IMR Press / JIN / Volume 18 / Issue 4 / DOI: 10.31083/j.jin.2019.04.1176
Open Access Original Research
Bioinformatics analysis of the molecular mechanism underlying Huntington’s disease
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1 Department of Neurology, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110004, P. R. China
*Correspondence: (Shuyan Cong)
J. Integr. Neurosci. 2019, 18(4), 369–376;
Submitted: 4 September 2019 | Accepted: 20 October 2019 | Published: 30 December 2019
Copyright: © 2019 Wang et al. Published by IMR Press.
This is an open access article under the CC BY 4.0 license

We explore the underlying molecular mechanisms and to identify key molecules in Huntington's disease by utilizing bioinformatics methods. The gene expression profile of GSE3621 was extracted from the gene expression omnibus. Differentially expressed genes between the R6/1 transgenic mouse model of Huntington's disease and controls at different time points were screened by limma package in R. Kyoto encyclopedia of genes and genomes database. It was used to analyze the pathways of differentially expressed genes. A searching tool of the protein-protein interaction network was constructed and visualized by Cytoscape. Molecular complex detection was utilized to performed module analysis. There were 513, 483, and 528 differentially expressed genes identified at weeks 18, 22 and 27, respectively, when compared with the control samples. Also, 24 significantly enriched R. Kyoto encyclopedia of genes and genomes database pathways were identified (9 in week 18, 6 in week 22, 9 in week 27), and 31 significant modules were identified from the protein-protein interaction network (13 in week 18, 8 in week 22, 10 in week 27). Hoxd8, Atf3, and Egr2 were confirmed as transcription factors related to Huntington’s disease. There are widespread gene expression changes in Huntington's disease at different time points. Some hub genes, such as Usp18, Oasl2, and Rtp4, may play important roles in the pathogenesis of Huntington’s disease.

Huntington's disease
differentially expressed gene
transcription factors
protein-protein interaction
1. Introduction

Huntington's disease (HD) is an autosomal dominant inherited neurodegenerative disease caused by a repeat expansion of prolonged CAG in the huntingtin (HTT) gene (Rodrigues et al., 2018). Mutant protein and production of transcriptional disorders, proteasome, autophagy, apoptosis, mitochondria, and metabolic dysfunction, neuroinflammation, oxidative stress, and consequent neurodegenerative changes, especially in the striatum. Among these, transcriptional dysregulation is one of the most important pathological mechanisms in HD (Bithell et al., 2009; Seredenina et al., 2012; Vuono et al., 2015). The clinical characteristics of HD are progressive motor, cognitive as well as emotional disturbances (Bano et al., 2011). There is a wide range of gene expression changes in HD, and there is evidence that these changes are due in part to complex clinical manifestations (Seredenina et al., 2012).

Inflammation is an emerging research area in neurodegenerative diseases (Kwan et al., 2012). Elevated circulating monocytes and levels of serum inflammatory cytokines are found in HD patients that considered to be hyper-responsive to immune stimuli (Ellrichmann et al., 2013). The elevated level of serum inflammatory cytokines was also found in some HD mouse models (Wild et al., 2008).

Trager et al. (2015) found myeloid cells from the HD mouse model showed increased cytokine production when stimulated by lip polysaccharides (LPS) in vitro, macrophage isolated from R6/2 mouse showed elevated level of phagocytosis, similarly to results in HD patients.

Gene expression profiling is a rapid, high-throughput method for identifying mRNA expression in cells. By comparing the different gene expressions of the HD with controls, we can better understand the pathogenesis of HD and help to find potential target genes for treatment. Recently, bioinformatics mining and network construction were considered to play a critical role in researching and predicting the pathogenesis of various neurodegenerative disorders, including HD (Thanh-Phuong et al., 2014; Trager et al., 2015). However, up to the present, there is no bioinformatics study on HD differential gene expression profiles at different time points.

Hodges et al. (2008b) analyzed the differential gene expression in the R6/1 mouse line throughout the progression of phenotypic signs from 18 to 27 weeks and uploaded data at the GEO accession GSE3621. By using GSE3621, we aim to further mine the key molecules and the potential molecular mechanisms related to HD pathogenesis. In this study, significant DEGs the R6/1 transgenic mouse model of Huntington's disease and controls at different time points were screened, followed by functional annotation. Potential HD related genes were revealed by PPI network analysis of proteins encoded by DEGs. In addition, some hub genes were also identified by MCODE analysis. The present study may provide new insights into the molecular mechanisms at different time points of HD pathogenesis.

2. Materials and methods
2.1 Microarray data

Gene expression profile of GSE3621 was downloaded from GEO (http://www.ncbi. database. Researches were designed to elucidate the relationship between mRNA differential expression and the emergence of the behavioral phenotype in the R6/1 mouse model of HD. Nine R6/1 transgenic mice (expressing full-length versions of mutant 122-131 glutamines) at week 18 (n = 3), week 22 (n = 2), week 27 (n = 4) and 9 R6/1 wild controls at week 18 (n = 3), week 22 (n = 3) and week 27 (n = 3) were used. Hodges et al. (2008a) detected a difference in activity at 18 weeks, such as rotarod performance, while novel object exploration and locomotor activity changed significantly at 22 and 27 weeks, so three-time points were selected to detect DEGs.The base data built on the platform of GPL1261 was analyzed by the Affymetrix mouse genome 430 2.0 array.

2.2 Data pre-processing and analysis of DEGs.

Identifiable expression forms of data were first converted from the original raw one. The limma package (linear models for microarray data) in the R language (Smyth et al., 2005) was used to identify DEGs between R6/1 transgenic mice and controls. The student's t-test was used to select the DEGs by the criteria of P values < 0.05 and |log2FC| > 1.

2.3 Cluster analysis of DEGs

To elucidate the changes of DEG expression at the three different time points, a clustered heatmap of DEGs was constructed by using Bioconductor (gplots package in R). Mean expression values of DEGs among the three different time-point between R6/1 transgenic mice and controls were utilized to constituent the expression matrix.

2.4 KEGG pathway enrichment analysis of DEGs

DAVID (Database for annotation visualization and integrated discovery) online analysis tools (http://david.abcc.ncifcrf.go) form an integrated bioinformatics database. The system can excavate the biological functions of numerous genes and protein ID, and play a vital role in further extraction of biological gene information (Huang et al., 2009). The KEGG ( pathway enrichment analysis was performed to identify DEGs by using DAVID (Kanehisa et al., 2008). Enriched terms including more than two genes and P values < 0.01 were considered statistically significant.

2.5 PPI network construction and analysis

The STRING (search tool for the retrieval of interacting genes, http:/ is an online comprehensive perspective database designed to evaluate protein interaction information (Szklarczyk et al., 2011). In this study, STRING was designed to obtain a PPI network of DEGs and visualized by Cytoscape subsequently (Smoot et al., 2011). The confidence score of 0.4 was confirmed as the cut-off standard. MCODE (Bader et al., 2003) is the preferred computational method to analyze protein complexes from PPI networks. MCODE can generate overlapping clusters by identifying densely connected subgraphs and filtering non-dense subgraphs (Spirin et al., 2003). In our study, MCODE was applied to screen modules of the PPI network with a node score cut-off = 0.2, degree cut-off = 2, max depth = 100 and k-core = 2.

2.6 TF annotation

TFs were annotated among DEGs basing on TRANSFAC version 6.0 ( (Matys et al., 2003). The differences and similarities between TFs at the three time-points as well as the degrees of TFs were analyzed.

3. Results
3.1 Identification of DEGs at three time points

A total of 513, 483, and 528 DEGs were identified at week 18, week 22, and week 27, respectively, when comparing with the control samples. Upregulated and downregulated DEGs are listed in Table 1. There are more downregulated DEGs than upregulated DEGs at the first two time-points, at week 27, the up and down-regulated genes are almost equal.

Table 1 DEG counts at three-time points in HD transgenic mouse
Timepoints DEGs Upregulated genes Downregulated genes
18 weeks 513 220 293
22 weeks 483 201 282
27 weeks 528 267 261
3.2 Cluster analysis of the DEGs

To further define the changes of DEG expression level at three-time points in HD transgenic mice, cluster analysis is conducted. Cluster heat maps are performed by comparing the R6/1 transgenic mice and controls at three-time points, respectively. The top fifty DEGs are shown in Fig. 1.

Figure 1.

Cluster heat maps of top fifty DEGs in three-time points. (A) Week 18, (B) week 22, and (C) week 27. Relative levels of gene expression are showed using a color scale: Yellow stands for high expression value, and blue stands for low expression value. Changes of color from blue to yellow represent the changes in expression value from low to high. DEGs differentially expressed genes.

3.3 KEGG pathway enrichment analysis

The KEGG pathways enriched for dysregulated genes are listed in Table 2. The results show that the pathways of dysregulated genes are relatively the same between week 18 and week 22, which are enriched in the pathways of neuroactive ligand-receptor interaction, protein digestion, and absorption, measles, influenza A and cytosolic DNA-sensing pathway. At week 27, downregulated genes are enriched in pathways related to cocaine addiction, amphetamine addiction, serotonergic synapse,and alcoholism; upregulated genes are enriched in pathways related to retinol metabolism, chemical carcinogenesis, rheumatoid arthritis, and cytokine-cytokine receptor interaction, respectively. In general, DEGs are predominantly involved in pathways of immune (infective disease) and neuropsychological system-related diseases.

Table 2 KEGG pathways of significantly up- and downregulated genes at three time-pointsin HD transgenic mouse
Contrast group KEGG pathway Gene counts Genes P-value
18 weeks
Downregulated genes mmu04080: Neuroactive ligand-receptor interaction 11 DRD1, PTH2R, A630033H20RIK, ADORA2A, GM10334, NPY2R, LPAR3, GRIN3B, VIPR1, PLG, CHRNG 4.93 × 10-04
mmu04974: Protein digestion and absorption 6 SLC15A1, GM10334, ATP1B4, MEP1B, CELA2A, COL10A1 2.0 × 10-03
mmu05031: Amphetamine addiction 5 ARC, DRD1, PPP1R1B, SLC6A3, GRIN3B 4.0 × 10-03
mmu05030: Cocaine addiction 4 RD1, PPP1R1B, SLC6A3, GRIN3B 0.01
mmu05034: Alcoholism 7 GNGT1, DRD1, PPP1R1B, ADORA2A, SLC6A3, CRH, GRIN3B 0.01
Upregulated genes mmu05162: Measles 5 IRF7, OAS1B, IFNA11, CD209B, MX2 4.0 × 10-03
mmu05164: Influenza A 5 IRF7, OAS1B, IFNA11, CCL5, MX2 9.0 × 10-03
mmu05168: Herpes simplex infection 5 IRF7, OAS1B, IFNA11, CCL5, LTA 0.01
mmu04623: Cytosolic DNA-sensing pathway 3 IRF7, IFNA11, CCL5 0.03
22 weeks
Downregulated genes mmu04080: Neuroactive ligand-receptor interaction 11 DRD1, PTH2R, A630033H20RIK, ADORA2A, GM10334, NPY2R, LPAR3, GRIN3B, VIPR1, PLG, CHRNG 1.0 × 10-04
mmu04974: Protein digestion and absorption 6 SLC15A1, GM10334, ATP1B4, MEP1B, CELA2A, COL10A1 2.0 × 10-04
mmu05034: Alcoholism 7 GNGT1, DRD1, PPP1R1B, ADORA2A, SLC6A3, CRH, GRIN3B 1.0 × 10-03
Upregulated genes mmu05162: Measles 5 IRF7, OAS1B, IFNA11, CD209B, MX2 3.0 × 10-03
mmu05164: Influenza A 5 IRF7, OAS1B, IFNA11, CCL5, MX2 9.0 × 10-03
mmu04623: Cytosolic DNA-sensing pathway 4 IRF7, IFNA11, CCL5 0.03
27 weeks
Downregulated genes mmu05030: Cocaine addiction 4 PPP1R1B, DRD2, SLC6A3, PDYN 7.0 × 10-03
mmu05031: Amphetamine addiction 4 ARC, PPP1R1B, SLC6A3, PDYN 0.01
mmu04726: Serotonergic synapse 5 PTGS2, SLC6A4, ALOX12B, TPH2, HTR5B 0.02
mmu05034: Alcoholism 6 PPP1R1B, DRD2, SLC6A3, CRH, PDYN, HIST1H3I 0.02
Upregulated genes mmu00830: Retinol metabolism 6 CYP4A12A, ADH1, CYP3A11, UGT2B5, SDR16C5, CYP2C50 4.02 × 10-04
mmu05204: Chemical carcinogenesis 5 ADH1, CYP3A11, NAT2, UGT2B5, CYP2C50 4.0 × 10-03
mmu05323: Rheumatoid arthritis 4 ITGB2L, H2-AA, IL15, MMP1A 0.02
mmu04060: Cytokine-cytokine receptor interaction 6 IFNA2, IL3, IL17B, TNFRSF18, IL15, CD27 0.02
mmu05150: Staphylococcus aureus infection 3 DSG1B, ITGB2L, H2-AA 0.04
3.4 PPI network construction and analysis

The PPI network of DEGs is visualized by Cytoscape following a PPI search; the PPI networks of the DEGs identified at three-time points are shown in Fig. 2. The PPI network of week 18 consisted of 134 protein nodes and 275 pairs; week 22 consists of 130 protein nodes and 253 pairs, and week 27 consisted of 159 protein nodes and 367 protein pairs. In analyzing the MCODE, degrees ≥ 5 are set as the cut-off criterion, and a total of 32 genes are identified as hub genes (Fig. 3) and summarized in Table 3. AVP is the hub gene at week 18 and had the highest score in the network, Rtp4, Oasl2, and Usp18 had higher scores both in week 22 and week 27.

Figure 2.

Protein-protein interaction networks of DEGs. Undirected network at (A) week 18, (B) week 22, and (C) week 27. Green nodes represent the downregulated DEGs, and red nodes represent the upregulated DEGs. DEG, differentially expressed gene.

Table 3 Dysregulated hub genes at three-time-pointsin HD transgenic mouse
Time-points Genes (Degree) Counts
18 weeks Avp (13), Trh (8), Crh (8), Pmch (8), Npff (7), Prok2 (7), Lpar3 (7), Prokr1 (7), Golt1a (7), Vip (6), Pth2r (6), Vipr1 (6),Drd1a (6), Adora2a (6) 13
22 weeks Mx2 (7), Rtp4 (7), Irf7 (7), Oasl2 (7), Usp18 (7), Parp12 (6), Gbp3 (6), Sp100 (5) 8
27 weeks Rtp4 (9), Ifi44 (9), Oasl2 (9), Ifit1 (9), Usp18 (9), Gbp6 (9), Isg15 (9), Ddx60 (9), Irgm2 (9), Parp9 (9) 10
3.5 TFs

TFs among the DEGs at the three-time points are listed in Table 4. The maximum number of TFs is found at week 18, including Hoxd8, Atf3and Stat4. The minimum number of TFs is found at week 22, including Egr2 and Atf3. By analyzing the PPI network, Atf3, Hoxd8, and Egr2 were the critical TFs in the development of HD.

Figure 3.

Significant modules screened from the PPI networks by using MCODE. Undirected network at (A) week 18 (14 nodes), (B) week 22 (8 nodes), and (C) week 27 (10 nodes). Green nodes represent the downregulated DEGs, and red nodes represent the upregulated DEGs. DEG, differentially expressed gene.

Table 4 Transcription factors among DEGs at three-time-pointsin HD transgenic mouse
Time-points Transcription factors
Upregulated genes Downregulated genes
18 weeks Atf3, Foxi1, Foxb2, Gcm1, Foxd3, Hoxb13, Hoxd8, Pax3, Stat4, Twist1, Irf7, Vsx1, Vgll2 Egr2, Fosl2, Pou2f3, Pparg, Ppp1r1b, Ankrd2, Asb11, Sall4
22 weeks Atf3, Foxb2, Gcm1, Foxd3, Hoxb13, Hoxd8, Stat4, Twist1, Irf7 Egr2, Fosl2, Ppp1r1b, Ankrd2, Asb11, Sall4
27 weeks Atf3, Hoxd8, Ascl2, Pax9, Sox3, Tead2, Twist1, Ankrd1 Runx3, Egr2, Gata3, Gfi1b, Hoxa1, Hoxd9, Ppp1r1b, Nr1h4, Prrx2, Fhl5, Nkd2, Zfp423
4. Discussion

So far, the exact pathophysiological mechanism of HD is still not clear (Kieburtz et al., 2018). In this study, we aim to explore the underlying molecular mechanisms and to identify key molecules in HD by using bioinformatics methods. By analyzing the gene expression profiles of the brain from the HD mouse model at three different time points, hundreds of DEGs are found in this study. DEGs are identified to be enriched in pathways related to immune (infective disease) and neuropsychological systems. According to the PPI network and MCODE analysis, some hub genes are identified, including Rtp4, Oasl2, and Usp18. TFs among DEGs are also identified, including Atf3, Hoxd8, and Egr2. We infer that these genes may participate in the pathophysiological mechanism of HD.

RTP4 (Receptor Transporter Protein 4) is a protein-coding gene (Saito et al., 2004). A study on the gene in neurodegenerative disease is relatively rare, and we do not retrieve the relevant research on RTP4 and HD. In the study, we find RTP4 is related to the pathway of signaling by GPCR (G protein-coupled receptor).

Yao et al. (2015) have demonstrated GPCR modulate huntingtin levels and toxicity in vitro and in vivo in STHdhQ7/Q111 cells, their research revealed that by modulating the GPCR function, mHTT levels and toxicity could be stabilized or reduced. However, the specific mechanism of RTP4 in GPCR and its possible impact on HD still need further study. OASL2 is an interferon stimulating gene and involves in innate immune response (Ellrichmann et al., 2013). It has been proved to be an important reaction module for macrophage activation (Zhu et al., 2014). Kwan et al. (2012) demonstrate that migration of macrophage under inflammatory stimulation is impaired in their BACDD mouse model of HD, which may predict one of the potential mechanisms of HD (Kwan et al., 2012).

McDermott et al. (2011) also suggest that OASL2 takes part in the responses which are crucial for macrophage activation and migration. OASL2 dysregulation may interrupt activation and migration of macrophage in the HD mouse model or interaction with mHTT fragments in immunological manners. USP18 (ubiquitin-specific protease 18) is an interferon-inducible protein and participate in regulation of interferon response on viral infection (Chen et al., 2015).

Goldmann et al. (2015) demonstrate that Usp18 is essentially contributedto microglial quiescence in white matter microglia. Microglia are regardedas macrophages in the central nervous system and are important for tissue homeostasis. Microglia dysfunctionis thought to be involved in the pathogenesis of certainneurodegenerative and neuroinflammatory disorders. Goldmann et al. (2015) further report that microglial Usp18 could negatively regulate the activation of Stat1 and inductinterferon-induced genes concomitantly, thereby terminating interferon signaling.

Valekova et al. (2016) find that prominent decline of IFNα in central nervous system of HD animals could influenceIFNαrelated innate immune response, lead to enhanced disease progression, further suggesting a highly critical role for IFNα in HD. These results conclude Usp18 as a key negative regulator of microglia activation and identify a protective role of Usp18 for microglia function on regulating the interferon pathway. By preventing destructive microgliopathy and influencing interferon levels, USP18 may take part in the pathogenesis and progression of HD.

ATF3 is a member of the mammalian activated transcription factor protein family. In our study, we findthat ATF3 is differentially expressed at all threetime points. ATF3 is considered to be an effective marker for regeneration after nerve injury and thought to be a new indicator for nerve injury (Linda et al., 2011). ATF3 can bind to other members of the ARF/CREB (cAMP-response element-binding protein) family, including C-Jun, ATF2and JUNB, by forming dimmers to actas a transcriptional activator and inhibitor (Wang et al., 2015).

Previously enormous researches have demonstrated the role of CREB in HD pathogenesis and disease progression (Chaturvedi et al., 2012; Choi et al., 2009; Giralt et al., 2013).

ATF3 may participate in HD by influencing CREB signaling. Hoxd8 is a protein encoded by the HOXD8 gene (Wilming et al., 2015). It is a highly conserved family of transcription factors and plays a key role in the morphogenesis of multicellular organisms (Brison et al., 2012).

Hoss et al. (2014) have demonstrated that a large amount of HOX genes differentially expressed in HD patients. Hox genes are a major transcription factor family in embryonic development (Lemons and McGinnis, 2006). They are highly associated with most aspects of early development and are significantly expressed in neural differentiation (Pearson et al., 2005). An increase in HOX transcriptional activity may be compensatory, helping to maintain or reconstitute cell polarity or indirect epigenetic regulation. EGR2 (Early growth response protein 2) is a protein encoded by the EGR2 gene. EGR2 is a transcriptional regulator containing two zinc finger DNA binding sites and is highly expressed in migrating neural crest cells (Swiatek and Gridley, 1993).

Many studies have focused on the abnormal expression of EGR2 on the Charcot-Marie-Tooth disease, which is a hereditary motor and sensory neuropathy (Krajewski et al., 2000). So far, we can confirm that EGR2 playsimportant roles in early neurodevelopment; however, its role in HD needs further study.

A large number of previous enrichment analyses have been applied on HD, and many KEGG pathways are involved, including DNA replication, p53, GnRH, and NF-kappa B signaling pathway (Li et al., 2015). In this study, we find infectious diseases, such as measles, herpes simplex infection, and immune system such as neuroactive ligand-receptor interaction and rheumatoid arthritis enriched pathways are more common than other pathways. Neurotoxicity of mHTT is the trigger point for HD, Tai et al. (2007) suggested that the presence of mHTT aggregation could initiateimmune responses in CNS and pointed out microglia and astrocytes were the major contributors to the inflammatory reaction. Tai et al. (2007) demonstrated that microglial activation in striatum was closely associated with the occurrence of cognitive function (Tai et al., 2007). Elevated IL-6 and IL-8 levels were detected in pre-manifest subjects in the study of Björkqvist (2016) and they extrapolated the results were possibly corresponding to the innate immune response (Wild et al., 2008).

Chang et al. (2015) also observed plasma inflammatory proteins, such as MMP-9 and TGF-β1 altered in pre-symptomatic, early, or late stages of HD (Ionescu et al., 2011). In addition to the indirect effects of mHTT neurotoxicity on immune system, Trager et al. (2014) demonstrated mHTT could directly interact with IKKγ, which was responsible for immune response. Khoshnan et al. (2004) also confirmed the mHTT could bind to and directly activate ΙΚΚβ, which regulated mHTT neurotoxicity via triggering the caspase-dependent cleavage of mHTT. Summarizing previous studies, we concluded that immune responses mightplay important roles in the pathogenesis of HD, and some immune-related cytokines can be used as biomarkers for disease progression and evaluate the prognosis of the disease. At the same time, interfering with certain immune pathway may prevent progression or improve prognosis in HD patients.

Our research had some limitations. First, we used bioinformatics to analyze DEGs based on R6/1 transgenic mouse model, which had 5' end of the human HD gene carrying115-150 CAG repeat at expansions expressed ubiquitously in the mouse genome (Menalled et al., 2002), such genetic manipulation may potentially alter the overall number of genes express or silenced, mismatching the preclinical results presented in our study with gene expression in HD patients. Secondly, potential genetic rearrangements of cellular or animal models on each generation leading to the unstable expansion or retraction of the CAG length repeat may also affect the results of DEGs (Gatto et al., 2015). Moreover, the regulation on the expression the microinjection fragments from the 5' end of the human HD gene isolated from a phage genomic clone (Mangiarini et al., 1996) are under the regulation of murine genes which may not be operated by the same gene regulation as in the human disease. However, the use of other full-length mouse models (such as the BACHD or YAC128 mice) as well as knock-in HD mice models (HdHQ125 and zQ175 mice), which includes on their genomic insertion part of the regulatory genes, could improve such limitations (Gatto et al., 2019).

In conclusion, by performing bioinformatics analysis of gene expression, hundreds of genes related to HD were identified, including Rtp4, Oasl2, and Usp18. Furthermore, our study suggests that HD may have similar pathways involved in several diseases, including measles, herpes simplex infection, and rheumatoid arthritis, and most of the involved pathways are mainly focused on the immune system. Our study provided novel insight into the molecular mechanisms of HD, which may help to explore the pathogenesis of HD and may improve the treatment strategy of the HD.

Author contributions

S.C. conceived and designed the study; Z.W. and X.D. performed the experiments, analyzed the data, and wrote the paper.


This work was funded by the National Natural Science Foundation of China (No. 81371271) and was sponsored by the Liaoning Bai Qian Wan Talents Program.

Conflict of interest

The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.

Bader, G. D. and Hogue, C. W. V. (2003) An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics 4, 2. 10.1186/1471-2105-4-212525261
Bano, D., Zanetti, F., Mende, Y., and Nicotera, P. (2011) Neurodegenerative processes in Huntington's disease. Cell Death & Disease 2, e228. 10.1159/00050467431910411
Bithell, A., Johnson, R., and Buckley, N. J. (2009) Transcriptional dysregulation of coding and non-coding genes in cellular models of huntington's disease. Biochemical Society Transactions 37, 1270-1275. 10.1042/BST037127019909260
Björkqvist, M. (2016) Immunomodulation - a disease-modifying avenue for treatment of Huntington's disease? Journal of Neurochemistry 137, 670-672. 10.1111/jnc.1353927059524
Brison, N., Tylzanowski, P. and Debeer, P. (2012) Limb skeletal malformations - what the hox is going on? European Journal of Medical Genetics 55, 1-7. 10.1016/j.ejmg.2011.06.00302314c43-a830-40da-9885-d857d3c80008
Chang, K. H., Wu, Y. R., Chen, Y. C. and Chen, C. M. (2015) Plasma inflammatory biomarkers for Huntington's disease patients and mouse model. Brain Behavior and Immunity 44, 121-127. 10.1016/j.bbi.2014.09.01125266150
Chaturvedi, R. K., Hennessey, T., Johri, A., Tiwari, S. K., Mishra, D., Agarwal, S., Kim, Y. S. and Beal, M. F. (2012) Transducer of regulated creb-binding proteins (torcs) transcription and function is impaired in huntingtons disease. Human Molecular Genetics 21, 3474-3488. 10.1093/hmg/dds178d38e9814-363d-4d41-a672-b3e09536afcb
Chen, C., Zhang, Y. B. and Gui, J. F. (2015) Expression characterization, genomic structure and function analysis of fish ubiquitin-specific protease 18 (Usp18) genes. Developmental and Comparative Immunology 52, 112-122. 10.1016/j.dci.2015.05.00325981749
Choi, Y. S., Lee, B., Cho, H. Y., Reyes, I. B., Pu, X. A., Saido, T. C., Hoyt, K. R. and Obrietan, K. (2009) Creb is a key regulator of striatal vulnerability in chemical and genetic models of huntington's disease. Neurobiology of Disease 36, 259-268. 10.1016/j.nbd.2009.07.014e1117df9-21f3-4307-a1ac-9e659ed86fab
Ellrichmann, G., Reick, C., Saft, C. and Linker, R. A.(2013) The role of the immune system in huntington's disease. Clinical & Developmental Immunology 2013,541259. 10.1111/1759-7714.1329631909928
Gatto, R. G., Chu, Y., Ye, A. Q., Price, S. D., Tavassoli, E., Buenaventura, A., Brady, S. T., Magin, R. L., Kordower, J. H. and Morfini, G. A. (2015) Analysis of Yfp(J16)-R6/2 reporter mice and postmortem brains reveals early pathology and increased vulnerability of callosal axons in huntington's disease. Human Molecular Genetics 24, 5285-5298. 10.1093/hmg/ddv24826123489
Gatto, R. G., Ye, A. Q., Colon-Perez, L., Mareci, T. H., Lysakowski, A., Price, S. D., Brady, S. T., Karaman, M., Morfini, G. and Magin, R. L. (2019) Detection of axonal degeneration in a mouse model of huntington's disease: comparison between diffusion tensor imaging and anomalous diffusion metrics. Magma 32, 461-471. 10.1007/s10334-019-00742-630771034
Giralt, A., Saavedra, A., Carreton, O., Arumi, H., Tyebji, S., Alberch, J. and Perez-Navarro, E. (2013) Pde10 inhibition increases glua1 and creb phosphorylation and improves spatial and recognition memories in a huntington's disease mouse model. Hippocampus 23, 684-695. 10.1002/hipo.2212829776d85-4e52-4f9b-8408-b5c7ba3b5efd
Goldmann, T., Zeller, N., Raasch, J., Kierdorf, K., Frenzel, K., Ketscher, L., Basters, A., Staszewski, O., Brendecke, S. M., Spiess, A., Tay, T. L., Kreutz, C., Timmer, J., Mancini, G. M. S., Blank, T., Fritz, G., Biber, K., Lang, R., Malo, D., Merkler, D., Heikenwaelder, M., Knobeloch, K. P. and Prinz, M. (2015) Usp18 lack in microglia causes destructive interferonopathy of the mouse brain. EMBO Journal 34, 1612-1629. 10.15252/embj.20149079125896511
Hodges, A., Hughes, G., Brooks, S., Elliston, L., Holmans, P., Dunnett, S. B. and Jones, L. (2008a) Brain gene expression correlates with changes in behavior in the R6/1 mouse model of huntington's disease. Genes, Brain and Behavior 7, 288-299. 10.1111/j.1601-183X.2007.00350.x17696994
Hodges, A., Hughes, G., Brooks, S., Elliston, L., Holmans, P., Dunnett, S. B. and Jones, L. (2008b) Brain gene expression correlates with changes in behavior in the R6/1 mouse model of huntington's disease. Genes Brain and Behavior 7, 288-299. 10.1111/j.1601-183X.2007.00350.x17696994
Hoss, A. G., Kartha, V. K., Dong, X., Latourelle, J. C., Dumitriu, A., Hadzi, T. C., MacDonald, M. E., Gusella, J. F., Akbarian, S., Chen, J.-F., Weng, Z. and Myers, R. H. (2014) Micrornas located in the Hox gene clusters are implicated in huntington's disease pathogenesis. Plos Genetics 10, e1004188. 10.1371/journal.pgen.100418824586208
Huang, D. W., Sherman, B. T. and Lempicki, R. A. (2009) Systematic and integrative analysis of large gene lists using david bioinformatics resources. Nature Protocols 4, 44-57. 10.1038/nprot.2008.21119131956
Ionescu, V. A., Villanueva, E. B., Hashioka, S., Bahniwal, M. and Klegeris, A. (2011) Cultured adult porcine astrocytes and microglia express functional interferon-gamma receptors and exhibit toxicity towards Sh-Sy5y cells. Brain Research Bulletin 84, 244-251. 10.1016/j.brainresbull.2010.12.011421a20d6-5b15-4a21-b9cb-47b74ec9255f
Kanehisa, M., Araki, M., Goto, S., Hattori, M., Hirakawa, M., Itoh, M., Katayama, T., Kawashima, S., Okuda, S., Tokimatsu, T. and Yamanishi, Y. (2008) Kegg for linking genomes to life and the environment. Nucleic Acids Research 36, D480-D484. 10.1093/nar/gkm88218077471
Khoshnan, A., Ko, J., Watkin, E. E., Paige, L. A., Reinhart, P. H. and Patterson, P. H. (2004) Activation of the I Kappa B Kinase complex and nuclear Factor-Kappa B contributes to mutant Huntingtin neurotoxicity. Journal of Neuroscience 24, 7999-8008. 10.1523/JNEUROSCI.2675-04.200415371500
Kieburtz, K., Reilmann, R. and Olanow, C. W. (2018) Huntington's disease: current and future therapeutic prospects. Movement Disorders 33, 1033-1041. 10.1002/mds.2736329737569
Krajewski, K. M., Lewis, R. A., Fuerst, D. R., Turansky, C., Hinderer, S. R., Garbern, J., Kamholz, J. and Shy, M. E. (2000) Neurological dysfunction and axonal degeneration in charcot-marie-tooth disease type 1a. Brain 123, 1516-1527. 10.1093/brain/123.7.151610869062
Kwan, W., Trager, U., Davalos, D., Chou, A., Bouchard, J., Andre, R., Miller, A., Weiss, A., Giorgini, F., Cheah, C., Moeller, T., Stella, N., Akassoglou, K., Tabrizi, S. J. and Muchowski, P. J. (2012) Mutant huntingtin impairs immune cell migration in huntington disease. Journal of Clinical Investigation 122, 4737-4747. 10.1172/JCI64484d6b69d59-2a33-4fd3-9ef6-8b0c9a1cc0a0
Lemons, D. and McGinnis, W. (2006) Genomic evolution of Hox gene clusters. Science 313, 1918-1922. 10.1126/science.113204017008523
Li, P., Nie, Y. and Yu, J. (2015) An effective method to identify shared pathways and common factors among neurodegenerative diseases. Plos One 10, e0143045. 10.1371/journal.pone.014304526575483
Linda, H., Skold, M. K. and Ochsmann, T. (2011) Activating transcription factor 3, a useful marker for regenerative response after nerve root injury. Frontiers in Neurology 2, 30-30. 10.3389/fneur.2011.0003021629765
Mangiarini, L., Sathasivam, K., Seller, M., Cozens, B., Harper, A., Hetherington, C., Lawton, M., Trottier, Y., Lehrach, H., Davies, S. W. and Bates, G. P. (1996) Exon 1 of the Hd gene with an expanded cag repeat is sufficient to cause a progressive neurological phenotype in transgenic mice. Cell 87, 493-506. 10.1016/s0092-8674(00)81369-08898202
Matys, V., Fricke, E., Geffers, R., Gossling, E., Haubrock, M., Hehl, R., Hornischer, K., Karas, D., Kel, A. E., Kel-Margoulis, O. V., Kloos, D. U., Land, S., Lewicki-Potapov, B., Michael, H., Munch, R., Reuter, I., Rotert, S., Saxel, H., Scheer, M., Thiele, S. and Wingender, E. (2003) Transfac (R): transcriptional regulation, from patterns to profiles. Nucleic Acids Research 31, 374-378. 10.1093/nar/gkg10812520026
McDermott, J. E., Archuleta, M., Thrall, B. D., Adkins, J. N. and Waters, K. M. (2011) Controlling the response: predictive modeling of a highly central, pathogen-targeted core response module in macrophage activation. Plos One 6, e14673. 10.1371/journal.pone.001467321339814
Menalled, L. B., Sison, J. D., Wu, Y., Olivieri, M., Li, X. J., Li, H., Zeitlin, S. and Chesselet, M. F. (2002) Early motor dysfunction and striosomal distribution of huntingtin microaggregates in huntington's disease knock-in Mice. Journal of Neuroscience 22, 8266-8276. 12223581
Pearson, J. C., Lemons, D. and McGinnis, W. (2005) Modulating Hox gene functions during animal body patterning. Nature Reviews Genetics 6, 893-904. 10.1038/nrg172616341070
Rodrigues, F. B. and Wild, E. J. (2018) Huntingtons disease clinical trials corner: february 2018. Journal of Huntington's Disease 7, 89-98. 10.3233/JHD-18900129480210
Saito, H., Kubota, M., Roberts, R. W., Chi, Q. Y. and Matsunami, H. (2004) Rtp family members induce functional expression of mammalian odorant receptors. Cell 119, 679-691. 10.1016/j.cell.2004.11.02115550249
Seredenina, T. and Luthi-Carter, R. (2012) What have we learned from gene expression profiles in Huntington's disease? Neurobiology of Disease 45, 83-98. 10.1016/j.nbd.2011.07.00198df604b-cb52-4ed0-90cc-103af3fcd9bc
Smoot, M. E., Ono, K., Ruscheinski, J., Wang, P. L. and Ideker, T. (2011) Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics 27, 431-432. 10.1093/bioinformatics/btq6753b7a4c26-9b3b-484a-9841-40b7f80590a3
Smyth, G. K. (2005) Limma: linear models for microarray data. Bioinformatics and Computational Biology Solutions Using R and Bioconductor, 397-420. 10.1016/j.ijsu.2019.11.01731756546
Spirin, V. and Mirny, L. A. (2003) Protein complexes and functional modules in molecular networks. Proceedings of the National Academy of Sciences of the United States of America 100, 12123-12128. 10.1073/pnas.203232410014517352
Swiatek, P. J. and Gridley, T. (1993) Perinatal lethality and defects in hindbrain development in mice homozygous for a targeted mutation of the zinc finger gene Krox20. Genes & Development 7, 2071-2084. 10.1038/s41386-020-0605-331910432
Szklarczyk, D., Franceschini, A., Kuhn, M., Simonovic, M., Roth, A., Minguez, P., Doerks, T., Stark, M., Muller, J., Bork, P., Jensen, L. J. and von Mering, C. (2011) The string database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Research 39, D561-D568. 10.1093/nar/gkq97321045058
Tai, Y. F., Pavese, N., Gerhard, A., Tabrizi, S. J., Barker, R. A., Brooks, D. J. and Piccini, P. (2007) Microglial activation in presymptomatic Huntington's disease gene carriers. Brain 130, 1759-1766. 10.1093/brain/awm04417400599
Thanh-Phuong, N., Caberlotto, L., Morine, M. J. and Priami, C. (2014) Network analysis of neurodegenerative disease highlights a role of Toll-Like receptor signaling. Biomed Research International 2014,686505. 10.1155/2014/21056025692130
Trager, U., Andre, R., Lahiri, N., Magnusson-Lind, A., Weiss, A., Grueninger, S., McKinnon, C., Sirinathsinghji, E., Kahlon, S., Pfister, E. L., Moser, R., Hummerich, H., Antoniou, M., Bates, G. P., Luthi-Carter, R., Lowdell, M. W., Bjoerkqvist, M., Ostroff, G. R., Aronin, N. and Tabrizi, S. J. (2014) Htt-lowering reverses huntington's disease immune dysfunction caused by Nf Kappa B pathway dysregulation. Brain 137, 819-833. 10.1093/brain/awt35547d24967-2ac2-4054-88ec-47737881137d
Trager, U., Andre, R., Magnusson-Lind, A., Miller, J. R. C., Connolly, C., Weiss, A., Grueninger, S., Silajdzic, E., Smith, D. L., Leavitt, B. R., Bates, G. P., Bjoerkqvist, M. and Tabrizi, S. J. (2015) Characterisation of immune cell function in fragment and full-length Huntington's disease mouse models. Neurobiology of Disease 73, 388-398. 10.1016/j.nbd.2014.10.01225447230
Valekova, I., Jarkovska, K., Kotrcova, E., Bucci, J., Ellederova, Z., Juhas, S., Motlik, J., Gadher, S. J. and Kovarova, H. (2016) Revelation of the Ifn Alpha, Il-10, Il-8 and Il-1 Beta as promising biomarkers reflecting immuno-pathological mechanisms in porcine Huntington's disease model. Journal of Neuroimmunology 293, 71-81. 10.1016/j.jneuroim.2016.02.01227049565
Vuono, R., Winder-Rhodes, S., de Silva, R., Cisbani, G., Drouin-Ouellet, J., Spillantini, M. G., Cicchetti, F., Barker, R. A. and Registry Investigators, E. (2015) The role of Tau in the pathological process and clinical expression of Huntington's disease. Brain 138, 1907-1918. 10.1093/brain/awv10725953777
Wang, W., Liu, R., Xu, Z., Niu, X., Mao, Z., Meng, Q. and Cao, X. (2015) Further insight into molecular mechanism underlying thoracic spinal cord injury using bioinformatics methods. Molecular Medicine Reports 12, 7851-7858. 10.3892/mmr.2015.444226497545
Wild, E. J., Björkqvist, M., Thiele, J., Silvestroni, A., Andre, R., Lahiri, N., Raibon, E., Lee, R. V., Benn, C. L., Soulet, D., Magnusson, A., Woodman, B. and Landles, C. (2008) A novel pathogenic pathway of immune activation detectable before clinical onset in Huntington's disease. Journal of Neurology Neurosurgery and Psychiatry 79, A4-A4. 10.1084/jem.2008017818625748
Wilming, L. G., Boychenko, V. and Harrow, J. L.(2015) Comprehensive comparative homeobox gene annotation in human and mouse. Database 2015,bav091. 10.1093/database/bav11726708988
Yao, Y., Cui, X., Al-Ramahi, I., Sun, X., Li, B., Hou, J., Difiglia, M., Palacino, J., Wu, Z.-Y., Ma, L., Botas, J. and Lu, B. (2015) A striatal-enriched intronic gpcr modulates huntingtin levels and toxicity. Elife 4, e05449. 10.7554/eLife.0544925738228
Zhu, J., Zhang, Y., Ghosh, A., Cuevas, R. A., Forero, A., Dhar, J., Ibsen, M. S., Schmid-Burgk, J. L., Schmidt, T., Ganapathiraju, M. K., Fujita, T., Hartmann, R., Barik, S., Hornung, V., Coyne, C. B. and Sarkar, S. N. (2014) Antiviral activity of human oasl protein is mediated by enhancing signaling of the rig-i rna sensor. Immunity 40, 936-948. 10.1016/j.immuni.2014.05.0077e3906a6-35d5-4cf5-8057-80f76947d683
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