Transcriptional regulatory networks underlying gene expression changes in Huntington's disease
2018; Springer Nature; Volume: 14; Issue: 3 Linguagem: Inglês
10.15252/msb.20167435
ISSN1744-4292
AutoresSeth A. Ament, Jocelynn R. Pearl, Jeffrey P. Cantle, Robert M. Bragg, Peter J. Skene, Sydney R. Coffey, Dani E. Bergey, Vanessa C. Wheeler, Marcy E. MacDonald, Nitin S. Baliga, Jim Rosinski, Leroy Hood, Jeffrey B. Carroll, Nathan D. Price,
Tópico(s)Microbial Metabolic Engineering and Bioproduction
ResumoArticle26 March 2018Open Access Transparent process Transcriptional regulatory networks underlying gene expression changes in Huntington's disease Seth A Ament Seth A Ament Institute for Systems Biology, Seattle, WA, USA Institute for Genome Sciences and Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA Search for more papers by this author Jocelynn R Pearl Jocelynn R Pearl Institute for Systems Biology, Seattle, WA, USA Molecular & Cellular Biology Graduate Program, University of Washington, Seattle, WA, USA Altius Institute for Biomedical Sciences, Seattle, WA, USA Search for more papers by this author Jeffrey P Cantle Jeffrey P Cantle Behavioral Neuroscience Program, Department of Psychology, Western Washington University, Bellingham, WA, USA Search for more papers by this author Robert M Bragg Robert M Bragg Behavioral Neuroscience Program, Department of Psychology, Western Washington University, Bellingham, WA, USA Search for more papers by this author Peter J Skene Peter J Skene Basic Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA Search for more papers by this author Sydney R Coffey Sydney R Coffey Behavioral Neuroscience Program, Department of Psychology, Western Washington University, Bellingham, WA, USA Search for more papers by this author Dani E Bergey Dani E Bergey Institute for Systems Biology, Seattle, WA, USA Search for more papers by this author Vanessa C Wheeler Vanessa C Wheeler Molecular Neurogenetics Unit, Center for Human Genetic Research, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA Search for more papers by this author Marcy E MacDonald Marcy E MacDonald Molecular Neurogenetics Unit, Center for Human Genetic Research, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA Search for more papers by this author Nitin S Baliga Nitin S Baliga orcid.org/0000-0001-9157-5974 Institute for Systems Biology, Seattle, WA, USA Search for more papers by this author Jim Rosinski Jim Rosinski CHDI Management, CHDI Foundation, Princeton, NJ, USA Search for more papers by this author Leroy E Hood Leroy E Hood Institute for Systems Biology, Seattle, WA, USA Search for more papers by this author Jeffrey B Carroll Jeffrey B Carroll orcid.org/0000-0003-1711-8868 Behavioral Neuroscience Program, Department of Psychology, Western Washington University, Bellingham, WA, USA Search for more papers by this author Nathan D Price Corresponding Author Nathan D Price [email protected] orcid.org/0000-0002-4157-0267 Institute for Systems Biology, Seattle, WA, USA Search for more papers by this author Seth A Ament Seth A Ament Institute for Systems Biology, Seattle, WA, USA Institute for Genome Sciences and Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA Search for more papers by this author Jocelynn R Pearl Jocelynn R Pearl Institute for Systems Biology, Seattle, WA, USA Molecular & Cellular Biology Graduate Program, University of Washington, Seattle, WA, USA Altius Institute for Biomedical Sciences, Seattle, WA, USA Search for more papers by this author Jeffrey P Cantle Jeffrey P Cantle Behavioral Neuroscience Program, Department of Psychology, Western Washington University, Bellingham, WA, USA Search for more papers by this author Robert M Bragg Robert M Bragg Behavioral Neuroscience Program, Department of Psychology, Western Washington University, Bellingham, WA, USA Search for more papers by this author Peter J Skene Peter J Skene Basic Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA Search for more papers by this author Sydney R Coffey Sydney R Coffey Behavioral Neuroscience Program, Department of Psychology, Western Washington University, Bellingham, WA, USA Search for more papers by this author Dani E Bergey Dani E Bergey Institute for Systems Biology, Seattle, WA, USA Search for more papers by this author Vanessa C Wheeler Vanessa C Wheeler Molecular Neurogenetics Unit, Center for Human Genetic Research, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA Search for more papers by this author Marcy E MacDonald Marcy E MacDonald Molecular Neurogenetics Unit, Center for Human Genetic Research, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA Search for more papers by this author Nitin S Baliga Nitin S Baliga orcid.org/0000-0001-9157-5974 Institute for Systems Biology, Seattle, WA, USA Search for more papers by this author Jim Rosinski Jim Rosinski CHDI Management, CHDI Foundation, Princeton, NJ, USA Search for more papers by this author Leroy E Hood Leroy E Hood Institute for Systems Biology, Seattle, WA, USA Search for more papers by this author Jeffrey B Carroll Jeffrey B Carroll orcid.org/0000-0003-1711-8868 Behavioral Neuroscience Program, Department of Psychology, Western Washington University, Bellingham, WA, USA Search for more papers by this author Nathan D Price Corresponding Author Nathan D Price [email protected] orcid.org/0000-0002-4157-0267 Institute for Systems Biology, Seattle, WA, USA Search for more papers by this author Author Information Seth A Ament1,2,‡, Jocelynn R Pearl1,3,4,‡, Jeffrey P Cantle5, Robert M Bragg5, Peter J Skene6, Sydney R Coffey5, Dani E Bergey1, Vanessa C Wheeler7, Marcy E MacDonald7, Nitin S Baliga1, Jim Rosinski8, Leroy E Hood1, Jeffrey B Carroll5 and Nathan D Price *,1 1Institute for Systems Biology, Seattle, WA, USA 2Institute for Genome Sciences and Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA 3Molecular & Cellular Biology Graduate Program, University of Washington, Seattle, WA, USA 4Altius Institute for Biomedical Sciences, Seattle, WA, USA 5Behavioral Neuroscience Program, Department of Psychology, Western Washington University, Bellingham, WA, USA 6Basic Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA 7Molecular Neurogenetics Unit, Center for Human Genetic Research, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA 8CHDI Management, CHDI Foundation, Princeton, NJ, USA ‡These authors contributed equally to this work *Corresponding author. Tel: +1 206 732 1204; E-mail: [email protected] Molecular Systems Biology (2018)14:e7435https://doi.org/10.15252/msb.20167435 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Transcriptional changes occur presymptomatically and throughout Huntington's disease (HD), motivating the study of transcriptional regulatory networks (TRNs) in HD. We reconstructed a genome-scale model for the target genes of 718 transcription factors (TFs) in the mouse striatum by integrating a model of genomic binding sites with transcriptome profiling of striatal tissue from HD mouse models. We identified 48 differentially expressed TF-target gene modules associated with age- and CAG repeat length-dependent gene expression changes in Htt CAG knock-in mouse striatum and replicated many of these associations in independent transcriptomic and proteomic datasets. Thirteen of 48 of these predicted TF-target gene modules were also differentially expressed in striatal tissue from human disease. We experimentally validated a specific model prediction that SMAD3 regulates HD-related gene expression changes using chromatin immunoprecipitation and deep sequencing (ChIP-seq) of mouse striatum. We found CAG repeat length-dependent changes in the genomic occupancy of SMAD3 and confirmed our model's prediction that many SMAD3 target genes are downregulated early in HD. Synopsis This study models the transcriptional network controlling mouse and human striatum, and predicts a central role of 13 transcription factors whose regulatory network patterns change as a result of CAG expansion in Huntington's disease. A genome-scale model for the target genes of transcription factors (TFs) in mouse and human striatum is built by integrating TF binding sites with transcriptomic data. The model identified 48 differentially expressed TF-target gene modules associated with gene expression changes in Htt CAG knock-in mouse striatum, and replicated many of these associations in independent transcriptomic and proteomic datasets. 13 of 48 of these predicted TF-target gene modules were also differentially expressed in striatal tissue from human disease. Experimental validation of the model prediction that SMAD3 regulates HD-related gene expression changes was produced using chromatin immunoprecipitation and deep sequencing (ChIP-seq) of mouse striatum. Introduction Massive changes in gene expression accompany many human diseases, yet we still know relatively little about how specific transcription factors (TFs) mediate these changes. Comprehensive characterization of disease-related transcriptional regulatory networks (TRNs) can help clarify potential disease mechanisms and prioritize targets for novel therapeutics. A variety of approaches have been developed to reconstruct interactions between TFs and their target genes, including models focused on reconstructing the physical locations of transcription factor binding (Gerstein et al, 2012; Neph et al, 2012), as well as computational algorithms utilizing gene co-expression to infer regulatory relationships (Friedman et al, 2000; Bonneau et al, 2006; Margolin et al, 2006; Huynh-Thu et al, 2010; Marbach et al, 2012; Reiss et al, 2015). These approaches have yielded insights into the regulation of a range of biological systems, yet accurate, genome-scale models of mammalian TRNs remain elusive. Several lines of evidence point to a specific role for transcriptional regulatory changes in Huntington's disease (HD). HD is a fatal neurodegenerative disease caused by dominant inheritance of a polyglutamine (polyQ)-coding expanded trinucleotide (CAG) repeat in the HTT gene (MacDonald et al, 1993). Widespread transcriptional changes have been detected in post-mortem brain tissue from HD cases versus controls (Hodges et al, 2006), and transcriptional changes are among the earliest detectable phenotypes in HD mouse models (Luthi-Carter et al, 2000; Seredenina & Luthi-Carter, 2012; preprint: Bragg et al, 2016; Langfelder et al, 2016; Ament et al, 2017). These transcriptional changes are particularly prominent in the striatum, the most profoundly impacted brain region in HD (Vonsattel et al, 1985; Tabrizi et al, 2013). Replicable gene expression changes in the striatum of HD patients and HD mouse models include downregulation of genes related to synaptic function in medium spiny neurons accompanied by upregulation of genes related to neuroinflammation (Seredenina & Luthi-Carter, 2012; Labadorf et al, 2015). Some of these transcriptional changes may be directly related to the functions of the huntingtin (HTT) protein. Both wild-type and mutant HTT (mHTT) protein have been shown to associate with genomic DNA, and mHTT also interacts with histone-modifying enzymes and is associated with changes in chromatin states (Benn et al, 2008; Thomas et al, 2008; Seong et al, 2010). Wild-type HTT protein has been shown to regulate the activity of some TFs (Zuccato et al, 2007). Also, high concentrations of nuclear mHTT aggregates sequester TF and co-factor proteins and interfere with genomic target finding, though it is unknown whether this occurs at physiological concentrations of mHTT (Wheeler et al, 2000; Shirasaki et al, 2012; Li et al, 2016). Roles for several TFs in HD have been characterized (Zuccato et al, 2003; Arlotta et al, 2008; Tang et al, 2012; Dickey et al, 2015), but we lack a global model for the relationships between HD-related changes in the activity of specific TFs and the downstream pathological processes that they regulate. The availability of large transcriptomics datasets related to HD is now making it possible to begin comprehensive network analysis of the disease, particularly in mouse models. Langfelder et al (2016) generated RNA-seq from the striatum of 144 knock-in mice heterozygous for an allelic series of HD mutations with differing CAG repeat lengths, as well as 64 wild-type littermate controls. They used gene co-expression networks to identify modules of co-expressed genes with altered expression in HD. However, their analyses did not attempt to identify any of the TFs responsible for these gene expression changes. Here, we investigated the roles of core TFs that are predicted to drive the gene expression changes in HD, using a comprehensive network biology approach. We used a machine learning strategy to reconstruct a genome-scale model for TF-target gene interactions in the mouse striatum, combining publicly available DNase-seq with brain transcriptomics data from HD mouse models. We identified 48 core TFs whose predicted target genes were overrepresented among differentially expressed genes in at least five of fifteen conditions defined by a mouse's age and CAG repeat length, and we replicated the predicted core TFs and differential gene expression associations in multiple datasets from HD mouse models and from HD cases and controls. Based on the coordinated downregulation in HD knock-in mice of transcripts and proteins for Smad3 and its predicted target genes, we hypothesized that SMAD3 may be a core regulator of early gene expression changes in HD. Using chromatin immunoprecipitation and deep sequencing (ChIP-seq), we demonstrate CAG repeat-dependent changes in SMAD3 occupancy and downregulation of SMAD3 target genes in mouse brain tissue. In conclusion, the results from our TRN analysis and ChIP-seq studies of HD reveal new insights into predicted transcription factor drivers of complex gene expression changes in this neurodegenerative disease. Results A genome-scale transcriptional regulatory network model of the mouse striatum We reconstructed a model of TF-target gene interactions in the mouse striatum by integrating information about transcription factor binding sites (TFBSs) with evidence from gene co-expression in the mouse striatum (Fig 1A). We predicted the binding sites for 871 TFs in the mouse genome using digital genomic footprinting. We identified footprints in DNase-seq data from 23 mouse tissues (Yue et al, 2014) using Wellington (Piper et al, 2013). Footprints are defined as short genomic regions with reduced accessibility to the DNase-I enzyme in at least one tissue. Our goal in combining DNase-seq data from multiple tissues was to reconstruct a single TFBS model that could make useful predictions about TF-target genes, even in conditions for which DNase-seq data were not available. We identified 3,242,454 DNase-I footprints. Genomic footprints are often indicative of occupancy by a DNA-binding protein. We scanned these footprints for 2,547 sequence motifs from TRANSFAC (Matys et al, 2006), JASPAR (Mathelier et al, 2014), UniProbe (Hume et al, 2015), and high-throughput SELEX (Jolma et al, 2013) to predict binding sites for specific TFs (TFBSs), and we compared these TFBSs to the locations of transcription start sites. We considered a TF to be a potential regulator of a gene if it had at least one binding site within a 5-kb region upstream and downstream of the TSS, which had been shown previously to maximize target gene prediction from digital genomic footprinting of the human genome (Plaisier et al, 2016). Figure 1. Reconstruction and validation of a transcriptional regulatory network (TRN) model of the mouse striatum Schematic for reconstruction of tissue-specific TRN models by combining information about TF binding sites with evidence from co-expression. Training (black) and test set (blue) prediction accuracy for genes in the mouse striatum TRN model. Genes are ordered on the x-axis according to their training set prediction accuracy (r2, predicted versus actual expression). The dotted black line indicates the cut off for the number of genes which the model explained > 50% of expression variation in training data. Distribution for the number of predicted regulators per target gene. Distribution for the number of predicted target genes per TF. Enrichments of TF-target gene interactions in the mouse striatum TRN for TFBSs supported by DNase footprints identified in 23 tissues. Download figure Download PowerPoint To assess the accuracy of this TFBS model, we compared our TFBS predictions to ChIP-seq experiments from ENCODE (Yue et al, 2014) and ChEA (Lachmann et al, 2010; Appendix Fig S1). For 50 of 52 TFs, there was significant overlap between the sets of target genes predicted by our TFBS model versus ChIP-seq (FDR < 1%). Our TFBS model had a median 78% recall of target genes identified by ChIP-seq and a median 22% precision. That is, our model identified the majority of true-positive target genes but also made a large number of false-positive predictions. Low precision is expected in this model, since TFs typically occupy only a subset of their binding sites in a given tissue. Nonetheless, low precision indicates a need for additional filtering steps to identify target genes that are relevant in a specific context. We sought to identify TF-target gene interactions that are active in the mouse striatum, by evaluating gene co-expression patterns in RNA-seq transcriptome profiles from the striatum of 208 mice (Langfelder et al, 2016). The general idea is that active regulation of a target gene by a TF is likely to be associated with strong TF-gene co-expression, and TFBSs allow us to identify direct regulatory interactions. This step also removes TFs with low expression: Of the 871 TFs with TFBS predictions, we retained as potential regulators the 718 TFs that were expressed in the striatum. We fit a regression model to predict the expression of each gene based on the combined expression patterns of TFs with one or more TFBSs ±5 kb of that gene's transcription start site. We used LASSO regularization to select the subset of TFs whose expression patterns together predicted the expression of the target gene. This approach extends several previous regression methods for TRN reconstruction (Tibshirani, 1996; Bonneau et al, 2006; Friedman et al, 2010; Chandrasekaran et al, 2011; Haury et al, 2012) by introducing TFBS-based constraints. In preliminary work, we considered a range of LASSO and elastic net (α = 0.2, 0.4, 0.6, 0.8, 1.0) regularization penalties and evaluated performance in fivefold cross-validation (see Materials and Methods). We selected LASSO based on the highest correlation between prediction accuracy in training versus test sets. We validated the predictive accuracy of our TRN model by comparing predicted versus observed expression levels of each gene. Our model explained > 50% of expression variation for 13,009 genes in training data (Fig 1B). Prediction accuracy in fivefold cross-validation was nearly identical to prediction accuracy in training data. That is, genes whose expression was accurately predicted in the training data were also accurately predicted in the test sets (r = 0.94; Fig 1B). Genes whose expression was not accurately predicted generally had low expression in the striatum (Appendix Fig S2). We removed poorly predicted genes, based on their training set accuracy before moving to the test set. The final TRN model contains 13,009 target genes regulated by 718 TFs via 176,518 interactions (Dataset EV1). Our model predicts a median of 14 TFs regulating each target gene and a median of 147 target genes per TF (Fig 1C and D). Fifteen TFs were predicted to regulate > 1,000 target genes (Appendix Fig S3). Importantly, TF-target gene interactions retained in our striatum-specific TRN model were enriched for genomic footprints in whole brains of 8-week-old C57BL/6 male mice (P = 1.4e-82) and in fetal brain (P = 2.1e-88), supporting the idea that these TF-target gene interactions reflect TF binding sites in the brain (Fig 1E). We defined "TF-target gene modules" as the sets of genes predicted to be direct targets of each of the 718 TFs. Of these 718 TF-target gene modules, 135 were enriched for a functional category from Gene Ontology (Ashburner et al, 2000; FDR < 5%, adjusting for 4,624 GO terms). Of the 718 TF modules, 337 were enriched (P < 0.01) for genes expressed specifically in a major neuronal or non-neuronal striatal cell type (Doyle et al, 2008; Dougherty et al, 2010; Zhang et al, 2014), including known cell-type-specific activities for both neuronal (e.g., Npas1-3) and glia-specific TFs (e.g., Olig1, Olig2) (Appendix Fig S4). These results suggest that many TRN modules reflect the activities of TFs on biological processes within specific cell types. Prediction of core TFs associated with transcriptional changes in HD mouse models We next sought to identify TFs that are core regulators of transcriptional changes in HD. Of the 208 mice in the RNA-seq dataset used for network reconstruction, 144 were heterozygous for a human HTT exon 1 allele knocked into the endogenous Htt locus (Wheeler et al, 1999; Menalled et al, 2003; Langfelder et al, 2016), and the remaining 64 mice were C57BL/6J littermate controls. Six distinct Htt alleles differing in the length of the CAG repeat were used. In humans, the shortest of these alleles—HttQ20—is non-pathogenic, and the remaining alleles—HttQ80, HttQ92, HttQ111, HttQ140, and HttQ175—are associated with progressively earlier onset of phenotypes. We used RNA-seq data generated by Langfelder et al (Langfelder et al, 2016) from four male and four female mice of each genotype at each of three time points: 2-month-old, 6-month-old, and 10-month-old mice. These mouse models undergo subtle age- and allele-dependent changes in behavior, and all of the ages profiled precede detectable neuronal cell death (Carty et al, 2015; Rothe et al, 2015; Alexandrov et al, 2016; preprint: Bragg et al, 2016). We evaluated gene expression differences between HttQ20/+ mice and mice with each of the five pathogenic Htt alleles at each time point, a total of 15 comparisons. The extent of gene expression changes increased in an age- and CAG length-dependent fashion, with extensive overlap between the DEGs identified in each condition (Fig 2). A total of 8,985 genes showed some evidence of differential expression (DEGs; P < 0.01) in at least one of the 15 conditions, of which 5,132 were significant at a stringent false discovery rate < 1%. These results suggest that robust and replicable gene expression changes occur in the striatum of these HD mouse models at ages well before the onset of neuronal cell death or other overt pathology. Figure 2. Robust changes in striatal gene expression in 2-, 6-, and 10-month-old HD knock-in miceCounts of differentially expressed genes in each mouse model at each time point (allele shown versus Q20; edgeR log ratio test; nominal P-value < 0.01). Download figure Download PowerPoint The predicted target genes of 209 TFs were overrepresented for DEGs in at least one of the 15 conditions (three ages × five mouse models; Fisher's exact test, P < 1e-6; Dataset EV2). Repeating this analysis in 1,000 permuted datasets indicated that enrichments at this level of significance never occurred in more than four conditions (i.e., zero instances in 718,000 tests across 1,000 permutations of 718 TF-target gene networks). We therefore focused on a core set of 48 TFs whose predicted target genes were overrepresented for DEGs in five or more conditions. Notably, 44 of these 48 TFs were differentially expressed (FDR < 0.01) in at least one of the 15 conditions (Appendix Fig S5). We refer to these 48 TFs as core TFs. Replication of core TFs in independent datasets We sought to replicate the associations of core TFs in HD by testing for enrichment of TF-target gene modules for differentially expressed genes or proteins in independent HD-related datasets. First, we conducted a meta-analysis of differentially expressed TF-target gene modules in four independent microarray gene expression profiling studies of striatal tissue from HD mouse models (Kuhn et al, 2007; Becanovic et al, 2010; Fossale et al, 2011; Giles et al, 2012). Targets of 46 of the 48 core TFs were enriched for DEGs (meta-analysis P-value < 0.01; Fig 3A and B) in the microarray data. The overlap between TFs whose target genes were differentially expressed in HD versus control mice in microarray datasets and the core TFs from our primary dataset was significantly greater than expected by chance (Fisher's exact test: P = 5.7e-32). These results suggest that transcriptional changes in most of the core TF-target gene modules were preserved across multiple datasets and mouse models of HD. Figure 3. Replication of core TFs in independent datasets Venn diagram showing overlap between core regulator TF-target gene modules identified in the primary RNA-seq dataset, compared to TF-target gene modules enriched for differentially expressed genes in three independent datasets. −log10(P-values) for the strength of enrichment of each of the core regulator TF-target gene modules for differentially expressed genes in each of the four datasets. Download figure Download PowerPoint Next, we asked whether the target genes of core TFs were also differentially abundant at the protein level. We studied quantitative proteomics data from the striatum of 64 6-month-old HD knock-in mice (Langfelder et al, 2016). These were a subset of the mice profiled with RNA-seq in our primary dataset. Targets of 22 of the 48 core TFs were enriched for differentially abundant proteins (Fisher's exact test, P < 0.01; Fig 3A and B). The overlap between TFs whose target genes were differentially abundant between CAG-expanded versus wild-type mice and the core regulator TFs was significantly greater than expected by chance (Fisher's exact test: P = 5.7e-20). Third, we asked whether TFs predicted to drive early gene expression changes in mouse models of HD might also regulate gene expression changes in human disease. This analysis is complicated by the fact that striatal samples available from post-mortem HD patients are almost universally from late-stage disease, whereas our studies in mice focus on much earlier time points. In addition, the striatum is heavily degraded in late-stage HD, with many dead neurons and extensive astrogliosis (Vonsattel et al, 1985). For these reasons, transcriptomic changes in HD cases versus controls that are closely related to pathogenesis may be masked by a multitude of transcriptomic changes that are secondary to pathology. To overcome these issues and maximize our ability to detect overlap with the mouse models, we performed two tests in which we considered either a restrictive set of TFs from the HD mouse models (the 48 core regulators), as well as a broader set of TFs (all 209 TFs whose predicted target genes were enriched in at least one condition from our primary mouse RNA-seq dataset). We reconstructed a TRN model specific to the human striatum by integrating a map of TFBSs (Plaisier et al, 2016) based on digital genomic footprinting of 41 human cell types (Neph et al, 2012) with microarray gene expression profiles of post-mortem striatal tissue from 36 HD cases and 30 controls (Hodges et al, 2006). As in our TRN model for the mouse striatum, we fit a LASSO regression model to predict the expression of each gene in human striatum from the expression levels of TFs with predicted TFBSs within 5 kb of its transcription start sites (Appendix Fig S6). A total of 616 TFs had one-to-one orthology and ≥ 10 predicted target genes in both the mouse and human striatum TRN models. Using these 616 human TF-target gene modules, we tested the enrichment of differentially expressed genes in the caudate nucleus (part of the dorsal striatum) from HD cases versus controls (Hodges et al, 2006; Durrenberger et al, 2015). Predicted target genes for 13 of the 48 core TFs from mouse striatum were also overrepresented among differentially expressed genes in HD cases versus controls. This overlap was not statistically greater than expected by chance (odds ratio = 1.79; P = 0.05; Fig 3A and B). However, when we considered the broader set of 209 TF-target gene modules from the primary mouse RNA-seq dataset, we found significant overlap for TF-target gene modules that were downregulated both in HD and in HD mouse models (28 shared TF-target gene modules; odds ratio = 3.6, P = 5.0e-5; Appendix Fig S6D) and for TF-target gene modules that were upregulated both in HD and in HD mouse models (26 shared TF-target gene modules; odds ratio = 1.8, P = 0.02; Appendix Fig S6E). These results suggest that some transcriptional programs are shared between the earliest stages of molecular progression (assayed in mouse models) and late stages of human disease. However, the human data support for relatively few of the core 48 TFs from mouse models. Fourth, we asked whether core TFs in striatum also regulate HTT CAG length-dependent gene expression changes in other tissues. We analyzed gene expression in the cortex, hippocampus, cerebellum, and liver of HTT knock-in mice, using RNA-seq of these tissues from 168 of the mice in our primary striatal dataset (Langfelder et al, 2016). For each tissue, we reconstructed a transcriptional regulatory network model equivalent to our TRN model for mouse striatum, and we tested for the enrichment of Htt-allele-dependent gene expression changes among the predicted targets of each TF (Dataset EV3). We found a statistically significant ove
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