Dissection of a Down syndrome-associated trisomy to separate the gene dosage-dependent and -independent effects of an extra chromosome

Zhuo Xing; Yichen Li; Eduardo Cortes-Gomez; Xiaoling Jiang; Shuang Gao; Annie Pao; Jidong Shan; Yinghui Song; Amanda Perez; Tao Yu; Max R Highsmith; Frimpong Boadu; Jeffrey M Conroy; Prashant K Singh; Andrei V Bakin; Jianlin Cheng; Zhijun Duan; Jianmin Wang; Song Liu; Benjamin Tycko; Y Eugene Yu

doi:10.1093/hmg/ddad056

. 2023 Apr 4;32(13):2205–2218. doi: 10.1093/hmg/ddad056

Dissection of a Down syndrome-associated trisomy to separate the gene dosage-dependent and -independent effects of an extra chromosome

Zhuo Xing ¹, Yichen Li ², Eduardo Cortes-Gomez ³, Xiaoling Jiang ⁴, Shuang Gao ^5,⁶, Annie Pao ⁷, Jidong Shan ⁸, Yinghui Song ⁹, Amanda Perez ¹⁰, Tao Yu ¹¹, Max R Highsmith ¹², Frimpong Boadu ¹³, Jeffrey M Conroy ^14,¹⁵, Prashant K Singh ¹⁶, Andrei V Bakin ¹⁷, Jianlin Cheng ¹⁸, Zhijun Duan ^19,²⁰, Jianmin Wang ²¹, Song Liu ²², Benjamin Tycko ^23,²⁴, Y Eugene Yu ^25,^26,^✉

¹The Children’s Guild Foundation Down Syndrome Research Program, Department of Cancer Genetics and Genomics, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA

²The Children’s Guild Foundation Down Syndrome Research Program, Department of Cancer Genetics and Genomics, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA

³Department of Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA

⁴The Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, Zhejiang, China

⁵Department of Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA

⁶ Bioinformatics, OmniSeq Inc., Buffalo, NY, USA

⁷The Children’s Guild Foundation Down Syndrome Research Program, Department of Cancer Genetics and Genomics, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA

⁸Molecular Cytogenetics Core, Albert Einstein College of Medicine, Bronx, NY, USA

⁹Molecular Cytogenetics Core, Albert Einstein College of Medicine, Bronx, NY, USA

¹⁰Department of Cancer Genetics and Genomics, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA

¹¹The Children’s Guild Foundation Down Syndrome Research Program, Department of Cancer Genetics and Genomics, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA

¹²Department of Electric Engineering and Computer Science, University of Missouri, Columbia, MO, USA

¹³Department of Electric Engineering and Computer Science, University of Missouri, Columbia, MO, USA

¹⁴Research and Development, OmniSeq Inc., Buffalo, NY, USA

¹⁵Research Support Services, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA

¹⁶Department of Cancer Genetics and Genomics, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA

¹⁷Department of Cancer Genetics and Genomics, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA

¹⁸Department of Electric Engineering and Computer Science, University of Missouri, Columbia, MO, USA

¹⁹Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA

²⁰Division of Hematology, Department of Medicine, University of Washington, Seattle, WA, USA

²¹Department of Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA

²²Department of Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA

²³ Hackensack-Meridian Health Center for Discovery and Innovation, Nutley, NJ, USA

²⁴John Theurer Cancer Center, Hackensack University Medical Center, Hackensack, NJ, USA

²⁵The Children’s Guild Foundation Down Syndrome Research Program, Department of Cancer Genetics and Genomics, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA

²⁶Genetics, Genomics and Bioinformatics Program, State University of New York at Buffalo, Buffalo, NY, USA

^✉

To whom correspondence should be addressed at: The Children’s Guild Foundation Down Syndrome Research Program, Department of Cancer Genetics and Genomics, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA. Tel: +1 7168451099; Fax: +1 7168451698; Email: yuejin.yu@roswellpark.org

PMCID: PMC10281752 PMID: 37014740

Abstract

As an aneuploidy, trisomy is associated with mammalian embryonic and postnatal abnormalities. Understanding the underlying mechanisms involved in mutant phenotypes is broadly important and may lead to new strategies to treat clinical manifestations in individuals with trisomies, such as trisomy 21 [Down syndrome (DS)]. Although increased gene dosage effects because of a trisomy may account for the mutant phenotypes, there is also the possibility that phenotypic consequences of a trisomy can arise because of the presence of a freely segregating extra chromosome with its own centromere, i.e. a ‘free trisomy’ independent of gene dosage effects. Presently, there are no reports of attempts to functionally separate these two types of effects in mammals. To fill this gap, here we describe a strategy that employed two new mouse models of DS, Ts65Dn;Df(17)2Yey/+ and Dp(16)1Yey/Df(16)8Yey. Both models carry triplications of the same 103 human chromosome 21 gene orthologs; however, only Ts65Dn;Df(17)2Yey/+ mice carry a free trisomy. Comparison of these models revealed the gene dosage-independent impacts of an extra chromosome at the phenotypic and molecular levels for the first time. They are reflected by impairments of Ts65Dn;Df(17)2Yey/+ males in T-maze tests when compared with Dp(16)1Yey/Df(16)8Yey males. Results from the transcriptomic analysis suggest the extra chromosome plays a major role in trisomy-associated expression alterations of disomic genes beyond gene dosage effects. This model system can now be used to deepen our mechanistic understanding of this common human aneuploidy and obtain new insights into the effects of free trisomies in other human diseases such as cancers.

Introduction

Trisomy is a chromosomal deviation that occupies a unique position in contemporary biomedicine, as it is responsible for conditions such as human trisomy 21 [Down syndrome (DS)], trisomy 13 (Patau syndrome) and trisomy 18 (Edwards syndrome). Questions regarding how these chromosomal alterations contribute to disease phenotypes have attracted extensive interest. There is a long-established consensus that altered gene dosages because of changes in the chromosome number drive the resulting phenotypes of organisms harboring such an aneuploidy, and this consensus has been widely tested and confirmed by many studies using different model systems, including ones that normalized the dosage of specific genes in mouse models (1–7). However, the possibility also exists that the presence of an extra free chromosome with its own centromere may contribute to the phenotypes of aneuploid organisms independent of gene dosage effects (7–14). Presently, such an important complementary hypothesis has not been examined experimentally in a mammalian system.

The possible impact of an extra chromosome beyond gene dosage effects is supported by observations that human trisomies 21, 13 and 18 have different genes triplicated but share many abnormalities, including those associated with brain, muscle and developmental cognitive deficits (15–21). These observations suggest that an extra chromosome may have a phenotypic impact regardless of the identities of the genes triplicated. However, conclusive evidence for such a possibility is lacking, largely because the addition of an extra chromosome in cell cultures or animal models will simultaneously increase the gene dosage, which makes it challenging to separate the effects of the gene dosage increases from the gene dosage-independent effects of the extra chromosome. In this study, we set out to separate gene dosage-dependent and -independent effects of an extra chromosome in relationship to human trisomy 21, as hypotheses for both gene dosage-dependent and -independent effects of human trisomy 21 have been proposed by the DS research community (8–13). To this end, we developed and analyzed a new mouse model system designed specifically for separating the gene dosage-dependent and -independent effects of a trisomy, including the phenotypic and transcriptomic impacts of an extra free chromosome with its own centromere, i.e. a ‘free trisomy’ beyond gene dosage effects.

In this report, we first describe the generation of Df(17)2Yey/+ and Df(16)8Yey/+ mouse mutants as genetic components for modeling and subsequently Ts65Dn;Df(17)2/+ and Dp(16)1/Df(16)8 mice as two comparable mouse models of DS in which dosages of all human gene orthologs were identical with or without an extra free chromosome. We then discuss the results of comparative cognitive tests (e.g. open field, nesting, T-maze, contextual fear-conditioning and Morris water maze tests) and transcriptome profiling. The experimental findings indicate that this model system will be useful for obtaining a deeper mechanistic understanding of dosage-dependent and -independent effects of trisomies.

Results

Generation of Df(17)2Yey/+ and Df(16)8Yey/+ as the components of the mouse model system to separate gene dosage-dependent and -independent effects of an extra chromosome

We devised a strategy that could effectively separate the gene dosage-dependent and -independent effects of an extra chromosome by generating new mouse models, which are depicted in Figure 1 along with other relevant models. The Ts65Dn and Dp(16)1Yey/+ [abbreviated as Dp(16)1/+] DS models have been used extensively for basic, translational and therapeutic research (22–26). The Ts65Dn model (Fig. 1, Model #1) carries a free marker chromosome with its own centromere that is notated as Ts(17¹⁶)65Dn (22,25), whereas the Dp(16)1/+ model (Fig. 1, Model #4) carries an interstitial tandem duplication (24). Both models carry three copies of the identical set of 103 human chromosome 21 (Hsa21) gene orthologs on Mmu16 (Fig. 1). Ts65Dn and Dp(16)1/+ mice exhibit many shared DS phenotypes with similar severity (24,27–33), likely because they share the same triplicated genes. However, these models also exhibit important phenotypic differences. For instance, Ts65Dn phenotypes are not always recapitulated in Dp(16)1/+ mice [e.g. prenatal forebrain defects (27,34) or swallowing alterations (35)]. In addition, Dp(16)1/+ mice have shown less severe phenotypes than Ts65Dn (27,34). For example, Ts65Dn mice are more severely impaired in the Morris water maze test than Dp(16)1/+ mice on the B6EiC3Sn strain background (P < 0.01; Supplementary Material, Fig. S1). Because the triplicated genes substantially overlap in the two models, and only Ts65Dn carries an extra chromosome, the presence of the extra chromosome may be responsible for the phenotypic differences observed between Ts65Dn and Dp(16)1/+ (27,34,35). However, the evidence is not yet conclusive. Although Ts65Dn and Dp(16)1/+ mice share the same 103 triplicated genes, Ts65Dn carries triplications of another 41 human gene orthologs that are not triplicated in Dp(16)1/+, and Dp(16)1/+ carries triplications of another 15 human gene orthologs that are not triplicated in Ts65Dn (Fig. 1). To overcome the associated uncertainty, we engineered new mouse models to normalize the dosage of all the Hsa21 gene orthologs located outside of the genomic regions encompassing the shared triplicated genes in both models, which are termed Df(17)2Yey/+ and Df(16)8Yey/+.

Schematic illustration of Mmu16 and Mmu17 in four mouse mutants. As depicted, to separate the gene dosage-dependent and -independent effects of an extra chromosome, we generated Ts65Dn;*Df(17)2*/+ and *Dp(16)1/Df(16)8* compound mouse mutants. Both mutants carry three copies of the *Mir155*-*Zbtb21* region highlighted by black. Thus, there are no differences in genes or gene dosages between Ts65Dn;*Df(17)2*/+ and *Dp(16)1/Df(16)8*. However, only Ts65Dn;*Df(17)2*/+ has an extra chromosome (trisomy). 16, Mmu16; 17, Mmu17.

Df(17)2Yey/+ and Df(16)8Yey/+ [hereafter referred to as Df(17)2/+ and Df(16)8/+, respectively] were generated using Cre/loxP-mediated chromosome engineering (Fig. 2). The deletion endpoints were determined on the basis of the breakpoints of Ts(17¹⁶)65Dn (8,36). The triplicated genes removed from Ts65Dn did not pertain to human DS or comparisons with Dp(16)1/+ and were located between Scaf8 and Pde10a on Mmu17 (Fig. 1). The triplicated genes removed from Dp(16)1/+ did not pertain to comparisons with Ts65Dn and were located between Lipi and Ncam2 on Mmu16 (Fig. 1). Thus, Df(17)2 (Fig. 2A–D) was used to normalize the triplicated Scaf8–Pde10a region on Mmu17 of Ts65Dn mice (Fig. 1), whereas Df(16)8 (Fig. 2E–H) was used to normalize the triplicated Lipi–Ncam2 region on Mmu16 of Dp(16)1/+ mice (Fig. 1). Both deletion mutants have a normal appearance and were fertile with the exception of the shortened tails in Df(17)2/+ mice because of the haploinsufficiency of Tbxt (37).

(A)–(D) Generation of *Df(17)2*/+ mice. (A) Strategy for generating *Df(17)2*. MICER vectors MHPN155l15 and MHPP338g19 were linearized with *Nde*I and *Hpa*I, respectively, before targeting. E, *Eco*RI; H, *Hpa*I. (B) Illustration of genomic locations of the BAC probes used in the FISH analysis. (C) FISH analysis of the metaphase spread prepared from the ES cells carrying *Df(17)2*. (D) Southern blot analysis of *Eco*RI-digested mouse tail DNA hybridized with probe 2B. Lane 1 and 2 represent *Df(17)2*/+ mice. (E)–(H) Generation of *Df(16)8*/+ mice. (E) Strategy for generating *Df(16)8*. MICER vectors MHPN121h17 and MHPP56i01 were linearized with *Swa*I and *Afl*II, respectively, before targeting. N, *Nde*I; V, *Eco*RV. (F) Illustration of genomic locations of the BAC probes used in the FISH analysis. (G) FISH analysis of the metaphase spread prepared from the ES cells carrying *Df(16)8*/+. (H) Southern blot analysis of *Nde*I-digested mouse tail DNA hybridized with probe 2D. Lane 1 and 2 represent *Df(16)8*/+ mice. These results demonstrate that the desired deletions are present in the engineered mutants. 5′, *5′ HPRT* fragment; 3′, *3′ HPRT* fragment; N, neomycin-resistance gene; P, puromycin-resistance gene; Ty, Tyrosinase transgene; Ag, Agouti transgene; arrowhead, *loxP* site.

Generation and analysis of the complete mouse model system to separate gene dosage-dependent and -independent effects of an extra chromosome

Ts65Dn mice were crossed with Df(17)2/+ mice to generate Ts65Dn;Df(17)2/+ mice (Fig. 1, Model #2; Fig. 3A), whereas Dp(16)1/+ mice were crossed with Df(16)8/+ mice to generate Dp(16)1/Df(16)8 mice (Fig. 1, Model #3; Fig. 3B). These compound mutants on the same strain background (see Supplementary Material, Fig. S2) were used to separate the gene dosage-dependent and -independent effects of an extra chromosome at the molecular and phenotypic levels independent of the gene dosage increases. The availabilities of Ts65Dn;Df(17)2/+ and Dp(16)1/Df(16)8 mice were prerequisites for our efforts because the dosages of all human gene orthologs are identical in these mice.

Agilent microarray CGH profile. DNA from (A) Ts65Dn;*Df(17)2*/+ and (B) *Dp(16)1/Df(16)8* mice were used for the analysis. CGH profiles for Mmu16 and Mmu17 for each mouse are shown. The data represent the log2-transformed hybridization ratios of (A) Ts65Dn;*Df(17)2*/+ or (B) *Dp(16)1/Df(16)8* mouse DNA versus WT mouse DNA.

At 2–4 months of age, both Ts65Dn;Df(17)2/+ and Dp(16)1/Df(16)8 mice had a normal appearance. To analyze the phenotypes of the mutant mice, we performed a number of behavioral tests (open field, nesting, T-maze, contextual fear-conditioning and Morris water maze tests) on the mutant mice and wild-type (WT) controls at 2–4 months of age. In the open field tests, the Ts65Dn;Df(17)2/+ and Dp(16)1/Df(16)8 mice showed no statistical difference (P > 0.05) (Supplementary Material, Fig. S3A, Table 1). The remaining tests examined the hippocampal function of the compound mouse models. In the nesting tests (38), both Ts65Dn;Df(17)2/+ and Dp(16)1/Df(16)8 mice had significantly lower scores for nest building compared with the WT control mice (P < 0.05); these mice left significantly more unused Nestlet material compared with the WT control mice (P < 0.05) (Fig. 4A). There were no differences observed between the Ts65Dn;Df(17)2/+ and Dp(16)1/Df(16)8 mice in this test (Fig. 4A, Table 1). In T-maze tests, the alternation rate of entry into the left and right arms of the maze is associated with reference and working memory (39). In this test, the alternation rate for the male Ts65Dn;Df(17)2/+ mice was significantly lower than that of the male WT and Dp(16)1/Df(16)8 mice (P < 0.05), thus indicating that the male Ts65Dn;Df(17)2/+ mice had impaired hippocampal function (Fig. 4B, Table 1). We performed the contextual fear-conditioning test to examine the capacity for hippocampal-mediated contextual memory (40,41). Before the delivery of foot shocks, the three mouse groups had a similar baseline freezing level (P > 0.05). After 24 h, both the mutant and WT mice showed similar increases in freezing behavior upon their return to the test chamber (P > 0.05) (Supplementary Material, Fig. S3B, Table 1). To examine hippocampal-mediated spatial learning and memory (41–43), we compared the performances of the Ts65Dn;Df(17)2/+, Dp(16)1/Df(16)8 and WT control mice using the Morris water maze test. Although the path length needed for the three groups to locate the hidden platform significantly decreased following training, there were no significant differences detected between Ts65Dn;Df((17)2/+ and Dp(16)1/Df(16)8 mice (Supplementary Material, Fig. S3C, Table 1). However, female Ts65Dn;Df(17)2/+ mice displayed a tendency to take a longer path length than female WT controls to locate the platform, but the post hoc analysis failed to detect a significant difference on any specific trial days (Supplementary Material, Fig. S3C, Table 1).

Table 1.

Summary of the phenotypic comparison between Ts65Dn;Df(17)2/+ and Dp(16)1/Df(16)8 mice

Genotype	Ts65Dn;Df(17)2/+ versus WT			Dp(16)1/Df(16)8 versus WT			Ts65Dn:Df(17)2/+ versus Dp(16)1/Df(16)8
Sex	Both sexes	Males	Females	Both sexes	Males	Females	Both sexes	Males	Females
Open field	−	−	−	−	−	−	−	−	−
Nesting	+^*	+^*	−	++^*	+^*	−	−	−	−
T-maze	+^^	+^^	−	−	−	−	−	+^^	−
Morris water maze	−	−	−	−	−	−	−	−	−
Contextual fear conditioning	−	−	−	−	−	−	−	−	−

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−, no significant difference detected; +, P < 0.05; ++, P < 0.01.

^*Evidence of the gene dosage effect.

^* ^*Evidence of the gene dosage-independent effect.

(A) Nesting test. WT (males, n = 13; females, n = 17), Ts65Dn;*Df(17)2*/+ (males, n = 15; females, n = 18) and *Dp(16)1/Df(16)8* (males, n = 15; females, n = 18) mice were supplied with a pre-weighed Nestlet in separate cages before the start of the dark cycle. The nesting results were recorded the next day. Each nest was assigned a score from 0 to 5 (0, nestlet untouched, no nest in the cage; 5, perfect nest, almost no nestlet remaining). Data are presented as the mean ± SEM for both sexes and for males and females separately. At the end of the test, the remaining Nestlet was weighed and recorded. Data are presented as the mean ± SEM for both sexes and for males and females separately. ^*^*, P < 0.01; ^*, P < 0.05. (B) T-maze test. WT (males, n = 16; females, n = 17), Ts65Dn;*Df(17)2*/+ (males, n = 15; females, n = 18) and *Dp(16)1/Df(16)8* (males, n = 15; females, n = 18) mice were allowed to explore the T-maze for 10 min. Each entry into each arm was recorded. The alternation rate was calculated by dividing the number of correct alternations by the total possible alternations. The alternation rates are shown for both sexes, for males and females separately. Data are presented as the mean ± SEM. ^*, P < 0.05.

In summary of the aforementioned results, the impairments observed in the nesting tests for both Ts65Dn;Df(17)2/+ and Dp(16)1/Df(16)8 mice indicate that the gene dosage increases can cause cognitive deficits regardless of whether the mutant mice carried an extra chromosome. The impairments observed in the T-maze tests for male Ts65Dn;Df(17)2/+ mice, but not male Dp(16)1/Df(16)8 mice, suggest that an extra free chromosome with its own centromere can have a phenotypic impact independent of a gene dosage effect because the triplicated Hsa21 gene orthologs are identical between the two compound mouse models.

Next, the ribonucleic acid (RNA)-seq data were obtained from the cerebral cortex tissue isolated from Ts65Dn;Df(17)2/+, Dp(16)1/Df(16)8 and WT control mice at 6 weeks of age. The RNA-seq data associated with this study have been deposited in the National Center for Biotechnology Information’s Gene Expression Omnibus. When compared with WT control mice, the two compound mutants showed similar elevated expressions of the same 47 and 53 genes in males and females, respectively (Fig. 5A and C; Supplementary Material, Tables S1 and S2). A total of 93.6% and 88.7% of these genes were mapped to the triplicated region shared by the males and females of these two compound mutants, respectively (Fig. 5C; Supplementary Material, Tables S1 and S2), a reflection of the consequence of gene dosage increases. The expressions of 45 and 134 genes located outside of the shared triplicated region were increased in Ts65Dn;Df(17)2/+ males and females, respectively, whereas the expressions of only two and five non-triplicated genes were increased in Dp(16)1/Df(16)8 males and females, respectively (Fig. 5A and C; Supplementary Material, Tables S1 and S2). The expressions of 29 and 269 non-triplicated genes were decreased in Ts65Dn;Df(17)2/+ males and females, respectively, whereas the expressions of only two and six non-triplicated genes were decreased in Dp(16)1/Df(16)8 males and females, respectively (Fig. 5A and C; Supplementary Material, Tables S1 and S2). The significant differences in the numbers of non-triplicated genes with altered expressions associated with Ts65Dn;Df(17)2/+ and Dp(16)1/Df(16)8 models suggest that the presence of an extra chromosome may play a crucial role in expression alterations of disomic genes in the trisomic mice beyond gene dosage effects. Transcriptomic differences were also observed in direct comparisons between Ts65Dn;Df(17)2/+ and Dp(16)1/Df(16)8, which yielded a relatively smaller number of differentially expressed (DE) genes (Fig. 5B; Supplementary Material, Tables S1 and S2). The earlier mentioned observations can be explained by the RNA-seq data showing that both compound mutations led to alterations in gene expression when compared with WT, but the magnitudes of the associated expression alterations were more pronounced in Ts65Dn;Df(17)2/+ mice (Fig. 5; Supplementary Material, Tables S1 and S2), which is also consistent with the aforementioned impact of the extra chromosome.

Analysis of differential expressions via RNA-seq-based transcriptome profiling of the cerebral cortex isolated from Ts65Dn;*Df(17)2*/+ (males, n = 5; females, n = 5), *Dp(16)1/Df(16)8* (males, n = 5; females, n = 5) and WT control mice (males, n = 6; females, n = 6). (A) The heatmaps of the DE genes between Ts65Dn;*Df(17)2/+* and WT as well as between *Dp(16)1*/*Df(16)8* and WT. (B) The heatmaps of the DE genes between Ts65Dn;*Df(17)2/+* and *Dp(16)1*/*Df(16)8*. (C) The Venn diagram illustrating the relationship between the higher-expressed and lower-expressed Ts65Dn;*Df(17)2*/+ genes versus the WT controls and the higher-expressed and lower-expressed *Dp(16)1/Df(16)8* genes versus the WT controls (see Supplementary Material, Table S1 for the names of the DE genes represented in the heatmaps and the Venn diagram as well as Supplementary Material, Table S2 for the numbers of the DE genes in various categories).

Verification of the genotype–phenotype relationship between Ts65Dn and Ts65Dn;Df(17)2/+ mice

There is extensive evidence showing that Ts65Dn mice exhibit abnormal phenotypes in the open field, T-maze and Morris water maze tests (Supplementary Material, Fig. S1) (44–49). However, Ts65Dn;Df(17)2/+ mice did not exhibit significant differences from the WT controls in the open field and Morris water maze tests in this study (Fig. 6), which may suggest that the dosage normalization of the sub-centromeric region of Mmu17 by Df(17)2 might have substantially eliminated the Ts65Dn-associated abnormal phenotypic features in Ts65Dn;Df(17)2/+ mice. Here we compared Ts65Dn and Ts65Dn;Df(17)2/+ mice directly on the same strain background (see Supplementary Material, Fig. S4) in the open field, T-maze and Morris water maze tests. Our results showed that Ts65Dn mice were more active in the open field test (Fig. 6A) than both the Ts65Dn;Df(17)2/+ and WT control mice. In the T-maze test, female Ts65Dn mice were impaired when compared with the female WT mice, whereas both male Ts65Dn and Ts65Dn;Df(17)2/+ mice were impaired compared with the male WT controls (Fig. 6B). In the Morris water maze test, the path lengths of male and female Ts65Dn mice were significantly longer than those of both male and female WT mice, respectively (P < 0.01; Fig. 6C, Table 2). Such differences were not detected between the male Ts65Dn;Df(17)2/+ and WT mice (Fig. 6C; Table 2). There was a tendency for female Ts65Dn;Df(17)2/+ mice to perform worse than female WT mice, but no significant difference was detected on any trial day by the post hoc analysis (Fig. 6C), which is consistent with the trend shown in Supplementary Material, Figure S3C. The effect of the sub-centromeric region of Mmu17 is evident through the direct comparison of the data derived from Ts65Dn and Ts65Dn;Df(17)2/+ (Fig. 6 and Table 2). These results suggest that the Ts65Dn phenotypes are the consequence of the compounding effects of the elevated dosages of genes orthologous to Hsa21 on Mmu16 and the sub-centromeric region on Mmu17 that does not consist of Hsa21 gene orthologs in addition to the gene dosage-independent effects of the extra Ts(17¹⁶)65Dn chromosome.

(A) Open field test. WT (males, n = 10; females, n = 13), Ts65Dn (males, n = 7; females, n = 8) and Ts65Dn;*Df(17)2*/+ (males, n = 7; females, n = 10) mice were allowed to explore the open field arena for 10 min. Total path length (m) was analyzed in both sexes and in males and females separately. (B) T-maze test. WT (males, n = 10; females, n = 13), Ts65Dn (males, n = 7; females, n = 8) and Ts65Dn;*Df(17)2*/+ (males, n = 7; females, n = 10) mice were allowed to explore the T-maze for 10 min. Each entry into each arm was recorded. The alternation rate was calculated by dividing the number of correct alternations by the total possible alternations. The alternation rates are shown for both sexes and for males and females separately. (C) Morris water maze test. WT (males, n = 9; females, n = 12), Ts65Dn (males, n = 7; females, n = 8) and Ts65Dn;*Df(17)2*/+ (males, n = 7; females, n = 9) mice were compared using the Morris water maze test. During the hidden platform test from day 1 to 6, the path length of Ts65Dn, but not Ts65Dn;*Df(17)2*/+, was significantly longer than that of the WT mice when both sexes were calculated together (P < 0.001). In male mice, the path length of Ts65Dn, but not Ts65Dn;*Df(17)2*/+, was significantly longer than that of the WT mice (P < 0.01). In female mice, the path length of Ts65Dn was significantly longer than that of the WT mice (P < 0.01); meanwhile, the Ts65Dn;*Df(17)2*/+ mice showed a tendency to take a longer path but the post hoc analysis did not reveal significant difference for any trial days. Data are presented as the mean ± SEM. ^*^*^*, P < 0.001; ^*^*, P < 0.01; ^*, P < 0.05.

Table 2.

Summary of the phenotypic comparison between Ts65Dn and Ts65Dn;Df(17)2/+ mice

Genotype	Ts65Dn versus WT			Ts65Dn;Df(17)2/+ versus WT			Ts65Dn versus Ts65Dn;Df(17)2/+
Sex	Both sexes	Males	Females	Both sexes	Males	Females	Both sexes	Males	Females
Open field	+	+	−	−	−	−	+^*	+^*	−
T-maze	+++	++	++	++	+	−	+^*	−	−
Morris water maze	+++	++	++	−	−	−	+++^*	+++^*	−

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−, no significant difference detected; +, P < 0.05; ++, P < 0.01; +++, P < 0.001.

^*Evidence of the impact of the sub-centromeric region on Mmu17.

Discussion

Successful separation of gene dosage-dependent and -independent effects of a trisomy modeling DS

Trisomy is associated with many medical conditions, including developmental disorders and cancers. Deciphering of the critical mechanisms underlying trisomy-associated phenotypes is essential for the rational design of new therapeutic interventions. This study is the first attempt to functionally dissect two components of a trisomy, i.e. the impacts of increased gene dosage and an extra chromosome with its own centromere independent of gene dosage. Such an effort became feasible because (1) Ts65Dn mice carry a free marker chromosome Ts(17¹⁶)65Dn and (2) precise chromosomal rearrangements, such as duplication and deletion with predetermined endpoints, can be efficiently engineered in mice.

The following conclusions were drawn from our comparative analysis of the Ts65Dn;Df(17)2/+ and Dp(16)1/Df(16)8 models. (1) The functional contributions of different components of a trisomy were effectively dissected using the compound mouse mutants, namely, Ts65Dn;Df(17)2/+ and Dp(16)1/Df(16)8. (2) When compared with WT control mice, both Ts65Dn;Df(17)2/+ and Dp(16)1/Df(16)8 showed impairments in the nesting test (Fig. 4A; Table 1). These findings indicate that the mice have impaired cognitive function, and the data provide further evidence that increased gene dosage plays a role in such cognitive impairments. On the other hand, neither showed impairments in some other important tests, such as the contextual fear-conditioning test, which suggests that the extent of their cognitive impairment may be relatively mild. (3) The cognitive function of these two models reflects the final output of the dosage increases of the specific 103 Hsa21 gene orthologs with or without an extra free chromosome at the organismal level, which may arise from complex functional interactions among the triplicated genes. For example, the impact of some critical genes, such as Dyrk1a, may have been partially neutralized by the triplications of other genes in the triplicated region. Regarding the genotype–phenotype relationship, these two compound mutants may be the best models for gaining a better understanding of the phenotypic consequences of the triplication of these specific 103 Hsa21 gene orthologs with or without an extra free chromosome. (4) These two models were designed specifically to examine the impact of an extra free chromosome beyond gene dosage effects. The detection of impairments in male Ts65Dn;Df(17)2/+ mice, but not male Dp(16)1/Df(16)8 mice, in T-maze tests (Fig. 4B; Table 1) revealed such a gene dosage-independent impact; therefore, these mutants fulfilled their unique role as the desired animal models of DS for this specific pursuit. Interestingly, the impact of the gene dosage-independent effect of the extra chromosome was sex specific. DS-associated sex-specific effects have been demonstrated in many model-based studies (50–55). Although details of such a phenomenon are still under intense exploration, abnormal interactions between marker chromosomes and sex chromosomes may have some mechanistic relevance (56).

Genome-wide expression analyses have been carried out by a number of teams on various mouse models of DS (11,27,57–62) In this study, we performed comparative transcriptome profiling on cerebral cortex tissue isolated from Ts65Dn;Df(17)2/+ and Dp(16)1/Df(16)8 as well as WT control mice. When the WT control mice were used as the reference, elevated expressions of genes were observed in both Ts65Dn;Df(17)2/+ and Dp(16)1/Df(16)8. Among those elevated expressed genes that were shared in both models, ⁓90% of them were located within the triplicated region (Fig. 5A and C; Supplementary Material, Tables S1 and S2). This reflects the consequence of gene dosage increases. Interestingly, 477 Ts65Dn;Df(17)2/+ genes located outside of the shared triplicated region showed altered expressions, whereas only 15 non-triplicated Dp(16)1/Df(16)8 genes showed the same (Fig. 5A and C; Supplementary Material, Tables S1 and S2). This contrast at the transcriptomic level supports the critical role of the extra chromosome in altering expressions of disomic genes in the trisomic mice. The comparison between Ts65Dn;Df(17)2/+ and Dp(16)1/Df(16)8 without the involvement of WT resulted in a relatively lower number of DE genes (Fig. 5B; Supplementary Material, Tables S1 and S2). This is because the expressions of the WT-referenced DE genes changed in both Ts65Dn;Df(17)2/+ and Dp(16)1/Df(16)8, but the degrees of change were higher in Ts65Dn;Df(17)2/+ (Fig. 5; Supplementary Material, Tables S1 and S2), which is also suggestive of the impact of the extra chromosome beyond gene dosage effects. Through all of these efforts, we have successfully teased out the impact of a trisomy-associated extra chromosome at the molecular level for the first time.

On the basis of the aforementioned, the two compound mutants generated in this study can serve as a pair of unparalleled models of DS to examine the impact of an extra free chromosome beyond gene dosage effects on other DS phenotypes, including those involving a swallowing dysfunction, which was detected in the Ts65Dn model but not in the Dp(16)1/+ model (35).

To dissect human trisomy 21 further, it would be very advantageous if a more relevant human model was also available but that is not possible at present. In that regard, the closest ones comparable to Ts65Dn;Df(17)2/+ and Dp(16)1/Df(16)8 are related to Robertsonian translocations (ROBs) in humans, i.e. rob(21q;21q) and rob(14q;21q) (63). Importantly, unlike the free trisomy 21, rob(21q;21q) and rob(14q;21q) do not implicate an extra free Hsa21. For all genes on human 21q, rob(21q;21q) and rob(14q;21q) individuals carry three copies, just like individuals carrying the free trisomy 21. However, such a feature does not extend to human 21p where the translocation breakpoints are located because the evidence shows that the translocations occur near ENSG00000286103, TPTE or BAGE2 on 21p (64,65). On the basis of the genome coordinates, a substantial number of microRNA (miRNA), ribosomal deoxyribonucleic acid (rDNA) and long noncoding RNA (LncRNA) genes are located proximal to these breakpoints, so the ROB carriers will have a lower dosage of these genes when compared with the free trisomy 21 carriers. These miRNA, rDNA and LncRNA genes play very important roles in many biological processes (63,66–68). Thus, the dosage differences of these genes between the ROB and the free trisomy 21 carriers will complicate the interpretation if these two types of chromosomal rearrangements are used to reveal the impact of an extra free chromosome beyond gene dosage effects.

Some effects of trisomies have been attributed to the specific genes located on the extra chromosome (11). However, all triplicated Hsa21 gene orthologs are identical between the Ts65Dn;Df(17)2/+ and Dp(16)1/Df(16)8 mice, which suggests that the specific transcriptomic and phenotypic differences associated with these two compound mouse models are not solely caused by the genes present on the extra chromosome.

Molecular and cellular changes associated with aneuploidy have been extensively investigated (7,14), including alterations of cellular proliferation and metabolism (69,70), genome stability (71,72) and nuclear membrane/structure/morphology (73). In the future, it would be interesting to further examine the gene dosage-dependent and -independent effects on these molecular and cellular alterations using our model system described herein.

Confirmation of the contribution by the sub-centromeric region of Mmu17 in Ts65Dn mice

Ts65Dn mice have been used as a DS mouse model for ˃20 years (22,25), and more DS studies have been published using this model than any other animal model. Many clinical trials have been launched on the basis of the preclinical data generated using Ts65Dn mice (74,75). Our data have shown that the sub-centromeric region of Mmu17 plays a decisive role in two important tests, the open field test and the Morris water maze test (Fig. 6A and C; Table 2). Our data are consistent with the results generated using Ts66Yah mice in which the sub-centromeric region of the Mmu17 region on the Ts(17¹⁶)65Dn marker chromosome was removed by a cis deletion (76,77). Because this genomic region of Mmu17 is not orthologous to any genomic region on Hsa21, the preclinical data generated using the Ts65Dn mouse model will need to be re-evaluated using other DS animal models, including Ts65Dn;Df(17)2/+ and Ts66Yah.

Importantly, the current study has shown that it is feasible to dissect gene dosage-dependent and -independent effects of a specific trisomy in vivo. Thus, we expect that our model system could also be effectively employed to separate these two types of effects in other phenotypes of human trisomy 21. The strategy utilized in our study may also inspire the development of new approaches to dissect the different components of other aneuploidies. Besides common human aneuploid conditions, insights from such investigations may also have general implications for understanding the effects of free trisomies in other human diseases such as cancers.

Materials and Methods

Generation of mouse models

We generated new chromosomal deletions between Scaf8 and Pde10a on Mmu17 and Lipi and Ncam2 on Mmu16 using Cre/loxP-mediated chromosome engineering (78). MICER clones (79), MHPN155l15 and MHPP338g19 on Mmu17 (Fig. 2A), and MHPN121h17 and MHPP56i01 on Mmu16 (Fig. 2E) were used as targeting vectors to deliver loxP to the two endpoints of each deletion in the genome of mouse AB2.2 embryonic stem (ES) cells (78). MHPP56i01 was constructed by switching the backbone of MHPN56i01, and the targeting vector with the desired orientation of the insertion of the mouse genomic DNA was selected on the basis of the restriction enzyme digestion and sequencing confirmation. Before gene targeting, MHPN155l15, MHPP338g19, MHPN121h17 and MHPP56i01 were linearized with NdeI, HpaI, SwaI and AflII, respectively. The linearized targeting vectors were electroporated into ES cells, which were then selected with G418 or puromycin. Double-targeted ES cell clones were identified by Southern blot analysis using polymerase chain reaction (PCR) products as probes. ES cell culture and electroporation were carried out as described previously (78). To induce recombination between the targeted loxP sites, the Cre-expression vector pOG231 (78) was electroporated into double-targeted cells. The ES cell clones carrying individual deletions were identified by Southern blot analysis. ES cell clones were also confirmed by fluorescence in situ hybridization (FISH) analysis (Fig. 2C and G). The ES cell lines carrying the aforementioned individual deletions were used to generate germline chimeras by injecting them into blastocysts isolated from C57BL/6 J mice, as described previously (78). The Df(17)2/+ and Df(16)8/+ deletion mice were confirmed by Southern blot analysis. Df(17)2/+ mouse tail DNA was digested with EcoRI and hybridized with probe 2B (Fig. 2A), and the 19.8 kb band in the Southern blot analysis indicated the presence of the Df(17)2 (Fig. 2D). Df(16)8/+ mouse tail DNA was digested with NdeI and hybridized with probe 2D (Fig. 2E), and the 25.3 kb band in the Southern blot analysis indicated the presence of the Df(16)8 (Fig. 2H).

Fluorescence in situ hybridization

Metaphase chromosome spreads from ES cells were prepared, and FISH analysis was performed as described previously (78). To detect the chromosomal deletions between Scaf8 and Pde10a on Mmu17, bacterial artificial chromosome (BAC) clones RP23-147G23 were selected (Fig. 2B). To detect the chromosomal deletion between Lipi and Ncam2 on Mmu16, BAC clone RP23-337 K15 was selected (Fig. 2F). These BAC clones carrying mouse genomic DNA mapped within these regions were labeled with digoxigenin and detected using an anti-digoxigenin rhodamine antibody. BAC clones, RP23-103F2 and RP23-81D13 on Mmu17 and Mmu16, respectively, carrying mouse genomic DNA for identifying Mmu16 and Mmu17 were labeled with biotin and detected using a fluorescein isothiocyanate–avidin conjugate (Fig. 2B and F). The chromosomes were counterstained with 4′,6′-diamidino-2-phenylindole.

Animals

All mice were maintained in a temperature- and humidity-controlled animal facility with a 12-h light/dark cycle and had ad libitum access to food and water. All mice used in the behavioral experiments were 2–4 months old. Before behavioral experiments, each mouse was pre-handled for 2 min every day for 1 week. All experimental procedures were approved by the Institutional Animal Care and Use Committee of Roswell Park Comprehensive Cancer Center.

Comparative genomic hybridization microarrays

To confirm the genomic content of the Ts65Dn;Df(17)2/+ and Dp(16)1/Df(16)8 mouse models, an oligonucleotide array containing 244 000 probes designed for mouse comparative genomic hybridization (CGH) was utilized (Agilent Technologies). The probes were comprised of 60-mer oligonucleotides with an average spatial resolution of 6.4 kb. The genome coordinates of the probes in the array were predetermined by Agilent Technologies. Genomic DNA was prepared from the tail tissue of a male mutant mouse. Genomic DNA isolated from a WT female littermate was used as a reference control. The genomic DNA from the mutant mouse and WT littermate (1 μg each) was fluorescently labeled using the Agilent Genomic DNA Labeling Kit. Hybridization to the CGH array was performed for 40 h at 65°C. After hybridization and washing, the slide was scanned using an Agilent microarray scanner to generate high-resolution images for both the Cy3 (the mutant mouse) and Cy5 (the WT littermate) channels.

Open field test

The mice were placed in an open field arena and tracked for 10 min. The arena was a transparent plastic box with the following dimensions: 40 cm (L) × 40 cm (W) × 40 cm (H). The experimental data were recorded and analyzed using the HVS Image All in One Tracking 2019 system (HVS Image Ltd, Twickenham, Middlesex, UK). The total distance traveled (m) was analyzed for each genotype.

Nesting test

Nesting tests were performed on the basis of the protocol of Deacon (38). In ⁓1 h before the dark phase, each mouse was separated into a testing cage that contained a 2-g pre-weighed pressed cotton square (Nestlet). After the 12-h dark phase, mice were returned to their home cages. The nests were scored, and the unused Nestlet was weighed. We used the following scoring system: 0 = Nestlet was not noticeably used; 1 = Nestlet was slightly used but not gathered; 2 = Nestlet was partly used and gathered, but there was no nest formed; 3 = Nestlet was partly used, and a flat nest was formed; 4 = Nestlet was mostly used to form a nest, but noticeable Nestlet remained; 5 = Nestlet was completely torn up to form a perfect nest, and no Nestlet remained.

T-maze spontaneous alternation test

T-maze spontaneous alternation tests were performed on the basis of the protocol of Deacon and Rawlins (39). The T-shaped maze was made of opaque Plexiglas with a start arm (50 cm × 10 cm × 20 cm) and left and right arms (37 cm × 10 cm × 20 cm for each arm). A sliding door was placed in the start arm to create a holding place that was separated from the rest of the maze, whereas a divider panel (20 cm × 20 cm), which extended 10 cm into the start arm, was centered in the middle of the ‘T’ between the left and right arms. Each mouse was placed into the start arm for 5 min of confined time, then the sliding door was opened, and the mouse had free access to the rest of the maze for 10 min. Any entry into one of the lateral arms was recorded, and entry into an opposite arm was defined as an alternation. Only valid entries were counted (i.e. the whole body was inside the lateral arm, including its tail). The percentage of times the mouse alternated between the left and right arms over the possible alternations (total entry number minus one) was defined as the alternation rate.

Contextual fear-conditioning test

The contextual fear-conditioning tests were performed using the Fear-Conditioning Video Tracking System (Med Associates Inc., St. Albans, VT, USA). The test chamber consisted of a grid floor of stainless-steel rods connected to an electric shock generator, a video camera in the front of the chamber and a ceiling light. On the first day of the test, each mouse was recorded for 2 min inside the chamber as baseline activity before conditioning. A foot shock (1 mA scrambled) was then delivered for 2 s using Video Freeze Software V.1.8 (Med Associates Inc., St. Albans, VT, USA). The mouse was removed from the chamber after an additional 30 s of stay. Each mouse was returned to the test chamber after 24 h and monitored for 3 min with no foot shock. The freezing behavior was recorded using Video Freeze Software. The percentage of freezing time during the 3 min contextual exposure was calculated as a measure of contextual learning.

Morris water maze test

A standard Morris water maze test (41–43) was performed using the HVS Image All in One Tracking 2019 system (HVS Image Ltd, Twickenham, Middlesex, UK). A 1.5-m circular pool was used, and the water temperature was maintained at 25 ± 1°C. Each mouse performed four trials on each training day. In each trial, the mouse started from one of four starting points (north, east, south or west) and was allowed to swim for a maximum of 90 s to reach the platform. Once on the platform, the mouse was allowed to stay there for 10 s before being removed from the pool. The hidden platform trials were performed on days 1–6, and the path length (travel distance) was collected.

RNA sequencing

Ts65Dn;Df(17)2, Dp(16)1/Df(16)8 and WT control male and female mice were generated by using the scheme outlined in Supplementary Material, Figure S2. RNA was extracted from the cerebral cortex of the aforementioned mice at 6 weeks of age by using an miRNeasy mini kit (Qiagen, Hilden, Germany) in accordance with the manufacturer’s recommendations. On-column DNAse digestion was performed to remove any residual genomic DNA contamination. RNA samples were quantitated with a Qubit Broad Range RNA kit (Thermo Fisher), and qualitative assessments were performed by using the 4200 Tapestation (Agilent Technologies, Santa Clara, CA). RNA-Seq libraries were prepared from 500 ng total RNA by using an RNA HyperPrep Kit along with RiboErase (HMR) kit (Roche Sequencing Solutions) in accordance with the manufacturer’s instructions. Final libraries were purified by using Pure Beads and validated for appropriate size on a 4200 TapeStation D1000 Screentape (Agilent Technologies, Inc). The DNA libraries were quantitated by using a KAPA Biosystems qPCR kit and pooled together in an equimolar fashion. The library pool was denatured and diluted to 350 pM with 1% PhiX control library added. The resulting pool was then loaded into the NovaSeq Reagent cartridge, for 100 paired end sequencing and sequenced on a NovaSeq6000 following the manufacturer’s recommended protocol (Illumina Inc.). For each library, an average of 50 million paired end reads were generated.

Analysis of RNA-seq data

Paired end raw sequencing reads passed through the quality filter from Illumina Real-Time Analysis (RTA) were first pre-processed by using FASTQC (v0.11.8) (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) (80) for sequencing base quality control (QC). The reads were mapped to the GRCm38 mouse reference genome and GENCODE (v25) (81) annotation database using STAR (v2.7.9a) (82). Alignment files were indexed using samtools (v1.14) (83). A second pass QC step was done using alignment output with RSeQC (v4.0.0) (84) in order to examine abundances of genomic features, splicing junction saturation and gene-body coverage. Gene expression was quantified by using featureCounts (v2.0.0) (85) with the fracOverlap 0.98 option and then formatted into a raw counts data matrix. Differential expression analyses were performed by using DESeq2 (v1.36.0) (86,87), a variance-analysis package developed to infer statistically significant differences in RNA-seq data. Genes were called DE when having a fold-change (FC) > 1.2 and false discovery rate (FDR) < 0.1 (using Benjamini–Hochberg method to control the FDR). Downstream heatmaps were constructed using a regularized-log2 transformation (rlog function implemented by DESeq2). Heatmaps were generated using the pheatmap (v1.0.8) (https://CRAN.R-project.org/package=pheatmap) (88) R package. Venn diagrams were produced to display DE genes from comparisons between either of the two compound mutants versus WT in both sexes separately using the following cut-offs: FC > 1.2 and FDR < 0.1.

Statistical analyses

Data from the open field, nesting, T-maze spontaneous alternation and contextual fear-conditioning tests were analyzed with the Student’s t-test. Data from 6-day hidden platform training trails in the Morris water maze were compared by using a two-way analysis of variance with the genotype as a between-subjects factor and day as a repeated-measures factor, followed by Tukey post hoc tests. In the behavioral experiments, n indicates the number of mice. All data reported in the text and figures are expressed as the mean ± standard error of the mean (SEM).

Supplementary Material

Supplemental_for_Manuscript_by_Xing_et_al_1_ddad056

Click here for additional data file.^{(1.2MB, pdf)}

Acknowledgements

This study was supported in part by grants from the National Institutes of Health (R01HD090180, R01HD109750, R01DC019735, R03TR003344, R21GM114645 and P30CA016056) and the Children’s Guild Foundation. The authors would like to thank the anonymous reviewers for their important comments.

Contributor Information

Zhuo Xing, The Children’s Guild Foundation Down Syndrome Research Program, Department of Cancer Genetics and Genomics, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA.

Yichen Li, The Children’s Guild Foundation Down Syndrome Research Program, Department of Cancer Genetics and Genomics, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA.

Eduardo Cortes-Gomez, Department of Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA.

Xiaoling Jiang, The Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, Zhejiang, China.

Shuang Gao, Department of Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA; Bioinformatics, OmniSeq Inc., Buffalo, NY, USA.

Annie Pao, The Children’s Guild Foundation Down Syndrome Research Program, Department of Cancer Genetics and Genomics, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA.

Jidong Shan, Molecular Cytogenetics Core, Albert Einstein College of Medicine, Bronx, NY, USA.

Yinghui Song, Molecular Cytogenetics Core, Albert Einstein College of Medicine, Bronx, NY, USA.

Amanda Perez, Department of Cancer Genetics and Genomics, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA.

Tao Yu, The Children’s Guild Foundation Down Syndrome Research Program, Department of Cancer Genetics and Genomics, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA.

Max R Highsmith, Department of Electric Engineering and Computer Science, University of Missouri, Columbia, MO, USA.

Frimpong Boadu, Department of Electric Engineering and Computer Science, University of Missouri, Columbia, MO, USA.

Jeffrey M Conroy, Research and Development, OmniSeq Inc., Buffalo, NY, USA; Research Support Services, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA.

Prashant K Singh, Department of Cancer Genetics and Genomics, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA.

Andrei V Bakin, Department of Cancer Genetics and Genomics, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA.

Jianlin Cheng, Department of Electric Engineering and Computer Science, University of Missouri, Columbia, MO, USA.

Zhijun Duan, Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA; Division of Hematology, Department of Medicine, University of Washington, Seattle, WA, USA.

Jianmin Wang, Department of Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA.

Song Liu, Department of Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA.

Benjamin Tycko, Hackensack-Meridian Health Center for Discovery and Innovation, Nutley, NJ, USA; John Theurer Cancer Center, Hackensack University Medical Center, Hackensack, NJ, USA.

Y Eugene Yu, The Children’s Guild Foundation Down Syndrome Research Program, Department of Cancer Genetics and Genomics, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA; Genetics, Genomics and Bioinformatics Program, State University of New York at Buffalo, Buffalo, NY, USA.

Conflict of Interest statement

None of the authors has any conflict of interests.

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PERMALINK

Dissection of a Down syndrome-associated trisomy to separate the gene dosage-dependent and -independent effects of an extra chromosome

Zhuo Xing

Yichen Li

Eduardo Cortes-Gomez

Xiaoling Jiang

Shuang Gao

Annie Pao

Jidong Shan

Yinghui Song

Amanda Perez

Tao Yu

Max R Highsmith

Frimpong Boadu

Jeffrey M Conroy

Prashant K Singh

Andrei V Bakin

Jianlin Cheng

Zhijun Duan

Jianmin Wang

Song Liu

Benjamin Tycko

Y Eugene Yu

Abstract

Introduction

Results

Generation of Df(17)2Yey/+ and Df(16)8Yey/+ as the components of the mouse model system to separate gene dosage-dependent and -independent effects of an extra chromosome

Figure 1.

Figure 2.

Generation and analysis of the complete mouse model system to separate gene dosage-dependent and -independent effects of an extra chromosome

Figure 3.

Table 1.

Figure 4.

Figure 5.

Verification of the genotype–phenotype relationship between Ts65Dn and Ts65Dn;Df(17)2/+ mice

Figure 6.

Table 2.

Discussion

Successful separation of gene dosage-dependent and -independent effects of a trisomy modeling DS

Confirmation of the contribution by the sub-centromeric region of Mmu17 in Ts65Dn mice

Materials and Methods

Generation of mouse models

Fluorescence in situ hybridization

Animals

Comparative genomic hybridization microarrays

Open field test

Nesting test

T-maze spontaneous alternation test

Contextual fear-conditioning test

Morris water maze test

RNA sequencing

Analysis of RNA-seq data

Statistical analyses

Supplementary Material

Acknowledgements

Contributor Information

Conflict of Interest statement

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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