Both MPH and ATX increase the locomotor activity of wild-type Drosophila
To investigate the cell type specific molecular mechanisms of ADHD drugs in the brain at single-cell resolution, we conducted behavioral experiments and scRNASEQ in wild-type (WT) adult male Drosophila melanogaster following exposure to MPH, ATX, and control treatment. Subsequently, we dissected and dissociated whole brains under three different conditions with phenotypes captured after drug administration and subjected single cells to 10X Genomics scRNASEQ. An overview of the workflow of the drug-exposed scRNASEQ study in adult Drosophila brain is shown in Fig. 1. MPH and ATX are commonly used drugs to treat ADHD symptoms in humans3. In the modified capillary feeder (CAFE) assay35, WT male flies were exposed to MPH, ATX, and control treatment for 24 h (Fig. 1A). The drugs were tested at four or five different doses (for ATX: 0.25 mg mL− 1, 0.5 mg mL− 1, 1 mg mL− 1, and 2 mg mL− 1; for MPH: 0.25 mg mL− 1, 0.5 mg mL− 1, 1 mg mL− 1, 1.5 mg mL− 1, and 2 mg mL− 1) according to the literature. The results show that 0.25 mg mL− 1 ATX (Supplementary Fig. 1A) and 1.5 mg mL− 1 MPH (Supplementary Fig. 1B) had the strongest effect; therefore, these concentrations were chosen for subsequent experiments. A single adult fruit fly was placed in each arena after drug exposure (a total of 24 flies per treatment, and 72 flies overall) and its behavior was recorded (Fig. 1B). Two distinct replications were carried out on July 12th (replicate 1) and August 10th (replicate 2) 2021. The locomotor activities of fruit flies were simultaneously tracked using EasyFlyTracker and the short-term distances were quantitated. Only 60 out of the 72 flies (20 flies per treatment) were used for subsequent behavioral calculations and experiments. We found that WT male flies produced hyperactivity-like behavior (higher locomotor activity) following exposure to MPH or ATX in comparison with the controls, as shown in Fig. 1C. Additionally, we observed a significant increase (Fig. 1C ①) in the average distance traveled per fly in a 10-min time period in MPH-exposed (Kruskal–Wallis test with Bonferroni correction: P = 1.463e-03) or ATX-exposed (Kruskal–Wallis test with Bonferroni correction: P = 3.766e-03) flies, which is consistent with previously published results16, 36. Moreover, the line plot of the average distance traveled by each fly at each time point during the 1.5-h recording period also shows markedly higher-level activities in drug-exposed groups as compared with control flies (Fig. 1C ②). Furthermore, EasyFlyTracker created angle-change plots (Supplementary Fig. 1C) and heatmaps (Supplementary Fig. 1D) of the different treatments to display more details of the behavioral activities of the fruit flies. We found a positive correlation (Pearson r of distances and angles in all groups: 0.6979, P = 6.71E-263) between the pattern of angle-change activities and locomotor activities in both the drug-exposed and control groups, as shown by the scatter plot in Supplementary Fig. 1C.
scRNASEQ identified 28 distinct primary clusters in adult male Drosophila brain
We dissected the brains from WT male fruit flies in the MPH-treated, ATX-treated, and control groups following the observation of significant hyperactivity-like behavior in comparison with the controls (Fig. 1D). A total of 60 male flies were dissected for one behavioral test (20 brains per treatment). Subsequently, three independent samples from each batch were processed for single-cell isolation and the mRNAs were barcoded and sequenced (Fig. 1E). We analyzed the sequencing data at different levels, as shown in Fig. 1F. Since the number of recovered cells was greater than expected, DoubletFinder 37 was used to predict and remove doublets. Details of the number of cells and other statistics are summarized in Supplementary Table 1. A total of 82,917 cells were retained for subsequent analysis. We primarily identified 28 distinct clusters at low resolution (0.1; 15 PCA), annotated the clusters based on previous understanding of canonical markers and the top 10 marker genes in each cluster, and were able to clearly distinguish between neurons and glial cells. These marker genes are summarized in Supplementary Table 2, and the preliminary visualization of the cell type annotation is shown in Fig. 2A. At first, neurons and glial cells were roughly regarded as two main types according to the classical marker genes elav and repo, and then detailed cell types were annotated according to Supplementary Table 2, including monoaminergic neurons (Monoamines), mushroom body Kenyon cells (MBKCs), ellipsoid body cells (EB), optic lobe cells (OL), projection neurons (PNs), unannotated clusters that cannot be identified according to the primary classification (Clusters A–H), and glial cells (Glia). It is well known that mushroom bodies, which contain three subclasses of neurons, \(\alpha \beta , {\alpha }^{{\prime }}{\beta }^{{\prime }}, \text{a}\text{n}\text{d} \gamma\), are essential for olfactory learning and memory. Using the well-known markers ey and Dop1R2, in addition to the other top 10 marker genes listed in Supplementary Table 2, we were able to directly distinguish between two different MBKC types. As shown in Fig. 2A, only a small fraction of cells expressed the known marker gene Vesicular Monoamine Transporter (Vmat), which were independently marked as monoaminergic neuronal cells. No obvious sub-clusters of Monoamines (C20) were found according to known marker genes corresponding to each of these neurons releasing 5-HT, tyramine (TA), octopamine (OA), and DA in Drosophila (details can be found in the Methods); thus, we regarded Monoamines (C20) as our research target representing dopaminergic neurons. Certain clusters related to the hypothesis or mechanism of drugs were fully considered and analyzed. The number of differentially expressed (drug-responsive) genes between the treatment and control groups was calculated for each primary cell type.
The general effects of MPH and ATX in neurons and glial cells
After careful annotation of the cell types, we used Seurat's default parameters (logfc.threshold = 0.25 and Bonferroni-adjusted P\(\le\) 0.05) to identify DEGs. We identified many drug-responsive DEGs across all clusters: 694 for MPH and 248 for ATX, with 230 genes shared between the two groups, as shown by the Venn diagram (Fig. 2B). Previous studies have shown that both drugs greatly enhance the cognitive function and symptoms of ADHD, which raises the possibility that they share overlapping mechanisms of action4, 38–40. Thus, the biological pathways of the 230 genes shared between MPH and ATX were analyzed, and the top 20 pathways are displayed in Fig. 2C. These top common pathways include those that modify chemical synaptic transmission, the Ca2+ pathway, the negative regulation of synaptic transmission, ion transport, the neuronal system, adult behavior, and the regulation of cell–cell communication. The full set of results can be found in Supplementary Table 3. These pathways are mainly associated with the regulation of neurotransmitters, which is in agreement with the general hypothesis of ADHD as a neurotransmitter disorder.
More specifically, a wider range of cell type responses was identified for the stimulant MPH in comparison with the non-stimulant ATX after analysis of DEGs in individual cell types (Fig. 2D). Exposure to MPH and ATX induced widespread changes in gene expression throughout the brain, with a stronger effect associated with MPH. The widespread “neurotransmitter imbalance” hypothesis was evaluated by focusing on the Monoamine (C20) cluster that explicitly expresses Vmat. Additionally, the top three clusters containing the greatest number of DEGs were Cluster E (C14), Glia (C7), and MBKC_b (C18), respectively. We performed pathway analysis of the neuronal cell types C20, C14, and C18 to identify cell type-specific signals, as shown in Fig. 2E (C7 was introduced later in “Subdivision of glial cells and their essential role in MPH and ATX effects”). The full plot of the biological pathways is shown in Supplementary Fig. 2. We found that the cell type-specific enrichment pathways are mostly implicated in MPH effects. For instance, C20 shows a limited number of enrichment pathways concentrated in MPH, with none in ATX. Moreover, these cell types perform different functions in the MPH-treated and ATX-treated groups, sharing only a limited number of pathways in Drosophila brain. For example, these cells only share two pathways (negative regulation of cell communication and courtship behavior) in C18, and the rest are related to MPH. C18 is the second-highest cluster, which is a subtype of MBKC and essential for olfactory learning and memory. MBKC forms numerous synapses with DA neurons, and recent results have highlighted the importance of DA-driven plasticity and activity in feedback and feedforward connections between various elements of the mushroom body neural networks41. Although we do not yet have the exact cell type mapping between Drosophila and humans, the different responses to MPH and ATX in Drosophila brain cell types further support the diversity of drug responses and the importance of precise treatment.
Neurotransmitter-related gene expression pattern in adult Drosophila brain at single-cell resolution
Prior knowledge demonstrates that the different neurotransmitter hypotheses are essential to the pharmaceutical treatment of psychiatric diseases42–45. Neurotransmitters can be analyzed at both the cell type and DEG level at single-cell resolution. We firstly assessed the proportion and distribution of cells expressing genes responsible for the release or synthesis of different neurotransmitters in Drosophila brain, which provides valuable information to fully understand the mechanisms induced by the two drugs. We classified cells as glutamatergic, cholinergic, GABAergic, and monoaminergic neurons based on the expression of key genes, vesicular glutamate transporter (VGlut) of Glu, vesicular acetylcholine transporter (VAChT) of ACh, Glutamic acid decarboxylase 1 (Gad1) of GABA, or vesicular monoamine transporter (Vmat) of monoamines, and repo of glial cells were classified as other type. These cells are plotted in different colors in Fig. 3A, and the aforementioned names of the primary cell types are also labeled to elaborate. Fortunately, we did not observe any detectable expression of neurotransmitter markers in glia, as shown in the heatmap in Fig. 3B. Cholinergic neurons were found to be the most abundant in the control samples, being expressed in 55.45% of all cells; however, glutamatergic, GABAergic, and monoaminergic neurons were expressed in only 17.44%, 7.09%, and 0.42% of cells, respectively (Fig. 3C). Since recent research in Drosophila revealed a list of co-expressed neuroactive substances22, 23, we also looked at the possibility of co-existing neurotransmitters in the adult male brain. As shown in Fig. 3C, cells expressing these neurotransmitter-specific marker genes were mainly exclusive, despite the presence of 5.70% VAChT and VGlut indicators and 3.19% VAChT and Gad1 markers. Some cells simultaneously release both excitatory and inhibitory neurotransmitters. This phenomenon shows that co-expression of excitatory and inhibitory neurotransmitters also occurs in the adult male Drosophila brain, which is consistent with scRNASEQ data for larval brain22 and midbrain23. The percentage of cells expressing the markers for Glu, GABA, or all three or four neurotransmitters was very low (< 1%) (Fig. 3C), despite the possibility that these markers represent multiple cells. Analyzing the distribution and proportion of these neurotransmitters can aid our understanding of the cellular and molecular processes related to ADHD drugs.
Even though the pathophysiology of ADHD remains largely unknown, the neurotransmitter imbalance hypothesis has been continuously described; thus, cell type proportions of the “neurotransmitter levels” induced by MPH and ATX treatment were quantitated according to the expression levels of key genes. As shown in Fig. 3D, the proportions of cell types induced by drug treatment changed only slightly, indicating that the significant effects (marked with *) on GABAergic and monoaminergic neurons only affected the gene expression level of a small number of cells. Specifically, we found that monoaminergic neurons only changed significantly following MPH treatment (Fisher Exact probability test: P = 2.89E-02), GABAergic neurons changed significantly following both MPH (Fisher Exact probability test: P = 2.13E-03) and ATX (Fisher Exact probability test: P = 5.30E-06) treatment, while changes in other neurons (cholinergic and glutamatergic) were non-significant. These may be drug-induced changes in the expression of certain genes or a result of the “neurotransmitter switch.” Neurotransmitter switching, the gain of one transmitter and the loss of another in the same neuron, can be driven by natural stimuli, drugs, and other programs used to manage neurological and psychiatric disorders that also affect neurotransmitter states and thus alter behavior, which has been observed in several studies46. Our results provide the possibility for further research to reveal the manner by which psychotropic drugs alter key gene expression and neurotransmitter switching in certain cells in the brain. Moreover, trends (although non-significant) in excitatory glutamatergic neurons increased following both MPH and ATX treatment in comparison with the control; however, excitatory cholinergic neurons decreased and inhibitory GABAergic neurons changed in different directions following MPH and ATX treatment. The imbalance between excitation and inhibition is associated with ADHD-like symptoms and drug-induced mechanisms. A previous study has shown that these imbalances may contribute to the development of ADHD-like phenotypes in a mouse model47. Moreover, the distinct direction of change following MPH and ATX treatment indicates that the underlying molecular mechanisms are different. Indeed, gene set association analysis in humans has revealed that Glu, and possibly also GABA, are associated with ADHD and ASD, although the direction of the effects remains undetermined48. Next, we evaluated the difference in expression levels of neurotransmitter marker genes (VAChT, VGlut, Gad1, Vmat, and repo) between the drug treatment and control groups and found that Gad1 was significantly expressed following treatment with both drugs (Wilcox test of MPH: p_val_adj = 5.47E-41, Wilcox test of ATX: p_val_adj = 3.28E-51). Additionally, we visualized the patterns following treatment with MPH and ATX by counting the normalized DEGs in the five groups of cells using a radar plot, as shown in Fig. 3E. We discovered similar neurotransmitter expression levels but different expression intensity of the normalized DEGs between the MPH-treated and ATX-treated groups for most cell classes, with the exception of monoaminergic neurons in which ATX treatment induced a lower number of DEGs in comparison with MPH. However, cell type groups such as cholinergic, glutamatergic, and GABAergic neurons, and even glial cells, showed similar expression preferences. These results support the notion that MPH is a much broader neurotransmitter inhibitor. Taken together, monoaminergic neurons react to both MPH and ATX treatment in distinct ways; for example, in the same direction but with different intensity.
In summary, both MPH and ATX treatment affects different neurotransmitter neurons, producing a slight change in cell type proportions. Since the chance of changes in cell type proportions is small, it is more likely that drug treatments have a strong effect on gene expression in a small proportion of cells, resulting in gene expression changes or neurotransmitter switching.
Dopamine metabolism and signaling respond to MPH and ATX
As mentioned previously, neurotransmitters are thought to be critical in the field of ADHD research, especially monoaminergic neurons. Catecholamines (DA, NE), 5-HT, and GABA display dysfunction or deficit in ADHD49–52. Most drug treatments for ADHD, such as MPH5 and ATX6, aim to regulate inter-synaptic neurotransmitter levels. The enhanced efflux of DA and NE associated with MPH or ATX exposure leads to increased availability for binding to their respective transporters (such as the DAT and NET) and receptors, as evidenced by existing studies53–57. Here, we aim to summarize the drug responses of DA and NE, by using OA in Drosophila to replace NE, which is the invertebrate homolog of mammalian NE and plays important roles in modulating behavior and synaptic functions. DA metabolism and signaling is discussed in this section, and the results for OA are shown in the subsequent section.
DA signaling is regulated by enzyme degradation and transporter reuptake, and the recycled metabolites can be reused to synthesize DA (Fig. 4A). These steps can occur in different cell types, such as DA-releasing cells, postsynaptic neurons, and glial cells58; therefore, we used our scRNASEQ data to determine which cell types expressed components of the DA recycling and metabolic pathways. The first step of DA synthesis, conversion of tyrosine into the DA precursor L-DOPA catalyzed by the ple-encoded Tyrosine hydroxylase, appears to primarily occur in Monoamines (C20) as compared with other cell clusters (Fig. 4B). In comparison, Ddc, which converts L-DOPA to DA, is present in several other neuronal populations, including OLs, PNs, and other non-specific clusters (Fig. 4B). It is unclear whether Ddc present in these neurons is involved in the metabolism of DA and other aromatic L-amino acids; however, these two genes were not significantly differentially expressed among the MPH, ATX, and control treatments. Three enzymes play a role in DA degradation and recycling (Fig. 4A). Firstly, the ebony (e) gene product converts DA into N-beta-alanyl-dopamine (NBAD)59, 60 and was almost exclusively expressed in glial cells, but only occupied 25% in our data (Fig. 4B). Secondly, Dopamine-N-acetyltransferase, encoded by speck, converts DA into N-acetyl-dopamine (NADA). speck was expressed in glial cells, PNs, and non-specific cell types (such as C0, C4, and C5) (Fig. 4B). Although these results highlight the important role of glia in DA reuptake, metabolism, and recycling, other cells appear to convert DA into NADA rather than into NBAD (Fig. 4A, B). The fate and consequence of these two metabolites in each cell type remain largely unknown. Thirdly, tan (t), a gene coding a hydrolase that can convert NBAD back to DA, was nearly not found in any cell population from the brain itself (Fig. 4B), suggesting that this recycling pathway is not utilized there. Nevertheless, these three genes were also not significantly differentially expressed among MPH, ATX, and control treatments.
The vesicular monoamine transporters (encoded by Vmat) transport DA, 5-HT, OA, and TA into synaptic vesicles61. As already mentioned, Vmat was mainly detected in Monoamines (C20). The DAT-encoded DA transporter mediates DA reuptake by dopaminergic neurons. Unlike Vmat, DAT was not only specifically expressed in dopaminergic cells, but also found in MBKCs (C9-MBKC_a” cluster in Fig. 4B), suggesting that other neurons may tightly regulate the duration and magnitude of DA signals that they receive. However, these two transporters were also not significantly differentially expressed among MPH, ATX, and control treatments. These results are not consistent with the previously discussed hypothesis that MPH acts primarily by inhibiting DAT62. Here, we propose several reasons for this discrepancy. Firstly, the high dose may be one reason for the non-significant differential expression of DAT following MPH treatment, since previous research has shown that different doses of MPH can lead to different results63. For example, one study found that a high dose of MPH appears to suppress intracranial self-stimulation through mechanisms other than DAT inhibition64. Although we used the inflection point of locomotor activities (as shown in Supplementary Fig. 1A, B), which followed previous experiments with MPH16, 36, no such experiments have been performed in Drosophila with ATX; thus, it is difficult to describe the dose effects of MPH and ATX in our experiments. Secondly, structural biology research has demonstrated that Drosophila DAT possesses differences in subsite B of the central binding site as compared with human DAT, which leads to much weaker inhibition of the stimulant amphetamine in humans65. Thirdly, it has been shown that the extracellular concentration of DA is significantly decreased in neuronal cell lines devoid of DAT following treatment with MPH7. Therefore, we suggest that these factors prevent us from viewing the pattern of DAT inhibition.
In addition to DAT, our data also explore the distribution and drug response of DA receptors, which are another important factor in the DA signaling pathway that influences DA levels in the synaptic cleft. As shown in Fig. 4B, DA receptors (Dop1R1, Dop1R2, Dop2R, and DopEcR) are not only distributed in MBKCs that form numerous synapses with DA neurons in the lobes of mushroom bodies, but also in Monoaminergic (C20) and projection neurons. Although we are unable to directly map the cell types between Drosophila and humans, various cell types in Drosophila brain expressed DA receptors and responded to the drugs, emphasizing the various cellular responses of DA. Our data demonstrate that DopEcR is expressed in almost all cell types, even glia, which is consistent with a previous study reporting this as an important receptor broadly expressed in Drosophila brain66. DopEcR is activated by DA as well as ecdysteroids (ecdysone and 20E) to increase cAMP levels and modulate multiple signaling cascades such as the phosphoinositide 3-kinase pathway67, 68. Other receptors, Dop1R1 and Dop1R2, were mainly expressed in MBKCs, which has also been reported previously69, 70. We found that Dop2R and DopEcR were differentially expressed between the MPH-treated and control groups, but not the ATX-treated group. We distinguished DopEcR as the DEG in PNs (C3 and C8), EB2 (C12), Cluster D (C13), OL3 (C15), and MBKC_a (C9), and distinguished Dop2R in Cluster B (C4), as displayed in Fig. 3C. Since C4 lacks a direct location marker, we did not initially assign a specific cell type; however, we subsequently found that C4 had a high expression level of Gad1 and thus annotated these cells as GABAergic neurons (Fig. 4A). These results revealed that the DA receptor Dop2R responded in GABAergic neurons. Dop2R encodes a G protein-coupled receptor that is activated by DA and regulates various phenotypes such as locomotor activity and olfactory associative learning. More importantly, the human ortholog of this gene, DRD2 (dopamine receptor D2, DIOPT v8.0 score = 9), is implicated in several diseases including ADHD, conduct disorder, and movement disease. For example, single nucleotide polymorphisms (SNPs) in DRD1 and DRD2 are considered potential risk factors for ADHD71, suggesting that the target of MPH is not only DAT; and more importantly, our results support an important role for DA receptors in MPH treatment. In addition to the above-mentioned common enzymes, transporters, and receptors, other genes are also involved in DA metabolism and signaling, as shown in Fig. 4B. Significant differential expression of these genes in at least one cell type is marked with an asterisk. These nine genes are all involved in the regulation of the DA secretory pathway of GO:0014059. As summarized in Fig. 4C, Syt1, Sytalpha, Syt7, and Ih were significantly differentially expressed in some specific clusters between the MPH-treated and control groups; Sytalpha was only significantly differentially expressed in OL3 (C15), Syt7 only in MBKC_b (C18), and Syt1 and Ih in various unannotated neuronal clusters (C0, C13, C14), PNs (C10, C11), EB2 (C12), OLs (C15, C17), and MBKC_b (C18); only Syt1 (PNs (C11), C14 (ClusterE)) and Ih (ClusterB (C4), PNs (C11), C14 (ClusterE)) were significantly differentially expressed between the ATX-treated and control groups.
In summary, genes involved in DA metabolism and signaling pathways were detected in different cell types in adult Drosophila brain, such as MBKC, EB, OLs, PNs, and some unknown cell types, reflecting the diversity of drug effects. Our findings also support the previous view that DA receptors are crucial in ADHD: Dop2R and DopEcR were differently expressed between the MPH-treated and control groups, while there was no difference in the ATX-treated group. Additionally, genes involved in the regulation of the secretory pathway of DA, such as Syt1, Sytalpha, Syt7, and Ih, were mainly responsive to MPH treatment, suggesting the existence of more targets for the two drugs in Drosophila and the requirement for further exploration in humans. Here, the expression levels of DA-related signaling genes were clearly different between the ATX-treated and MPH-treated groups, highlighting the difference in the underlying mechanism that exhibits hyperactivity (higher locomotor activities) at the current dose. Moreover, these findings suggest that further genes responsive to ADHD drugs can be mined and prospective candidates can be applied to other species such as mice, rats, and humans.
Genes for other neurotransmitter receptors and synaptic proteins also respond to MPH and ATX
It has been reported that MPH not only plays a role in regulating the DA pathway, but also NE, 5-HT, Glu, and even other more general cellular processes8, 9. Moreover, our previous evidence suggests that MPH may have multiple dimensional targets, such as receptors for different neurotransmitters. Thus, in addition to DA receptor genes, we also describe several drug-induced receptor changes for both MPH and ATX. We found that many cell types also significantly expressed the receptor genes for OA (similar to NE in humans), 5-HT, GABA, Glu, and acetylcholine (Fig. 4C), the patterns of which are summarized in Fig. 4C.
There was a lower expression level of all three OA receptors in the MPH-treated group, but only Octbeta2R was expressed at a lower level in the ATX-treated group. As previous research has shown, ATX increases the extracellular levels of NE, for which NET is its target6. ATX has a high affinity and selectivity for NET, but little-to-no affinity for other neurotransmitter transporters or receptors6, 72. Our results in Drosophila brain demonstrate the sparse inhibitory targets of ATX for OA and even other receptor genes, which is similar to previous results for NE. Octbeta2R enables the activity of the OA receptor in Drosophila and OA produces specific biochemical responses such as increased synthesis of cyclic AMP (cAMP) and phosphorylase activation73. Octbeta2R also participates in the adenylate cyclase-activating G protein-coupled receptor signaling pathway and positively regulates synaptic growth74, 75. In addition, Octbeta1R may also inhibit cAMP production via inhibitory G0α76. Therefore, we propose that cAMP plays an important role in the action of ATX and MPH in ADHD and requires further attention, which is supported by several studies. Firstly, reduced expression levels of cAMP response element modulator (CREM) were found in an ADHD rat model77. Secondly, CREM mutant mice display ADHD-like behaviors such as increased levels of physical activity78. Thirdly, enhanced glutamate release and phosphorylation of cAMP response element binding protein (CREB) at serine 133 may be associated with attention deficit79. The expression of Octbeta1R was reduced within C15 (OL3) and C18 (MBKC_b) in the MPH-treated group, and Octbeta2R plays a role in enabling OA receptor activity. Thus, we examined the similarities and differences between MPH and ATX treatment, which may reveal the potential targets and different underlying mechanisms of the two drugs; for example, ATX only inhibits Octbeta2R and not Octbeta1R, which we speculate is linked to the amount of cAMP.
The expression level of 5-HT1B was lower following the administration of both drugs, but 5-HT1A was only expressed at a lower-level following MPH administration (Fig. 4C). 5-HT1B is regarded as the modulator of drug reinforcement, stress sensitivity, mood, anxiety, and aggression. In addition, reduced 5-HT1B auto-receptor activity may have an antidepressant-like effect80. Previous studies have shown that DA and 5-HT neurons can interact anomalously in ADHD at the soma, terminal, and distant levels81. Moreover, 5-HT regulates DA activity through its receptors 5-hydroxytryptamine receptor 1B (HTR1B) or 5-hydroxytryptamine receptor 2A (HTR2A), and their dysfunction can lead to problems in “5-HT-DA dynamics” resulting in ADHD symptoms82, 83. These preliminary data suggest an important role for the serotonin system in the development of ADHD. Moreover, studies in animal models of ADHD indicate intimate interplay between 5-HT and dopaminergic neurotransmission51. At the optimal dose, we observed a marked decrease in its expression, especially in the MPH group. Although human studies have not confirmed these associations, animal studies have found MPH to be a HTR1A agonist84, and it is speculated that the activation of 5-HT1A may play a partial role in MPH-mediated DA release in the brain.
The receptor genes for GABA and Glu belong to “Amino acid receptor genes.” Changes were observed in different cell types in the MPH-treated group; however, there was almost none significant difference in expression in the ATX-treated group, which may suggest that these are targets of MPH but not ATX. Previous research has shown that ADHD may be related to insufficient responses of the GABAergic system in frontostriatal circuitry85; thus, we propose that inhibition of receptor genes such as GABA-B-R1 may be a potential mechanism for the treatment of ADHD using MPH. Additionally, many receptor genes for Glu, such as GluClalpha, GluRIA, GluRIB, mGluR, and KaiR1D, respond to MPH, supporting its important role. There was also a reduction in the expression of “Acetylcholine receptor genes” following treatment with MPH, as shown in Fig. 4C. For example, nAChRalpha6 and nAChRalpha7 had lower expression levels in a variety of cell clusters, nAChRalpha1 had a lower expression level in C22 (OL6), and nAChbeta1 had a lower expression level in C18(MKBC_b). This pattern of lower expression was not obvious in the ATX group, which also supports the notion that MPH is a broad inhibitor of amino acids and acetylcholine in the brain. Generally, the number of differentially expressed receptor genes following MPH treatment is much higher than that following ATX treatment, which is consistent with the existing evidence that MPH is a much broader inhibitor. The patterns of receptor genes for different neurotransmitters support the view that MPH also has an effect on the regulation of OA (NE), 5-HT, Glu, and even other more general cellular processes8, 9.
Neurotransmitter release requires the involvement of synaptic vesicles, and a variety of molecules and proteins play essential roles in mediating the binding and release of synaptic vesicles to neurotransmitters. The DEGs for “Neuronal and synaptic activity” and “Regulation of synaptic transmission” are summarized in Fig. 4C. The DEGs of the cell types in the MPH-treated group changed more broadly than those in the ATX-treated group. In addition, it must be emphasized that the expression level of Hr38 changed in opposite directions in the MPH-treated and ATX-treated groups. Hr38 is a Drosophila homolog of the mammalian Nr4a1/Nr4a2/Nr4a3 gene family, which is transcriptionally activated by MEF2 in humans. Evidence shows that Hr38 is also a downstream gene of Mef2 in Drosophila, and alcohol activates Mef2 to induce Hr38. An increased level of Hr38 is associated with higher tolerance and increased preference for alcohol86. Moreover, knockdown of dopaminergic (dMEF2) neurons results in increased locomotor activity and reduced sleep, which is concordant with the human phenotype18. It should also be highlighted that Snap25 was only responsive in the MPH-treated group. It is well known that synaptosome-associated protein of 25 kDa (SNAP25) is one of the critical proteins of the soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE) complex, which is essential for calcium-dependent exocytosis of synaptic vesicles. Dysfunction of the SNARE complex and related proteins is involved in neurological disorders and highlights their significant contribution to the pathology of various neurological disorders such as ADHD and epilepsy, and genetic and pharmacogenetic evidence suggests that they may also be important biological targets for these diseases87.
In summary, in addition to DA-related genes, MPH and ATX also inhibit other receptor genes, such as those for OA/5-HT/GABA/Glu/Ach, to similar or differing degrees in Drosophila brain, and their patterns are summarized as follows. Firstly, the stimulant MPH induced a wider range of inhibitory effects than the non-stimulant ATX, and published research indicates that MPH regulates multiple pathways in Drosophila brain8, 9. MPH exhibits a larger inhibition of receptor genes than ATX, not only DA receptors but also others, and the expression response patterns of the two drugs are generally distinct. Some receptor genes share certain characteristics with one another, suggesting that they serve the same purpose in several pharmacological therapies. These findings provide the basic effects of psychotropic drugs at single-cell resolution in Drosophila brain. Inspiringly, a recent study mapped the transcriptome of the caudate nucleus and anterior cingulate cortex in post-mortem tissue from 60 individuals with and without ADHD, uncovering significant downregulation of neurotransmitter gene pathways, especially glutamatergic. Specifically, glutamate receptor genes are enriched by DEGs in the caudate nucleus, DEGs in the anterior cingulate cortex (ACC) are involved in serotonin and GABA receptor activity, and a broad set of genes for neurotransmitter receptor activity is enriched by DEGs in both regions49. This transcriptomics evidence highlights corticostriatal neurotransmitter abnormalities in the pathogenesis of ADHD, especially receptor genes for different neurotransmitters, suggesting that our Drosophila drug discovery results are consistent with these neurotransmitter models in ADHD and have the potential for large-scale drug screening. These are the most promising data for ADHD, including the target genes and their corresponding repurposing drugs; therefore, it is essential to translate these results into human orthologous genes and investigate more deeply.
Subdivision of glial cells and their essential role in MPH and ATX effects
As mentioned previously, glia possesses a large number of DEGs with which to address the potential roles of drug treatment. Moreover, glial cells are primarily important for sustaining and maintaining appropriate neuronal function. Emerging evidence indicates that distinct subtypes of glia play a crucial role in the control of neuronal development, apoptosis, metabolism, sleep, and other physiological activities in Drosophila14. There exist five types of glial cells in Drosophila including astrocyte-like, ensheathing, cortex, perineurial, and subperineurial glia, with the last two types belonging to surface glial cells. Identification of specific subtypes of glial cells is essential to explore their function in response to drugs in our data; therefore, we extracted cells from the Glial (C7) cluster, re-clustered them at a low resolution, and filtered the mixed neuronal cells. We identified four sub-clusters of glial cells: ensheathing, astrocyte-like, surface, and cortex glia, as shown in Fig. 5. All the subglial cells are labeled in the UMAP plot in Fig. 5A, and the top 10 genes with positive expression in each distinguished glial population are shown in the dot plot in Fig. 5B. Some of the top 10 genes consistent with published markers of glia were confirmed. Surface-associated glia are located in the outer adult brain and form the blood-brain-barrier (BBB) to help fruit flies detect their required nutrients88. Cortex glial cells constitute the layer below the surface cells and wrap around multiple neuronal cell bodies, participating in the metabolic support of neurons and providing nutrients89. Ensheathing and astrocyte-like glial cells belong to neuropil-associated glia and are located around the nerve bundle, forming a sheath structure. Ensheathing glial cells have been shown to remove degenerated axon fragments after brain injury90. Astrocyte-like glial cells play an important role in information transmission between neurons and glial cells, in addition to guaranteeing neurotransmitter homeostasis by expressing a set of specific transporter proteins such as excitatory amino acid transporter 1 (EAAT1) or GABA transporter (GAT)91–93. Previous studies have indicated that drugs can also induce a response in glial cells; for instance, esketamine alleviates cortical microglial activation, alters microglial number, and maintains morphological features in mice94. Moreover, MPH increases Glu uptake in chick cerebellum Bergmann glial cells95, and different doses of MPH induce different glial cells (e.g., astrocytes, microglia) under ADHD or non-ADHD conditions in rats96. More direct evidence in humans shows that neuronal and glial cell numbers are altered in a cortical layer-specific manner in autism97. Overall, although there exists no direct evidence for the effect of MPH or ATX at the cellular level in the human brain, our results demonstrate that both drugs induced a different response in Drosophila brain, which needs to be addressed. More specifically, in comparison with the control group, the subtype proportions estimated based on the expression of marker genes changed in both the MPH-treated and ATX-treated groups (Fig. 5C): there were relatively increased surface and astrocyte-like glia but a relatively decreased proportion of ensheathing and cortex glia. These changes show that MPH and ATX play a specific role in regulating subglial cells; however, the underlying mechanism needs to be further analyzed.
The DEGs for each subglial cell type were calculated separately and the counts are shown in the bar graph in Fig. 5D. Astrocyte-like and ensheathing glia had strikingly apparent gene expression changes, with more than 10 DEGs in each group. Detailed results of the DEGs can be found in Supplementary Table 4. Subsequently, pathway analysis was conducted in subglial cells possessing more than 10 DEGs. Pathways for the DEGs in ensheathing glia following MPH treatment were enriched in various biological processes, such as responses to stimuli, rhythmic processes, locomotion, metabolic processes, and developmental processes, revealing key processes that respond to MPH treatment; however, no specific pathways were identified for DEGs following ATX treatment. For example, semaphorin 1a (Sema1a), which was differentially expressed following MPH treatment, encodes a transmembrane protein involved in the negative regulation of locomotion, and a previous study showed that glial cells overexpressing another family member, Sema2a, cause abnormal traveling of flies98. Additionally, bendless (ben) is expressed in ensheathing glial cells, which encodes an E2 ubiquitin-conjugating enzyme that plays essential roles in multiple processes such as synaptic growth and maturation, axon guidance, innate immunity, genomic integrity, tumor growth, apoptosis, and long-term memory. Furthermore, the pathways related to DEGs in astrocyte-like glial cells following MPH treatment were also enriched in metabolic processes, cellular processes, and responses to stimuli. Accordingly, MPH has been shown to activate astrocytes in limbic neuronal/glial co-cultures99. Differential expression of the excitatory amino acid transporter 1 (Eaat1) in astrocyte-like glial cells following MPH treatment participates in glia–neuron communication100 and tightly regulates extracellular Glu levels to control neurotransmitter functions in locomotor behavior93.
In summary, four subtypes of glial cells were identified and shown to have different functions in adult Drosophila brain. Genes and pathways responsive to MPH and ATX treatment indicate the key role of various subglial cells at the brain level, especially ensheathing and astrocyte-like glia. Moreover, human neurodevelopmental and neurodegenerative central nervous system diseases associated with glial dysfunction in Drosophila models were reviewed and summarized101, uncovering the contribution of glial cells to brain function and disease susceptibility.
Cytochrome P450 genes occupy only a small fraction of cells in adult Drosophila brain, most of which are expressed in glial cells
As mentioned previously, most DEGs between drug-treated and control cells are involved in metabolic processes; therefore, we also analyzed the cytochrome P450 (CYP) genes in our data. P450 enzymes are heme-thiolate proteins best known for their role as monooxygenases and are present in almost all living organisms. Insect CYP genes can be assigned to four different phylogenetically related “clans,” three named after the founding family in vertebrates (CYP3, CYP4, and CYP2 clans) and one named according to their subcellular location (mitochondrial CYP clan). We analyzed 87 genes from these groups in Drosophila melanogaster recorded in FlyBase and found that only a small proportion were expressed in our fly brain data (Supplementary Fig. 4). In addition, a single-nucleus RNA sequencing study in the adult Drosophila renal system reported the presence of CYP genes Cyp6g1 and Cyp12d1102 in tubules, which are known to detoxify insecticides. However, Cyp6g1 was found within our fly brain data in very few cells, and no cells expressed Cyp12d1, indicating tissue differences in the expression of CYP genes in Drosophila.
The detected genes were primarily expressed in glial cells, but only occupied a small percentage of glia and were present at varying levels (Supplementary Fig. 4A); their distribution in subglial cells is shown in Supplementary Fig. 4B. Moreover, CYP genes were not identified as DEGs following drug treatment due to the small proportion of expressing cells in adult Drosophila brain (Supplementary Fig. 4C). Comparison of the expression levels of CYP genes in glial subtypes between the drug-treated and control groups revealed some related functions that should be noted. Cyp6a2103 and Cyp12a4104 are associated with insecticide resistance; Cyp6a20 is thought to be related to aggressive behavior105; Cyp28c1, Cyp311a1, and Cyp4d2 were shown to be lethal in RNAi screen, and Cyp4s3 was sublethal in that screen106; and Cyp28a5 was expressed in different directions in different subglial cells and is related to monooxygenase activity, and has been reported to be induced by caffeine107.
In general, CYP genes only occupied a small proportion of cells in adult Drosophila brain, which were mainly glia, and the functions of most of these genes are not well understood. We did not identify any significantly differentially expressed CYP genes involved in drug metabolism in adult Drosophila brain, which may be due to their limited percentage or tissue specificity.
Cell–cell communication analysis identified plausible interactions between monoaminergic neurons and glial cells
FlyPhoneDB108 can effectively identify active ligands and receptors and predict cell–cell communication events between cell clusters in adult Drosophila brain. We analyzed the 28 major pathways separately in the three treatments (MPH, ATX, and control) and uncovered their cell–cell interaction pairs between the different cell types (Supplementary Fig. 5A). Notably, the Hippo, JAK/STAT, and Torso signaling pathways displayed no interactions within any cell clusters in our adult Drosophila brain data. Further, in most cases, glial cells were the major center of the cell communication network, especially in the EGFR, FGFR, Hedgehog, Insulin, Notch, TNF-α, and Toll signaling pathways, also suggesting its central role in adult Drosophila brain. In addition, monoaminergic cells showed strong connections with glial cells in the EGFR, FGFR, Insulin, Notch, and TGF-β signaling pathway. In particular, we found some differences in specific signaling pathways between the drug-treated and control groups; for example, the TNF-α signaling pathway connects monoaminergic neurons and glia following MPH treatment, while this connection is made through the egr_wgn ligand–receptor pair rather than in ATX or control (Supplementary Fig. 5B). It has been reported that another similar stimulant, methamphetamine, activates microglia by critically modulating astrocyte-derived TNF and Glu in adult mouse brain109. Additionally, the Toll signaling pathway is a major regulator of innate immunity in Drosophila and indicates more connections and different directions of expression following MPH or ATX treatment as compared with the control group. The FGFR signaling pathway highlights the importance of glia in the assembly and maintenance of neural circuits and the functions of FGF signaling in these processes110. Detailed results can be found in Supplementary Table 5. Despite the fact that the precise effects are unknown, these findings suggest a potential immunological response triggered by both drugs. A previous study has shown that astrocyte signaling and gliotransmitters represent the highly evolved integrative interface in brain communication that is coupled to slow modulatory signaling from multiple sources with fast synaptic transmission111. As mentioned previously, our results show the important role of glia in DA reuptake, metabolism and recycling (Fig. 4), and responsive genes, cells, and pathways following MPH and ATX treatment, indicating the key role of various subglial cells at the brain level, especially ensheathing and astrocyte-like glia (Fig. 5). Our cell–cell communication prediction results show that after MPH and ATX treatment, glial cells specifically interact with monoaminergic neurons through a variety of ligand–receptor pairings. We harbor the view that these connections participate in glia–neuron communication to regulate and control the neurotransmission elicited by MPH and ATX; nevertheless, it is necessary to confirm the specifics of these interactions in the future.
Drug-responsive DEGs can be translated to human orthologs to generate drug potential
As mentioned earlier, our results provide the most promising candidates and foundation for ADHD in Drosophila brain; therefore, it is essential to translate these data to human orthologous target genes and their corresponding repurposing drugs. More than 70% of human orthologs (DIOPT score \(\ge\) 3) were found in cells following both MPH and ATX treatment (Supplementary Table 6), which provides raw drug-response mapping data that further contribute to the establishment of a theoretical basis in humans. Existing data were used to confirm the possibility of our pattern in Drosophila, and then prospective target genes and repurposing drugs were analyzed. A general workflow and the datasets used are shown in Fig. 6.
Known mapping data of targets to drugs confirms the possibility of potential drug screening in Drosophila
After comparing the orthologs of the DEGs with the target genes of nine FDA-approved ADHD drugs recorded in DrugBank (Supplementary Table 7), overlaps were found for DRD2 (fly: Dop2R), HTR1A (fly: 5-HT1A, 5-HT1B), SLC22A5 (fly: CG7084), SLC22A4 (fly: CG7084), ADRB2 (fly: Octbeta2R), and ADRA2A (fly: Octalpha2R). These are targets of different psychotropic drugs including Amphetamine, Methylphenidate, Clonidine, Guanfacine, and Methamphetamine; for example, DRD2 is the target gene of various drugs, among which is Amphetamine112, but the pharmacological response is unknown. Additionally, DRD2 is also associated with several neuropsychiatric disorders including ADHD, autism spectrum disorder (ASD), and bipolar disorder (BD)113. These results strongly support our pipeline to efficiently identify potential drug targets for ADHD.
Overlaps between drug-responsive genes, GWAS candidate genes, and druggable genes
The relationship between susceptibility genes and drug target genes in psychiatric disorders and ADHD is a controversial topic. Previous studies have shown little association between drug target genes and ADHD susceptibility genes29, 114, which may be due to the limited number of known ADHD drug target genes and published GWAS candidate genes. Thus, after obtaining the drug-responsive genes for Drosophila, we reconsidered the relationship among them. More crucially, focusing on genes known to encode druggable proteins will help to translate the results to humans. Beyond the first estimated core pharmacological principles27, an increasing number of potentially druggable genes have been defined by Finan et al. as set of 4479 divided into 3 tiers based on druggability levels28. Several studies have been conducted using these druggable genes to explore more targets or repurposing drugs for different conditions including ADHD29, nootropics32, Parkinson’s disease33, and schizophrenia34. Some putative genes targeted by existing drugs potentially available for repurposing have been identified through this approach; therefore, drug repurposing is further embraced in the framework of our Drosophila single-cell brain study to uncover more possibilities.
We demonstrated the relationship between the unique orthologs of the DEGs following treatment with MPH or ATX (688 orthologs with DIOPT score \(\ge\) 3 in Supplementary Table 6) and the genome-wide association genes for ADHD (385 genes reported previously29 and shown in Supplementary Table 7). Drugs with genetic evidence to support their target in relation to the indication are more likely to be successfully approved than drugs lacking such evidence115. Large-scale GWAS studies have uncovered new drug targets for the treatment of psychiatric disorders116–118. For ADHD, 25 overlapping genes were found after expanding the drug target genes to the Drosophila drug-responsive genes, among which 12 genes have Tier 1 druggability and 13 genes with Tier 2/3 can be treated as new potential targets. Detailed information can be found in Supplementary Table 8. In particular, Tier 1 contains targets of approved small molecules, biotherapeutic drugs, and drugs in clinical trials28. Annotation of these 12 druggable genes using the Open Target Platform119 (https://platform.opentargets.org/), which provides multi-level evidence, showed that drugs targeting CHRNB2, such as AZD1446 (interacts with cholinergic neurons), have been in clinical trials for ADHD (e.g., NCT01012375: https://clinicaltrials.gov/ct2/show/study/NCT01012375). Other genes and their 41 mapped drugs and indications providing evidence for drug repurposing can be found in Supplementary Table 8.
More prospective target genes and repurposing drugs are supported by existing evidence
Without relying on any particular hypothesis, we also examined the prospective capabilities of these orthologs and presented them according to their levels of druggability. In total, 232 druggable DEGs were identified in Drosophila following MPH treatment and 105 druggable DEGs were found following ATX treatment. Among these, 95 unique genes belong to Tier 1. Next, we searched all published targets that have multi-level evidence associated with ADHD (2427 genes) in the Open Target Platform to support our unique Tier 1 druggable genes, and 38 genes were retained after filtering. Moreover, information related to the target genes, such as the corresponding drugs, indications, mechanism of action (MoA), action type, and clinical phase and status were added to aid exploration of the clinical trials database (https://clinicaltrials.gov). After combining this information, 11 genes corresponding to various drugs related to ADHD that were in different clinical stages (drugs approved by the FDA or in on-going investigation) were found. For example, GUANFACINE and CLONIDINE are approved by the FDA and used clinically; others (more than 20 drugs), such as LIDOCAINE, BUSPIRONE, MK-8777, and MOLINDONE, have been investigated at different clinical stages of ADHD. Details of the clinical trials are summarized in Supplementary Table 8. These results confirm the efficacy of our Drosophila drug screening system and provide a foundation for future large-scale extension. The hope is that these drugs (mapped to 27 genes) that respond to other indications have not yet been tested for ADHD and may be repurposed in the future. Multiple lines of evidence suggest that these 27 genes have the most robust evidence for ADHD-associated risk. These potential drugs and their corresponding indications are listed in Supplementary Table 8. Genes belonging to Tier 2 and Tier 3 were treated as additional potential targets and are not shown due to their vast number.
Here, we summarize and emphasize a number of these prospective new targets and drugs for repurposing with different levels of evidence as shown in Supplementary Table 8. Drug and target information can also be retrieved through our web tool ADHDrug (http://adhdrug.cibr.ac.cn/). These data are richer and more comprehensive than previously published data for ADHD. Ongoing or completed examples at different clinical stages of ADHD give us the confidence to elucidate new treatments for ADHD using drug repurposing approaches. To conclude, we present a framework for the exploration of potential druggable genes in Drosophila using two ADHD drugs and the possibilities for drug repurposing (Supplementary Table 8) as potential novel avenues for ADHD treatment. Our findings add to the knowledge of known ADHD drugs at the single-cell level and expand our exploration of ADHD-related drug repurposing, which may provide interventions at the multi-evidence level of the disease.