Skip to main content
  • Loading metrics

Fruitless decommissions regulatory elements to implement cell-type-specific neuronal masculinization

  • Margarita V. Brovkina,

    Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Supervision, Visualization, Writing – original draft, Writing – review & editing

    Affiliation Graduate Program in Cellular and Molecular Biology, University of Michigan Medical School, Ann Arbor, Michigan, United States of America

  • Rachel Duffié,

    Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Writing – review & editing

    Affiliations Department of Biochemistry and Molecular Biophysics, Columbia University, New York, New York, United States of America, Mortimer B. Zuckerman Mind Brain and Behavior Institute, Columbia University, New York, New York, United States of America

  • Abbigayl E. C. Burtis,

    Roles Conceptualization, Data curation, Investigation, Methodology, Visualization, Writing – review & editing

    Affiliation Department of Molecular, Cellular, and Developmental Biology, The University of Michigan, Ann Arbor, Michigan, United States of America

  • E. Josephine Clowney

    Roles Conceptualization, Funding acquisition, Investigation, Methodology, Supervision, Writing – original draft, Writing – review & editing

    Affiliation Department of Molecular, Cellular, and Developmental Biology, The University of Michigan, Ann Arbor, Michigan, United States of America


In the fruit fly Drosophila melanogaster, male-specific splicing and translation of the Fruitless transcription factor (FruM) alters the presence, anatomy, and/or connectivity of >60 types of central brain neurons that interconnect to generate male-typical behaviors. While the indispensable function of FruM in sex-specific behavior has been understood for decades, the molecular mechanisms underlying its activity remain unknown. Here, we take a genome-wide, brain-wide approach to identifying regulatory elements whose activity depends on the presence of FruM. We identify 436 high-confidence genomic regions differentially accessible in male fruitless neurons, validate candidate regions as bona fide, differentially regulated enhancers, and describe the particular cell types in which these enhancers are active. We find that individual enhancers are not activated universally but are dedicated to specific fru+ cell types. Aside from fru itself, genes are not dedicated to or common across the fru circuit; rather, FruM appears to masculinize each cell type differently, by tweaking expression of the same effector genes used in other circuits. Finally, we find FruM motifs enriched among regulatory elements that are open in the female but closed in the male. Together, these results suggest that FruM acts cell-type-specifically to decommission regulatory elements in male fruitless neurons.

Author summary

Courtship behavior in male Drosophila melanogaster is controlled by a well-defined neural circuit that is labeled by the male-specific transcription factor Fruitless (FruM). While FruM is known to change the number, anatomy and connectivity of neurons which comprise the circuit and has been suggested to repress the expression of a few gene targets, the mechanism of how FruM regulates genes across many different kinds of neurons is unknown. Using an approach to identify gene regulatory elements based on their chromatin accessibility states (ATAC-seq), we identified a large set of chromatin accessibility changes downstream of Fruitless. By examining the activity of these regulatory elements in vivo, we found that their activity was 1) sexually dimorphic and 2) specific to a single class of FruM neurons, suggesting that FruM acts on different chromatin targets in different neuron classes comprising the courtship circuit. Further, we found a known FruM-regulated enhancer of the FruM-repressed gene Lgr3 to have closed chromatin specifically in FruM neurons. Combined with an enrichment of FruM motifs in regions which are closed in FruM neurons, we present a mechanism where FruM directs the decommissioning of sex-shared regulatory elements to masculinize neurons in a cell-type specific manner.


In many species, male and female brains generate distinct behavioral repertoires. The ability to compare behavior, brains, neurons, and gene expression across the sexes makes sexually dimorphic behaviors premier models for understanding structure-function relationships in neural circuits. In both vertebrates and invertebrates, master regulators of neuronal sex are induced downstream of the sex determination hierarchy and alter the composition of specific neurons and brain areas [14]. Circuit changes produced by these transcription factors are complex and heterogeneous, including differences in the numbers of specific types of neurons, their anatomy and connectivity, and their mature physiology [27]. Collectively, these sex-specific alterations to circuits cause males and females to perform sex-specific innate behaviors. While the causal role of these master regulators in shaping behavior is clear, the transcriptional events through which they do so are opaque.

In insects, neural circuits that regulate mating are masculinized by the action of the Fruitless transcription factor. Male Drosophila melanogaster flies selectively perform courtship displays toward conspecific virgin females, extending and vibrating a single wing to sing a courtship song [8]. Fruitless is both necessary and sufficient for masculinization of behavior: While 2–5% of neurons in both male and female brains express fru transcript from its sexually dimorphic promoter, fruP1, sex-specific splicing results in functional protein, FruM, only in the male [914]. Males mutant for fruitless exhibit dysregulated courtship behaviors, while females genetically manipulated to produce FruM protein perform courtship displays to other females [11,12,1518]. Sex-specific morphological differences in >60 classes of fru+ neurons have been catalogued [2,3,19], and many of these dimorphic populations are implicated in the regulation and performance of male mating behaviors [8].

The fru+ neurons are found in every part of the nervous system, including sensory structures, the central brain, and motor output regions [9,10]. Most subclasses derive from distinct neuroblasts and are only a small proportion of the cells born from each neuroblast [3]. The sexual differentiation of different types of fruitless neurons produces changes to male and female cell number, anatomy, connectivity, function, or a combination of these [2,3,1922]. Work from many labs over the last 15 years has suggested that fruitless neurons throughout the nervous system preferentially interconnect to form a modular circuit dedicated to sex-specific behaviors [20,21,23,24].

FruM is a sequence-specific DNA binding protein that likely turns on and off the transcription of specific gene targets [11,12]. Previous studies have identified a handful of FruM effectors but have not addressed broader logical principles about what it means to be a male neuron [2528]: Does FruM act on the same targets across different fruitless cells? Are the genes it regulates dedicated to the mating circuit? Does it stimulate or repress transcription? Ultimately, defining the transcriptional role for FruM in masculinizing this circuit will allow us to ask how differences in overall circuit architecture and behavior emerge from independent gene regulatory events in its cellular constituents [29].

To increase resolution for identifying candidate enhancers and repressors directly or indirectly regulated by FruM, we performed the Assay for Transposase-Accessible Chromatin (ATAC-seq) [30] on FAC-sorted fru+ and fru- neurons from male and female brains. Using this exquisitely sensitive method, we identified 436 genomic elements differentially accessible in the presence of FruM. To measure the gene regulatory activity of these elements and to define the specific subpopulations of fru+ cell types in which they are capable of regulating gene expression, we analyzed the ability of matched genomic fragments to drive reporter expression across the brain. The combination of these genome-wide and brain-wide approaches allows us to define cell-type-specific enhancers dependent on FruM and the logic of FruM action across different populations of fru+ cells. We find that individual regulatory elements differentially accessible in the presence of FruM are each used in only a small population of fru+ neurons, suggesting that each subpopulation of fru+ cells has a distinct set of FruM effectors. We therefore conclude that FruM acts as a “switch gene” [29]: It flags cells as male and interacts differently with the cell-type-specific transcriptional milieus of different cell types to induce diverse masculinizing adjustments. While regulatory elements may be dedicated to specific FruM populations, the genes they regulate are shared with other circuits. Finally, we identify differentially accessible genomic regions with strong FruM motifs; these are enriched in regions specifically closed in the presence of FruM protein, suggesting that FruM acts to decommission its direct targets.


To determine how fruitless-expressing neurons in male flies are differentially patterned to allow males to perform distinct behaviors from females, we sought to identify genetic elements whose regulatory state correlated with the presence of FruM protein. As chromatin accessibility often correlates with the activity of regulatory elements, we used ATAC-seq, a method for identifying open chromatin regions genome-wide [30]. fruitless is expressed across much of the life cycle: expression begins in late larvae, peaks in mid-pupae, and continues robustly in the adult [13]. FruM-dependent sexual dimorphisms, including in developmental apoptosis, neuronal arbor patterning, and functional properties, encompass differences that likely arise at each of these time points [6,20,3134]. We chose to complete our initial analysis at the adult stage, as comprehensive knowledge of the repertoire of adult fruitless neurons allows us to identify the neurons in which individual regulatory dimorphisms occur.

We used expression of GFP under control of fruP1-Gal4 [10] to report transcription from the fruP1 promoter, and then FAC-sorted and analyzed four populations of cells: fru+ and fru- cells from male and female (Fig 1A–1C). These four populations allow us to define transcriptional differences related to sex, fru transcriptional status, and FruM protein status.

Fig 1. Genomic profiling of a core population of fruitless neurons.

A. 2-photon maximum intensity projection of fruP1-GAL4 driving mCD8-GFP in the adult male central brain, shown with and without MB247-Gal80 masking GFP signal in mushroom body Kenyon cells. Here and throughout, brain images show anterior view. B. Scheme of dissection and sorting. C. Scheme of datasets collected. D. UCSC genome browser screenshot of the fruitless locus. fruitless transcript structure is schematized below. Here and throughout, each signal track shows a transparent overlay of two independent biological replicates. Gray bars under ATAC signal indicate MACS2-called peaks at FDR threshold <0.001, with orange bar indicating peak summit. E. Signal heatmap of ATAC signal (average of two independent biological replicates) summarized over all annotated genes. F. Binary heatmap of genomic regions which contain a peak of open chromatin (MACS2 FDR <0.001). G. Axes that separate the four analyzed cell populations.

fruitless neurons in the central brain are morphologically diverse and derive from >60 neuroblasts [2,3,19]. To enrich for cell populations of interest, from the central brain, we removed optic lobes and the ventral nerve chord. In addition, the largest population of fruitless neurons is γ Kenyon cells. Kenyon cells are required for olfactory learning, including courtship learning; however, Kenyon cells are not required for core courtship programs [35]. To prevent these numerous cells from dominating our analyses, we used MB247-Gal80 to remove reporter expression in γ Kenyon cells and therefore sorted them into the fru- populations (Fig 1A and 1B); we estimate that γ KCs comprise 3% of our fru- libraries. For each of two biological replicates, we sorted GFP+ cells, and matched numbers of GFP- cells, from >20 male and female brains; this yielded 6,000–10,000 cells per sample. GFP+ cells were 1.8–3.6% of cells in female, and 3.6–5% of cells from male, in line with previous estimates of fru+, non-Kenyon cells from the central brain [10,13]. Samples were then subjected to ATAC-seq (two replicates) or RNA-seq (two replicates) (Fig 1C and 1D). Instructions for viewing this data through our public UCSC “trackhub” can be found in the Data Availability Statement.

fruitless is transcribed from several promoters: The fruP1 promoter drives expression of the sexually dimorphic transcripts, including the male transcript coding for FruM, while three downstream promoters drive expression of the FruCOM protein, which is not dimorphic and which is required in both sexes for embryogenesis [12,14]. We do not observe fruCOM transcripts in adult neurons (Fig 1D), and previous analyses have found that FruCOM protein is not present after pupation [13], thus ensuring that our analyses are restricted to differences that arise from the sexually dimorphic FruM protein.

We captured strong (~4.8 fold) enrichment of fruitless transcript in the fru+ RNA-seq libraries (Fig 1D and S1 Table). The small amount of fru mRNA signal in the fru- libraries is expected to derive from the fruitless mRNA expressed in γ Kenyon cells, which we sorted into the fru- pool. We observe fru mRNA in both male and female cells, with the expected sexually dimorphic splicing of the S exon clearly visible. While close to 100% of the fru transcript in female cells matches the expected female splicing variant, a small subset of transcripts in the male have the female exon. In addition to sexually dimorphic splicing, fru transcripts are alternatively spliced at the 3’ end, yielding several possible DNA binding domains [12]. Protein products of these isoforms are designated FruMA, FruMB, and FruMC, and previous results have shown that expression of these three isoforms is largely overlapping, such that most fruitless neurons contain all three [31]. We observe all three 3’ splice isoforms in our libraries. QC metrics are presented in S1A–S1F Fig.

For ATAC-seq, we isolated nuclei and subjected them to TN5 transposition, DNA purification, library preparation, and paired-end sequencing [30], with modifications made for low input cell numbers [36]. After mapping reads to the D. mel genome (dm6) and removing duplicates, we obtained 9–16 million distinct reads per sample. The eight libraries showed strong Spearman correlation overall, as expected given their common source (S1A Fig). Male and female libraries clustered separately; male fru+ samples clustered together, while female samples, all of which lack FruM protein, intermingled (S1A Fig). Likely due to the high overall correlations, principle component analysis was dominated by read depth (S1B Fig). We aggregated ATAC-seq signal across genes and found strong enrichment at promoters, weaker enrichment 3’ of genes, and depletion of signal from transcribed regions (Fig 1E). We then used MACS2 to call peaks in each of the four cell types, yielding >11,000 peaks genome-wide for each sample at FDR<0.001 [37]. ~60% of peaks were called universally across the four sample types, and the remaining 40% were condition-specific (Fig 1F). While we observe many intronic peaks in our dataset, these peaks occupy diverse positions in TSS-anchored gene models and thus do not show strong aggregate signal (Fig 1E). Comparisons across these four cell types allow us to investigate three axes of neuronal difference: sex, fru transcript status, and FruM protein status (Fig 1G).

Sexually dimorphic, FruM-independent peaks reflect dosage compensation

Sexually dimorphic splicing of fruP1 transcripts represents a late, tissue-specific event in the sex differentiation hierarchy [11,12]. In order to test the validity of our data, we first assessed whether we could detect hallmarks of X chromosome dosage compensation in adult neurons, which is expected to be sexually dimorphic but FruM independent (Fig 2A). In Drosophila melanogaster, the dosage compensation complex (DCC) binds to ~700 regions on the male X to upregulate gene expression chromosome-wide (Fig 2A) [38,39]. Two X-linked lncRNAs that help to target the DCC to the male X, roX1 and roX2, were expressed only in our male samples and expressed similarly in fru+ and fru- cells, as expected (Fig 2B) [40]. We also observe male-specific ATAC-seq signals at the roX1 and roX2 loci (Fig 2B). Finally, we observe expected sexually-dimorphic splicing of transformer (tra), sex lethal (sxl), and doublesex (dsx) (S2C–S2E Fig). Just as we find both functional (“male”) and non-functional (“female”) fru transcripts in the male, but only non-functional transcripts in the female (Fig 1D), we observe both non-functional (“male”) and functional (“female”) sxl and tra isoforms in female, and only non-functional isoforms in male (S2C–S2E Fig).

Fig 2. Sexually dimorphic, FruM-independent peaks reflect dosage compensation.

A. Schematic of dosage compensation in D. mel. B. UCSC genome browser screenshot of ATAC-seq and RNA-seq signal at canonical dosage compensation genes lncRNA:roX1 and lncRNA:roX2. roX2 CHART (black bars) shows known tethering sites of lncRNA:roX2 to chromatin [39]. C. Manhattan plot of log2(female/male) chromatin accessibility in fru+ and fru- neurons across chromosomes. Points represent genomic regions. Vertical axes show the relative change in accessibility of a region from DiffBind output. Horizontal axes represent scaled chromosomal locations. Regions with no change in accessibility between samples (FDR > 0.05) are plotted in gray. Regions with male-biased accessibility (chartreuse) appear as negative fold change, and regions with female-biased accessibility (purple) appear as positive fold change. D. Summary of X versus autosome distribution of differentially accessible genomic regions (DiffBind, FDR<0.05) versus all peaks (MACS2, FDR<0.001). E. Relative distance of regions specifically accessible in female versus male fru- neurons to lncRNA:roX2 binding sites identified by CHART [39]. Sex-biased regions which directly overlap a roX2 tethering site are highlighted in magenta. F. Signal heatmap of log2 fold change (log2FC) in ATAC-seq coverage between female fru- and male fru- neurons. Line plot shows mean log2FC in signal across reference-anchored regions. Signal heatmaps below are split into regions with female-biased (purple) or male-biased (chartreuse) accessibility. G. I-cisTarget analysis of male-biased regions shows enrichment of a GAGA motif matching the known CES motif for the dosage compensation machinery. The normalized enrichment score of the motif is 12.3.

To define differentially accessible peaks across our sex-specific datasets, we first developed methods to correct for the different numbers of X chromosomes in male and female. When the X chromosome and autosomes were analyzed together using common pipelines, most differential peaks genome-wide were found to be female-biased and from the X chromosome, presumably due to the two-fold difference in genetic material from female versus male X. As the X chromosome comprises a large proportion of the fly genome, the bias this induced in our analysis was strong: the X chromosome constituted ~20% of our total ATAC-seq reads in female flies, and only 14% in male flies. To correct for this disparity, we separated X chromosome and autosome reads in each dataset (S2A Fig). We then used DiffBind to call differential peaks on autosomes alone, and on the X chromosome alone [41,42]. This method increased our sensitivity to detect differential peaks on the autosomes between the two sexes and allowed us to predict true accessibility differences on the X chromosome (S2B Fig).

For all MACS2-called peaks genome-wide, we calculated the change in chromatin accessibility between fru- male and female samples, and between fru+ male and female samples (Fig 2C and 2D and S2 Table). The genomic distribution of differential peaks was strikingly different between the fru- samples and between the fru+ samples. In fru- samples, 93 of 95 differential peaks were located on the X chromosome, suggesting that the main differences in chromatin accessibility between these samples derived from the process of dosage compensation itself. In contrast, differentially accessible regions between male and female fru+ neurons were evenly distributed across the genome (Fig 2C and 2D). Together, these patterns validate our ability to identify FruM-dependent regulatory events in ATAC-seq data and allow us to subtract signatures of dosage compensation from our analysis.

On the X chromosome, we observed ~44 regions that were strongly male-biased, similar to signals in rox1 and rox2 (Fig 2B, 2C, 2E and 2F). These peaks, which we predicted represented DCC binding sites, were common to both fru+ and fru- samples. To test this, we took advantage of known male-specific DCC binding profiles mapped by analysis of rox2 chromatin binding by CHART (capture hybridization analysis of RNA targets) [39]. Indeed, male-biased peaks were relatively closer to DCC binding sites identified by roX2 CHART than were female-biased peaks, and >50% overlapped DCC binding sites (Fig 2E). Using i-cisTarget, we found sharp enrichment of the MSL recognition element (MRE) in these sequences (Fig 2F–2G) [43].

Transcriptional regulation of fruP1

We next characterized accessibility differences between the fru+ versus fru- datasets. We found just 22 regions genome-wide whose accessibility was common to the two fru+ or two fru- datasets, consistent with a model where fruitless neurons are heterogeneous in the absence of FruM protein production (S3A and S3B Fig). To ask whether these morphologically and lineally distinct neurons activate fruitless transcription through common or distinct mechanisms, we next turned our attention to the fru locus itself. fruitless has a high intron:exon ratio, which has been suggested to correlate with diversification of cis-regulatory elements and expression patterns [44,45]. The fruP1 promoter was accessible regardless of transcriptional status (Fig 1D); hierarchical control of promoter opening versus transcription has been observed in other neural systems [46]. There was an enrichment of called peaks in the locus in the two fru+ datasets, particularly in the first two introns downstream of fruP1 (Fig 3A). There also appeared to be pervasive enrichment of reads across the locus in the fru+ datasets. We reasoned that if each subpopulation of fru neurons uses a different enhancer within the fru locus to activate transcription from fruP1, averaging of these accessibility signals across a variety of fru+ cell types could lead to the observed pervasive opening. To quantify accessibility across the 120kb locus, we measured coverage, i.e. number of reads per base pair. We observed up to 4-fold enrichment in reads across the locus as a whole in fru+ samples compared to fru- samples (Fig 3B). We re-mapped histone-mark ChIP from adult brain neurons [47] and found that this region was also enriched for H3K27Ac, a mark of active enhancers (Fig 3A).

Fig 3. Diverse regions of the fruitless locus act as enhancers in subsets of fru+ neurons.

A. UCSC genome browser screenshot of the fruitless locus (dm6 assembly). Blue signal tracks represent INTACT histone ChIP data for R57C01(Nsyb)-labeled neurons in the adult head [47]. The P1 promoter of fru shows enrichment of H3K4me3, a promoter mark, while the whole gene body shows signal for H3K27ac, an enhancer mark, compared to low signal upstream of fruP1. ATAC-seq signal tracks show numerous accessible regions across the gene body. Regions whose enhancer activity is imaged in Fig 3C are highlighted. B. Aggregate reads across the fru locus in fru+ versus fru- male neurons plotted in 5kb windows. Pervasive opening is observed across much of the >100kb gene, with up to four-fold more reads in the fru+ condition. C. Peak landscapes and conservation across four genomic fragments in the Vienna Tiles collection, as indicated. Corresponding genomic locations are highlighted in A. D. Maximum intensity 2-photon stacks of enhancer activity of the four tiles in Fig 3C within fruitless neurons in the male overlaid with the fruP1-LexA expression pattern (top) and as GFP signal alone (bottom). Each tile drives expression in distinct fruitless neurons. Remarkably, VT043701, which has higher ATAC signal in fru- cells, drives expression in γ Kenyon cells, which we sorted into the fru- population.

To ask whether this enhanced gene-wide coverage was unique to fruitless, we used featureCounts to count reads across a gene locus and used DESeq2 to measure changes in coverage between sample conditions using a cutoff of p.adj <0.05 (S3C and S3D Fig). fruitless was near the top of the list of genes with differential coverage in this analysis, and other high-ranked genes were not like fru: They were short genes dominated by localized, robust peaks (like those found in rox1 and rox2). We also made use of this gene-scale ATAC coverage measurement to compare accessibility and RNA expression levels in the male fru+ condition; we found mild correlation (r2 = 0.21, S3E Fig).

To ask if diverse regions of the fru locus act as enhancers in different subpopulations of fru+ cells, we first examined male brain expression patterns of 69 reporter lines carrying pieces of DNA from the fru locus. These alleles, from the “Vienna Tiles” collection, contain 1-3kb pieces of genomic DNA placed upstream of a minimal promoter and the coding sequence of the Gal4 transcription factor; they are recombined into a standard genomic location [48]. We viewed these expression patterns in silico, using the Brainbase database ( Using reference anatomies of fru neurons, we found that three swathes of the fru locus had many tiles labeling fru neurons: The P1 promoter, the intron upstream of the sexually dimorphic exon, and the second intron (S3 Table) [2,3]. Remarkably, each of the 14 tiles that labeled fru neurons labeled only 1–3 fru classes, suggesting that individual regions of the fru locus act as fru enhancers in small subsets of fru neurons. To test this further, we obtained four tiles from the first and second introns and used a genetic intersection approach to identify fru neurons in which these fragments of the fru locus could act as enhancers (Fig 3C and 3D). Indeed, each region we examined drove expression in distinct fruitless subpopulations, and none of the regions we examined was able to drive gene expression across all fruitless neurons. We therefore conclude that the fruitless locus is densely packed with enhancer elements that each drive fru transcription in a subset of fru+ cells, i.e. that distinct gene regulatory mechanisms control fru expression across the diverse neurons that express it. This is consistent with analyses of the transcriptional control of neurotransmitter systems across ontogenetically diverse neuronal populations in C. elegans [49].

Identification of candidate FruM-regulated genomic elements

We sought to identify genomic regions whose accessibility robustly correlated with FruM status: Peaks present in male fru+ cells and absent from all other datasets represent genomic regions opened in the presence of FruM, while peaks absent in male fru+ cells and present in all other datasets represent regions closed in the presence of FruM. To identify these regions, we used DiffBind to call differentially accessible peaks between male and female samples, and between fru+ and fru- samples (Figs 4A–4C and S4A–S4C and S2 Table). We observed 1037 differential peaks between male fru+ and male fru-, 861 between male fru+ and female fru+, and only 58 between the two female datasets (Fig 4A). The depletion of differential peaks between the two female datasets, both lacking FruM, supports our hypothesis that the female fru+ cells, lacking FruM, are a heterogeneous population whose constituents are no more similar to one another than those in the fru- population. Further, we attributed a small number of chromatin accessibility changes which depend on sex or fruitless transcriptional status alone, suggesting that the large number of accessibility changes depend on the activity of FruM itself (Fig 4A).

Fig 4. Chromatin changes downstream of FruM are near genes involved in neuronal projection and synaptic matching.

A. Number of differentially accessible peaks between different sample comparisons at FDR<0.05. A “biased” region indicates a region which is relatively more open in a given comparison. Labels at right describe how compared conditions differ. B. Examples of peaks with increased (left) and decreased (right) accessibility specific to FruM neurons. C. UpSet plot showing intersection of DiffBind results at FDR <0.05 from male versus female fru+ neurons (861 sites) and male fru+ versus fru- neurons (1037 sites). D. Distribution of fold change between the binary comparisons shown in (C). Each point is a peak. Points are colored according to their status in (C). All peaks that are differentially accessible in both comparisons vary in the same direction in both comparisons. Tail of aqua points at left represent dosage compensation signals from the X chromosome. E. Distribution of peak locations for all peaks versus FruM-specific peaks. F. 436 FruM-specific peaks are located near 303 unique genes, which are enriched for the gene categories noted. G. GO analysis using gProfiler of the 303 genes shown in Fig 4F. We include only terms with <1000 members. Genes with FruM-specific annotated peaks were used as input gene lists, and all genes with peaks were used as background. Green terms relate to axonogenesis and teal to adhesion.

To identify changes in the activity of gene regulatory elements downstream of FruM, we took the intersection of (1) the 1037 peaks specific to male fru+ versus male fru- and (2) the 861 peaks specific to male fru+ versus female fru+ (Fig 4C and 4D). This resulted in 436 high-confidence peaks (FDR<0.05 in both comparisons) genome-wide (S4 Table). Comparing between the two male samples allows us to filter out sex-specific, FruM-independent elements, while comparing between male and female fru+ cells allows us to compare populations of cells with roughly matched identities and thus filter out peaks associated with cell type distribution. In line with our biological expectations, all peaks that satisfied both conditions were biased in the same direction in both comparisons i.e. present in the male fru+ dataset and absent in the other three (255 peaks), or absent in the male fru+ dataset and present in the other three (181 peaks) (Figs 4D and S4D). Most differential peaks were intronic or intergenic, while promoters were sharply underrepresented among differential peaks (Fig 4E). We consider these 436 genomic elements to be candidate enhancers or repressors whose activity is regulated directly or indirectly by FruM.

The fly genome is compact relative to common model vertebrates, and regulatory elements are often found in or near the genes they regulate [48]. We therefore hypothesize that our 436 candidate elements regulate the genes closest to them. Our FruM-dependent peaks were in or near 303 unique genes, which were particularly enriched for membrane proteins (122 genes), transcription factors (22) and immunoglobulin superfamily (IgSF) members (27) (Fig 4F). GO terms for cell adhesion and axonogenesis were also highly enriched compared to all the genes containing peaks across our dataset (Fig 4G). Of course, these are attractive candidate factors for determination of neuronal identity or connectivity, and IgSF proteins have been implicated previously in the fru circuit [50].

We manually inspected a subset of the 436 candidate regulatory elements; they were clearly differentially accessible by both qualitative inspection and statistical thresholds, were reproducible between replicates, and were found in genes plausibly involved in the biological processes under study. However, these differential peaks were much smaller than the peaks we observed in promoters, which were of common height across samples (Figs 4E and 1D). Because “accessibility” as measured by ATAC is essentially quantized—each region of the chromosome is tagged 0, 1, or 2 times per cell—we reasoned that peak height provides a rough measure of the proportion of cells in the analyzed population in which a locus is open, or unbound by nucleosomes. If this is the case, we would predict that each regulatory element we identified is being used by only a small portion of the fru+ cell population, suggesting that FruM might induce different gene regulatory programs in different ontogenetic classes of fru+ neurons.

Genomic elements specifically accessible in male fruitless neurons act as enhancers in subsets of male fruitless cells

To test the ability of candidate gene regulatory elements to drive gene expression, we identified reporter Gal4 alleles matched to their genomic loci, as in the fru locus shown in Fig 3C–3E [48,51]. Such reporter alleles are available for many of our 436 FruM-dependent elements, and we selected the top seven ATAC-seq peaks specifically open in FruM cells for which reporter alleles were available (Fig 5A, 5D and 5G). In order to visualize neurons with recent transcription from the fruP1 promoter, we constructed animals in which fruP1-LexA [52] drove expression of a red fluorophore, and reporter Gal4 constructs drove GFP expression. These animals allowed us to examine overlap between the fruitless population and neurons in which the candidate genomic region was capable of acting as a transcriptional enhancer (Fig 5B, 5C, 5E and 5F). To simplify observation of enhancer activity within the fruitless population, we also used an intersectional genetic approach to identify neurons positive for enhancer activity and current or past expression from fruP1 (S5A and S5B Fig). Enhancer activity of all seven fragments was tested with both genetic strategies, with consistent results.

In six of seven cases, we observed male-specific reporter expression in one or a few anatomic classes of fruitless neurons, validating these differential peaks as bona fide enhancers with sex-specific activity and confirming that our ATAC-seq peaks demarcated accessible chromatin from subpopulations of fru+ neurons (Figs 5B, 5C and 5E–5G and S5B). Reporter expression driven by the seventh construct was mutually exclusive with fru-LexA expression (84D12 tile, S5B Fig). We also note that three classes of fruitless neurons, aSP2, aSP6, and aDT6, were repeatedly labeled by these six fragments. These fruitless classes are particularly numerous, and we assume their over-representation reflects the fact that we analyzed reporters matched to the strongest differential peaks. Reporters never labeled all the aDT6, aSP2, or aSP6 neurons, suggesting that these anatomic groups contain multiple transcriptional subtypes.

Fig 5. Regulatory elements specifically accessible in FruM neurons have sexually dimorphic and cell-type-specific activity in vivo.

A. UCSC genome browser screenshot of an intergenic region with accessibility specific to FruM neurons. The 80B02 fragment encompasses this peak. B. Confocal maximum intensity projections of reporter element 80B02 driving 10XUAS-IVS-myr::GFP in male (left) and female (right). 80B02 activity is sexually dimorphic in cells with somata located at the dorsal midline (arrowhead), and common to the sexes in dorsolateral cells that innervate the central complex. Representative of 3–4 images per sex. Imaging conditions were matched between sexes. C. Overlap of 80B02 signal with fruP1-LexA-driven myr::TdTomato expression in the brains shown in (B). Insets show double-positive somata at the male dorsal midline and fibers in the lateral protocerebral complex. D. UCSC genome browser screenshot of a second intergenic region with accessibility specific to FruM neurons. The 91A09 fragment encompasses this peak. E. Maximum intensity projection of two-photon stack of 91A09-Gal4 driving 10XUAS-IVS-myr::GFP in male and female. 91A09 expression is shared between the sexes in lateral and subesophageal regions, but male-specific dorsally (arrowhead). Representative of 2–3 images per sex. Imaging conditions were matched between sexes. F. Overlap of 91A09 signal with fruP1-LexA-driven myr::TdTomato expression in the brains shown in (B). Insets show double-positive somata at the male dorsal midline and fibers in the lateral protocerebral complex. G. Summary of fru+ cell types in which analyzed genomic fragments drive reporter expression.

In addition to sex-specific labeling of fru+ neurons, each allele also drove non-dimorphic reporter expression in some fru- neurons (e.g. as seen in Fig 5C and 5F). Because reporter tiles were much larger than our peaks (2–3 kb versus 500bp), they could comprise multiple enhancer elements which act in fru+ versus fru- neurons. To ask whether our identified peaks played a causal role in driving gene expression in fru neurons, we examined Brainbase images of overlapping tiles when possible (S5D Fig). For enhancer reporter 91A09, only tiles containing our ATAC peak labeled fru+ neurons, while overlapping tiles that did not cover the peak labeled fru- neurons.

Finally, while most reporter constructs drove sex-biased expression in subpopulations of male fruitless neurons, none of the reporters drove expression across the whole fruitless population. Together with the scale of the differential peaks we observe, these data lead us to conclude that FruM has different direct and/or indirect genetic targets in different ontogenetic/anatomic subpopulations of fruitless neurons. Moreover, individual FruM-regulated enhancers are activated downstream of FruM only in specific subpopulations of fruitless neurons. Future enhancer-bashing experiments will be required to delineate minimal regulatory elements and to characterize whether individual elements act independently to drive the activity we observe, or in concert with nearby enhancers or repressors.

Transcriptional effectors of FruM are neither universal across fru+ cells nor dedicated to fru+ cells

Our ATAC-seq and enhancer activity assays suggest that FruM genomic targets vary across distinct anatomic populations of fruitless neurons. To ask whether there are any genes broadly regulated by FruM status across the fruitless population, we used DESeq2 on our RNA-seq data to call differentially expressed genes between male and female fru+ neurons, between male fru+ and fru- neurons, and between female fru+ and fru- neurons (Figs 6A and S6A and S6B and S1 Table) [53]. As shown in Fig 1, fruitless transcript quantity and splice isoform tracked the cell type and sex of the library. Aside from fruitless, we observed <300 genes differentially expressed between male fru+ and male fru- cells, and most of these were also differentially expressed, in the same direction, between female fru+ and female fru- cells (Figs 6A and S6A and S6B). We interpret these to be signatures of the particular populations of cells we analyzed, and that these differences in expression at the population level are independent of FruM. For example, eyeless is a marker of Kenyon cells, which we sorted into the fru- population in both sexes; eyeless transcripts are enriched in both fru- datasets, as expected. Many other genes common to fru+ or fru- datasets were involved in neurotransmitter or neuropeptide production or reception suggesting the potential for distinct distributions of transmitter usage between sexually dimorphic (fru+) versus sex-shared (fru-) cells (S6C Fig).

Fig 6. FruM-regulated genes are neither distinct to nor universal across male fruitless neurons.

A. UpSet plot showing intersection of differential gene expression (DESeq2 results at FDR <0.05) from male versus female fru+ neurons (33 genes) and male fru+ versus fru- neurons (272 genes). Only 7 genes are differential across both comparisons. B. Distribution of fold change between the binary comparisons shown in (A). Points are colored according to their status in (A). Only 5 genes have differential expression specific to FruM cells (i.e. fold changes in the same direction in both comparisons). C. Adult female brain expression pattern of R19B09 (green) driving expression in aDT6/median bundle neurons with nc82 counterstain (magenta). Image from Janelia FlyLight database. D. UCSC genome browser screenshot of RNA-seq and ATAC-seq signal at Lgr3, a gene that is expressed in female but not male median bundle neurons [25]. Highlighted is R19B09.3A, which Meissner et al. found to be a minimal regulatory element bound by FruMB.

We observed only 33 statistically significant differences (DESeq p. adj <0.05) in gene expression between male and female fru+ neurons, most of which also differed between male versus female fru- neurons (S6A Fig). We interpret these as being sex-specific but FruM independent.

If FruM alters transcription of distinct genes across the dozens of classes of fruitless neurons, we would expect these differences to average out when the whole pool of fruitless neurons is analyzed en masse. In contrast, if FruM regulated the same effectors across all fruitless classes, we would expect to observe a strong transcriptional signature specific to male fru+ cells, containing FruM. We identified only seven transcripts that are uniquely active or inactive in cells containing FruM (Fig 6A and 6B). The implication of this analysis is that aside from fruitless itself, there are unlikely to be strong differences in gene expression that are dedicated to or shared across the fru+ cell population in the adult. These findings are consistent with our results that FruM regulates different genomic elements in different populations of fru+ cells and with prior research suggesting that the same neuronal specificity factors are used throughout the brain in different combinations [5456].

While cell-type-specific gene expression changes are difficult to detect in pooled cells, genome-wide expression trends may still be apparent. To ask how FruM-specific regulatory elements influence transcriptional regulation genome-wide, we analyzed expression of the 303 genes near FruM-specific ATAC peaks in male versus female fru+ neurons (S6D Fig). While genes near FruM-open peaks were neither activated nor repressed on average, we found a small and statistically significant female bias in expression of genes near FruM-closed elements.

FruMB was shown previously to bind to a minimal regulatory element in Lgr3 and to repress Lgr3 expression in a subset of male FruM median bundle/aDT6 neurons (Fig 6C) [25]. Remarkably, we observe increased transcription of lgr3 in pooled female fru+ cells, which we presume emanates from the aDT6 subpopulation; aDT6 cells comprise 5–10% of our fru+ populations (Fig 6D). FruMB could decrease Lgr3 expression in the male by decommissioning an Lgr3 enhancer or commissioning an Lgr3 repressor. To discriminate between these, we visualized this minimal region in our ATAC-seq data and found it to be accessible in female fru+ cells and not in male fru+ cells (Fig 6D). This suggests that the regulatory element in Lgr3 identified by Meissner et al. is an enhancer that is decommissioned by FruM, resulting in downregulation of Lgr3 expression in male aDT6 cells.

Enrichment of FruM binding sites in peaks specifically closed in male fru+ neurons

To ask whether FruM acts to decommission regulatory elements more generally, we first needed to separate direct targets (i.e. those bound by FruM), from indirect targets (i.e. those regulated by direct FruM targets). To explore this using our ATAC data, we searched for Fru motifs across the 436 FruM-specific peaks. FruM has three DNA-binding domains, termed FruA, FruB, and FruC. The three isoforms are expressed in largely overlapping neuronal populations, with FruB and FruC expressed more broadly than FruA [31,57,58]. The FruB and FruC isoforms of FruM are each independently required in the male for courtship behavior and male aggression, while loss of FruA has little effect on courtship [22,31,57,58]. Loss of FruC causes feminization of neuronal anatomy, while loss of FruA and FruB have little anatomic effect [22,31,58]. The three isoforms have been shown by SELEX to bind distinct DNA motifs (Figs 7A and S7A) [59].

We used FIMO to search our peak datasets for FruA,B, and C motifs. In each peak, we quantified the strongest match to each of these motifs [60]. We were surprised to find that motifs for all three DNA binding domains were more common among peaks specifically closed in the presence of FruM than in those specifically open in the presence of FruM (Figs 7A and 7B and S7A–S7C). This pattern was particularly apparent for FruB motifs, which were strongly enriched in peaks closed in the presence of FruM (Fig 7A and 7B). Together, these results suggest that, in the adult, FruM decommissions (or closes) the regulatory elements to which it directly binds. Moreover, these results suggest that the 181 regions inaccessible in male fru+ cells are those most likely to be direct FruM targets, while regions specifically opened in male fru+ cells are likely to be downstream effectors of primary targets. Analysis of additional FruA motifs shown in S7A–S7C Fig.

Fig 7. Strong Fru motifs are enriched among peaks closed in the presence of FruM.

A. SELEX motif for Fru [59] and cumulative frequency distribution of FruB motif strengths identified using FIMO across peaks open (orange), closed (magenta), and unchanged (grey) in the presence of FruM. All three motifs are depleted from regions specifically open in FruM neurons, and FruB motifs are enriched among peaks specifically closed in the presence of FruM. The p-value threshold for a well-matched motif (p <0.001) is marked as a dashed line. B. Volcano plots showing the strength of the best match to each motif across our 436 FruM-specific peaks. Regions opened in the presence of FruM have negative values. C. FruCOM binding profiles across our FruM-closed and FruM-opened peaks. FruCOM data is from whole L3 larvae [62]. FruCOM signal is enriched at FruM-specific peaks, especially those closed in the presence of FruM. D. UCSC genome browser screenshot of ATAC-seq signal across a FruM-closed region covered by the enhancer reporter element R64C09. E. Genetic intersection of R64C09 enhancer activity with fruitless expression. R64C09 labels a distinct neuron population in female that is not labeled in male fruitless neurons, consistent with it acting as an enhancer that is decommissioned in the presence of FruM. 64C09 also drives sex-shared expression in fru+ olfactory sensory neurons.

FruM and FruCOM share most of their protein coding regions and their DNA binding domains, and FruCOM can rescue loss of FruM in behavioral experiments [61]. We therefore asked whether our putative FruM-regulated sites overlapped with FruCOM-bound sites identified by ChIP from larvae through the modERN project (ENCODE ENCGM860XOW) [62]. We re-analyzed raw data from the modERN dataset and compared ChIP enrichments across our peaks closed in the presence of FruM versus open in the presence of FruM. We found strong enrichment of FruCOM binding centered over peaks closed in the presence of FruM, and weaker FruCOM binding enrichment at peaks opened in the presence of FruM (Fig 7C). This analysis suggests that despite distinct cellular contexts, FruM and FruCOM may have targets in common, and supports our hypothesis that Fru decommissions regulatory elements to which it directly binds.

To test whether additional regions closed in FruM cells function as enhancers in female fruitless neurons, we selected one such region, an intergenic peak located near cry, vib, and CG31475 (Fig 7D). We used our intersectional genetic strategy to analyze expression of 64C09, a Janelia fragment encompassing this peak (Fig 7E). Remarkably, we found that this element drove robust, female-specific reporter expression in a subset of fru neurons, while overlapping fragments that do not encompass the peak drove expression in fru- cells (S7E and S7F Fig). Together, these results suggest that FruM acts cell-type-specifically to decommission enhancer elements in male fruitless neurons.


Here, we have analyzed the landscape of gene regulatory elements upstream and downstream of the fruitless transcription factor. Together, our results suggest that FruM is a single node of commonality across cells that are otherwise transcriptionally diverse. The mechanisms upstream of fru transcription are likely to be distinct across different cells; once FruM is translated in males, it does not unify gene expression programs across the disparate cells that express it, but rather executes distinct programs in each type of fru+ cell. FruM therefore serves as an evolutionary and developmental handle on neurons that need to be made sexually dimorphic, and likely intersects differently with the gene regulatory identities of the individual neuron types in which it is expressed to alter them in different ways.

FruM as a decommissioner of regulatory elements

Despite decades of work on the cellular, circuit, and behavioral functions of FruM, we know very little about what FruM does as a transcription factor to masculinize neurons. Our findings suggest that FruM directs genome-wide changes in chromatin accessibility and decommissions its direct targets. This mechanism is strikingly consistent with previous work: The two genes previously identified as direct FruM targets, robo1 and Lgr3, are both downregulated in the male [25,26]. A third gene, teiresias, was recently shown to be downregulated by FruM in the male and predicted to contain FruB binding motifs [28]. Moreover, FruM can recruit the cofactor Bonus, followed by the chromatin modifiers HDAC1 or HP1 [63]; HDAC1 and HP1 both convert euchromatin to heterochromatin.

This decommissioning activity raises an important question of whether FruM remains bound to such loci. Currently, few transcription factors show ability to bind nucleosomes (“pioneer activity”). And while currently no high-resolution binding profiles exist for FruM, ChIP enrichment of FruCOM in whole third instar larvae correlates with accessible chromatin in third instar larval brains. This suggests Fru proteins bind nucleosome-displaced and not nucleosome-occupied regions. FruM may then use a “hit and run” mechanism–binding a locus, recruiting chromatin modifiers, and leaving when the region is no longer accessible. If so, ChIP-based methods may miss direct targets of FruM which can nevertheless be detected by looking at resultant chromatin changes with ATAC-seq.

Beyond a putative molecular mechanism for the action of FruM on gene expression, this finding suggests that neuronal masculinization in the insect proceeds through cell-type-specific dismantling of female or sex-shared gene expression programs. Maleness is typically conceived as an addition to female programming, as exemplified by the striking male-specific courtship routines performed in birds and insects. However, FruM is not required for courtship actions per se, but rather for their regulation [11,64]. Moreover, fru-expressing neurons in the female control egg-laying, and ectopic FruM expression in female flies dismantles egg-laying behavior [15,65]. Together, these findings are consistent with masculinization as a process of loss of female programming.

FruM masculinizes transcription differently in different classes of fruitless neurons

Fruitless masculinizes dozens of classes of neurons in the central brain alone, and does so by altering cell genesis, apoptosis, arbor anatomy, connectivity, and neurophysiology [2,3,6,20,21,32,33]. Consistent with the variable nature of these masculinizing mechanisms, we find that FruM status alters the activity of distinct gene regulatory elements across distinct classes of fruitless neurons. Indeed, we find that each relevant genomic region is likely FruM-regulated in just one or a few fruitless populations.

The specificity of FruM action across distinct fruitless neurons could result from distinct availability of cofactors with different DNA-binding domains, or from differences in the pre-existing chromatin landscape that FruM encounters in each cell type. Fruitless is one of 40 members of the BTB/POZ family of transcription factors in Drosophila. The BTB/POZ domain is a dimerization domain, and family members have been shown to both homo- and hetero-dimerize. The presence of different FruM heterodimers across neuron types that bind different composite motifs is thus a feasible mechanism. Another interesting possibility is that the distinct factors that activate fruitless transcription in different populations of fruitless neurons could themselves cooperate with FruM in regulating cell-type-specific effectors.

Comparison with previous genome-wide datasets

Two previous studies attempted to identify FruM-bound sites genome-wide through ectopic expression of tagged isoforms that enzymatically label DNA. Each of these experiments is conceptually difficult to interpret because FruM is studied outside of the typical cellular context and transcriptional millieu. A DamID study [57] analyzed central nervous systems in which FruM-Dam expression is driven by leak from an uninduced UAS-driven construct, allowing expression in any cell at any time of development; a BirA/BLRP study [66] induced FruM expression in the S2 cell line, which is not neural [67]. Both studies reported broad peaks (e.g. median length 10kb in the BirA study). The low resolution, likely a result of the spatial resolution of enzymatic tagging, the accretion of covalent DNA labeling over long time scale labeling, and the use of microarrays for sequence detection, preclude a crisp comparison with our nucleosome-scale peak landscapes. Gene-level intersection of putative Fru binding from the BirA/BLRP study [66] versus genes with differential peaks in our analysis is shown in S7D Fig.

We argue here that FruM alters expression of distinct genes across fru subpopulations, and that these transcriptional differences are masked when the whole fru populations are analyzed by RNA-seq. However, a previous study identified 772 genes enriched in male fru+ neurons in adult by TRAP [68]. We were somewhat surprised to find that we did not re-discover these genes, especially as our analysis is more sensitive than TRAP (i.e. we observe 4.8-fold enrichment of fru versus 1.5-fold enrichment by TRAP). One possibility is that differences in TRAP were dominated by fru+ Kenyon cells, which we filtered out in our analysis (Fig 1A). The TRAP approach also relied on comparing polysome-bound transcripts from fru+ neurons with input from the whole head, which would be expected to enrich neural transcripts generally.

Action of FruM across the life cycle

fruitless expression begins in late larvae, peaks in mid-pupal stages, and continues robustly in the adult; new populations of cells turn on fruitless expression across the life cycle, with Kenyon cell expression arising only in late pupae [13]. FruM exerts masculinizing effects that could arise at each of these stages. We have begun our analysis here with the adult stage, and observe gene regulatory alterations of synaptic matching molecules, transcription factors, and ion channels that are consistent with neural specificity functions required in the adult. We expect that some of these differences, especially in synaptic matching molecules, would also be observed at earlier developmental stages—synaptic matching molecules are used to guide synaptogenesis and often continue to be expressed to maintain the synapse [56,69,70]. At earlier stages, we might begin to observe FruM-dependent regulation of genes required for axon guidance or the induction or suppression of apoptosis. Alternatively, as differentiation is thought to proceed through loss of gene regulatory potential over time, the adult state of these cells as measured by ATAC-seq could represent a summation of all the gene regulatory alterations that occurred during earlier stages of cellular development; this is particularly likely if FruM decommissions regulatory elements. Finally, we observe in adults that FruM-dependent regulatory elements are cell-type-specific. We cannot rule out the possibility that these or other regulatory elements are used more broadly across fruitless neuron types at earlier developmental stages.

Effects of FruM on connectivity are unlikely to occur through convergent gene expression

The construction of neural circuits from diverse cellular parts is an extraordinarily complex problem, and the idea that circuit construction could be simplified by expression of the same factors across cells of a circuit has arisen repeatedly as a potentially simplifying mechanism [7173]. Sexually dimorphic transcription factors are particularly compelling examples in which expression of a particular gene appears to “paint” multi-layered circuits dedicated to particular behaviors [1,29,74]. Our results here suggest that even when the same transcription factor does label lineally diverse, connected cells, this pattern is unlikely to make the process of establishing circuit connectivity any simpler. First, we suggest that lineally diverse neurons use distinct gene expression programs to activate the shared transcription factor, i.e. that expression of fruitless occurs through multiple convergent mechanisms rather than a single mechanism. The diverse paths to fruitless expression suggest that sexual behavior could be evolutionarily diversified by adding and removing cells from the fruitless-expressing network [58]. The extreme length and low exon/intron ratio of many genes that function as neural specificity factors suggest that, like the fruitless locus, they are packed with regulatory elements that modularly govern their expression across distinct neuronal populations.

Second, once FruM is produced, it does not homogenize the expression profiles of these diverse cells, but rather alters expression of distinct gene batteries in each population of cells. While Fruitless is thus shared across these cells, the gene regulatory elements upstream and downstream of it are not. If anything, maintaining Fruitless as a shared node across these cells imposes an additional layer of complexity and constraint on the gene regulatory events that construct the brain. We expect that common expression of transcription factors across layers of sexually dimorphic neural circuits is evolutionarily retained to allow categorization of a set of neuronal transformations as sex-related—these are “switch genes,” not “terminal selectors” [29,75]. While examples of shared expression of transcription factors or homophilic adhesion molecules across connected cells will certainly occur from time to time, we do not expect this to be a general model for the construction of circuits.

Modular control of gene expression

Our findings suggest that aside from fruitless itself, there are not genes whose expression is specific to or universal across the circuit. In this view, FruM does not transcriptionally unify cells of the circuit or simplify circuit specification. Rather, FruM flags these cells as “male,” and induces male-specific circuit function by tweaking expression of the same effector genes as are used in other combinations in other circuits. The data presented here are consistent with a model where FruM acts in concert with the distinct transcriptional milieu of each subpopulation of fruitless neurons to enact distinct gene regulatory programs and thus alters each class of neurons in unique ways. Gene regulatory programs therefore diverge downstream of FruM. We propose that this modular organization allows evolutionary diversification, as mutations to regulatory elements would be expected to alter gene expression only in individual populations of cells, rather than across the circuit.


Resources table


Flies were maintained on cornmeal-molasses food, or on cornmeal food with a yeast sprinkle (‘R’ or ‘B’ recipes, Lab Express, Ann Arbor, MI) in a humidified incubator at 25C on a 12:12 light:dark cycle. Flies analyzed in all experiments were 2–7 day old adults who were housed in mixed-sex groups.


Flow cytometry

Brains were dissected for up to 90 minutes in Schneider’s medium supplemented with 1% BSA and placed on ice. Optic lobes were removed during dissection. Brain dissections were interspersed such that both male and female brains were dissected throughout the dissection period. About 20 brains were obtained for each sex. After all dissections were completed, collagenase was added to a final concentration of 2mg/mL and samples were incubated at 37C for 20 minutes, without agitation.

Samples were dissociated by trituration and spun down at 300g, 4C, for 5 minutes. Collagenase solution was removed and replaced with PBS+0.1% BSA, and cells were passed through a cell strainer cap and supplemented with 50ng/mL DAPI before being subjected to flow cytometry on an a FACS Aria II. Plasticware for cell dissociation and collection was pre-treated by rinsing with PBS+1% BSA to prevent cells from sticking to bare plastic.

During flow cytometry, dead and dying cells were excluded using DAPI signal, and forward scatter and side scatter measurements were used to gate single cells. Using our dissociation methods, 50–80% of singlets appeared viable (DAPI-low), and 2–5% of viable singlets were GFP+. We collected 6,000–10,000 GFP+ cells for each fru+ sample and analyzed matched numbers of GFP-/fru- cells. For each replicate, we sorted male and female cells during the same session and performed transposition or RNA extraction in parallel. During sorting, we made two adjustments to protect the fly primary cells, which were very delicate—we disabled agitation of the sample tube, and sorted using the “large nozzle,” e.g. 100μm, i.e. using larger droplet size and lower pressure. For ATAC-seq, we sorted cells into PBS supplemented with 0.1% BSA. For RNA-seq, we sorted cells directly into Trizol-LS.

Brain dissections, staining, and imaging

Brains were dissected in external saline (108 mM NaCl, 5 mM KCl, 2 mM CaCl2, 8.2 mM MgCl2, 4 mM NaHCO3, 1 mM NaH2PO4, 5 mM trehalose, 10 mM sucrose, 5 mM HEPES pH7.5, osmolarity adjusted to 265 mOsm). For two-photon imaging, brains were then transferred fresh to 35mm imaging dishes and pinned to sylgard squares with tungsten wire. Imaging was performed on a Bruker Investigator using a 1.0 NA, 20x, water-dipping objective. Stacks were collected along the anterior-posterior axis with 1 micrometer spacing in Z and ~350nm axial pixel size.

For immunostaining and confocal imaging, brains were dissected for up to twenty minutes before being transferred to 1% paraformaldehyde in PBS, on ice. All steps were performed in cell strainer baskets (caps of FACS tubes) in 24 well plates, with the brains in the baskets lifted from well to well to change solutions. Brains were fixed overnight at 4C in 1% PFA in PBS. On day 2, brains were washed 3x10’ in PBS supplemented with 0.1% triton-x-100 on a shaker at room temperature, blocked 1 hour in PBS, 0.1% triton, 4% Normal Goat Serum, and then incubated for at least two overnights in primary antibody solution, diluted in PBS, 0.1% triton, 4% Normal Goat Serum. Primary antibody was washed 3x10’ in PBS supplemented with 0.1% triton-x-100 on a shaker at room temperature, then brains were incubated in secondary antibodies for at least two overnights, diluted in PBS, 0.1% triton, 4% Normal Goat Serum. DAPI (1 microgram/mL) was included in secondary antibody mixes. Antibodies and concentrations can be found in the resources table.

Brains were mounted in 1x PBS, 90% glycerol supplemented with propyl gallate in binder reinforcement stickers sandwiched between two coverslips. Samples were stored at 4C in the dark prior to imaging. The coverslip sandwiches were taped to slides, allowing us to perform confocal imaging on one side of the brain and then flip over the sandwich to allow a clear view of the other side of the brain. Scanning confocal stacks were collected along the anterior-posterior axis on a Leica SP8 with 1 micrometer spacing in Z and ~150nm axial pixel size.

Assay for transposase-accessible chromatin

After FAC-sorting, nuclear isolation and Tn5 transposition were performed as in [30] with modifications made for small numbers of cells as described in [36]. Transposed DNA was isolated and stored at -20C until library preparation. Transposed DNA was amplified with barcoded primers in NEBNext High Fidelity 2X PCR Master Mix (NEB) and purified with Ampure XP beads (Beckman Coulter) at a ratio of 1.6uL beads per 1uL library. Purified library was eluted in 30uL of 10mM Tris-HCl pH8, 0.1mM EDTA.

The quality of prepared libraries was verified with a Bioanalyzer 2100 using a high sensitivity DNA kit (Agilent). Libraries were quantified using KAPA qPCR assay (KAPA Biosystems), and multiplexed and sequenced on a NextSeq Illumina machine with 75 base pair, paired-end reads. Libraries were sequenced to a depth of 35–70 million reads.

Processing of ATAC-seq data.

Adapters were trimmed using cutadapt and reads below 18 bases were discarded. Reads were aligned to dm6 with Bowtie2 with option–X 1000 to set the maximum fragment length for paired-end reads. About 70% of reads mapped uniquely to the fly genome. Aligned reads were processed with samtools to create a bam file. Picard MarkDuplicates was used to mark duplicates and samtools was used to generate a final bam file with de-duplicated reads passing a q30 quality filter. After de-duplication, we obtained 8.9–16.3 million unique reads per library.

The sequencing data were uploaded to the Galaxy web platform, and we used the public server at to analyze the data [83]. Peaks were called with MACS2 “callpeak” using–nomodel at an FDR threshold of <0.001, which gave a reproducible and robust peak calls across replicates and by eye.

Analysis of differential accessibility.

For comparisons between neurons of the same sex status (i.e. male fru+ vs male fru- or female fru+ vs female fru-), DiffBind was run with default parameters using filtered bam files for each replicate and MACS2 called peaks for each replicate as input.

In female flies, the X chromosomes accounted for 20–21% of all ATAC-seq reads, while only accounted for 14% of all male ATAC-seq reads across samples. For comparisons of chromatin accessibility between sexes, this difference in X:A DNA ratio introduced extremely large bias in regions called as differentially accessible. We initially identified 1374 differentially accessible regions between male fru- neurons and female fru- neurons, compared to the 95 regions reported in this paper. This is due to whole-genome rather than per-chromosome normalization performed by DiffBind and results in artificially high numbers of regions to be female-biased on X and male-biased on the autosomes.

Therefore, we separated aligned reads from the X chromosome and autosomes using “Slice BAM by genomic regions”, selecting for chrX, or for chr2L, 2R, 3L, 3R, and 4. DiffBind was run using the same MACS2 called peaks, but with either X chromosome or autosome aligned reads. Separate lists of changed regions (FDR <0.05) were concatenated per comparative analysis.

Tables of genomic regions were downloaded and processed in R as follows: Peaks were annotated using ChIPSeeker–nearest gene, +/- 50bp promoter and exported as tables. FruM-specific regions with annotated protein-coding genes were filtered for unique genes. List of genes were loaded into gProfiler with default settings. Annotation from combined MACS2 peak calls used as custom background list. FruM-specific gene lists were also loaded into DAVID for domain annotation, without a custom background.

RNA sequencing

RNA extraction.

Cells were sorted into Trizol-LS and stored at -80C until RNA isolation. For RNA extraction, we followed the standard Trizol-LS protocol until the aqueous phase was isolated. We then passed the aqueous phase over Arcturus Picopure columns, including a DNAse treatment on the column (Qiagen RNAse-free DNAse set #70254) using the following unpublished protocol with unknown authors: RNA Purification from TriZol samples using PicoPure columns (Ed R. 2010-12-07, Modifications from ABI help line for Acturus PicoPure (Candice)). We summarize the protocol as follows:

Trizol sample

  • Process TriZol samples with 0.2 vol of chloroform
  • Spin max speed for 5 minutes (start PicoPure column conditioning)
  • Take aqueous layer
  • Add equal volume of 70% ethanol (RNAse-free)

PicoPure column conditioning

  • Condition PicoPure column for 5 minutes
  • Spin out at max speed for 1 minute.

Bind RNA to picoPure column

  • Load up to 300 uL of the supernatant/70% ethanol mix at a time
  • Spin at 100g x 2 minutes for each load
  • After last load, spin 16,000 x 30 seconds
  • Discard flowthrough
  • Wash with 100 uL of Wash Buffer 1 (WB1) and spin 8,000g x 1 minute

DNAse treatment

  • For each sample, combine 35 uL of RDD buffer with 5uL DNAse I stock solution (previously resuspended per Qiagen protocol
  • Add 40 uL of DNAse mix onto membrane.
  • Incubate at RT x 15 minutes
  • Add 40 uL of Wash Buffer 1 (WB1) and spin 8,000g x 15 seconds
  • Wash with 100 uL of Wash Buffer 2 (WB2) at 8,000g x 1 minute
  • Empty flow-through
  • Wash again with with 100uL of Wash Buffer 2 (WB2) at 16,000g x 2 minutes
  • Check to make sure no wash buffer remains.


  • Transfer to a new 0.5 mL tube (in kit)
  • Add 11 uL of 42C pre-warmed elution buffer to membrane
  • Wait 1 minute
  • Spin at 1,000g x 1 minute to distribute buffer
  • Elute at 16,000g x 1 minute
  • Consider doing a second elution to capture as much flowthrough as possible.
  • Store at -80C

RNA quantification and quality assessment were performed on an RNA TapeStation at the UMich Genomics Core. We typically obtain 0.1–0.3pg of RNA per cell, depending on cell type and developmental stage. Note that insect 28S rRNA is processed to a size similar to 18S rRNA, thus “RNA Integrity Number” or similar that are calculated by these machines will not reflect the true RNA quality.

Library preparation and sequencing.

RNA libraries were prepared by the UMich Genomics Core with the following protocol:

Samples were subjected to quality control on an RNA TapeStation. Total starting RNA was around 1 ng per sample. Library preparation was performed using the NEBNext Single Cell/Low Input RNA Library Prep Kit for Illumina, with amplification cycles calibrated to the amount of total RNA. Unstranded, poly-A selected libraries were sequenced on an Illumina NovaSeq using 150bp paired end reads to a depth of ~30 million reads per library.

Alignment and analysis of RNA-seq data.

All processing steps were done through the Galaxy web platform. Reads were trimmed with Trim Galore! with automatic adapter detection and aligned to dm6 using HiSAT2 in paired end mode. Uniquely aligning reads above MAPQ 30 were selected using JSON and MarkDuplicates, retaining 25.4–39.6 million reads per library. FastQC and MultiQC were used to visualize quality metrics across samples. Coverage tracks were generated using deeptools “bamCoverage”.

Exon reads were counted and aggregated at a gene-level using featureCounts. TPM values were calculated using StringTie and averaged between replicates. Differential expression was determined using DESeq2 using an p.adj threshold of >0.05 for reporting significance.

Additional genomic analyses

Analysis of lncRNA:roX2 tethering near sites of sex-specific chromatin accessibility.

Genomic locations of lncRNA:roX2 binding to chromatin using CHART was lifted over from dm3 into dm6 using UCSC LiftOver. Lifted-over bed files were sorted using bedtools “sortBed” and relative distance of lncRNA:roX2 tethering sites was determined using bedtools “RelDist” for the X chromosomes only.

Neuron-specific histone marks.

Histone chromatin immunoprecipitation from adult neurons [47] was downloaded in FASTQ format from SRA [88] using “Download and Extract Reads in FASTQ”, trimmed using Trim Galore! [89] with automatic adapter detection. Trimmed reads were aligned to dm6 using Bowtie2 and made into coverage tracks for comparison using deepTools “bamCoverage”.

Fru DamID analysis.

Ratio files of FruA, FruB, and FruC DamID enrichment over dam-only control were downloaded in GFF format from GEO (GSE52247). Regions were resized to remove 5bp on each flank, thus removing overlapping regions of microarray probes and rebased to bed/bedGraph compatible format. Genomic regions were then lifted over from dm3 into dm6 using RLiftOver. Tables were exported as bedGraph files, uploaded to the Galaxy web platform and converted from bedGraph to bigWigs using “Wig/BedGraph-to-bigWig” using default parameters.

Analysis of Fru motifs.

All ATAC-seq genomic regions from DiffBind comparison (FDR > = 1) between female fru+ and male fru+ were converted to FASTA using bedtools MakeFasta. FASTA sequences were run through the web version of FIMO using Fru motifs identified by SELEX [59] with no threshold (Q val = 1) to re-capture all sequences which were input. We used p-values rather than Q-values in our analysis because the SELEX motifs are 8bp, and near-identical matches to sequences do not stand up well to p-value correction. The top match per region per Fru isoform was selected using R based on lowest p-value match. Regions previously selected (Fig 4) as FruM-specific were flagged. Tables were converted to cumulative frequencies and plotted.

Supporting information

S1 Fig. Quality control metrics for ATAC and RNA sequencing.

A. Example FACS plot showing gates for fru+ (GFP+) neurons collected. Example plot corresponds to male neurons from replicate 2 of RNA-seq experiments. B. Heatmap of Spearman correlations of uniquely aligned, deduplicated reads from ATAC-seq libraries. C. PCA analysis of uniquely aligned, deduplicated reads from ATAC-seq libraries. PC1 reflects read depth. D. Fragment length of ATAC-seq libraries. E. Heatmap of Spearman correlations of uniquely aligned, deduplicated reads from RNA-seq libraries. F. PCA analysis of uniquely aligned, deduplicated reads from RNA-seq libraries. G. UCSC genome browser screenshots of ATAC-seq and RNA-seq signal across neural (nSyb), glial (repo), and neuroblast (dpn) specific genes.


S2 Fig. Analysis of sexually dimorphic genes.

A. Flowchart of computational pipeline used to identify differentially accessible regions between male and female samples using ATAC-seq. B. Barplots displaying number of differentially accessible regions using DiffBind FDR <0.05 as a cutoff. Corrected regions are produced by the pipeline in (A), while uncorrected regions are produced without splitting of the X chromosome and autosomes before running DiffBind. Colors correspond to Fig 2C, where purple are regions that have female-biased accessibility, and chartreuse represents regions with male biased accessibility. C. UCSC genome browser screenshot of ATAC-seq and RNA-seq signal in the dsx locus. Blue signal tracks represent INTACT histone ChIP data for R57C01(Nsyb)-labeled neurons in the adult head [47]. D. UCSC genome browser screenshots of the sxl and tra loci. E. Zoom in of highlighted regions in D, corresponding to sexually dimorphic splicing events. Displayed are RNA-seq coverage tracks and 20 aligned reads in male fru+ neurons and female fru+ neurons. Reads correspond to replicate 1 data. For both genes, male mRNA contains only non-functional isoforms, while female mRNA contains both protein-coding and non-functional isoforms.


S3 Fig. Chromatin accessibility changes in neurons based on transcriptional status of fruitless.

A. UpSet plot showing the intersection of regions which are differentially accessible (DiffBind FDR <0.05) between fru+ and fru- neurons in both sexes. B. Of 22 regions differentially accessible between fru+ and fru- neurons, 19 correspond to protein-coding genes (labeled on the X axis). The log2(Fold Change) in accessibility of these genes relative in female neurons is plotted. C. Schematic of calculating chromatin accessibility across a gene locus. D. Barplot of log2(Fold Change) of genes with gene-scale differential coverage between male fru+ neurons and male fru- neurons. ey is a Kenyon cell marker. E. Scatterplot of whole-gene chromatin accessibility versus gene expression level in the male fru+ dataset.


S4 Fig. Identification of FruM-regulated elements.

A. Volcano and MA plots of regions with differential accessibility (DiffBind < 0.05) between female and male fru+ neurons. Points at the top border in the volcano plot and along the bottom border in the MA plot have been thresholded such that they are visually comparable to plots in B. B. Volcano and MA plots of regions with differential accessibility (DiffBind < 0.05) between male fru- and fru+ neurons. C. Binary heatmap of regions called differentially accessible in 1 or more comparisons. D. Scatterplot of log2 fold changes in accessibility compared to male fru+ neurons. Figure shows analysis in Fig 4D with autosomal regions (chromosomes 2, 3, and 4) separated from sex chromosomal regions (X and Y). E. Signal heatmaps of FruM-specific regions, separated by regions which are selectively open or closed in male fru+ neurons.


S5 Fig. Sexually dimporphic enhancer usage in distinct FruM neurons.

A. UCSC genome browser screenshots of genomic regions covered by each reporter. B. 2-photon stack of adult male (B) and female brains. Arrows point to cells with sex-specific expression (89D01, 80F10, 75F01) or dimorphic labeling intensity (92B12). C. UCSC genome browser screenshot of region covering enhancer reporter 84D12 and 2-photon stack of adult male and female brains. Labeling is mutually exclusive with fru expression. Brain-wide speckle signal is autofluorescence. D. In-silico enhancer bashing of enhancer reporter element 91A09 (Fig 5D–5F). UCSC genome browser screenshot showing overlapping enhancer reporters from the Vienna Tiles collection. Maximum intensity projection of matching substacks for each overlapping tiles from Brainbase. Images correspond to adult male brains.


S6 Fig. Analysis of transcription in fru neurons.

A. MA plots of differential RNA expression analysis between four datasets. B. Clustered heatmap of TPM values of genes with differential expression in one or more comparisons. Differential expression is dominated by fru status. C. Gene ontology analysis of genes enriched in both fru+ datasets (male and female) over fru- datasets. Enrichment is over a custom background of genes with expression > 10 TPM across the four datasets. D. Expression of 303 genes near FruM-closed or FruM-open ATAC-seq peaks in male versus female fru+ cells. P-value by Welch two-sample t-test.


S7 Fig. Strong FruM motifs are enriched among peaks closed in the presence of FruM.

A. SELEX and SANGER FruA motifs. B. Cumulative frequency plots for SELEX and SANGER Fru (FruA) motifs (FlyFactorSurvey) across FruM-open, FruM-closed and unchanged regions. C. Volcano plots showing motif strengths across FruM-specific regions. D. Binary heat map of overlap of FruM-specific regions with previously reported FruM targets in S2 cells [66]. E. Expanded UCSC genome browser screenshot of ATAC-seq signal across a FruM-closed region covered by the enhancer reporter element R64C09 and two flanking regions R66B05 and R64C10. F. Adult female brain expression pattern of R66B05, R64C09, and R64C10 (green) driving expression with nc82 counterstain (magenta). Arrows point to region of neurons which show sexually dimorphic enhancer activity in R64C09. Images from Janelia FlyLight database.


S1 Table. Results of DESeq2 comparisons of transcript levels across different sample pairs.


S2 Table. Results of diffbind comparisons of peak accessibility across different sample pairs.

Tables display peak location and statistical comparisons, annotation of peak location, and data on nearest gene.


S3 Table. Description of neurons labeled by Vienna Tile reporters in images from the Brainbase database.

Annotations were performed by downloading stacks from using our knowledge of neural anatomy and reference images from [2,3].


S4 Table. 436 “FruM-specific” peaks that meet statistical cutoffs in both male fru+ versus male fru- and male fru+ versus female fru+ comparisons.

Statistical results of each comparison are shown in separate sheets. Tables display peak location and statistical comparisons, annotation of peak location, and data on nearest gene.



We are grateful to the Bloomington Drosophila Stock Center and others who provided flies, as listed in the Resources Table. We thank Nipun Basrur, Patricia Wittkopp, Cameron Palmer, and members of the Clowney lab for critical reading of the manuscript.


  1. 1. Knoedler JR, Shah NM. Molecular mechanisms underlying sexual differentiation of the nervous system. Curr Opin Neurobiol. 2018;53:192–197. pmid:30316066
  2. 2. Yu JY, Kanai MI, Demir E, Jefferis GSXE, Dickson BJ. Cellular organization of the neural circuit that drives Drosophila courtship behavior. Curr Biol. 2010;20:1602–1614. pmid:20832315
  3. 3. Cachero S, Ostrovsky AD, Yu JY, Dickson BJ, Jefferis GSXE. Sexual Dimorphism in the Fly Brain. Curr Biol. 2010;20:1589–1601. pmid:20832311
  4. 4. Barr MM, García LR, Portman DS. Sexual Dimorphism and Sex Differences in Caenorhabditis elegans Neuronal Development and Behavior. Genetics. 2018;208:909–935. pmid:29487147
  5. 5. Bayless DW, Shah NM. Genetic dissection of neural circuits underlying sexually dimorphic social behaviours. Philosophical Transactions of the Royal Society B: Biological Sciences. 2016;371:20150109. pmid:26833830
  6. 6. Kimura K-I, Ote M, Tazawa T, Yamamoto D. Fruitless specifies sexually dimorphic neural circuitry in the Drosophila brain. Nature. 2005;438:229–233. pmid:16281036
  7. 7. Welch JD, Kozareva V, Ferreira A, Vanderburg C, Martin C, Macosko EZ. Single-Cell Multi-omic Integration Compares and Contrasts Features of Brain Cell Identity. Cell. 2019;177:1873–1887.e17. pmid:31178122
  8. 8. Yamamoto D, Koganezawa M. Genes and circuits of courtship behaviour in Drosophila males. Nat Rev Neurosci. 2013;14:681–692. pmid:24052176
  9. 9. Manoli DS, Foss M, Villella A, Taylor BJ, Hall JC, Baker BS. Male-specific fruitless specifies the neural substrates of Drosophila courtship behaviour. Nature. 2005;436:395–400. pmid:15959468
  10. 10. Stockinger P, Kvitsiani D, Rotkopf S, Tirián L, Dickson BJ. Neural circuitry that governs Drosophila male courtship behavior. Cell. 2005;121:795–807. pmid:15935765
  11. 11. Ito H, Fujitani K, Usui K, Shimizu-Nishikawa K, Tanaka S, Yamamoto D. Sexual Orientation in Drosophila is Altered by the Satori Mutation in the Sex-Determination Gene Fruitless that Encodes a Zinc Finger Protein with a BTB Domain. Proceedings of the National Academy of Sciences of the United States of America. 1996;93:9687–9692. pmid:8790392
  12. 12. Ryner LC, Goodwin SF, Castrillon DH, Anand A, Villella A, Baker BS, et al. Control of Male Sexual Behavior and Sexual Orientation in Drosophila by the fruitless Gene. Cell. 1996;87:1079–1089. pmid:8978612
  13. 13. Lee G, Foss M, Goodwin SF, Carlo T, Taylor BJ, Hall JC. Spatial, temporal, and sexually dimorphic expression patterns of the fruitless gene in the Drosophila central nervous system. J Neurobiol. 2000;43:404–426. pmid:10861565
  14. 14. Goodwin SF, Taylor BJ, Villella A, Foss M, Ryner LC, Baker BS, et al. Aberrant splicing and altered spatial expression patterns in fruitless mutants of Drosophila melanogaster. Genetics. 2000;154:725–745. pmid:10655225
  15. 15. Demir E, Dickson BJ. fruitless splicing specifies male courtship behavior in Drosophila. Cell. 2005;121:785–794. pmid:15935764
  16. 16. Gailey DA, Hall JC. Behavior and cytogenetics of fruitless in Drosophila melanogaster: different courtship defects caused by separate, closely linked lesions. Genetics. 1989;121:773–785. pmid:2542123
  17. 17. Gill KS. A mutation causing abnormal courtship and mating behavior in males of Drosophila melanogaster. Am Zool. 1963;3:507.
  18. 18. Hall JC. Courtship among males due to a male-sterile mutation inDrosophila melanogaster. Behav Genet. 1978;8:125–141. pmid:99136
  19. 19. Kimura K-I, Hachiya T, Koganezawa M, Tazawa T, Yamamoto D. Fruitless and doublesex coordinate to generate male-specific neurons that can initiate courtship. Neuron. 2008;59:759–769. pmid:18786359
  20. 20. Kohl J, Ostrovsky AD, Frechter S, Jefferis GSXE. A bidirectional circuit switch reroutes pheromone signals in male and female brains. Cell. 2013;155:1610–1623. pmid:24360281
  21. 21. Ruta V, Datta SR, Vasconcelos ML, Freeland J, Looger LL, Axel R. A dimorphic pheromone circuit in Drosophila from sensory input to descending output. Nature. 2010;468:686–690. pmid:21124455
  22. 22. Wohl M, Ishii K, Asahina K. Layered roles of fruitless isoforms in specification and function of male aggression-promoting neurons in Drosophila. Ramaswami M, VijayRaghavan K, editors. eLife. 2020;9:e52702. pmid:32314957
  23. 23. Clowney EJ, Iguchi S, Bussell JJ, Scheer E, Ruta V. Multimodal Chemosensory Circuits Controlling Male Courtship in Drosophila. Neuron. 2015;87:1036–1049. pmid:26279475
  24. 24. Koganezawa M, Kimura K-I, Yamamoto D. The Neural Circuitry that Functions as a Switch for Courtship versus Aggression in Drosophila Males. Curr Biol. 2016;26:1395–1403. pmid:27185554
  25. 25. Meissner GW, Luo SD, Dias BG, Texada MJ, Baker BS. Sex-specific regulation of Lgr3 in Drosophila neurons. Proc Natl Acad Sci USA. 2016;113:E1256–1265. pmid:26884206
  26. 26. Ito H, Sato K, Kondo S, Ueda R, Yamamoto D. Fruitless Represses robo1 Transcription to Shape Male-Specific Neural Morphology and Behavior in Drosophila. Curr Biol. 2016;26:1532–1542. pmid:27265393
  27. 27. Dauwalder B, Tsujimoto S, Moss J, Mattox W. The Drosophila takeout gene is regulated by the somatic sex-determination pathway and affects male courtship behavior. Genes Dev. 2002;16:2879–2892. pmid:12435630
  28. 28. Sato K, Ito H, Yamamoto D. teiresias, a Fruitless target gene encoding an immunoglobulin-superfamily transmembrane protein, is required for neuronal feminization in Drosophila. Communications Biology. 2020;3:1–10. pmid:31925316
  29. 29. Baker BS, Taylor BJ, Hall JC. Are complex behaviors specified by dedicated regulatory genes? Reasoning from Drosophila. Cell. 2001;105:13–24. pmid:11300999
  30. 30. Buenrostro J, Wu B, Chang H, Greenleaf W. ATAC-seq: A Method for Assaying Chromatin Accessibility Genome-Wide. Curr Protoc Mol Biol. 2015;109:21.29.1–21.29.9. pmid:25559105
  31. 31. von Philipsborn AC, Jörchel S, Tirian L, Demir E, Morita T, Stern DL, et al. Cellular and behavioral functions of fruitless isoforms in Drosophila courtship. Curr Biol. 2014;24:242–251. pmid:24440391
  32. 32. Ren Q, Awasaki T, Huang Y-F, Liu Z, Lee T. Cell Class-Lineage Analysis Reveals Sexually Dimorphic Lineage Compositions in the Drosophila Brain. Curr Biol. 2016;26:2583–2593. pmid:27618265
  33. 33. Sethi S, Lin H-H, Shepherd AK, Volkan PC, Su C-Y, Wang JW. Social Context Enhances Hormonal Modulation of Pheromone Detection in Drosophila. Curr Biol. 2019;29:3887–3898.e4. pmid:31679932
  34. 34. Zhao S, Deanhardt B, Barlow GT, Schleske PG, Rossi AM, Volkan PC. Chromatin-based reprogramming of a courtship regulator by concurrent pheromone perception and hormone signaling. Sci Adv. 2020;6:eaba6913. pmid:32494751
  35. 35. Keleman K, Vrontou E, Krüttner S, Yu JY, Kurtovic-Kozaric A, Dickson BJ. Dopamine neurons modulate pheromone responses in Drosophila courtship learning. Nature. 2012;489:145–149. pmid:22902500
  36. 36. Monahan K, Schieren I, Cheung J, Mumbey-Wafula A, Monuki ES, Lomvardas S. Cooperative interactions enable singular olfactory receptor expression in mouse olfactory neurons. Elife. 2017;6. pmid:28933695
  37. 37. Feng J, Liu T, Qin B, Zhang Y, Liu XS. Identifying ChIP-seq enrichment using MACS. Nature Protocols. 2012;7:1728–1740. pmid:22936215
  38. 38. Chu C, Qu K, Zhong FL, Artandi SE, Chang HY. Genomic Maps of Long Noncoding RNA Occupancy Reveal Principles of RNA-Chromatin Interactions. Molecular Cell. 2011;44:667–678. pmid:21963238
  39. 39. Simon MD, Wang CI, Kharchenko PV, West JA, Chapman BA, Alekseyenko AA, et al. The genomic binding sites of a noncoding RNA. Proceedings of the National Academy of Sciences. 2011;108:20497–20502. pmid:22143764
  40. 40. Gelbart ME, Kuroda MI. Drosophila dosage compensation: a complex voyage to the X chromosome. Development. 2009;136:1399–1410. pmid:19363150
  41. 41. Ross-Innes CS, Stark R, Teschendorff AE, Holmes KA, Ali HR, Dunning MJ, et al. Differential oestrogen receptor binding is associated with clinical outcome in breast cancer. Nature. 2012;481:389–393. pmid:22217937
  42. 42. Stark R, Brown G. DiffBind: Differential binding analysis of ChIP-Seq peak data: 33.
  43. 43. Imrichová H, Hulselmans G, Kalender Atak Z, Potier D, Aerts S. i-cisTarget 2015 update: generalized cis-regulatory enrichment analysis in human, mouse and fly. Nucleic Acids Res. 2015;43:W57–W64. pmid:25925574
  44. 44. McCoy MJ, Fire AZ. Intron and gene size expansion during nervous system evolution. BMC Genomics. 2020;21:360. pmid:32410625
  45. 45. Wenick AS, Hobert O. Genomic cis-Regulatory Architecture and trans-Acting Regulators of a Single Interneuron-Specific Gene Battery in C. elegans. Developmental Cell. 2004;6:757–770. pmid:15177025
  46. 46. Markenscoff-Papadimitriou E, Allen WE, Colquitt BM, Goh T, Murphy KK, Monahan K, et al. Enhancer interaction networks as a means for singular olfactory receptor expression. Cell. 2014;159:543–557. pmid:25417106
  47. 47. Henry GL, Davis FP, Picard S, Eddy SR. Cell type-specific genomics of Drosophila neurons. Nucleic Acids Res. 2012;40:9691–9704. pmid:22855560
  48. 48. Kvon EZ, Kazmar T, Stampfel G, Yáñez-Cuna JO, Pagani M, Schernhuber K, et al. Genome-scale functional characterization of Drosophila developmental enhancers in vivo. Nature. 2014;512:91–95. pmid:24896182
  49. 49. Serrano-Saiz E, Gulez B, Pereira L, Gendrel M, Kerk SY, Vidal B, et al. Modular Organization of cis-Regulatory Control Information of Neurotransmitter Pathway Genes in Caenorhabditis elegans. Genetics. 2020. pmid:32444379
  50. 50. Goldman TD, Arbeitman MN. Genomic and Functional Studies of Drosophila Sex Hierarchy Regulated Gene Expression in Adult Head and Nervous System Tissues. PLoS Genet. 2007;3. pmid:18039034
  51. 51. Jenett A, Rubin GM, Ngo T-TB, Shepherd D, Murphy C, Dionne H, et al. A GAL4-Driver Line Resource for Drosophila Neurobiology. Cell Reports. 2012;2:991–1001. pmid:23063364
  52. 52. Mellert DJ, Knapp J-M, Manoli DS, Meissner GW, Baker BS. Midline crossing by gustatory receptor neuron axons is regulated by fruitless, doublesex and the Roundabout receptors. Development. 2010;137:323–332. pmid:20040498
  53. 53. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550. pmid:25516281
  54. 54. Carrillo RA, Özkan E, Menon KP, Nagarkar-Jaiswal S, Lee P-T, Jeon M, et al. Control of Synaptic Connectivity by a Network of Drosophila IgSF Cell Surface Proteins. Cell. 2015;163:1770–1782. pmid:26687361
  55. 55. Li H, Horns F, Wu B, Xie Q, Li J, Li T, et al. Classifying Drosophila Olfactory Projection Neuron Subtypes by Single-Cell RNA Sequencing. Cell. 2017;171:1206–1220.e22. pmid:29149607
  56. 56. Tan L, Zhang KX, Pecot MY, Nagarkar-Jaiswal S, Lee P-T, Takemura S-Y, et al. Ig Superfamily Ligand and Receptor Pairs Expressed in Synaptic Partners in Drosophila. Cell. 2015;163:1756–1769. pmid:26687360
  57. 57. Neville MC, Nojima T, Ashley E, Parker DJ, Walker J, Southall T, et al. Male-specific fruitless isoforms target neurodevelopmental genes to specify a sexually dimorphic nervous system. Curr Biol. 2014;24:229–241. pmid:24440396
  58. 58. Billeter J-C, Villella A, Allendorfer JB, Dornan AJ, Richardson M, Gailey DA, et al. Isoform-Specific Control of Male Neuronal Differentiation and Behavior in Drosophila by the fruitless Gene. Current Biology. 2006;16:1063–1076. pmid:16753560
  59. 59. Dalton JE, Fear JM, Knott S, Baker BS, McIntyre LM, Arbeitman MN. Male-specific Fruitless isoforms have different regulatory roles conferred by distinct zinc finger DNA binding domains. BMC Genomics. 2013;14:659. pmid:24074028
  60. 60. Grant CE, Bailey TL, Noble WS. FIMO: scanning for occurrences of a given motif. Bioinformatics. 2011;27:1017–1018. pmid:21330290
  61. 61. Ferri SL, Bohm RA, Lincicome HE, Hall JC, Villella A. fruitless Gene Products Truncated of their Male-Like Qualities Promote Neural and Behavioral Maleness in Drosophila If these Proteins Are Produced In the Right Places At the Right Times. Journal of Neurogenetics. 2008;22:17–55. pmid:18363163
  62. 62. Kudron MM, Victorsen A, Gevirtzman L, Hillier LW, Fisher WW, Vafeados D, et al. The ModERN Resource: Genome-Wide Binding Profiles for Hundreds of Drosophila and Caenorhabditis elegans Transcription Factors. Genetics. 2018;208:937–949. pmid:29284660
  63. 63. Ito H, Sato K, Koganezawa M, Ote M, Matsumoto K, Hama C, et al. Fruitless recruits two antagonistic chromatin factors to establish single-neuron sexual dimorphism. Cell. 2012;149:1327–1338. pmid:22682252
  64. 64. Thistle R, Cameron P, Ghorayshi A, Dennison L, Scott K. Contact chemoreceptors mediate male-male repulsion and male-female attraction during Drosophila courtship. Cell. 2012;149:1140–1151. pmid:22632976
  65. 65. Wang F, Wang K, Forknall N, Patrick C, Yang T, Parekh R, et al. Neural circuitry linking mating and egg laying in Drosophila females. Nature. 2020;579:101–105. pmid:32103180
  66. 66. Vernes SC. Genome wide identification of fruitless targets suggests a role in upregulating genes important for neural circuit formation. Sci Rep. 2014;4:4412. pmid:24642956
  67. 67. Schneider I. Cell lines derived from late embryonic stages of Drosophila melanogaster. Development. 1972;27:353–365. pmid:4625067
  68. 68. Newell NR, New FN, Dalton JE, McIntyre LM, Arbeitman MN. Neurons That Underlie Drosophila melanogaster Reproductive Behaviors: Detection of a Large Male-Bias in Gene Expression in fruitless-Expressing Neurons. G3 (Bethesda). 2016;6:2455–2465. pmid:27247289
  69. 69. Barik A, Lu Y, Sathyamurthy A, Bowman A, Shen C, Li L, et al. LRP4 Is Critical for Neuromuscular Junction Maintenance. J Neurosci. 2014;34:13892–13905. pmid:25319686
  70. 70. Mosca TJ. On the Teneurin track: a new synaptic organization molecule emerges. Front Cell Neurosci. 2015;9. pmid:25698924
  71. 71. Berns DS, DeNardo LA, Pederick DT, Luo L. Teneurin-3 controls topographic circuit assembly in the hippocampus. Nature. 2018;554:328–333. pmid:29414938
  72. 72. Dubreuil V, Barhanin J, Goridis C, Brunet J-F. Breathing with phox2b. Philos Trans R Soc Lond, B, Biol Sci. 2009;364:2477–2483. pmid:19651649
  73. 73. Lin JH, Saito T, Anderson DJ, Lance-Jones C, Jessell TM, Arber S. Functionally Related Motor Neuron Pool and Muscle Sensory Afferent Subtypes Defined by Coordinate ETS Gene Expression. Cell. 1998;95:393–407. pmid:9814709
  74. 74. Pereira L, Aeschimann F, Wang C, Lawson H, Serrano-Saiz E, Portman DS, et al. Timing mechanism of sexually dimorphic nervous system differentiation. Elife. 2019;8. pmid:30599092
  75. 75. Hobert O. Terminal Selectors of Neuronal Identity. Curr Top Dev Biol. 2016;116:455–475. pmid:26970634
  76. 76. Lee T, Luo L. Mosaic analysis with a repressible cell marker for studies of gene function in neuronal morphogenesis. Neuron. 1999;22:451–461. pmid:10197526
  77. 77. Pfeiffer BD, Truman JW, Rubin GM. Using translational enhancers to increase transgene expression in Drosophila. Proc Natl Acad Sci USA. 2012;109:6626–6631. pmid:22493255
  78. 78. Shang Y, Griffith LC, Rosbash M. Light-arousal and circadian photoreception circuits intersect at the large PDF cells of the Drosophila brain. Proceedings of the National Academy of Sciences. 2008;105:19587–19594. pmid:19060186
  79. 79. Gordon MD, Scott K. Motor Control in a Drosophila Taste Circuit. Neuron. 2009;61:373–384. pmid:19217375
  80. 80. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nature Methods. 2012;9:357–359. pmid:22388286
  81. 81. Ramírez F, Ryan DP, Grüning B, Bhardwaj V, Kilpert F, Richter AS, et al. deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic Acids Res. 2016;44:W160–W165. pmid:27079975
  82. 82. Kim D, Langmead B, Salzberg SL. HISAT: a fast spliced aligner with low memory requirements. Nat Methods. 2015;12:357–360. pmid:25751142
  83. 83. Afgan E, Baker D, van den Beek M, Blankenberg D, Bouvier D, Čech M, et al. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2016 update. Nucleic Acids Res. 2016;44:W3–W10. pmid:27137889
  84. 84. Yu G, Wang L-G, He Q-Y. ChIPseeker: an R/Bioconductor package for ChIP peak annotation, comparison and visualization. Bioinformatics. 2015;31:2382–2383. pmid:25765347
  85. 85. Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4:44–57. pmid:19131956
  86. 86. Raudvere U, Kolberg L, Kuzmin I, Arak T, Adler P, Peterson H, et al. g:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update). Nucleic Acids Res. 2019;47:W191–W198. pmid:31066453
  87. 87. Zhu LJ, Christensen RG, Kazemian M, Hull CJ, Enuameh MS, Basciotta MD, et al. FlyFactorSurvey: a database of Drosophila transcription factor binding specificities determined using the bacterial one-hybrid system. Nucleic Acids Res. 2011;39:D111–117. pmid:21097781
  88. 88. Leinonen R, Sugawara H, Shumway M, International Nucleotide Sequence Database Collaboration. The sequence read archive. Nucleic Acids Res. 2011;39:D19–21. pmid:21062823
  89. 89. Kechin A, Boyarskikh U, Kel A, Filipenko M. cutPrimers: A New Tool for Accurate Cutting of Primers from Reads of Targeted Next Generation Sequencing. J Comput Biol. 2017;24:1138–1143. pmid:28715235