To study the contributions of neurons and glia to HD pathogenesis, we first needed to define a transcriptomic signature that would enable us to move across species (human, mouse, and fly) (Figure 1A). We began with human tissue. Since the striatum is the brain region most prominently affected in HD, we compared the gene expression profiles of human post-mortem striatal samples from healthy individuals and patients with HD, from different stages of the disease (i.e., Vonsattel Grade 0–4) (Hodges et al., 2006; Vonsattel et al., 1985). We identified 1852 downregulated and 1941 upregulated differentially expressed genes (DEGs) in patients with HD compared to healthy individuals (Figure 1B).

We then reanalyzed published RNA-seq data from mouse striata using an allelic series of knock-in mouse models with varying CAG repeat lengths at 6 months of age (Langfelder et al., 2016). Because it is unclear which CAG tract length in mice most faithfully recapitulates HD pathogenesis, the triplet repeat length was treated as a continuous trait, and we narrowed our analysis to DEGs that correlate with increasing CAG repeat length. Comparing the striata of wildtype mice to the knock-in HD mouse models, there were 3575 downregulated and 3634 upregulated DEGs (Figure 1B). (The greater genome coverage provided by RNA-seq [Miller et al., 2014] yielded larger datasets for mouse and, below, for Drosophila than for humans.)

We performed RNA-seq leveraging Drosophila HD models (Kaltenbach et al., 2007; Romero et al., 2008) (see Materials and methods) to compare the effect of expressing mHTT in either neurons or glia. The binary GAL4-UAS system was used to drive the expression of human mHTT either in neurons (elav >GAL4) or glia (repo >GAL4). Both full-length (HTT^FLQ200) and N-terminal (HTT^NT231Q128) models were used in this set of experiments since both the full protein and N-terminal HD fragments accumulate in the human brain as a result of proteolysis and mis-splicing (Kim et al., 2001; Neueder et al., 2017; Sathasivam et al., 2013; Wellington et al., 2002). Principal component analysis (PCA) showed that the greatest differences between samples are attributable to the cell-specific drivers, and not to the use of N-terminal versus full-length protein (Figure 1—figure supplement 1). Expressing mHTT in neurons resulted in 3058 downregulated and 2979 upregulated DEGs, while expressing mHTT in glia resulted in 3127 downregulated and 3159 upregulated DEGs. There were also DEGs common to both neurons and glia expressing mHTT: 1293 downregulated and 1181 upregulated (Figure 1B).

With these transcriptomic signatures in hand, we were able to compare gene expression profiles across the three species. We focused on genes with significantly altered expression (using a false discovery rate [FDR] < 0.05; see Materials and methods) in the same direction (i.e., upregulated or downregulated) in response to mHTT expression across these three species, including both Drosophila HD models. We call genes that meet this criterion concordantly altered DEGs (Supplementary file 1).

We compared DEGs using a graph-based approach (see Materials and methods) that allows for evolutionary divergence and convergence, instead of imposing one-to-one relationships. 815 upregulated DEGs observed in HD patient-derived striatal tissue had an orthologous gene in the HD mouse model and at least one Drosophila model of HD that was concordantly upregulated. Similarly, 791 DEGs identified in HD patients had an orthologous gene in mouse and Drosophila models that was concordantly downregulated (Figure 1C). About 40% of the alterations in gene expression in patient striatal samples are concordant with orthologous genes in both Drosophila and mice models of HD. To determine whether this result could be an artifact of overlapping a large number of DEGs in each model, we randomly selected and overlapped 815 and 791 orthologous genes across the three species 20,000 times. Based on the resulting distribution, we concluded that the overlap of concordant, orthologous DEGs across the various HD models was not random (p=6.37×10⁻¹⁵⁸ and p=1.66×10⁻¹⁶⁵, probability distribution test).

To compare the consequence of expressing mHTT in glia versus neurons, we recalculated the overlaps between the three species, distinguishing DEGs from the neuron-only and glia-only HTT-expressing Drosophila. There were 425 concordantly upregulated and 545 concordantly downregulated DEGs in glia. We also found 522 upregulated DEGs and 453 downregulated specific to neurons. Out of these groups of DEGs, 310 were upregulated and 320 were downregulated in both neurons and glia. To acknowledge the proportion of transcriptional alterations we excluded by specifying concordant expression with the HD Drosophila models, we also calculated the overlap between concordant DEGs observed only in striata from HD patients and mice. We found that 83.7% of upregulated DEGs and 77.7% of downregulated DEGs that were altered concordantly in human and mouse HD striata were also concordantly altered in the brains of the neuronal and/or glial HD Drosophila models (Figure 1D). Of the genes that showed concordantly altered expression only in human and mouse striata, 64 (40%) of the upregulated and 68 (30%) of the downregulated DEGs did not have an ortholog in Drosophila.

To investigate the cellular pathophysiology represented by DEGs in neurons and glia, we constructed protein-protein interaction (PPI) networks using the STRING-db database (Szklarczyk et al., 2015). The upregulated and downregulated networks of DEGs responding to mHTT expression in neurons or glia had a significant PPI enrichment compared to networks constructed from an equivalent number of random genes selected from a whole-proteome background (Supplementary file 2). To control for potential artifacts that could arise from using the whole proteome background, we performed a more stringent analysis using only proteins that are found in the striatum (Al-Ramahi et al., 2018). Using average node degree and betweenness as proxies for connectivity, we found that the glial and neuronal networks show higher network connectivity than expected by random chance among proteins present in the striatum (Supplementary file 2).

This high connectivity suggested that the networks are enriched in specific biological processes and/or pathways. We therefore clustered the glial mHTT response and neuronal mHTT response networks using the InfoMap random walks algorithm (iGraph Package for R and Python) (Rosvall and Bergstrom, 2007). Clusters that had fewer than four nodes were filtered out of subsequent analysis. The glial networks formed 23 and 24 clusters for upregulated and downregulated DEGs, respectively. Both the upregulated and downregulated neuronal networks formed 29 clusters. We applied this clustering method to the networks of randomly selected striatal proteins in order to determine the expected number of clusters for networks of a similar size. Both the glial and neuronal networks formed significantly more clusters than would be expected from random selection (Supplementary file 2).

To gain insight into biological processes represented by each cluster, we queried the five most significantly enriched terms (FDR < 0.05) using the GO Biological Process and Kyoto Encyclopedia of Genes and Genomes (KEGG) terms within each cluster (Supplementary file 3). A synthesis of these terms was used to identify clusters in both the glial and neuronal networks (Supplementary file 3, Figure 2—figure supplement 1B, C). We compared the membership within clusters across the glial and neuronal networks using a pairwise hypergeometric test and identified 14 clusters of upregulated DEGs common to both glial and neuronal networks. Similarly, there were 15 clusters of downregulated DEGs common to the both networks (Figure 2—figure supplement 1A).

Given the aims of our study, the clusters of DEGs specific to glia (represented by nodes in Figure 2) were of particular interest to us. Six clusters were specifically upregulated in response to mHTT expression, enriched in genes involved in transcription and chromatin remodeling, amino acid metabolism, cell proliferation, cytokine signaling/innate immunity, arachidonic acid metabolism, and steroid synthesis (Figure 2A). Six clusters were downregulated in response to glial mHTT expression, containing genes involved in synapse assembly, calcium ion transport, immune system regulation, phagocytosis, mRNA processing, and fatty acid degradation (Figure 2B).

We applied the same network analysis to genes that had concordantly altered expression in HD patient striata and HD mouse model striata but not in HD Drosophila models (Figure 2—figure supplement 2A). We observed that clusters comprising DEGs specific to the HD patients and the mouse models were functionally related to DEGs in both the glial and neuronal networks (Figure 2—figure supplement 2B).

Gene expression data from bulk tissue does not provide the resolution required to define cell-autonomous gene expression alterations resulting from mHTT toxicity. Therefore, we compared DEGs (false discovery rate [FDR] < 0.1) in human embryonic stem cells from individuals with HD (carrying 40–48 CAG repeats) with healthy embryonic stem cells that have been differentiated into either CD140+ oligodendrocyte progenitor cells (OPCs) or CD44+ astrocyte progenitor cells (APCs) (Osipovitch et al., 2019). We compared the resulting list of DEGs identified in the HD OPCs (1439 genes) and HD APCs (193 genes) to the list of conserved HD DEGs from flies expressing mHTT in glia.

We identified 46 upregulated and 91 downregulated DEGs in common (Figure 3A). APCs had 4 upregulated and 12 downregulated genes in common. We next asked whether any clusters in the fly glial networks were enriched in genes dysregulated in HD OPCs or APCs. The Synapse Assembly cluster (Figure 2B) was significantly enriched in genes with reduced expression in HD OPCs (Fisher’s exact test, p<0.001), including SYT13, LRRTM1, GRM1, EPB41L2, DLGAP3, and AGAP2; the only gene of this cluster that was upregulated in HD OPCs was NRXN3 (Figure 3B, C).

In sum, by using a comparative, network-based analysis of the HD transcriptome, we associated dysregulation of several biological processes with the expression of mHTT in glia. Layering the gene expression profile of homogenous glial populations affected by mHTT onto these networks, we were able to extract from the bulk-tissue analysis a cluster of genes related to synaptic assembly that are altered in response to glial mHTT toxicity.

The next question we sought to answer is whether changes in expression of synaptic assembly genes are compensatory or pathogenic. We reasoned that if lowering the expression of a downregulated HD DEG aggravated mHTT-induced toxicity, then the downregulation of that gene is pathogenic. Conversely, if reducing the expression of a DEG led to an improvement in HD-related phenotypes, we considered that reduction to be compensatory. We previously used this approach, which takes advantage of the genetic tractability of Drosophila and the availability of high-throughput behavioral screening as a proxy for neurological function, to discover modifier genes that reduce HTT protein levels in HD patient cells (Al-Ramahi et al., 2018). Here we assessed the effect of various genetic changes in the same group of animals over time, following the expression of mHTT in either glia, neurons, or both cell types. We used a custom, robotic assay system that video-records flies climbing upwards to the top of a vial after being knocked to the bottom (negative geotaxis) to track the behavior of individual Drosophila in real time and measure several motor metrics including speed (see Materials and methods). Healthy flies reliably climb to the top at a steady rate until the effects of aging gradually reduce their speed. In contrast, animals expressing mHTT specifically in glia or neurons show much more rapid, if still age-dependent, loss of climbing speed compared to animals expressing a non-targeting hairpin RNA (hpRNA). While we only focus on the effect of these genetic perturbations on speed, we also observe impairments in coordination, balance, and direction (output as number of turns and stumbles) in Drosophila expressing mHTT (data not shown).

The expression of SYT13, LRRTM1, GRM1, EPB41L2, DLGAP3, and AGAP2 is reduced in HD OPCs derived from human embryonic stem cells (Osipovitch et al., 2019), which is consistent with the expression patterns we observed in patient-derived striatal tissue, knock-in mouse model striatal tissue, and in neuronal tissue from Drosophila expressing mHTT in glia (Figure 3C). We performed genetic perturbation analysis on the Drosophila orthologs of these genes to assess whether their downregulation was pathogenic or compensatory in glia. Diminishing expression of the Drosophila orthologs of these six genes mitigated the behavioral deficits induced by mHTT expression in glia (Figure 3D, additional controls in Figure 3—figure supplement 1B). We concluded that reduced expression of these genes is a compensatory response to mHTT expression in glia.

There were additional protein interactors in Synapse Assembly whose expression was not altered in the HD-affected OPCs or APCs compared with controls but that were nonetheless downregulated across all three HD models. In our behavioral assay, reducing expression of these interactors, including NLGN3, NLGN4X, HOMER1, and SLITRK5, was also protective against glial mHTT toxicity (Figure 3—figure supplement 1A, Supplementary file 4; additional controls in Figure 3—figure supplement 1B).

In sum, comparative transcriptomic analysis indicated that genes within the Synapse Assembly cluster are associated with the glial response to HD, and the high-throughput behavioral assay further defined this response as compensatory.

NRXN3 was identified as a DEG in both our cross-species comparative transcriptomic analysis and in the gene expression profile of the HD glial progenitor population. NRXN3 expression was lower in the bulk HD transcriptome across species compared to their respective controls, but it was more highly expressed in the HD OPCs than in controls. This discordance between the bulk and single-cell-type gene expression profiles might be a result of time-dependent changes in gene expression as neurons age, but it prevented us from classifying the NRXN3 expression changes as being compensatory or pathogenic. We were particularly interested in neurexins, including NRXN3, because they mediate contact between pre- and post-synaptic neurons (Ushkaryov et al., 1992; Zeng et al., 2007).

We therefore asked whether downregulation of Drosophila NRXN3 (dNRXN3, also known as nrx-1) is damaging or protective when both neurons and glia express mHTT. In the Drosophila behavioral assay, heterozygous loss of dNRXN3 function in animals expressing mHTT in both neurons and glia mitigated mHTT toxicity and improved behavior (Figure 4A, left panel). Reproducing this experiment with flies expressing mHTT only in glia yielded the same benefit (Figure 4A, middle panel). The obvious next question, given its canonical role in neuron-neuron contact, was whether dNRXN3 heterozygosity would protect against mHTT pathogenesis in neurons. Interestingly, the answer was no (Figure 4A, right panel). Consistent with this, glia-specific knockdown of dNRXN3 (using the repo-GAL4 driver) mitigated mHTT toxicity in glia (Figure 4B, left panel), but neuron-specific knockdown (using the elav-GAL4 driver) of dNRXN3 did not mitigate mHTT toxicity in neurons (Figure 4B, right panel). In sum, reducing dNRXN3 in both neurons and glia protects against glial pathogenesis—and the combination of neuronal and glial pathogenesis—but not neuronal pathogenesis. This implies that mHTT disrupts some aspect of glial-neuronal interaction that is driven by the glia since lowering expression of dNRXN3 in glia is necessary and sufficient to mitigate behavioral impairments caused by mHTT.

To investigate whether Nrxn3 is expressed in astrocytes in the striatum of HD mice, we performed in situ hybridization (ISH) in coronal sections of striatal tissue taken from a mouse model of HD (Hdh^zQ175/+) to probe Nrxn3 mRNA. Nrxn3 was expressed in striatal astrocytes (Figure 4C, D, Figure 4—figure supplement 1). In conclusion, modulating the expression genes other than mHTT in glia could be an effective strategy for ameliorating HD-induced central nervous system (CNS) dysfunction.

We were curious to identify modifiers that concordantly affect mHTT-induced pathogenesis in both neurons and glia as these might be particularly attractive therapeutic targets for HD. We were particularly interested to discover whether any such shared modifiers exert their effect by reducing mHTT levels, which is considered a promising approach to therapy (Al-Ramahi et al., 2018; Barker et al., 2020; Caron et al., 2020; Li et al., 2019; Tabrizi et al., 2019; Wang et al., 2014; Wood et al., 2018; Yamamoto et al., 2000; Yao et al., 2015). We therefore again integrated network analysis with high-throughput experimentation.

Genes were sampled from both the neuronal and glial mHTT response networks by prioritizing those candidates with high centrality (calculated as a cumulative rank-score of node betweenness and node degree) within each cluster. When available, we used alleles that perturb the expression or activity of the Drosophila orthologs in the same direction as the gene expression change in the HD patient population (Figure 5A). We screened 411 alleles, representing 248 Drosophila genes homologous to 211 human genes, for perturbations that improve the age-dependent behavior of Drosophila expressing mHTT in neurons or glia (Supplementary file 5). Alleles that ameliorated neuronal or glial function were verified in a subsequent trial in animals expressing mHTT across the CNS (in both neurons and glia). In all, we identified 25 genes with altered expression in HD that suppressed mHTT-induced behavioral deficits in neurons, glia, or both (Figure 5B, C, Figure 5—figure supplement 1, Supplementary file 6).

Many of the modifiers common to neuronal and glial mHTT-induced dysfunction are involved in the regulation of the actin cytoskeleton (RHOC, TIAM1, ENAH, and CFL2), vesicular trafficking (SNAP23, SNX9, and SNX18), and inflammation (JUN, GTF3A, and ATF3). Multiple reports have implicated components of these pathways in the pathogenesis of not only HD, but in other neurodegenerative disorders as well (Al-Ramahi et al., 2018; Bardai et al., 2018; Bondar et al., 2018). We previously established an axis of genes with altered expression that regulate actin cytoskeleton and inflammation pathways driving forward HD pathogenesis (Al-Ramahi et al., 2018). Our current results would indicate that these pathways are not only critical to disease progression in neurons, but also in glia.

We previously observed that reducing the activity of RAC GTPase, a regulator of the actin cytoskeleton, and inflammation mediating nuclear factor kappa-light-chain-enhancer of activated B cells (NF Kappa-B) ameliorated pathogenesis by lowering mHTT protein levels through the activation of autophagy (Al-Ramahi et al., 2018). Thus, in a secondary screen we tested whether these disease modifiers common to both neurons and glia exerted their beneficial effects by lowering levels of the mutant HTT protein.

We collected protein lysates from Drosophila expressing mHTT across the CNS that also bore alleles that suppressed mHTT-induced behavioral deficits in both neurons and glia. We assessed the quantity of mHTT protein in these lysates by western blot, comparing experimental (candidate modifiers) and control animals (carrying a non-targeting hpRNA). This secondary screen identified Spn42De as a modifier whose knockdown lowered mHTT levels. Spn42De is one of the four Drosophila homologues of human SERPINA1 (which encodes alpha-1-antitrypsin, a member of a large group of protease inhibitors). Spn42De, human SERPINA1, and mouse Serpina1 are all upregulated in HD, and they are part of the Wound Healing and Inflammation cluster in both the neuronal and glial mHTT response networks (Figure 2—figure supplement 1C). Knockdown of Spn42De (henceforth dSERPINA1) in Drosophila expressing mHTT in both neurons and glia mitigated behavioral impairments (Figure 6A). In independent immunoblots, dSERPINA1 knockdown consistently reduced mHTT protein levels in lysates extracted from the heads of Drosophila expressing mHTT in both neurons and glia (Figure 6B, C). As a control, we performed immunoblot analysis of lysates from a green fluorescent protein (GFP) reporter line to ensure that this allele of dSERPINA1 did not reduce the function of the GAL4-UAS system (Figure 6—figure supplement 1).

To validate this observation across model systems, we performed homogenous time-resolved fluorescence (HTRF) on Hdh^Q111/Q7 mouse striatal cell lysates that were treated with either a pool of non-targeting scramble small interfering RNAs (siRNAs), a pool of siRNAs against Htt, or a pool of siRNAs against Serpina1a (the murine ortholog of SERPINA1). Serpina1a knockdown significantly reduced mHTT signal (Figure 6D). Knockdown of SERPINA1 thus protected against mHTT toxicity in neurons and glia by reducing levels of mutant HTT. Verifying this effect in multiple model organisms increases confidence in this observation and suggests that SERPINA1 could potentially prove useful as a target for treating HD. Interestingly, SERPINA1 expression is low in the healthy brain but it is upregulated in several disease conditions, consistent with a potential role in neuroinflammation (Abu-Rumeileh et al., 2020; Cabezas-Llobet et al., 2018; Gollin et al., 1992; Peng et al., 2015). We found increased Serpina1a protein staining in the striatum of Hdh^zQ175/+ compared to wildtype mice at 8.5 months (Figure 6—figure supplement 2), confirming its upregulation from the transcriptomic data. Previously we had shown that other genes in the subnetwork implicated in neuroinflammation can be manipulated to lower mHTT protein levels (Al-Ramahi et al., 2018). SERPINA1 may thus warrant investigation as a target for other neurological disorders as well.

Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Juan Botas (jbotas@bcm.edu).

We began with Drosophila models expressing either N-terminal human HTT (HTT^NT231Q128) or full-length HTT (HTT^FLQ200) (Kaltenbach et al., 2007; Romero et al., 2008). The mHTT was expressed using either a pan-neuronal (elav) or a pan-glial driver (repo). Mutant strains for screening were obtained from Bloomington Drosophila Stock Center, GenetiVision, and the Vienna Drosophila Resource Center. All strains were maintained at 18°C in standard molasses, yeast extract, and agar media until their experimental use. For RNA-sequencing, the full-length models were raised at 29°C and the N-terminal models were raised at 28°C. All behavioral experiments were performed on females raised at 28°C.

In Figure 3D, we used the following mutants to assess the effect of reduced expression of synaptic genes in mHTT animals on behavior: UAS-non-targeting^hpRNA (Vienna Drosophila Resource Center, ID:13974), CenG1A^LOF or y¹w*;Mi{MIC}CenG1A^MI06024 (Bloomington Drosophila Stock Center, ID: 44301), vlc^LOF or y¹w^67c23;P{w^+mc = lacW}vlc^k01109/CyO (Bloomington Drosophila Stock Center, ID: 10366), trn^LOF or y¹w^67c23;P{w^+mc = lacW}trn^S064117/TM3, Sb¹ Ser¹ (Bloomington Drosophila Stock Center, ID: 4550), cora^LOF or P{ry^t7.2=neoFRT}43D cora¹⁴/CyO (Bloomington Drosophila Stock Center, ID: 9099), UAS-Sytbeta^{hpRN A} (Vienna Drosophila Resource Center, ID:106559), and UAS-mGluR^RNAi (National Institute of Genetics, Japan, ID: 11144-R3).

To generate Drosophila that expressed siRNA that knocked down human HTT (UAS-siHTT), we cloned a 378 bp inverted EcoRI, XbaI fragment of N-terminal Htt into the pMF3 vector (Drosophila Genome Resource Center). This fragment maps to base pairs 406–783 of the human mRNA Huntingtin, which we cloned using the following primers:

We first digested the PCR product with EcoRI and ligated it with itself to obtain inverted repeats. We then digested the inverted repeat with XbaI and pasted the fragment into the pMF3 vector (also cut with XbaI); the resulting plasmid was injected into Drosophila embryos using standard methods (Dietzl et al., 2007). We validated that this line lowers mHTT levels.

Immortalized mouse striatal cells heterozygous for mHTT (STHdh^Q111/Q7) were obtained from Coriell Cell Repositories (Camden, NJ) and cultured in DMEM (Life Technologies, cat. no. 11965) supplemented with 10% fetal bovine serum (Life Technologies, cat. no. 10082–147). The cells were tested every two months by a TransDetect PCR Mycoplasma Detection Kit (Transgen Biotech, cat. no. FM311-01) to ensure that they are mycoplasma free. The identity has not been authenticated by STR profiling, but has been validated by western blot, morphology, and phenotypic experiments.

We performed RNA-seq on head tissue collected from Drosophila expressing N-terminal (UAS-HTT^NT231Q128) or full-length (UAS-HTT^FLQ200) human mHTT in neurons (elav-GAL4) or glia (repo-GAL4). For each combination of HD model and driver, RNA-seq was performed at three timepoints to capture the early, middle, and late phases of disease pathogenesis, corresponding to behavioral deficits caused by mHTT-induced neuronal or glial dysfunction. At each timepoint, samples for HD and age-matched controls were collected in triplicate. Drosophila expressing the N-terminal construct and corresponding controls were obtained at 7, 9, and 11 days post-eclosion for the neuronal driver, and at 5, 7, and 8 days post-eclosion for the glial driver. Drosophila expressing the full-length construct, samples were obtained at 18, 20, and 22 days post-eclosion for both the neuronal and glial driver. For RNA-seq, the neuronal N-terminal, glial N-terminal, and glial full-length model Drosophila were raised at 28°C. The neuronal full-length model Drosophila were raised at 29°C. For each genotype at each timepoint, we collected an equivalent number of control animals (elav-GAL4 or repo-GAL4) that were raised in the same conditions.

Three replicates of 50 virgin females were collected for each genotype and timepoint. Animals were aged in the appropriate temperature and were transferred to fresh food daily until tissue was harvested. At the selected ages, animals were transferred to 1.5 mL tubes, flash frozen in liquid nitrogen, vigorously shaken, and then sieved to collect 50 heads/genotype/replica (~5 mg tissue/replica). Total RNA was extracted using the miRNeasy Mini Kit (Qiagen cat. no. 210074).

RNA-seq profiling and preprocessing was performed by Q2 Solutions (Morrisville, NC). Samples were converted into cDNA libraries using the Illumina TruSeq Stranded mRNA sample preparation kit (Illumina cat. no. 20020595) and were sequenced using HISeq-Sequencing-2 × 50 bp-PE. Initial analysis was performed using Q2 Solution in-house mRNAv7 pipeline with a median of 49 million actual reads. After adapter sequences were removed, the reads were aligned to the Drosophila melanogaster transcriptome using Bowtie version 0.12.9 (Langmead and Salzberg, 2012). Expression was quantified using RSEM version 1.1.19, resulting in a median of 11,214 genes and 18,604 isoforms detected (Li and Dewey, 2011).

Three homology maps were constructed to define conserved genes that were concordantly dysregulated in response to mHTT toxicity: a Drosophila-human map, a Drosophila-mouse map, and a mouse-human map. The Drosophila-human map and Drosophila-mouse map were both obtained from DIOPT version 6.0.2 (Hu et al., 2011). To capture homology that results from evolutionary convergence and divergence, we included lower DIOPT scores between Drosophila and mammals instead of fitting one-to-one mappings between these species. The mouse-human homology mapping was obtained from the Mouse Genome Informatics (MGI) database hosted by Jackson Laboratories (Blake et al., 2017).

We integrated these three homology maps by representing each map as an undirected bipartite graph, where nodes are genes of one species and edges represent homology between two genes across species. All components were then merged to form an undirected graph where each node represents a gene name and corresponding species. We applied this integrated homology map consisting of nodes representing the Drosophila, mouse, and human dysregulated genes, and all edges induced by the corresponding nodes, to obtain a subgraph consisting of multiple connected components. If any individual connected component contained nodes that belong to all three species, we characterized all genes within the connected component as concordant.

To examine how the upregulated and downregulated core genes interact functionally, we used STRING v10.5 (Szklarczyk et al., 2015). Only high-confidence interactions (edge weight >0.7) were considered. Each node is converted from an ENSEMBL ID to human Entrez ID via the provided mapping file (v10, 04-28-2015). Four subgraphs of STRING were then induced on each core gene set separately. Nodes were further clustered with the InfoMap community detection algorithm (Rosvall and Bergstrom, 2008), implemented in the Python iGraph package, with the default settings (trials = 10) (Csardi and Nepusz, 2000).

We crossed female virgins that carried the mHTT transgene under the control of either the neuronal or glial driver, or the cell-specific driver alone, to males carrying the experimental allele. We introduced a heat-shock-induced lethality mutation on the Y chromosome (Y^P{hs-hid}) to the disease and cell-specific driver stocks to increase the efficiency of virgin collection (Starz-Gaiano et al., 2001). For crosses involving alleles that were lethal or sterile mutations on the X chromosome, this mating strategy was reversed. For behavioral assays, elav >HTTNT^NT231Q128 and repo >HTTNT^NT231Q128 animals were raised and maintained at 28.5°C. elav, repo >HTTNT^NT231Q128 animals were raised and maintained at 25°C. Individual Drosophila in each genotype were randomly grouped into replicates of 10.

The negative geotaxis climbing assay was performed using a custom robotic system (SRI International, available in the Automated Behavioral Core at the Dan and Jan Duncan Neurological Research Institute). The robotic instrumentation elicited negative geotaxis by ‘tapping’ Drosophila housed in 96-vial arrays. After three taps, video cameras recorded and tracked the movement of animals at a rate of 30 frames per second for 7.5 s. For each genotype, we collected 4–6 replicates of 10 animals to be tested in parallel (biological replicates). Each trial was repeated three times (technical replicates). The automated, high-throughput system is capable of assaying 16 arrays (1536 total vials) in ~3.5 hr. To transform video recordings into quantifiable data, individual Drosophila were treated as an ellipse, and the software deconvoluted the movement of individuals by calculating the angle and distance that each ellipse moves between frames. Replicates were randomly assigned to positions throughout the plate and were blinded to users throughout the duration of experiments. The results of this analysis were used to compute more than two dozen individual and population metrics, including distance, speed, and stumbles.

Software required to run and configure the automation and image/track the videos include Adept desktop, Video Savant, MatLab with Image Processing Toolkit and Statistics Toolkit, RSLogix (Rockwell Automation), and Ultraware (Rockwell Automation). Additional custom-designed software include Assay Control – SRI graphical user interface for controlling the assay machine; Analysis software bundles: FastPhenoTrack (Vision Processing Software), TrackingServer (Data Management Software), ScoringServer (Behavior Scoring Software), and Trackviewer (Visual Tracking Viewing Software).

mRNA ISH and immunofluorescence were performed on 25-µm-thick coronal brain sections cut from fresh-frozen brain harvested from a 6-month-old Hdh^zQ175/+ mouse. We generated digoxigenin (DIG)-labeled mRNA antisense probes against Nrxn3 using reverse-transcribed mouse cDNA as a template and an RNA DIG-labeling kit from Roche (Sigma). Primer and probe sequences for the Nrxn3 probe are available in Allen Brain Atlas (http://www.brain-map.org). ISH was performed by the RNA In Situ Hybridization Core at Baylor College of Medicine using an automated robotic platform as previously described (Yaylaoglu et al., 2005) with modifications of the protocol for fluorescent ISH. In brief: after the described washes and blocking steps, the DIG-labeled probe was visualized using a tyramide-Cy3 Plus kit (1:50 dilution, 15 min incubation, Perkin Elmer). Following washes in phosphate buffered saline (PBS), the slides were stained with 1:500 anti-GFAP rabbit polyclonal antibody (DAKO, Z0334) diluted in 1% blocking reagent in Tris buffered saline (Roche Applied Science, 11096176001) overnight at 4°C. After washing, slides were treated with 1:500 anti-rabbit IgG Alexa 488 secondary antibody for 30 min at room temperature (Invitrogen, A-11008). The slides were stained with DAPI and cover slipped using ProLong Diamond (Invitrogen, P36970). Images were taken at ×63 magnification using a Leica SP8 confocal microscope.

For visualizing Serpina1, 8.5-month-old male zQ175 mice (four wildtype and three knock-in) were deeply anesthetized and transcardially perfused with 1× PBS. The tissues were then treated with 70% ethanol for 24 hr, 95% ethanol overnight, 100% ethanol for 4 hr, and chloroform overnight. Next tissues were treated with paraffin at room temperature overnight and again with paraffin at 65°C for 2 hr. Paraffin-embedded tissue blocks were coronally sectioned at the thickness of 8 μm, starting from Bregma 0.98 mm. Immunofluorescence for Serpina1 was conducted with rabbit anti-Serpina1a primary antibody (Invitrogen, PA5-16661), followed by the biotin labeled secondary antibody and detected by Alexa Fluor 488 conjugated streptavidin. Fluorescent imaging of the striatal region was performed on a Leica Sp8 confocal microscope.

For all immunoblot experiments, Drosophila were raised and maintained at 25°C. Female F1 progeny were collected and flash-frozen 24 hr after eclosion. Heads were separated by genotype and divided into eight individuals per replicate. Drosophila heads were lysed and homogenized in 30 µL of lysis buffer (1× NuPage LDS Sample Buffer, 10% beta-mercaptoethanol) and boiled at 100°C for 10 min. Lysates were loaded on a 4–12% gradient Bis-Tri NuPage (Invitrogen) gel and run at a constant voltage of 80 V for an hour and then 120 V for 30 min. For mHTT levels, a 20% methanol transfer buffer was used to transfer proteins at 4°C overnight using a 200 mA current. For mCD8::GFP, proteins were transferred using a 10% methanol buffer for 2 hr at 4°C using a 200 mA current.

Prior to antibody treatment, all membranes were treated with blocking solution (5% non-fat milk in 1× TBST). For primary antibody treatment, all antibodies were diluted in blocking solution. To assess mHTT levels, membranes were then treated with a 1:500 mouse anti-HTT solution (mAb5490, EMD Millipore) overnight. For a loading control, membranes were subsequently treated with a 1:1000 alpha-tubulin antibody (Abcam EP1332Y). 1:1000 Rabbit anti-GFP (ThermoFisher A-11122) was used to assess levels of mCD8::GFP, and 1:1000 anti-lamin C (Hybridoma Bank LC28.26) was used as a loading control. All blots were treated with 1:5000 Goat anti-Mouse (IRDye 800CW Goat anti-Mouse IgG) and Goat anti-Rabbit (RDye 680RD Goat anti-Rabbit IgG) secondary antibodies diluted in blocking solution for 1 hr and imaged using the Odyssey CLx imager (LI-COR Biosciences).

STHdh^Q111/Q7 cells were reverse transfected with pooled siRNAs using Lipofectamine 2000 (Life Technologies, cat. no. 11668). Cells were treated with a pool of four small siRNAs per gene with the following sequences (Qiagen 1027280):

Following siRNA treatment, cell lysis buffer (1× PBS with 1% TrintonX-100% and 1% EDTA-free protease inhibitor; Calbiochem, #539134) was added to each well and the plate was put on ice for 30 min. After incubation, cells were homogenized and lysates were extracted. Separately, HTRF assay buffer was prepared using 50 mM NaH₂PO₄ (pH 7.4), 400 mM KF, 0.1% bovine serum albumin, 0.05% Tween-20, and Quant-ITTM PicoGreen (1:1500). The donor antibody, 2B7 conjugated to terbium, was diluted in HTRF assay buffer to a concentration of 0.023 µg/mL, and the acceptor antibody, mAb2166 (SigmaAldrich) conjugated to fluorescent dye D2, was diluted to a final concentration of 1.4 μg/mL. 5 µL of the HTRF buffer was added to 5 µL of cell lysates (5 µL) in each well of a 384-well plate. Lysates were then incubated at 4°C overnight.

HTRF was performed in a Perkin Elmer EnVision multilabel plate reader (model #2104), measuring the 615 nM and 665 nM, as well as the PicoGreen signal at 485 nM. Each sample was measured following 30 cycles of the excitation at an interval of 16.6 ms.

Differential expression analysis used the DESeq2 R package on a total of 12 comparisons (two HD models, two cell-specific drivers, and three timepoints) (Love et al., 2014). Outlier detection was performed using PCA on normalized gene expression data, resulting in one sample being removed. To establish a list of upregulated and downregulated DEGs in Drosophila, we examined the FDR at every timepoint in both genetic models. If the FDR was <0.05 at any data point in the HD models compared to control, we established that that gene was dysregulated due to the presence of mHTT in either neurons or glia. We did not take the magnitude of fold-change into account, only the direction (upregulated or downregulated) (Langfelder et al., 2016).

The identification of DEGs from humans was based on microarray data from brain tissue collected post-mortem in patients with HD and age-matched, healthy individuals. For consistency with the reported results, we examined the summary statistics of the caudate probe on the Affymetrix U133 A and B microarrays. We computed the FDR by applying the Benjamini–Hochberg procedure to the p-values reported in Hodges et al., 2006. A probe was said to be dysregulated if the absolute value of its fold-change was >1.2 (or log₂FC > 0.263) and the FDR was <0.05. Since multiple Affymetrix probes can match to the same Entrez ID, we specified that an Entrez-identified human gene was dysregulated, if there exists a matching probe that is also dysregulated.

We established the lists of upregulated and downregulated DEGs in mice from RNA-seq data presented in Langfelder et al., 2016, where the authors profiled mRNA of an allelic series in a HD knock-in mouse model. We reanalyzed data from the striatum at 6 months, identifying gene expression alterations that were significant (FDR < 0.05) in the continuous-Q case, a summary regression variable derived from DESeq that tests the association of the expression profile with Q-length as a numeric variable (Love et al., 2014).

We randomly sampled 471 proteins (equivalent to the average number of input proteins in the mHTT Responding networks) 1000 times from 15,884 proteins that are expressed in the striatum. Implementing the same parameters that were used for the mHTT responding networks, we constructed clustered PPI networks with the random striatal protein lists as inputs. We calculated the average node degree and average node betweenness within each network of random genes and compiled a distribution using these results. A Z-score was calculated using the distribution compiled from the random striatal networks. These Z-scores were then used to calculate the p-values that are reported in Supplementary file 2. All simulations and statistical calculations were performed in R—this script can be found as Source code 2.

We assessed behavior in Drosophila as the speed at which individual animals within one vial moved as a function of age and genotype using a nonlinear random mixed effects model regression. Specifically, we looked at differences in regression between genotypes with time (additive effect, represented by a shift in the curve) or the interaction of genotype and time (interactive effect, represented by a change in the slope of the curve). We estimated the expected statistical power to detect differences by each of our models using a stringent threshold for statistical significance (alpha = 0.001). We reported p-values representative of the pairwise post-hoc tests for testing whether all possible pairs of genotype curves are different in both models. We considered differences between positive controls and experimental perturbations of p<0.001 to be significant. p-values were adjusted for multiplicity using Holm’s procedure. Code for this analysis is available upon request from the Botas Laboratory. All graphing and statistical analyses were performed in R.

Images of western blots were analyzed using the Image Studio Lite software. We used an equivalent area to measure signal intensity across all replicates. We present proteins of interest as a ratio of the target protein to loading control (n = 5 immunoblots). Experimental replicates were compared to controls using a one-sided Student’s t-test. For HTRF, levels of mHTT were calculated by taking the ratio of the fluorescence signals (665 nM/615 nM) and normalizing to the PicoGreen signal in experimental groups after subtracting the signal from wells containing only sample buffer and HTRF buffer, without protein lysates. Results are presented as the average and standard error of the mean of the ΔF (%) (ΔF (%) = (Sample ratio − blank ratio)/blank ratio ×100). Each treatment group consisted of nine replicates (n = 9). p-values were calculated using Fisher’s LSD test.

The mean intensity for images of Serpina1a stained brain slices was measured using ImageJ. For each sample, five images were measured and the mean was calculated The control group consisted of four samples (n = 4), and the HD group consisted of three samples (n = 3). Groups were compared using a two-tailed t-test assuming unequal variances.