments in which two probes were used, 2 μL of each probe was added. After addition of probes, eye discs were incubated at 91 °C for three minutes and at 37°C for 16-20 hours with shaking. Eye discs were then washed for 1 hour at 37°C with shaking in 50% formamide+2X SSCT. 1 mL of each secondary probe was added at a concentration of 100 pmol/μL in 50 μL of 50% formamide+2X SSCT. Secondary probes were hybridized for 1 hour at 37°C with shaking. Eye discs were then washed twice for 30 minutes in 50% formamide+2X SSCT at 37°C with shaking, followed by three 10-minute washes at room temperature in 20% formamide+2X SSCT, 2X SSCT, and 2X SSC with nutation. Discs were mounted in SlowFade Gold immediately after the final 2X SSC wash, and imaged using a Zeiss LSM700 confocal microscope.
Generation of CRISPR lines
CRISPR lines were generated as described in references (Anderson et al., 2017; Gratz et al., 2013; Port et al., 2014; Yan et al., 2017). For both ssenh del and ssupstream del, sense and antisense DNA oligos for the forward and reverse strands of four gRNAs were designed to generate BbsI restriction site overhangs. The oligos were annealed and cloned into the pCFD3 cloning vector (Addgene, Cambridge, MA). A single-stranded DNA homology bridge was generated with 60-bp homologous regions flanking each side of the predicted cleavage site and an EcoRI (for ssenh del) or NaeI (for ssupstream del) restriction site to aid in genotyping. The gRNA constructs (125 ng/μl) and homologous bridge oligo (100 ng/μl) were injected into Drosophila embryos (BestGene, Inc., Chino Hills, CA). Single males were crossed with a balancer stock (yw; +; TM2/TM6B), and F1 female progeny were screened for the insertion via PCR, restriction digest, and sequencing. Single F1 males whose siblings were positive for the deletion were crossed to the balancer stock (yw; +; TM2/TM6B), and the F2 progeny were screened for the deletion via PCR, restriction digest, and sequencing. Deletion-positive flies from multiple founders were used to establish independent stable stocks.
The oligos used for the ssenh deland ssupstream del CRISPR are listed in Table S7.
Scanning electron microscopy
Adult Drosophila heads were removed and immediately mounted on a pin stub without fixation or sputtering. Heads were imaged at high vacuum at a voltage of 1.5 kV. All SEM was performed on a FEI Quanta ESEM 200 scanning electron microscope. SEM was used for Fig. 7B, D, F, H and Fig. S8C, F, I, L, S, V, Y, BB.
Hi-C tissue preparation
Larval eye discs and antennal discs were prepared for Hi-C using the following steps: 3 replicates of 125 third instar larval eye or antennal discs (i.e. N=3, n=125) were dissected in 1X PBS and stored in 1X PBS on ice until fixation. Eye and antennal discs were separated manually by dissection. After all discs were collected, discs were centrifuged at 17,600g for 1 minute and PBS was removed. Residual PBS was removed with a pipette, and discs were left to air dry for 3 minutes. Fixative (750 μL heptane, 237.4 μL cross-linking solution, 12 μL fresh 37% formaldehyde) was added to the discs, and discs were fixed for 15 minutes with vigorous shaking. Discs were then centrifuged at 17,600g for 1 minute and fixative was removed. 1 mL of glycine solution (125 mM glycine, 0.1% Triton in PBS) was added to the discs, and the discs were incubated for ≥1 minute with vigorous shaking to quench the crosslinking reaction. Discs were then centrifuged at 17,600g for 1 minute and glycine solution was removed. Discs were then washed 2X with 1 mL of ice-cold PBX and centrifuged at 17,600g for 1 minute. PBX was removed, and discs were then centrifuged again at 17,600g for 1 minute. Any residual PBX was removed with a pipette, and discs were air dried for 3 minutes. Discs were then snap-frozen in liquid nitrogen and stored at −80°C.
Cross-linking solution: 50 mM Hepes (11.92 g), 1 mM EDTA (0.292 g), 0.5 mM EGTA (0.19 g), 100 mM NaCl (5.844 g) in 1L ultra-pure water. Solution was brought to pH 8, sterile filtered, and stored at room temperature.
Glycine solution: 0.14 g glycine, 15 mL 1X PBS, 15 μL Triton-X. Solution was made fresh for each experiment and was stored at 4°C until use.
Hi-C library preparation and sequencing
Prior to the Hi-C experiment, biotinylated bridge oligos were prepared as follows: 10 μl 10X NEBuffer 2 was mixed with 90 μl of a 100 μM mixture of the two oligos (Bridge oligo forward: GATCGAGCTCGAGAA/iBiodT/T, Bridge oligo reverse: CTCGAGCTC). The mixture was heated to 98Ό for 6 min, then ramped down to RT at −0.1Ό / sec and stored at −20Ό until use.
Antennal and eye discs were thawed on ice and 1 ml of lysis buffer (10 mM Tris-Cl pH 8.0, 10 mM NaCl, 0.2% IGEPAL CA-630, 1x complete protease inhibitor (Roche, Cat. No. 11 873 580 001)) was added. Samples were incubated on ice for 30 min and then spun at 1000g at 4°C for 5 min. The supernatant was removed and pellets were resuspended in 200 μl of 0.5% SDS and incubated at 65Ό for 10 min. The SDS was then quenched by addin g 100 μl of 10% Triton X-100 and incubating at 37°C for 15 min. 50 μl of 10x NEBuffer 3.1, 130 μl nuclease-free H2O and 8 μl of DpnII (NEB) were added and samples were incubated at 37Ό overnight. Tubes were centrifuged for 5 min at 1000g at RT and pellets were resuspended in 30 μl nuclease-free H2O. 5 μl biotinylated bridge oligos (90 uM) were ligated to the DNA fragments overnight at 16°C using 1 μl of T4 DNA ligase HC (Thermo Fisher), 5 μl ligation buffer, 4 μl 10x BSA, and 5 μl PEG 4000.
After adding 2.5 μl of 0.5 M EDTA, the samples were centrifuged at 1000g at RT for 5 min and resuspended in 300 μl 1x BSA and 0.2% SDS in water. The pellets were centrifuged again at 1000xg at RT for 5 min and the pellets were resuspended in 300 μl 1x BSA and 0.1% SDS in water. This step was repeated for a second time. After another centrifugation, the samples were resuspended in 245 μl of 1.22x BSA and 0.2% Triton X-100 in water. The bridge oligos were phosphorylated by adding 30 μl 10x T4 ligase buffer (Thermo Fisher) and 20 μl PNK (10 U/ μl NEB). Tubes were incubated at 37Ό for 1 h. Bridge oligos were ligated to each oth er using 5 μl T4 DNA ligase HC (Thermo Fisher), 70 μl 10x T4 ligase buffer, 7 μl 100x BSA and 618 μl nuclease-free H2O. Samples were incubated for 4 h at RT. The samples were then spun down at 1000g at 4Ό for 5 min and the pellet was resuspended in 500 μL of extraction buffer (50 mM Tris-Cl pH 8.0, 50 mM NaCl, 1 mM EDTA, 1% SDS).
To digest proteins, 20 μL of 20 mg/ml Proteinase K (Invitrogen) was added and the mixture was incubated at 55°C at 1000 rpm on a thermal shak er for 30 min. For decrosslinking, 130 μL of 5 M sodium chloride were added and the mixture was incubated at 68Ό at 1000 on a thermal shaker overnight. To precipitate DNA, 63 μL of 3 M sodium acetate pH 5.2, 2 μL of 15 mg/ml GlycoBlue (Thermo Fisher) and 1000 μL of absolute ethanol were mixed with the sample, followed by incubation at −80Ό for 1 hour. The samples were then spun at 20,000xg at 4°C for one hour. The supernatant was removed and the DNA pellet was washed two times using 800 μL of 70% ethanol. All traces of remaining supernatant were removed; the was pellet air-dried for 2 min and then solubilized in 99 μl of 10 mM Tris-Cl pH 8.0. RNA was digested by incubation with 1 μL of 10 mg/ml RNase A at 37Ό for 15 min. The DNA was sheared in a total volume of 100μl to −200-400 bp with Diagenode Bioruptor Pico (4Ό, ON/OFF 30’’/90’’, 8 cycles). DNA fragmen ts were size-selected with SPRIselect Beads (Beckman Coulter) following the manufacturer’s instructions. For right side size selection, 0.6x volume of beads were used, and for left side size selection 1x was used. DNA was eluted in 100μL of 10mM Tris-Cl pH 8.0. For biotin enrichment, 30 μl of Dynabeads Streptavidin M-280 (Life Technologies) per sample were prepared following the manufacturer’s instructions and added to the purified DNA. The biotinylated bridge oligos were allowed to bind to the beads for 20 min at RT. The beads were washed 4 times with 200 μL each of 1x B&W buffer (5 mM Tris-HCl pH 7.5, 0.5 mM EDTA, 1 M NaCl) + 0.1% Tween-20. This was followed by 2 washes with 10 mM Tris-Cl pH 8.0. The beads were then resuspended in 40 μL of the same solution.
Library preparation was performed using the Swift Biosciences Accel-NGS 2S Plus DNA Library Kit, with the following modification: throughout the library preparation there was no SPRIselect clean-up performed. Instead, the samples were washed using the already bound Streptavidin beads. Each of the SPRI steps were replaced by the washes described hereafter: Samples were placed against the magnet and incubated until the solution was clear. The supernatant was discarded and samples removed from the magnet. Beads were resuspended in 150 μL of 1x B&W buffer + 0.1% Tween-20, and incubated at 55Ό in a thermal cycler for 2 min. This wash step was repeated a second time. An additional wash step with 100μL of 10 mM Tris-Cl pH 8.0 was performed. Here, the samples were immediately put against the magnet after resuspension, without any incubation. The supernatant was discarded and samples removed from the magnet. Beads were resuspended in the mixes as described in the Swift Biosciences Library Kit for Repair II, Ligation I and Ligation II. After Ligation II and the above described washes, beads were resuspended in 20μL of 10 mM Tris-Cl pH 8.0.
25 μl HiFi HotStart ReadyMix and 5 μl 10x Primer Mix (both Kapa Biosystems) were added and the PCR program was run as described in the Kapa HiFi HotStart ReadyMix manual. A 60Ό annealing temperature, 30 sec extension time and 12 cycles were used for the amplification. After the PCR, two 0.9x SPRI select clean-ups were performed and library concentration was measured using the Qubit dsDNA HS Assay kit (Thermo Fisher). Fragment size of the libraries was determined with the High Sensitivity DNA Kit for Bioanalyzer (Agilent Technologies). Libraries were sequenced using standard Illumina reagents with a read length of 150 bp, paired-end.
Hi-C mapping and TAD calling
Directionality index (DI) scores were calculated across 15 Kb windows, stepping every 5 Kb, by finding the log2 transform of the difference in the ratios of downstream versus upstream summed observed over expected interactions ranging from 15 Kb to 100 Kb in size. The expected value of a bin was defined as the sum of the product of fragment corrections for each valid fragment pair with both interaction fragments falling within the bin.
TAD calls were based on a Hidden Markov Model (HMM) segmentation of the DI scores. The HMM was initialized with three states (downstream bias, neutral, upstream bias), each with a three-part equally Gaussian mixture model. All mixture model components had variance of one and means in 0.5 increments centered on 1.0, 0.0, and −1.0 for the three states, respectively. Transitions to states (0, 1, 2) were set to (0.8, 0.05, 0.15), (0.25, 0.5, 0.25), and (0.15, 0.05, 0.8) for the downstream, neutral, and upstream states, respectively. Initial state frequencies were set at 0.3, 0.4, and 0.3. Parameters were learned from all chromosome DI scores after dividing each chromosome DI score set by its standard deviation. TADs were defined as starting at the first downstream bias state following an upstream bias state with any number of intervening neutral states. TADs extended through the last upstream bias state followed by a downstream bias state with any number of intervening neutral states.
mRNA sequencing and analysis
RNA-seq was performed on three biological replicates, each consisting of 30 third instar larval eye or antennal discs. Eye-antennal discs were dissected in 1X PBS and separated from the mouth hooks. Eye discs and antennal discs were then manually separated and placed directly into 300 μL of Trizol. RNA was purified using a Zymo Direct-zol RNA MicroPrep kit (catalog number R2062). mRNA libraries were prepared using an Illumina TruSeq Stranded mRNA LT Sample Prep Kit (catalog number RS-122-2101). Sequencing was performed using an Illumina NextSeq 500 (75 bp, paired end). Sequencing returned an average of 23,048,349 reads per replicate.
The following pipeline was used for mRNA-sequencing analysis: 1) FASTQ sequencing datasets were assessed for quality using FastQC; 2) Pseudoalignment with the Drosophila dm6 transcriptome and read quantifications were performed using kallisto (Bray et al., 2016); 3) Transcript abundance files generated by kallisto were joined to a file containing the genomic coordinates of all Drosophila mRNA transcripts (dmel-all-r6.20.gtf, available from Flybase); 4) The joined transcript coordinate file was compared to a file containing the coordinates of all tested transgenes using the bedtools intersect tool (http://bedtools.readthedocs.io/en/latest/content/tools/intersect.html)(Quinlan and Hall, 2010). The output file contained a list of all TPMs for each gene contained in each transgene.
Assessment of ChIP peaks and ncRNA density
ncRNA content of transgenes was assessed manually using the GBrowse tool on FlyBase. tRNAs, miRNAs, snoRNAs, and lncRNAs were included in the analysis of ncRNA content.
Transgenes were evaluated for insulator binding sites, Polycomb Group Complex binding sites, and the presence of chromatin marks using publicly available ChIP-chip and ChIP-seq datasets (Cuartero et al., 2014; Negre et al., 2010; Ong et al., 2013; Van Bortle et al., 2012; Wood et al., 2011). The datasets used for this analysis are listed in Table S8.
To ensure a higher likelihood of selecting true ChIP peaks rather than false positives, only those insulators present in data from multiple cell types were considered when assessing the number of insulator sites per transgene. .bed files containing the genomic coordinates of all ChIP peaks in each dataset were downloaded and classified by cell type. All datasets from the same cell type were merged into one file using the bedtools merge tool (http://bedtools.readthedocs.io/en/latest/content/tools/merge.html)(Quinlan and Hall, 2010). Insulator ChIP peaks present in more than one cell type were identified using the bedtools multiIntersectBed tool(Quinlan and Hall, 2010). The coordinates of insulator ChIP peaks present in multiple cell types were compared to a .bed file containing the genomic coordinates of all transgenes using the bedtools intersect tool (http://bedtools.readthedocs.io/en/latest/content/tools/intersect.html)Quinlan and Hall, 2010). This pipeline output the number of insulator ChIP peaks contained in each transgene.
To identify clusters of insulators, files containing the ChIP peak coordinates for each insulator were compared using the the bedtools multiIntersectBed tool(Quinlan and Hall, 2010), which output a .bed file listing all of the locations of overlap between insulator binding sites. Insulators were considered to cluster if their binding site coordinates overlapped or were directly adjacent to each other. The coordinates of clusters containing specific numbers or combinations of insulators were selected from the intersected .bed file and compared to a .bed file containing the genomic coordinates of all transgenes using the bedtools intersect tool (http://bedtools.readthedocs.io/en/latest/content/tools/intersect.html)(Quinlan and Hall, 2010). This pipeline output the number of insulator clusters contained in each transgene.
For Polycomb Group Complex proteins and chromatin marks, .bed files containing the genomic coordinates of all ChIP peaks in each dataset were downloaded and merged into one file using the bedtools merge tool (http://bedtools.readthedocs.io/en/latest/content/tools/merge.html)(Quinlan and Hall, 2010). The merged file was compared to a .bed file containing the genomic coordinates of all transgenes using the bedtools intersect tool (http://bedtools.readthedocs.io/en/latest/content/tools/intersect.html)(Quinlan and Hall, 2010). This pipeline output the number of protein or chromatin mark ChIP peaks contained in each transgene.
QUANTIFICATION AND STATISTICAL ANALYSIS
Pairing quantifications
All quantifications were performed in 3D on z-stacks with a slice thickness of 0.2 μm. Quantifications were performed manually using Fiji (Schindelin et al., 2012; Schneider et al., 2012). To chart the z position of each FISH dot, a line was drawn through the dot and the Plot Profile tool was used to assess the stack in which the dot was brightest. Due to homologous chromosome pairing between both copies of the endogenous chromosome and both copies of the transgene insertion chromosome, we observed a single dot for each chromosome. Therefore, we measured a single distance between the two paired transgene chromosome copies and the two paired endogenous chromosome copies. To determine the x-y distance between FISH dots, a line was drawn from the center of one dot to the center of the other dot and the length of the line was measured with the Plot Profile tool. The distance between the FISH dots was then calculated in 3D. A total of 100 nuclei from 3 eye discs (i.e. N=3, n=100) were quantified for Fig. 2F; 3E; 6C-E, G; S1E-F; S2E; S3C; and S7C, J. A total of 50 nuclei from three eye discs (i.e. N=3, n=50) were quantified for Fig. 3I; S1D, N; S2H; S2L; S4H; S5B, J; and S6U.
For experiments in which the transgene and endogenous site were both labeled with red fluorescent probes, FISH punctae ≤0.4 mm apart could not be distinguished as separate and were assigned a distance of 0.4 mm apart. For all controls in Fig. 3E; 6D; S3C; S4H; and S5J, green probes labeling the transgene insertion site were pseudocolored red and data were quantified in the same way as experiments in which the transgene and endogenous site were both labeled with red probes. 3L-X control data in Fig. 3E are taken from the same experiment as in Fig. S2E, but the data were re-quantified with the green probes pseudocolored red. 2L-3R eye control data in Fig. 3E; S3C; and S5J are taken from the same experiment as in Fig. 2F; 3I; S1F; and S6U, but the data were re-quantified with the green probes pseudocolored red. 2L-3R antenna control data in Fig. 6D are taken from the same experiment as in Fig. 6C, but the data were re-quantified with the green probes pseudocolored red. 3L-3R control data in S4H taken from the same experiment as in Fig. S2H and Fig. S6U, but the data were re-quantified with the green probes pseudocolored red.
Adult eye quantifications
The frequencies of Rh4- and Rh3-expressing R7s were scored manually for at least eight eyes per genotype. R7s co-expressing Rh3 and Rh4 were scored as Rh4-positive. 100 or more R7s were scored for each eye. For Fig. S8U, X, and AA, only the ventral half of each eye was scored.
Statistical analysis
All datasets were tested for a Gaussian distribution using a D’Agostino and Pearson omnibus normality test and a Shapiro-Wilk normality test. If either test indicated a non-Gaussian distribution for any of the datasets in an experiment, datasets were tested for statistical significance using a Wilcoxon rank-sum test (for single comparisons) or a one-way ANOVA on ranks with Dunn’s multiple comparisons test (for multiple comparisons). If both the D’Agostino and Pearson and the Shapiro-Wilk tests indicated a Gaussian distribution for all datasets in an experiment, datasets were tested for statistical significance using an unpaired t-test with Welch’s correction (for single comparisons) or an ordinary one-way ANOVA with Dunnett’s multiple comparisons test (for multiple comparisons). These statistical tests were performed using GraphPad Prism. Statistical tests and p-values are described in the figure legends.
Maximum likelihood calculations:
Parameters for either a single or double Gaussian distribution were estimated using maximum likelihood, and model selection was subsequently performed using the Bayesian Information Criterion (BIC). For the double Gaussian, parameters were estimated using a nonlinear recursion (Levenberg-Marquardt) algorithm to maximize the log likelihood of the distribution. These tests were performed using MATLAB (The Mathworks, Inc.), and are described further in the Results section.
Maximum likelihood calculations were performed for all transgenes tested in Fig. 2F. Maximum likelihood estimation was not possible for transgenes in Fig. 3E, as the 0.4 mm distance cutoff for 1-color FISH was too high to allow separation of paired and unpaired distributions into two Gaussian distributions.
DATA AND CODE AVAILABILITY
The Hi-C data and RNA seq data generated during this study have been uploaded to the NCBI Gene Expression Omnibus (GEO). The GEO accession number for the Hi-C data is GSE136267. The GEO accession number for the RNA-seq data is GSE136063. The custom script used to generate 19-bp barcoding primers for Oligopaints probe design is available at https://github.com/kviets0913/Oligopaints-Primers-Custom-Script.
Supplementary Material
Table S2. Genotypes of fly lines used in this study, Related to STAR Methods.
Highlights.
Identification of genomic buttons that drive homologous chromosome pairing in flies
DNA regions spanning TADs display button activity, but not all buttons contain TADs
Button-driven homologous pairing is necessary but not sufficient for transvection
Homologous chromosome pairing and transvection are cell-type-specific
Acknowledgements
We thank Charles Zuker, Steve Britt, Yuh-Nung Jan, the Bloomington Stock Center, the Developmental Studies Hybridoma Bank, and the BACPAC Resources Center for generously providing published fly stocks, BACs, and antibodies. Additionally, we thank Eric Joyce for his extensive assistance with DNA Oligopaints, Marco Gallio for generously sharing his laboratory space and equipment, and Peter DeFord for his assistance with modENCODE ChIP data analysis. Finally, we are grateful to C-ting Wu, Jelena Erceg, Jumana AlHaj Abed, Claude Desplan, Geraldine Seydoux, Victor Corces, Jeff Corden, and members of the Johnston lab for helpful comments on the manuscript. R.J.J. was supported by NIH R01EY025598. K.V. was supported by NIH 1F31EY026786.
Footnotes
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Declaration of Interests
The authors declare no competing interests.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Table S2. Genotypes of fly lines used in this study, Related to STAR Methods.
Data Availability Statement
The Hi-C data and RNA seq data generated during this study have been uploaded to the NCBI Gene Expression Omnibus (GEO). The GEO accession number for the Hi-C data is GSE136267. The GEO accession number for the RNA-seq data is GSE136063. The custom script used to generate 19-bp barcoding primers for Oligopaints probe design is available at https://github.com/kviets0913/Oligopaints-Primers-Custom-Script.