1. Chromosomes and Gene Expression
  2. Physiology

Gene dosage imbalance disrupts systemic metabolism in the Dp16 Down syndrome mouse model

  1. Fangluo Chen
  2. Muzna Saqib
  3. Christy M Nguyen
  4. Dylan C Sarver
  5. Y Eugene Yu
  6. Susan Aja
  7. Marcus M Seldin
  8. G William Wong  Is a corresponding author
  1. Department of Physiology, Pharmacology and Therapeutics, Johns Hopkins University, School of Medicine, United States
  2. Center for Metabolism and Obesity Research, Johns Hopkins University, School of Medicine, United States
  3. Department of Biological Chemistry, University of California, Irvine, United States
  4. Center for Epigenetics and Metabolism, University of California Irvine, United States
  5. The Children's Guild Foundation Down Syndrome Research Program, Department of Cancer Genetics and Genomics, Roswell Park Comprehensive Cancer Center, United States
  6. Genetics, Genomics and Bioinformatics Program, State University of New York at Buffalo, United States
  7. Department of Neuroscience, Johns Hopkins University School of Medicine, United States
https://doi.org/10.7554/eLife.110476.3
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Cite this article
  1. Fangluo Chen
  2. Muzna Saqib
  3. Christy M Nguyen
  4. Dylan C Sarver
  5. Y Eugene Yu
  6. Susan Aja
  7. Marcus M Seldin
  8. G William Wong
(2026)
Gene dosage imbalance disrupts systemic metabolism in the Dp16 Down syndrome mouse model
eLife 15:RP110476.
https://doi.org/10.7554/eLife.110476.3
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eLife Assessment

This article describes the comprehensive metabolic phenotype of a mouse model of Down Syndrome, together with supporting transcriptomic, metabolomic, and biochemical data. The evidence presented is compelling and highlights several core phenotypes including insulin resistance, dyslipidemia, and tissue signatures indicating inflammatory and cellular stress pathways. Similarities and differences in male and female mice are highlighted. This important study provides essential groundwork for the further genetic dissection of dosage-sensitive genes causing metabolic dysregulation in Down Syndrome.

https://doi.org/10.7554/eLife.110476.3.sa0

Abstract

Gene dosage imbalance resulting from an extra copy of human chromosome 21 (Hsa21) contributes to numerous clinical features in Down syndrome (DS). While dysregulated metabolism has long been noted in DS, the underlying cause is poorly understood and vastly understudied. To fill this critical knowledge gap, we conducted a comprehensive metabolic analysis of Dp(16)1Yey/+mice (abbreviated Dp16), a segmental duplication model carrying ~58% of the triplicated Hsa21 gene orthologs. Our multi-tissue transcriptomic analyses reveal shared and sex-specific increases in expression dosage of the triplicated genes in white and brown adipose tissues, liver, skeletal muscle, and hypothalamus. Despite sexual dimorphism in body weight, body temperature, food intake, and physical activity, Dp16 males and females share striking core phenotypes of pronounced insulin resistance, glucose intolerance, impaired lipid clearance, and dyslipidemia. Functional assessments, combined with biochemical, transcriptomic, and metabolomic analyses reveal tissue signatures of immune activation and a pro-inflammatory state, ER and oxidative stress, fibrosis, impaired glucose and fatty acid catabolism, altered lipid and bile acid profiles, and reduced mitochondrial respiratory capacity in Dp16 mice. These concerted changes disrupt homeostatic mechanisms that underpin metabolic health, contributing to systemic metabolic dysfunction. An obesogenic diet further exacerbates insulin resistance in Dp16 males and females despite divergent weight gain. The collective phenotypes broadly reflect the metabolic profile of DS. Our extensive molecular, biochemical, and physiological data provide an essential foundation for genetic dissection of dosage-sensitive genes affecting glucose and lipid metabolism, and for testing therapeutic strategies to improve metabolic outcomes in DS.

Introduction

Down syndrome (DS) is the most common aneuploidy compatible with postnatal survival and it affects ~1/700 live births (Sherman et al., 2007; Antonarakis et al., 2020). The presence of an extra human chromosome 21 (Hsa21) in DS alters the expression dosage of a large number of triplicated genes, which has major functional impacts across many organ systems (Zhu et al., 2018; Korenberg et al., 1994; Antonarakis, 2017; Duchon et al., 2021; Korbel et al., 2009). The core clinical features of DS include cognitive deficits, craniofacial dysmorphology, hypotonia, and the development of Alzheimer (AD)-like pathology in mid-life (Antonarakis et al., 2020; LaCombe and Roper, 2020). With variable degrees of penetrance and expressivity, individuals with DS also frequently exhibit congenital heart defects, hearing and vision loss, leukemia, reduced bone mass, and gastrointestinal diseases (Antonarakis et al., 2020; LaCombe and Roper, 2020).

In the last few decades, DS research has largely been centered on understanding how trisomy 21 affects brain development and its consequences on learning and cognitive function (Haydar and Reeves, 2012). However, a major clinical feature that has frequently been overlooked and neglected, although increasingly appreciated, is that adolescents and adults with DS also have a much higher incidence of obesity, insulin resistance, type 2 diabetes, and dyslipidemia (Van Goor et al., 1997; Bertapelli et al., 2016; Fonseca et al., 2005; Aslam et al., 2022; Valentini et al., 2017; Buonuomo et al., 2016; Adelekan et al., 2012; Gross et al., 2019; Garcia-de la Puente et al., 2021; de la Piedra et al., 2017). A recent large retrospective study in the UK, spanning three decades, found that the median age at diabetes diagnosis was 15 years earlier in individuals with DS and diabetes was more than four times more common in children and young adults with DS than in individuals without DS (Aslam et al., 2022). There was also an increased incidence of obesity in children and young adults with DS, with rates increasing over time (Aslam et al., 2022). Despite the fact that metabolic dysfunction in DS was first noted in the 1960s (Milunsky and Neurath, 1968) and well documented (Van Goor et al., 1997; Bertapelli et al., 2016; Fonseca et al., 2005; Milunsky and Neurath, 1968; Real de Asua et al., 2014), the underlying cause is largely unknown.

Beyond clinical and epidemiological observations (Van Goor et al., 1997; Bertapelli et al., 2016; Fonseca et al., 2005; Aslam et al., 2022), only limited studies have been conducted to determine the mechanistic underpinnings of DS-associated metabolic dysfunction. Most human studies involving adolescents or adults with DS assessed the impact of trisomy 21 on food intake, adiposity, physical activity level, and energy expenditure in adolescents or adults with DS (Hill et al., 2013; González-Agüero et al., 2011; Fernhall et al., 2005; Ptomey et al., 2020; Allison et al., 1995; Gutierrez-Hervas et al., 2020; Magenis et al., 2018; Fox et al., 2019; Luke et al., 1996), with one study documenting a deficit in mitochondrial function in the skeletal muscle (Phillips et al., 2013). However, the relative contribution of genetics versus lifestyle to altered metabolic parameters seen in DS remains challenging to untangle. At the cellular level, altered mitochondrial morphology, dynamics, and function have been well documented in vitro in cultured cells derived from DS (Helguera et al., 2013; Valenti et al., 2010; Valenti et al., 2011; Panagaki et al., 2019; Parra et al., 2018; Xu et al., 2022; Mollo et al., 2021; Anderson et al., 2021; Piccoli et al., 2013; Izzo et al., 2014; Izzo et al., 2017); it is unclear, however, whether this translates into changes in systemic metabolism in vivo.

To fill this critical knowledge gap, we have recently conducted a comprehensive and in-depth analysis of changes in systemic metabolism in two trisomic DS mouse models (Ts65Dn and TcMAC21; Sarver et al., 2023a; Sarver et al., 2023b). Ts65Dn mice were the workhorse of DS models for over two decades (Reeves et al., 1995). Due to a translocation event between mouse chromosome 16 (Mmu16) and Mmu17, Ts65Dn mice carry a freely segregating marker chromosome, Ts(1716), that contains ~59% of the gene orthologs found on Hsa21 (Reeves et al., 1995; Davisson et al., 1990; Davisson et al., 1993); they lack ~70 Hsa21 gene orthologs (Gupta et al., 2016). Under the basal state, chow-fed Ts65Dn mice of both sexes are glucose intolerant (Sarver et al., 2023b). Deterioration in metabolic homeostasis becomes much more apparent when mice are challenged with a high-fat diet (HFD). While obese Ts65Dn mice of both sexes exhibit dyslipidemia, male mice also show impaired systemic insulin sensitivity, reduced mitochondrial activity, and elevated fibrotic and inflammatory gene signatures in the liver and adipose tissue. Our systems-level analysis also reveals major changes in gene connectivity and pathways in liver and adipose tissues that contribute to dysregulated glucose and lipid metabolism seen in Ts65Dn mice (Sarver et al., 2023b). The metabolic phenotypes of Ts65Dn mice are largely consistent with the clinical and epidemiological findings of DS, namely the proclivity of individuals with DS to develop diabetes (Van Goor et al., 1997; Bertapelli et al., 2016; Fonseca et al., 2005; Aslam et al., 2022), dyslipidemia (Adelekan et al., 2012; Garcia-de la Puente et al., 2021; de la Piedra et al., 2017; Magge et al., 2019), and reduced mitochondrial function (Helguera et al., 2013). However, Ts65Dn has one major limitation. In addition to the 103 triplicated Hsa21 gene orthologs, Ts65Dn mice also carry an additional 41 triplicated protein-coding genes (from the sub-centromeric region of Mmu17) unrelated to Hsa21 (Reinholdt et al., 2011; Duchon et al., 2011). This confounds and complicates the genotype-phenotype correlations in this mouse model (Duchon et al., 2022; Guedj et al., 2023).

TcMAC21 is a recently generated transchromosomic mouse model carrying a non-mosaic Hsa21q (Kazuki et al., 2020). Due to multiple deletions, TcMAC21 mice are missing ~7% of the Hsa21 genes located on the long arm. Our systematic analysis of TcMAC21 mice has led to completely unexpected findings–the TcMAC21 mice are hypermetabolic, showing a dramatic increase in mitochondrial respiration and energy expenditure, and are markedly leaner despite consuming greater amounts of food, and are strikingly more insulin sensitive (Sarver et al., 2023a). These phenotypes are inconsistent with the clinical profile of DS. Thus, TcMAC21 mice do not model the metabolic phenotypes seen in DS. This phenomenon may be partly caused by abnormal interactions between human and mouse proteins, including the orthologous proteins of these two species, as well as the presence of over 400 non-coding human genes with uncertain effects on the mouse transcriptome (Sarver et al., 2023a).

To overcome the caveats and limitations associated with the metabolic studies of Ts65Dn and TcMAC21, and to help inform the selection of mouse model that best reflects the metabolic profile of DS, we undertook a comprehensive metabolic analysis of Dp(16)1Yey/+(abbreviated Dp16), another widely used mouse model of DS (Li et al., 2007). The Dp16 mice carry a duplicated segment of Mmu16 syntenic to Hsa21, with 115 triplicated Hsa21 gene orthologs (Li et al., 2007). Unlike the Ts65Dn mice generated from chromosomal translocation (Davisson et al., 1990; Davisson et al., 1993), the Dp16 mice were generated by chromosomal engineering based on precise Cre/LoxP-mediated recombineering (Li et al., 2007) and therefore carry no extra non-Hsa21 gene orthologs. Also, unlike the TcMAC21 mice, all the triplicated Hsa21 gene orthologs in Dp16 mice are of mouse origin. The Dp16 mouse model thus allows us to address the contribution of gene dosage imbalance to changes in systemic metabolism.

While we observed striking sex differences in various metabolic parameters, Dp16 male and female mice also shared important metabolic deficits that are hallmarks of dysregulated systemic metabolism; both sexes developed pronounced glucose intolerance, insulin resistance, impaired lipid clearance, and dyslipidemia, and these phenotypes were further exacerbated by a HFD. Using a multi-omics approach, we showed that Dp16 mice displayed transcriptomic, metabolomic, and biochemical signatures associated with tissue fibrosis, oxidative stress, a pro-inflammatory state, impaired metabolism and mitochondrial function, and dyslipidemia. Our pathway enrichment analyses revealed global changes in gene expression and biological pathways across major metabolic tissues in Dp16 mice. These collective changes underpin and contribute to the metabolic dysfunction seen in Dp16 mice. Collectively, our data suggest that dosage imbalance arising from the triplication of Hsa21 gene orthologs, along with its cascading effects across tissue transcriptomes and metabolomes, are causally linked to insulin resistance and impaired systemic glucose and lipid metabolism. Our present study lays critical and essential groundwork for genetic dissection of dosage-sensitive genes underpinning widespread metabolic deficits in DS.

Results

Increased gene expression dosage of the triplicated Hsa21 gene orthologs across major metabolic tissues

The Dp16 mice are triplicated for ~58% of Hsa21 gene orthologs (Li et al., 2007; Lana-Elola et al., 2016; Figure 1A). First, we wanted to establish which among the 115 triplicated Hsa21 gene orthologs on Mmu16 are expressed in six major metabolic tissues: gonadal white adipose tissue (gWAT; visceral fat), inguinal white adipose tissue (iWAT; subcutaneous fat), brown adipose tissue (BAT), skeletal muscle (gastrocnemius), and hypothalamus. Bulk RNA-sequencing showed that the majority of the triplicated genes are indeed expressed at the expected higher dosage (1.5-fold or higher) across tissues and their expression are regulated in a tissue- and sex-specific manner (Figure 1B and C). Interestingly, female gWAT is the only tissue with eight triplicated Hsa21 gene orthologs (Rbm11, Chodl, Cldn8, Sh3bgr, Igsf5, Itgb2l, Pcp4 and Tmprss2) whose expressions are significantly suppressed relative to WT controls (Figure 1B). Except female gWAT, we observed little evidence of dosage compensation, consistent with recent findings (Hunter et al., 2023). Of the six tissues examined, hypothalamus expresses the largest number of triplicated Hsa21 gene orthologs, as well as the largest number of shared triplicated genes between males and females (Figure 1C). Visceral fat (gWAT) and skeletal muscle (gastrocnemius) have the lowest number of shared differentially expressed triplicated genes between sexes, with females expressing a significantly higher number of triplicated genes compared to males (Figure 1C). Together, these data indicate increased expression dosage of many triplicated Hsa21 gene orthologs across major metabolic tissues in Dp16 mice, with sex differences noted.

Figure 1
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Increased gene expression dosage of the triplicated Hsa21 gene orthologs on mouse chromosome 16 (Mmu16) across tissues.

(A) Graphical representation of human chromosome 21 (Hsa21) and the syntenic Mmu16 segment that is duplicated in Dp16 mice. (B) Global view of the expression of 108 triplicated Hsa21 gene orthologs on Mmu16 in gonadal white adipose tissue (gWAT), inguinal white adipose tissue (iWAT), interscapular brown adipose tissue (BAT), skeletal muscle (gastrocnemius), and hypothalamus. Red denotes transcript that is expressed at >1.5-fold the WT level, whereas blue denotes transcript that is expressed at significantly lower level compared to WT control. The Ktrap gene cluster (23 Ktrap genes) located between Cldn8 and Tiam1 is not shown. (C) Overlap analysis showing differentially expressed Hsa21 gene orthologs that are shared between males and females across six tissues. The criteria for differentially expressed genes (DEGs) is log2(FC)>0 with padj <0.05. n=6 RNA samples per genotype per sex per tissue-type. Chow-fed WT and Dp16 mice were at 27.5 weeks of age at the time of tissue collection.

Sexual dimorphism in body weight, food intake, physical activity, and body temperature in Dp16 mice

We next determined the impact of gene dosage imbalance on systemic metabolism under the basal state when mice were fed a standard chow. The body weights of Dp16 male mice from 7 to 16 weeks old were not different from WT controls (Figure 2A). Body composition analysis by NMR revealed a modest increase in fat mass in Dp16 male mice but no difference in lean mass (Figure 2B). At 27.5 weeks of age, body weights and the absolute and relative (% of body weight) weights of visceral (gWAT) and subcutaneous (iWAT) fat, liver, and heart were not different between genotypes in male mice (Figure 2—figure supplement 1). The absolute and relative weights of the kidney, however, were significantly higher in Dp16 male mice. Female body weights were similar between genotypes from 6 to 8 weeks of age; however, Dp16 females gained markedly more weight compared to WT controls from 9 weeks of age onward (Figure 2C). Increased body weight in Dp16 females was due to increased fat and lean mass (Figure 2D). At 27.5 weeks of age, body weights and the absolute weights of gWAT and iWAT were significantly higher in Dp16 females compared to WT controls (Figure 2—figure supplement 1). The absolute and relative weights of liver, heart, and kidney were not different between genotypes in female mice.

Figure 2 with 4 supplements see all
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Sexual dimorphism in body weight, body temperature, food intake, and physical activity in chow-fed Dp16 mice.

(A) Body weight of chow-fed male Dp16 and WT mice over time. (B) Absolute and relative (% of body weight) fat and lean mass in male mice (WT = 15; Dp16 = 12). (C) Body weight of chow-fed female Dp16 and WT mice over time. (D) Absolute and relative (% of body weight) fat and lean mass in female mice (WT = 15; Dp16 = 15). (E–F) Food intake, total physical activity level, and energy expenditure of male (E) and female (F) Dp16 and WT mice across the circadian cycle (light and dark) and metabolic states (ad libitum fed, fast, refeed). Sample size for male (WT = 10–12; Dp16 = 11–12) and female (WT = 13–15; Dp16 = 5–6) mice. (G–H) Fecal frequency, average fecal weight, and fecal energy content (per gram and total) in male (G) and female (H) Dp16 and WT mice. Sample size for male (WT = 6; Dp16 = 6) and female (WT = 6; Dp16 = 6) mice. (I–J) Body temperature in the light and dark cycle of male (I) and female (J) Dp16 and WT mice. Sample size for male (WT = 10; Dp16 = 10) and female (WT = 15; Dp16 = 15) mice. All data are presented as mean ± SEM. * p<0.05; *** p<0.001; **** p<0.0001. For body weight over time, data were analyzed by 2-way ANOVA with Sidek post hoc tests.

We performed indirect calorimetry analysis to determine food intake, physical activity level, metabolic rate (VO2), and energy expenditure across the circadian cycle (light and dark phase) and metabolic states (ad libitum fed, fasted, and refed). None of the parameters measured were different between genotypes in male mice (Figure 2E). In females, however, food intake was significantly higher in Dp16 mice compared to WT controls in the ad libitum fed state and during the refeeding period following a fast (Figure 2F). Physical activity levels were also higher in Dp16 females during the refed period. Energy expenditure (normalized to lean mass), however, was not different between Dp16 females and WT controls across the circadian cycles and metabolic states (Figure 2F). Normalization of energy expenditure to lean mass can lead to an overestimation of energy expenditure (Tschöp et al., 2012). We therefore also performed ANCOVA analyses (using lean mass as a covariate of energy expenditure) (Tschöp et al., 2012). Both types of analyses indicated no differences in energy expenditure between genotypes of either sex across the circadian cycles and metabolic states (Figure 2—figure supplement 2).

To account for any potential differences in nutrient absorption in the intestine, we measured fecal output, frequency, and weight, as well as the fecal energy content by fecal bomb calorimetry. None of the fecal parameters were significantly different between genotypes of either sex (Figure 2G–H). Interestingly, Dp16 males had higher body temperature compared to WT controls in both the light and dark cycle, whereas Dp16 females had lower body temperature (Figure 2I–J). This prompted us to assess whether Dp16 mice have altered mitochondrial function in BAT, a major thermogenic tissue. Despite differences in body temperature, both Dp16 males and females had reduced maximal mitochondrial respiratory capacity in BAT, an effect more pronounced in males (Figure 2—figure supplement 3). Circulating Triiodothyronine (T3) levels, a hormone that also controls body temperature, were not different between genotypes of either sex (Figure 2—figure supplement 4). We measured serum levels of sex and stress hormones, as these could contribute to sex differences in metabolic outcomes. Corticosterone levels were not different between genotypes of either sex. Testosterone levels in males were variable and not significantly different between genotypes, whereas estradiol levels were higher in Dp16 females (Figure 2—figure supplement 4), possibly contributing to greater physical activity (Krause et al., 2021; Correa et al., 2015) and lower body temperature (Zhang et al., 2020; Krajewski-Hall et al., 2018). Our results suggest that lower body temperature, coupled with higher food intake, likely contributes to greater weight gain over time in Dp16 females. Together, these data reveal striking sex differences in body weight, food intake, physical activity, and body temperature in Dp16 mice.

Glucose intolerance, insulin resistance, impaired lipid clearance, and dyslipidemia in Dp16 mice

Type 2 diabetes and altered lipid profile are well documented in DS population (Van Goor et al., 1997; Bertapelli et al., 2016; Fonseca et al., 2005; Aslam et al., 2022; Valentini et al., 2017; Buonuomo et al., 2016; Adelekan et al., 2012; Gross et al., 2019; Garcia-de la Puente et al., 2021; de la Piedra et al., 2017). To determine baseline insulin, glucose, and lipid profiles, we measured blood glucose, serum insulin, triglyceride (TG), cholesterol, non-esterified free fatty acids (NEFA), and β-hydroxybutyrate (BHB; ketone) in overnight fasted (16 hr) mice. The Dp16 males fed a standard chow had significantly higher fasting insulin levels (Figure 3A). Fasting hyperinsulinemia likely contributed to lower fasting blood glucose seen in Dp16 male mice. Serum TG and NEFA levels were also significantly higher in Dp16 males compared to WT controls, suggesting enhanced fat mobilization in the fasted state. Serum β-hydroxybutyrate (ketone) levels were not different between genotypes whereas serum cholesterol levels were markedly lower in Dp16 male mice (Figure 3A). Like the males, Dp16 females also had significantly higher fasting serum insulin levels (Figure 3B). Unlike the males, blood glucose, TGs, cholesterol, and ketone levels were not different between genotypes in females. Also, different from the males, Dp16 females had lower NEFA levels relative to WT controls.

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Glucose intolerance, insulin resistance, and impaired lipid clearance in chow-fed Dp16 mice.

(A–B) Overnight fasting insulin, blood glucose, serum triglyceride, cholesterol, non-esterified free fatty acids (NEFA), and β-hydroxybutyrate (ketone) in male (A) and female (B) Dp16 and WT mice. Sample size for male mice (WT = 15; Dp16 = 12) and female mice (WT = 14; Dp16 = 15). (C–F) Impaired glucose tolerance as determined by the glucose tolerance test (GTT) in male (C) and female (E) Dp16 mice compared to WT controls. Impaired insulin sensitivity as determined by the insulin tolerance test (ITT) in male (D) and female (F) Dp16 compared to WT controls. Sample size for male mice (WT = 15; Dp16 = 12) and female mice (WT = 14; Dp16 = 15). (G–H) Impaired triglyceride clearance in response to lipid gavage as determined by the lipid tolerance test (LTT) in male (G) and female (H) Dp16 relative to WT controls. Sample size for male mice (WT = 10; Dp16 = 14) and female mice (WT = 15; Dp16 = 15). (I–J) Pooled mouse sera from male (I) and female (J) Dp16 and WT mice were fractionated by fast protein liquid chromatography (FPLC), and the triglyceride and cholesterol content of each fraction was quantified. Fractions corresponding to very-low density lipoprotein (VLDL), low-density lipoprotein (LDL), intermediate-density lipoprotein (IDL), and high-density lipoprotein (HDL) are indicated. All data are presented as mean ± SEM. * p<0.05; ** p<0.01; *** p<0.001; **** p<0.0001. For all tolerance tests, data were analyzed by two-way ANOVA with Sidek post hoc tests.

Higher fasting insulin levels in Dp16 males and females suggest insulin resistance. To further assess glucose metabolism in these mice, we performed glucose and insulin tolerance tests (ITTs) to determine the rate of glucose clearance in response to glucose or insulin injection. Both Dp16 males and females showed impaired glucose clearance after glucose loading (Figure 3C and E). To confirm that Dp16 mice have impaired insulin action, we directly assessed insulin sensitivity via ITT. The rate of glucose clearance in response to insulin injection was markedly impaired in both Dp16 males and females (Figure 3D and F). Impaired insulin action, glucose intolerance, and fasting hyperinsulinemia strongly indicate an insulin resistance phenotype in Dp16 mice.

We next assessed whether Dp16 mice have altered lipid handling capacity by performing a lipid tolerance test. The rate of TG clearance in response to an acute lipid load was significantly impaired in Dp16 mice of either sex, with Dp16 females showing a much more striking deficit in lipid clearance (Figure 3G and H). To determine whether Dp16 mice have altered lipoprotein profiles, we subjected pooled sera to FPLC fractionation followed by the quantification of TG and cholesterol levels in each fraction. Dp16 males had lower TG and cholesterol levels in the LDL/IDL and HDL fractions (Figure 3I). In contrast, Dp16 females had higher TG and cholesterol levels in the LDL/IDL fractions, as well as higher TG levels in the HDL fractions. Since liver is a key organ in lipid and lipoprotein synthesis and export, we measured hepatic TG, diacylglycerol (DAG), and cholesterol contents. No genotypic differences in either sex was observed (Figure 3—figure supplement 1). Dp16 males had lower maximal liver mitochondrial respiratory capacity, though not significant; and this was not different between genotypes in females (Figure 3—figure supplement 2). Altogether, our data indicate that Dp16 mice of either sex developed pronounced insulin resistance, glucose intolerance, dyslipidemia, and impaired lipid clearance.

Altered hepatic and serum metabolome in Dp16 mice

Since Dp16 mice showed profound disturbances in systemic energy metabolism, we performed untargeted metabolomic analyses to assess possible changes in liver and serum metabolome. A total of 4182 metabolites were identified from the 48 serum and liver samples. Partial Least Square Discriminant Analysis (PLS-DA) indicated that liver and serum metabolome of Dp16 males and females are clearly distinguishable from that of their corresponding WT controls (Figure 4A and B). In male and female liver, we observed 319 and 337 differential metabolites, respectively, in Dp16 mice vs WT controls (Figure 4—figure supplement 1; Figure 4—source data 1–4). In Dp16 male and female serum samples, we observed 422 and 835 differential metabolites, respectively (Figure 4—figure supplement 1). When comparing the differential metabolites found in liver and serum, there appeared to be limited overlap between the two compartments (Figure 4C). Major sex differences were seen in the differential metabolites found in liver and serum. There were 32 differential metabolites shared between Dp16 male and female liver, and 155 differential serum metabolites shared between the sexes (Figure 4D). The majority of differential metabolites found in liver and serum, however, were not shared between the sexes (Figure 4—source data 5).

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Altered liver and serum metabolome in Dp16 male and female mice.

(A–B) Partial least squares discrimination analysis (PLS-DA) of liver and serum metabolites of Dp16 and WT males and females. N=6 samples per genotype per sex. (C) Venn diagram of differential metabolites shared between liver and serum in Dp16 male or female mice. (D) Venn diagram of differential liver or serum metabolites shared between Dp16 males and females. (E) KEGG enrichment showing altered metabolic processes in Dp16 female serum. ES, enrichment score; NES, normalized enrichment score.

Figure 4—source data 1

Differential metabolites in Dp16 male mouse liver vs WT controls.

Differential metabolites criteria: VIP >1.0, fold change (FC)>1.2 or FC <0.833 and p-value <0.05. Sample name notation: male WT liver (M_WT_L), male WT serum (M_WT_S), male Dp16 liver (M_16_L), male Dp16 serum (M_16_S), female WT liver (F_WT_L), female WT serum (F_WT_L), female Dp16 liver (F_16_L), female Dp16 serum (F_16_S).

https://cdn.elifesciences.org/articles/110476/elife-110476-fig4-data1-v1.xlsx
Figure 4—source data 2

Differential metabolites in Dp16 female mouse liver vs WT controls.

Differential metabolites criteria: VIP >1.0, fold change (FC)>1.2 or FC <0.833 and p-value <0.05. Sample name notation: male WT liver (M_WT_L), male WT serum (M_WT_S), male Dp16 liver (M_16_L), male Dp16 serum (M_16_S), female WT liver (F_WT_L), female WT serum (F_WT_L), female Dp16 liver (F_16_L), female Dp16 serum (F_16_S).

https://cdn.elifesciences.org/articles/110476/elife-110476-fig4-data2-v1.xlsx
Figure 4—source data 3

Differential metabolites in Dp16 male mouse serum vs WT controls.

Differential metabolites criteria: VIP >1.0, fold change (FC)>1.2 or FC <0.833 and <i>P-value <0.05. Sample name notation: male WT liver (M_WT_L), male WT serum (M_WT_S), male Dp16 liver (M_16_L), male Dp16 serum (M_16_S), female WT liver (F_WT_L), female WT serum (F_WT_L), female Dp16 liver (F_16_L), female Dp16 serum (F_16_S).

https://cdn.elifesciences.org/articles/110476/elife-110476-fig4-data3-v1.xlsx
Figure 4—source data 4

Differentially expressed metabolites in Dp16 female mouse serum vs WT controls.

Differential metabolites criteria: VIP >1.0, fold change (FC)>1.2 or FC <0.833 and <i>P-value <0.05. Sample name notation: male WT liver (M_WT_L), male WT serum (M_WT_S), male Dp16 liver (M_16_L), male Dp16 serum (M_16_S), female WT liver (F_WT_L), female WT serum (F_WT_L), female Dp16 liver (F_16_L), female Dp16 serum (F_16_S).

https://cdn.elifesciences.org/articles/110476/elife-110476-fig4-data4-v1.xlsx
Figure 4—source data 5

Shared and distinct differential metabolites in Dp16 male and female mouse liver and serum vs WT controls.

Sample name notation: male WT liver (M_WT_L), male WT serum (M_WT_S), male Dp16 liver (M_16_L), male Dp16 serum (M_16_S), female WT liver (F_WT_L), female WT serum (F_WT_L), female Dp16 liver (F_16_L), female Dp16 serum (F_16_S).

https://cdn.elifesciences.org/articles/110476/elife-110476-fig4-data5-v1.xlsx

As shown by Kyoto Encyclopedia of Genes and Genomes (KEGG) classification, differential metabolites related to global, as well as lipid and amino acid, metabolism accounted for the major differences seen in liver and serum of Dp16 males and females (Figure 4—figure supplements 2 and 3). In Dp16 female serum, KEGG enrichment analysis indicated altered metabolic pathways related to bile secretion, steroid hormone biosynthesis, nucleotide metabolism, and ABC transporters (Figure 4E). In female and male liver, as well as male serum, KEGG enrichment analyses did not yield any pathways with a false discovery rate less than 0.05.

To provide greater detail, we highlighted some of the differential metabolites found in Dp16 male and female mice (Tables 1 and 2). Consistent with recent findings (Dunn et al., 2026), we also observed changes in hepatic bile acids content in both sexes, with most of the bile acids showing a reduced level in Dp16 mice. In contrast to the liver, we observed a more extensive changes in circulating bile acids, all of them except two were elevated in Dp16 mice of both sexes. Many bile acids serve as ligands for nuclear hormone receptors (e.g. FXR and TGR5) that control various aspects of glucose and lipid metabolism (Fuchs et al., 2025; Perino et al., 2021), and extensive changes in circulating bile acids may potentially contribute, at least in part, to the systemic metabolic phenotypes in Dp16 mice. In addition to bile acids, circulating levels of many phospholipids (e.g. LysoPC, LysoPA, LysoPE), some with signaling roles (O’Donnell et al., 2018), were also altered. In both liver and serum, multiple acyl-carnitines (intermediates in fat oxidation) were elevated in liver and serum, suggesting impaired fatty acid catabolism in Dp16 mice; this phenotype is further supported by our transcriptomic data indicating reduced expression of fat oxidation genes in liver and BAT (data are discussed further below).

Table 1
Selective differential metabolites in the liver and serum of Dp16 male mice.

Metabolites are considered significantly different if fold change (FC)>1.2 or<0.833, p-value <0.05, and the variable importance in projection (VIP) score is >1. Sample size: WT (n=6) and Dp16 (n=6).

NameClasslog2FCp-valueVIPUp.Down
Liver
ChenodeoxycholylmethionineBile acids0.9060.00031.23Up
23-Nordeoxycholic acidBile acids–0.9580.00661.08Down
Taurolithocholate sulfateBile acids–0.7960.04902.11Down
7 a,12a-Dihydroxy-cholestene-3-one (DHCHO)Cholestane steroids3.7710.01232.17Up
UndecanedioylcarnitineAcylcarnitine1.0160.02171.68Up
(6E)-Tridec-6-enedioylcarnitineAcylcarnitine0.8030.03031.88Up
(2E,5Z,7E)-DecatrienoylcarnitineAcylcarnitine–2.3150.02121.05Down
(9Z,11E,13Z)-Octadeca-9,11,13-trienoylcarnitineAcylcarnitine–1.2770.03491.44Down
Icosadienoic acidFatty acids and conjugates2.0300.00422.80Up
12-HHTrEFatty acids and conjugates–0.6790.04021.56Down
LysoPC(18:4(6Z,9Z,12Z,15Z)/0:0)Glycerophosphocholines0.9940.02141.48Up
LipoyllysineLipoamides–1.2260.04401.54Down
all-trans-4-Oxoretinoic acidRetinoids–1.0650.02541.88Down
Sphingosine (d17:1)Amines2.6430.01481.70Up
Serum
hyocholic acidBile acids2.4930.00303.06Up
Taurolithocholate sulfateBile acids1.5160.01093.29Up
Apocholic acidBile acids1.8370.01733.29Up
Methyl cholateBile acids1.4170.01823.29Up
Taurochenodeoxycholic acid (TCDCA)Bile acids2.0490.01952.55Up
Tauro-omega-muricholic acidBile acids1.5970.02012.91Up
(3b,5b,7a,12a)–3,7,12-trihydroxy-Cholan-24-oic acidBile acids2.3150.02092.50Up
Glycohyocholic acid (GHCA)Bile acids1.7090.02103.26Up
3beta-Glycocholic acidBile acids1.7640.02563.01Up
7-Ketodeoxycholic acid (7-keto DCA)Bile acids1.6350.03402.51Up
6,7-Diketolithocholic acid (6,7-diketo LCA)Bile acids2.1710.04002.18Up
7,12-diketolithocholic acid (7,12-diketo LCA)Bile acids2.0680.04141.75Up
Glycoursodeoxycholic acid (GUDCA)Bile acids–1.3080.04031.60Down
23-Nordeoxycholic acid (23-nor- DCA)Bile acids–1.1900.00321.18Down
6,15-diketo-13,14-dihydro Prostaglandin F1alphaEicosanoids1.1730.00082.20Up
Prostaglandin B1Eicosanoids2.0940.00201.89Up
Prostaglandin D2Eicosanoids1.1570.00411.28Up
11-Dehydro-thromboxane B2Eicosanoids1.1810.01161.08Up
8-Isoprostaglandin F2aEicosanoids0.9160.02371.10Up
Non-7-enoylcarnitineAcyl-carnitine–1.5630.00071.79Down
3,6-DihydroxydecanoylcarnitineAcyl-carnitine–2.0950.00081.83Down
(6E)-Tridec-6-enedioylcarnitineAcyl-carnitine0.5530.02211.16Up
trans-2-DodecenoylcarnitineAcyl-carnitine0.5010.02761.24Up
O-dodecanedioylcarnitineAcyl-carnitine0.6630.02631.46Up
7-Keto-dehydroepiandrosteroneAndrostane steroids1.1220.00051.70Up
TestosteroneAndrostane steroids1.9840.00812.99Up
Dehydroepiandrosterone (DHEA)Androstane steroids2.4700.02563.12Up
5Alpha-Androstan-17-Beta-Ol-3-One (DHT)Androstane steroids1.5440.03683.48Up
19-Hydroxyandrost-4-ene-3,17-dioneAndrostane steroids1.7810.04042.10Up
AndrostenedioneAndrostane steroids0.4410.04861.49Up
FAHFA(22:6(4Z,7Z,10Z,13Z,16Z,19Z)/14-O-22:6(4Z,7Z,10Z,13Z,16Z,19Z))Fatty acids and conjugates–1.1230.03051.99Down
PC(20:4(6Z,8E,10E,14Z)–2OH(5 S,12R)/2:0)Phospholipid2.4980.00051.75Up
LysoPC(22:4(7Z,10Z,13Z,16Z)/0:0)Phospholipid0.7660.00081.89Up
LysoPC(18:4(6Z,9Z,12Z,15Z)/0:0)Phospholipid1.5900.01383.04Up
PC(MonoMe (11,3)/MonoMe (1,3))Phospholipid2.0720.02283.07Up
1,2-Dilauroyl-sn-glycero-3-phosphocholinePhospholipid–1.0420.00551.31Down
LysoPE(0:0/15:0)Phospholipid–1.3530.00871.72Down
1-Heptadecanoyl-glycero-3-phosphoethanolaminePhospholipid–0.9500.01161.64Down
LysoPE(20:5(5Z,8Z,11Z,14Z,17Z)/0:0)Phospholipid–1.0870.04781.89Down
19-NordeoxycorticosteroneHydroxysteroids1.4470.00431.01Up
LipoamideLipoamides–1.7070.01621.31Down
Table 2
Selective differential metabolites in the liver and serum of Dp16 female mice.

Metabolites are considered significantly different if fold change (FC)>1.2 or<0.833, p-value <0.05, and the variable importance in projection (VIP) score is >1. Sample size: WT (n=6) and Dp16 (n=6).

NameClasslog2FCp-ValueVIPUp.Down
Liver
23-Norcholic acid (23-NCA)Bile acids–4.5570.0000084.86Down
6,7-Diketolithocholic acidBile acids–2.0490.00861.56Down
3-Oxo-7-hydroxychol-4-enoic acidBile acids–1.8170.01591.21Down
Apocholic acidBile acids–2.5770.01861.31Down
23-Nordeoxycholic acid (23-NDCA)Bile acids–2.5630.03502.72Down
(3b,5b,7a,12a)–3,7,12-trihydroxy-Cholan-24-oic acidBile acids–3.3750.04252.05Down
20-Hydroxy-leukotriene E4Eicosanoids1.2140.02612.66Up
6-Keto-prostaglandin E1Eicosanoids0.8860.02821Up
Prostaglandin E1Eicosanoids0.8220.03561.56Up
Prostaglandin A1Eicosanoids0.7210.03821.16Up
4-HydroxydecanedioylcarnitineAcyl-carnitine1.6900.00031.41Up
3-OxobutanoylcarnitineAcyl-carnitine0.7880.01001.1Up
4-HydroxyhexanoycarnitineAcyl-carnitine0.7010.01822.29Up
(3E)-GlutaconylcarnitinAcyl-carnitine1.6540.02041.27Up
O-(17-Carboxyheptadecanoyl)carnitineAcyl-carnitine1.7220.02621.55Up
(6E)-Tridec-6-enedioylcarnitineAcyl-carnitine0.6630.02781.61Up
LysoPE(22:5(7Z,10Z,13Z,16Z,19Z)/0:0)Phospholipid1.7920.01512.61Up
PC(MonoMe (11,3)
/MonoMe (11,3))		Phospholipid–5.9800.00542.17Down
TG(20:3n6/O-18:0/18:3(9Z,12Z,15Z))Triacylglycerols1.2930.00081.68Up
Serum
3-Oxo-7-hydroxychol-4-enoic acid (7-HOCA)Bile acids5.6451.7E-072.84Up
3beta-Glycocholic acidBile acids3.7271.8E-062.46Up
Taurochenodeoxycholic acid (TCDCA)Bile acids5.0694.0E-062.75Up
Tauro-omega-muricholic acidBile acids5.6826.9E-062.72Up
Glycohyocholic acid (GHCA)Bile acids3.5251.2E-052.46Up
Taurolithocholic acid (TLCA)Bile acids6.7592.6E-053.26Up
lithocholic acid (LCA)Bile acids3.4362.8E-042.49Up
Cholan-24-oic acid, 12-hydroxy-3-(sulfooxy)-, disodium salt, (3alpha,5beta,12alpha)- (9 CI)Bile acids1.9489.3E-041.98Up
3beta-Hydroxy-5-cholestenoic acidBile acids2.0449.0E-031.54Up
3alpha,7alpha-Dihydroxy-12-oxo-5beta-cholanateBile acids1.7251.1E-021.46Up
Glycochenodeoxycholate-3-sulfate (GCDCA-S)Bile acids1.9891.2E-022.19Up
(3b,5b,7a,12a)–3,7,12-trihydroxy-Cholan-24-oic acidBile acids3.2451.7E-021.47Up
Beta-Hyodeoxycholic acid (β-HDCA)Bile acids3.3112.9E-021.86Up
hyocholic acid (HCA)Bile acids1.8003.8E-021.66Up
23-Norcholic acid (23-NCA)Bile acids–2.3063.5E-031.82Down
23-Nordeoxycholic acid (23-NDCA)Bile acids–1.9234.0E-021.58Down
BiliverdinBilirubins0.8851.9E-022.64Up
5,6-Dihydroxyprostaglandin F1aEicosanoids0.9242.5E-031.06Up
2-glyceryl-11,12-EETEicosanoids1.2951.1E-021.39Up
11-Dehydro-thromboxane B2Eicosanoids1.6543.9E-021.38Up
13,14-dihydro-15-keto-PGA2Eicosanoids–1.3041.4E-042.18Down
15(S)-HETrEEicosanoids–1.5175.0E-042.63Down
THROMBOXANE B2Eicosanoids–1.0403.0E-031.03Down
FAHFA(22:6(4Z,7Z,10Z,13Z,16Z,19Z)/14-O-22:6(4Z,7Z,10Z,13Z,16Z,19Z))Fatty acids and conjugates–2.1482.7E-052.92Down
FAHFA 38:5Fatty acids and conjugates–2.4153.1E-051.62Down
Arachidonic acidFatty acids and conjugates–0.9282.8E-042Down
N-Palmitoyl GlutamineFatty acids and conjugates–1.3045.2E-031.33Down
Resolvin D1Fatty acids and conjugates–0.8769.8E-031.44Down
LysoPC(22:4(7Z,10Z,13Z,16Z)/0:0)Phospholipid0.9511.9E-041.96Up
LysoPC(22:5(7Z,10Z,13Z,16Z,19Z)/0:0)Phospholipid0.7621.0E-031.56Up
LysoPC(22:5(4Z,7Z,10Z,13Z,16Z)/0:0)Phospholipid0.6542.9E-031.28Up
PC(MonoMe (11,3)/MonoMe (11,3))Phospholipid6.4528.6E-031.9Up
1-O-Palmitoyl-2-O-acetyl-sn-glycero-3-phosphorylcholinePhospholipid0.8163.2E-021.2Up
LysoPC(18:4(6Z,9Z,12Z,15Z)/0:0)Phospholipid1.1483.7E-021.47Up
LysoPA(20:3(8Z,11Z,14Z)/0:0)Phospholipid–0.9344.3E-032.15Down
LysoPC(0:0/18:1(9Z))Phospholipid–1.4941.5E-031.14Down
PC(20:3(8Z,11Z,14Z)/18:3(9Z,12Z,15Z))Phospholipid–0.8687.3E-031.54Down
LysoPE(P-18:1(9Z)/0:0)Phospholipid–1.3059.8E-041.18Down
Glycerophospho-N-Oleoyl EthanolaminePhospholipid–1.1891.7E-031.32Down
1-heptadecanoyl-glycero-3-phosphoethanolaminePhospholipid–1.0712.4E-031.57Down
LPE(14:0)Phospholipid–1.0017.4E-031.01Down
LysoPE(20:3(8Z,11Z,14Z)/0:0)Phospholipid–0.9879.4E-031.07Down
LysoPE(20:5(5Z,8Z,11Z,14Z,17Z)/0:0)Phospholipid–1.0679.9E-031.55Down
PregnenolonePregnane steroids–1.0315.7E-041.18Down
17alpha-HydroxyprogesteronePregnane steroids–0.8073.9E-032Down
Dehydroepiandrosterone sulfate (DHEAS)Sulfated steroids–1.3344.6E-041.31Down

The levels of multiple eicosanoids, a class of lipids with pro- and anti-inflammatory roles (Dennis and Norris, 2015), were also changed in Dp16 mice. For example, several pro-inflammatory eicosanoids (e.g., 11-dehydro-thromboxane B2, prostaglandin B1, prostaglandin D2, 20-hydroxy-leukotriene E4) were elevated while some anti-inflammatory eicosanoids (e.g., 13,14-dihydro-15-keto-PGA2, 15(S)-HETrE) were reduced. In addition to eicosanoids, we observed lower levels of several fatty acids with anti-inflammatory and/or anti-diabetic roles (e.g., FAHFA, resolvin D1, all-trans-4-oxoretinoic acid) (Yore et al., 2014; Kuda et al., 2016; Serhan and Levy, 2018) and a concomitant increase in fatty acids with pro-inflammatory roles (e.g., icosadienoic acid, sphingosine) (Huang et al., 2011; Gomez-Larrauri et al., 2025). The general pro-inflammatory state in Dp16 mice, reflected by the metabolite data, parallel our transcriptomic data in BAT, liver, muscle, and hypothalamus showing a pro-inflammatory and heightened immune activation state (data are discussed further below).

We also would like to highlight that a crucial biomarker for oxidative stress, 8-Isoprostaglandin F2α (Roberts and Morrow, 2000), was significantly elevated in Dp16 male serum, whereas metabolites (e.g., lipoyllysine and lipoamide) that act as indirect antioxidants (Li et al., 2008) to maintain mitochondrial health were reduced. Further, N-palmitoyl glutamine, a recently discovered acylated amino acid that stimulates mitochondrial biogenesis and efficiency (Robbins et al., 2026), was also reduced in Dp16 female serum. Interestingly, 19-Nor-deoxycorticosterone (19-nor-DOC), a powerful mineralocorticoid hormone that increases blood pressure (Gomez-Sanchez et al., 1979; Gorsline and Morris, 1985), was elevated in Dp16 male serum, raising the possibility of altered blood pressure in these animals. In Dp16 female serum, we also observed elevated biliverdin (precursor of bilirubin), suggesting potential liver injury, which was confirmed by an increase in serum alanine transaminase (ALT) levels (Figure 4—figure supplement 4).

We showed using an ELISA method that estradiol levels are significantly higher in females and testosterone levels in males exhibited high variations (Figure 2—figure supplement 4). Our metabolite data, however, indicated that six androstane steroids, including testosterone, were significantly elevated in Dp16 male serum, whereas the precursors of female sex hormones (e.g., pregnenolone and 17α-hydroxyprogesterone) were reduced in female serum, presumably due to its greater conversion to estradiol. Given the pleiotropic systemic metabolic effects of testosterone and estradiol (Kelly and Jones, 2013; Hevener and Correa, 2025), changes in circulating sex hormones in Dp16 mice are likely contributing, at least in part, to the sex differences in metabolic phenotypes. Taken together, our metabolomic analyses reveal major sex-dependent and independent changes in liver and serum metabolites likely contributing to the systemic metabolic deficits in Dp16 mice.

Transcriptomic signatures of immune activation, fibrosis, ER stress, and impaired metabolic processes in Dp16 mice

To address the molecular underpinnings of the metabolic phenotypes seen in chow-fed Dp16 mice, we performed bulk RNA sequencing to assess global changes in the transcriptome and biological pathways across six metabolic tissues (Figure 5—source data 1–24). Except the liver, females have significantly more differentially expressed genes (DEGs) across tissues compared to males (Figure 5A). In Dp16 females, gWAT, iWAT, and BAT together accounted for the majority of DEGs, with the least number of DEGs seen in skeletal muscle (Figure 5A). In Dp16 males, BAT and liver have the highest number of DEGs, with skeletal muscle having the least DEGs. All tissues except gWAT have more upregulated than downregulated DEGs. Overlap analysis in males and females indicate shared DEGs across tissues, as well as DEGs that are seen only in males or females (Figure 5B). BAT and liver have the highest number of shared DEGs across sex, with skeletal muscle having the least shared DEGs. iWAT, gWAT and BAT have the highest number of DEGs that are female-specific, whereas liver and BAT having the highest number of DEGs that are male-specific (Figure 5B).

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Transcriptomic changes and altered biological pathways across tissues in chow-fed Dp16 male and female mice.

(A) Number of differentially expressed genes (DEGs) that up or down regulated across six tissues in male and female Dp16 mice and their WT littermate controls. DEG is defined as any gene with log2(FC)>0.5 and padj <0.05. N=6 per genotype per tissue. gWAT, gonadal white adipose tissue; iWAT, inguinal white adipose tissue; BAT, brown adipose tissue. (B) Overlap analysis showing DEGs that are shared between males and females, as well as those DEGs found in males or females only, across six tissues. (C) Gene ontology highlighting some of the top biological pathways altered across six tissues in male and female Dp16 mice.

Figure 5—source data 1

Differentially expressed genes (DEGs) upregulated in the gonadal white adipose tissue (gWAT) of chow-fed Dp16 male mice relative to WT controls.

https://cdn.elifesciences.org/articles/110476/elife-110476-fig5-data1-v1.xls
Figure 5—source data 2

Differentially expressed genes (DEGs) down-regulated in the gonadal white adipose tissue (gWAT) of chow-fed Dp16 male mice relative to WT controls.

https://cdn.elifesciences.org/articles/110476/elife-110476-fig5-data2-v1.xls
Figure 5—source data 3

Differentially expressed genes (DEGs) upregulated in the inguinal white adipose tissue (iWAT) of chow-fed Dp16 male mice relative to WT controls.

https://cdn.elifesciences.org/articles/110476/elife-110476-fig5-data3-v1.xls
Figure 5—source data 4

Differentially expressed genes (DEGs) down-regulated in the inguinal white adipose tissue (iWAT) of chow-fed Dp16 male mice relative to WT controls.

https://cdn.elifesciences.org/articles/110476/elife-110476-fig5-data4-v1.xls
Figure 5—source data 5

Differentially expressed genes (DEGs) upregulated in the brown adipose tissue (BAT) of chow-fed Dp16 male mice relative to WT controls.

https://cdn.elifesciences.org/articles/110476/elife-110476-fig5-data5-v1.xls
Figure 5—source data 6

Differentially expressed genes (DEGs) down-regulated in the brown adipose tissue (BAT) of chow-fed Dp16 male mice relative to WT controls.

https://cdn.elifesciences.org/articles/110476/elife-110476-fig5-data6-v1.xls
Figure 5—source data 7

Differentially expressed genes (DEGs) upregulated in the liver of chow-fed Dp16 male mice relative to WT controls.

https://cdn.elifesciences.org/articles/110476/elife-110476-fig5-data7-v1.xls
Figure 5—source data 8

Differentially expressed genes (DEGs) down-regulated in the liver of chow-fed Dp16 male mice relative to WT controls.

https://cdn.elifesciences.org/articles/110476/elife-110476-fig5-data8-v1.xls
Figure 5—source data 9

Differentially expressed genes (DEGs) upregulated in the skeletal muscle (gastrocnemius) of chow-fed Dp16 male mice relative to WT controls.

https://cdn.elifesciences.org/articles/110476/elife-110476-fig5-data9-v1.xls
Figure 5—source data 10

Differentially expressed genes (DEGs) down-regulated in the skeletal muscle (gastrocnemius) of chow-fed Dp16 male mice relative to WT controls.

https://cdn.elifesciences.org/articles/110476/elife-110476-fig5-data10-v1.xls
Figure 5—source data 11

Differentially expressed genes (DEGs) upregulated in the hypothalamus of chow-fed Dp16 male mice relative to WT controls.

https://cdn.elifesciences.org/articles/110476/elife-110476-fig5-data11-v1.xls
Figure 5—source data 12

Differentially expressed genes (DEGs) down-regulated in the hypothalamus of chow-fed Dp16 male mice relative to WT controls.

https://cdn.elifesciences.org/articles/110476/elife-110476-fig5-data12-v1.xls
Figure 5—source data 13

Differentially expressed genes (DEGs) upregulated in the gonadal white adipose tissue (gWAT) of chow-fed Dp16 female mice relative to WT controls.

https://cdn.elifesciences.org/articles/110476/elife-110476-fig5-data13-v1.xls
Figure 5—source data 14

Differentially expressed genes (DEGs) down-regulated in the gonadal white adipose tissue (gWAT) of chow-fed Dp16 female mice relative to WT controls.

https://cdn.elifesciences.org/articles/110476/elife-110476-fig5-data14-v1.xls
Figure 5—source data 15

Differentially expressed genes (DEGs) upregulated in the inguinal white adipose tissue (iWAT) of chow-fed Dp16 female mice relative to WT controls.

https://cdn.elifesciences.org/articles/110476/elife-110476-fig5-data15-v1.xls
Figure 5—source data 16

Differentially expressed genes (DEGs) down-regulated in the inguinal white adipose tissue (iWAT) of chow-fed Dp16 female mice relative to WT controls.

https://cdn.elifesciences.org/articles/110476/elife-110476-fig5-data16-v1.xls
Figure 5—source data 17

Differentially expressed genes (DEGs) upregulated in the brown adipose tissue (BAT) of chow-fed Dp16 female mice relative to WT controls.

https://cdn.elifesciences.org/articles/110476/elife-110476-fig5-data17-v1.xls
Figure 5—source data 18

Differentially expressed genes (DEGs) down-regulated in the brown adipose tissue (BAT) of chow-fed Dp16 female mice relative to WT controls.

https://cdn.elifesciences.org/articles/110476/elife-110476-fig5-data18-v1.xls
Figure 5—source data 19

Differentially expressed genes (DEGs) upregulated in the liver of chow-fed Dp16 female mice relative to WT controls.

https://cdn.elifesciences.org/articles/110476/elife-110476-fig5-data19-v1.xls
Figure 5—source data 20

Differentially expressed genes (DEGs) down-regulated in the liver of chow-fed Dp16 female mice relative to WT controls.

https://cdn.elifesciences.org/articles/110476/elife-110476-fig5-data20-v1.xls
Figure 5—source data 21

Differentially expressed genes (DEGs) upregulated in the skeletal muscle (gastrocnemius) of chow-fed Dp16 female mice relative to WT controls.

https://cdn.elifesciences.org/articles/110476/elife-110476-fig5-data21-v1.xls
Figure 5—source data 22

Differentially expressed genes (DEGs) down-regulated in the skeletal muscle (gastrocnemius) of chow-fed Dp16 female mice relative to WT controls.

https://cdn.elifesciences.org/articles/110476/elife-110476-fig5-data22-v1.xls
Figure 5—source data 23

Differentially expressed genes (DEGs) upregulated in the hypothalamus of chow-fed Dp16 female mice relative to WT controls.

https://cdn.elifesciences.org/articles/110476/elife-110476-fig5-data23-v1.xls
Figure 5—source data 24

Differentially expressed genes (DEGs) down-regulated in the hypothalamus of chow-fed Dp16 female mice relative to WT controls.

https://cdn.elifesciences.org/articles/110476/elife-110476-fig5-data24-v1.xls

We performed gene ontology (GO) analysis to reveal which major biological pathways are altered in Dp16 mice that could contribute to their metabolic phenotypes. Among the top biological pathways upregulated in female mice are ER stress (gWAT, iWAT), immune activation (iWAT, BAT, liver, and muscle), fibrosis (iWAT and hypothalamus), and the top downregulated pathways are ATP synthesis and cellular respiration (BAT and skeletal muscle) and lipid metabolism (liver) (Figure 5C). In male mice, some of the top biological pathways upregulated include fibrosis (gWAT and iWAT), immune activation (BAT, liver, and hypothalamus), lipid metabolism (BAT), and receptor signaling (skeletal muscle), and the down-regulated pathways include glucose and lipid metabolism (iWAT, BAT, and liver) and cellular respiration (BAT and liver) (Figure 5C). We highlighted some of the DEGs involved in immune activation, ER stress, fibrosis, glucose and lipid metabolism, fat oxidation, mitochondrial respiration, and signaling in iWAT, BAT, liver, skeletal muscle, and hypothalamus (Figure 5—figure supplements 1–5). These transcriptomic changes parallel our metabolite data (Tables 1 and 2); both sets of data highlighted impaired lipid metabolism, a pro-inflammatory state, reduced mitochondrial health, and oxidative stress. Altogether, these data underscore major sex differences in tissue transcriptomes, but also revealed common and shared biological pathways affected in Dp16 males and females that underpin their shared metabolic phenotypes.

To confirm the biochemical correlates of our RNA-seq data, we examined markers of fibrosis (hydroxyproline) and oxidative stress (malondialdehyde) in liver, gWAT, and iWAT. Collagen content was significantly higher in Dp16 female liver and lower in male gWAT (Figure 5—figure supplement 6). Oxidative stress was significantly higher in Dp16 male liver and lower in gWAT; in females it was lower in gWAT (Figure 5—figure supplement 6). These results partly corroborate our transcriptomic and metabolomic data, and further indicate that fibrosis and oxidative stress occur in Dp16 mice in a sex- and tissue-dependent manner.

Sexually dimorphic response of Dp16 mice to an obesogenic diet

Since the DS population is prone to developing obesity (Bertapelli et al., 2016; Aslam et al., 2022), we challenged the Dp16 mice with an obesogenic HFD and assessed how they handle metabolic stress associated with chronic high-fat feeding. There were striking sex differences in the response of Dp16 mice to an HFD. For the first five weeks on HFD, Dp16 males gained a similar amount of weight as the WT controls. From six weeks onward, their body weight diverged, with the WT males gaining significantly more weight compared to Dp16 males (Figure 6A), and this was reflected in much higher adiposity seen in the WT males (Figure 6B). Absolute lean mass was not different between genotypes in males, but % lean mass (when normalized to body weight) was higher in Dp16 males. In contrast to males, Dp16 females gained weight rapidly in the first 6 weeks on HFD, but by 7 weeks onward, the body weights of Dp16 females were no longer significantly different from WT controls (Figure 6C). Body composition analysis revealed no difference in fat mass, but modestly higher lean mass, in Dp16 females after 16 weeks on HFD (Figure 6D).

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Sexually dimorphism in body weight, body temperature, food intake, and physical activity in Dp16 mice in response to a high-fat diet (HFD).

(A) Body weight of HFD-fed male Dp16 and WT mice over time. (B) Absolute and relative (% of body weight) fat and lean mass in male mice (WT = 15; Dp16 = 12). (C) Body weight of HFD-fed female Dp16 and WT mice over time. (D) Absolute and relative (% of body weight) fat and lean mass in female mice (WT = 14; Dp16 = 14). (E–F) Food intake, total physical activity level, and energy expenditure of male (E) and female (F) Dp16 and WT mice across the circadian cycle (light and dark) and metabolic states (ad libitum fed, fast, refeed). Sample size for male (WT = 8; Dp16 = 11) and female (WT = 12; Dp16 = 12) mice. (G–H) Fecal frequency, average fecal weight, and fecal energy content (per gram and total) in male (G) and female (H) Dp16 and WT mice on HFD. Sample size for male (WT = 6; Dp16 = 7) and female (WT = 6; Dp16 = 6) mice. (I–J) Body temperature in the light and dark cycle of male (I) and female (J) Dp16 and WT mice on HFD. Sample size for male (WT = 15; Dp16 = 12) and female (WT = 14; Dp16 = 15) mice. All data are presented as mean ± SEM. * P<0.05; *** P<0.001; **** P<0.0001. For body weight over time, data were analyzed by 2-way ANOVA with Sidek post hoc tests.

We performed indirect calorimetry analysis after the mice were on HFD for 12 weeks. We observed lower ad libitum food intake in Dp16 males in the dark/active cycle (Figure 6E), and this could contribute to lower weight gain over time. Dp16 males had modestly higher physical activity levels in the light cycle during fasting, but energy expenditure was not different from WT controls across the circadian cycles and metabolic states (fed, fasted, and refed) (Figure 6E). In contrast to the males, food intake was not different between genotypes in females (Figure 6F). However, Dp16 females had consistently higher physical activity levels in the dark cycle across different metabolic states (fed, fasted, refed). Although energy expenditure (normalized to lean mass) appeared to be slightly higher in Dp16 females, it was only significantly different in the light cycle during the refed period (Figure 6F). To rule out potential overestimation of energy expenditure, we performed ANCOVA analyses (using lean mass as a covariate of energy expenditure) (Tschöp et al., 2012). ANCOVA analyses indicated no differences in energy expenditure between genotypes of either sex across the circadian cycles and metabolic states (Figure 6—figure supplement 1).

Next, we determined whether there are any differences in nutrient absorption in the intestine by quantifying fecal output and frequency, as well as fecal energy content in mice on HFD. No differences in any of the fecal parameters were noted between genotypes of either sex (Figure 6G–H). Because we observed differences in the body temperature of chow-fed mice, we again measured the body temperature of mice on HFD. In contrast to higher body temperature of chow-fed males, Dp16 males on HFD appeared to have slightly lower body temperature in the light cycle, though not significant (Figure 6I). Like the chow-fed females, Dp16 females on HFD also had lower body temperature in both the light and dark cycles (Figure 6J). We measured serum triiodothyronine (T3) to determine whether altered thyroid hormone level contributes to lower body temperature. Contrary to expectation, both Dp16 males and females had elevated serum T3 levels (Figure 6—figure supplement 2). We also measured serum levels of sex and stress hormones, as these could contribute to our phenotypes. Corticosterone levels were not different between genotypes of either sex. Testosterone levels in males appeared lower but not significant; estradiol levels, however, were higher in Dp16 females (Figure 6—figure supplement 2), possibly contributing to their lower body temperature and higher physical activity (Krause et al., 2021; Correa et al., 2015; Zhang et al., 2020; Krajewski-Hall et al., 2018).

At termination of study, we assessed whether there are differences in tissue weight in Dp16 mice on HFD. Tissues were collected from male mice at 50 weeks of age when they were on HFD for 34.5 weeks. At the time of termination, body weights and tissue weights of gWAT, iWAT, and liver were significantly lower in Dp16 males relative to WT controls (Figure 6—figure supplement 3). Although the absolute weights of heart and kidney were not different between genotypes, the relative weights (% of body weight) of heart and kidney were significantly higher in Dp16 males. For females, tissues were collected at 45 weeks of age (on HFD for 26 weeks). Although the body weights of Dp16 females on HFD were not different from WT controls at 35 weeks of age (on HFD for 16 weeks), there were significantly lower at 45 weeks of age (on HFD for 26 weeks) (Figure 6—figure supplement 3). The absolute and relative weights of gWAT and iWAT were significantly lower in Dp16 females. The absolute and relative weights of heart and kidney, however, were higher in Dp16 females.

We also noted that a marker of fibrosis (hydroxyproline content) was significantly higher in Dp16 male liver, as well as in Dp16 female liver, gWAT, and iWAT. A marker of oxidative stress (malondialdehyde content) was not significantly different between genotypes of either sex in liver, gWAT, and iWAT (Figure 6—figure supplement 4). Taken together, these data indicate major sex differences in the physiological response of Dp16 mice to an obesogenic diet.

High-fat diet exacerbates glucose intolerance and insulin resistance in Dp16 mice

Since Dp16 mice on a standard chow diet developed overt insulin resistance and dyslipidemia, we determined whether HFD would further exacerbate these phenotypes. In overnight (16 h) fasted males, serum insulin, TG, cholesterol, NEFA, and β-hydroxybutyrate (ketone) levels were not different between genotypes (Figure 7A), even though Dp16 males had significantly lower body weight and adiposity. Fasting blood glucose levels, however, were lower in Dp16 males, likely reflecting lower adiposity. The Dp16 females appeared to have higher fasting insulin levels, though not significant (Figure 7B). Fasting blood glucose and serum TG and cholesterol levels were not different between genotypes in female mice. Serum NEFA and β-hydroxybutyrate (ketone) levels were significantly lower in Dp16 females (Figure 7B), suggesting lower fasting-induced lipolysis and hepatic fat oxidation.

Figure 7
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Exacerbated glucose intolerance and insulin resistance in Dp16 mice fed a high-fat diet (HFD).

(A–B) Overnight fasting insulin, blood glucose, serum triglyceride, cholesterol, non-esterified free fatty acids (NEFA), and β-hydroxybutyrate (ketone) in male (A) and female (B) Dp16 and WT mice on HFD. Sample size for male mice (WT = 15; Dp16 = 12) and female mice (WT = 14; Dp16 = 14). (C–F) Exacerbated glucose intolerance as determined by the glucose tolerance test (GTT) in male (C) and female (E) Dp16 compared to WT controls on HFD. Exacerbated insulin resistance as determined by the insulin tolerance test (ITT) in male (D) and female (F) Dp16 compared to WT controls. Sample size for male mice (WT = 15; Dp16 = 12) and female mice (WT = 14; Dp16 = 14). (G–H) The rate of triglyceride clearance in response to lipid gavage as determined by the lipid tolerance test (LTT) in male (G) and female (H) Dp16 and WT mice. Sample size for male mice (WT = 15; Dp16 = 12) and female mice (WT = 14; Dp16 = 14). (I–J) Pooled mouse sera from male (I) and female (J) Dp16 and WT mice were fractionated by fast protein liquid chromatography (FPLC), and the triglyceride and cholesterol content of each fraction was quantified. Fractions corresponding to very-low density lipoprotein (VLDL), low-density lipoprotein (LDL), intermediate-density lipoprotein (IDL), and high-density lipoprotein (HDL) are indicated. All data are presented as mean ± SEM. * p<0.05; ** p<0.01; *** p<0.001; **** p<0.0001. For all tolerance tests, data were analyzed by two-way ANOVA with Sidek post hoc tests.

Next, we subjected Dp16 mice on HFD to glucose tolerance test. Both Dp16 males and females showed exacerbated glucose intolerance compared to WT controls (Figure 7C and E). Direct assessment of insulin sensitivity showed that Dp16 males and females have significantly reduced glucose clearance in response to insulin injection compared to WT controls, with Dp16 females exhibiting a more pronounced insulin resistance phenotype (Figure 7D and F).

Given that Dp16 mice on a standard chow diet had marked deficit in lipid clearance, we again performed lipid tolerance tests to assess whether Dp16 mice on HFD have worsening lipid handling capacity. No differences were observed between genotypes in male mice, whereas Dp16 females had a modest impairment in lipid clearance compared to WT controls (Figure 7G–H). Analysis of lipoprotein profiles showed that both Dp16 males and females have higher VLDL-TG and lower LDL-C compared to WT controls, with the effect more pronounced in males (Figure 7I–J). Taken together, these data indicate that despite divergent weight gain in response to HFD, both Dp16 males and females show exacerbated insulin resistance and dyslipidemia.

Discussion

In this study, we show that triplication of Hsa21 gene orthologs in the Dp16 mouse model causes profound disruption in systemic metabolism. By combining deep phenotyping with multi-omics approaches, we show that gene dosage imbalance contributes to major changes in transcriptomes, metabolomes, and biological pathways that link to systemic insulin resistance, glucose intolerance, impaired lipid clearance, dyslipidemia, and exacerbate metabolic stress induced by an obesogenic diet. Our findings provide valuable insights and plausible mechanistic explanations for the prevalence of obesity, dyslipidemia, and diabetes in the DS population.

We show that most triplicated Hsa21 gene orthologs are expressed at the expected ~1.5-fold or higher across six metabolic tissues, but in a striking sex- and tissue-specific manner. Variegated overexpression of Hsa21 genes has been previously noted in individuals with DS (Donovan et al., 2024). In Dp16 mice, female adipose depots exhibit the highest number of differentially expressed triplicated genes, and this is associated with the substantial weight gain observed in Dp16 females. Males, in contrast, have relatively stable body weight and adiposity under standard chow diet despite having similar gene dosage imbalance. These sex differences underscore the complex interactions between triplicated genes and sex-dependent regulatory mechanisms that influences tissue transcriptomes (Blencowe et al., 2022), fat mass expansion and systemic energy metabolism (Kelly and Jones, 2013; Hevener and Correa, 2025). Our data reinforces sex as an important biological determinant of metabolic risk in DS, consistent with documented sex differences in the susceptibility of individuals with DS to developing obesity and metabolic impairments (Aslam et al., 2022; González-Agüero et al., 2011; Hsieh et al., 2014; Pierce et al., 2019; Bhaumik et al., 2008; Rajkovic Vuletic et al., 2025).

Although their weight trajectories diverge, both male and female Dp16 mice exhibit hallmark features of metabolic dysfunction, including fasting hyperinsulinemia, glucose intolerance, insulin resistance, and impaired lipid clearance. These core phenotypes shared between the sexes arise in spite of divergent adiposity, indicating that dysregulated systemic metabolism is a primary consequence of triplicated gene dosage imbalance rather than a secondary effect of increased fat mass. The insulin resistance phenotype, as well as impaired TG clearance and alterations in lipoprotein profiles are consistent with the propensity of individuals with DS to developing type 2 diabetes, dyslipidemia and altered fasting lipid profiles (Van Goor et al., 1997; Bertapelli et al., 2016; Fonseca et al., 2005; Aslam et al., 2022; Valentini et al., 2017; Buonuomo et al., 2016; Adelekan et al., 2012; Gross et al., 2019; Garcia-de la Puente et al., 2021; de la Piedra et al., 2017). These parallels strengthen the translational relevance of the Dp16 model. Future studies are needed to determine which aspects of lipid metabolism—hepatic lipid export, adipose tissue lipolysis, lipoprotein turnover—are dysregulated in DS. Nevertheless, our assessments of mitochondrial function and biochemical markers of fibrosis and oxidative stress, as well as our transcriptomic, metabolomic, and pathway enrichments analyses have provided some mechanistic insights. Our data point to a combination of changes—impaired glucose and lipid metabolism, mitochondrial function, ER and oxidative stress, fibrosis, and low-grade inflammation—all of which are known drivers of adverse metabolic outcomes (Hotamisligil, 2010; Sun et al., 2013; Bhatti et al., 2017). These collective changes act in concert across major metabolic tissues to disrupt local and systemic energy metabolism in Dp16 mice.

Our metabolomic analyses highlighted extensive remodeling of the metabolome of liver and serum. Strikingly, the majority of differential metabolites are not shared between the sexes. This once again underscores the unexpected and complex interactions of triplicated genes and biological sex in determining phenotypic outcomes. The differential metabolites are clustered around pathways related to lipid, amino acid, and bile acid metabolism; some of these pathways have been previously documented in DS (Dunn et al., 2026; Powers et al., 2019). Despite sex differences, we noted that several classes of metabolites—bile acids, acylcarnitine, eicosanoids, phospholipids, fatty acid conjugates—are shared by Dp16 males and females and that the directionality of change is also broadly consistent between the sexes. Altered bile acid pools, including both primary and conjugated bile acids made in the liver (e.g. taurochenodeoxycholic acid, chenodeoxycholymethionine, hyocholic acid, glycohyocholic acid) and secondary bile acids made by gut bacteria (e.g. lithocholic acid, taurolithocholate sulfate, glycoursodeoxycholid acid, keto and diketo lithocholic acid), suggest potential changes in gut–liver axis and bile-acid–regulated metabolic signaling (Perino et al., 2021; Fleishman and Kumar, 2024; Wang et al., 2024) that can contribute to metabolic dysfunction. The directionality of change in multiple immuno-regulatory eicosanoids (Dennis and Norris, 2015) and fatty acids (Yore et al., 2014; Kuda et al., 2016; Serhan and Levy, 2018) appears to promote a pro-inflammatory state in Dp16 mice, a phenotype that is also supported by our multi-tissue transcriptomic data. Interestingly, several hepatic and serum phospholipid species (e.g. Lyo-PC, Lyso-PA, Lyso-PA) are either elevated or reduced in Dp16 mice. Given the complex signaling roles for some of these phospholipids (O’Donnell et al., 2018), we speculate that these changes may underline some aspects of the metabolic deficits in Dp16 mice. Thus, remodeling of tissue and serum metabolomes, in combination with major changes in transcriptomes across tissues, likely contributed to the pronounced metabolic dysfunction in Dp16 mice.

The introduction of an obesogenic diet helped us to reveal the complex interplay of gene and environment in the context of Hsa21 gene dosage imbalance. This becomes relevant as individuals with DS live increasingly longer lives, and can have varied lifestyles and diet. Despite opposite weight trajectories on HFD–males gaining less and females initially gaining more–both Dp16 male and female mice develop worsened glucose intolerance and insulin resistance. This dissociation between weight gain and metabolic impairment again point to gene dosage imbalance as the primary cause of metabolic dysfunction rather than a secondary effect of altered adiposity. The increased fibrosis observed in adipose tissue and liver in Dp16 mice on HFD suggests that chronic nutritional stress exacerbates extracellular matrix remodeling, a change in tissue architecture that is known to compromise adipose tissue and liver function (Sun et al., 2013; Koyama and Brenner, 2017).

Although not the focus of the present study, we unexpectedly discovered that an obesogenic diet causes heart enlargement in Dp16 mice; this phenotype was not observed in Dp16 mice fed a standard chow. The cause of heart enlargement in response to a HFD is presently unknown. Given that congenital heart defect is frequently seen in DS (Dimopoulos et al., 2023), prior studies in DS mouse models have been focused on the contribution of triplicated genes to developmental heart defects (e.g. atrial or ventricular septal defect; Lana-Elola et al., 2016; Li et al., 2016). Our data suggests that the complex interactions of genetics and diet may predispose adult individuals with DS to cardiovascular complications independent of congenital heart abnormalities.

Among the 115 Hsa21 triplicated gene orthologs located on the syntenic region of mouse chromosome 16, several are known to affect metabolism, oxidative stress, inflammatory response, and/or fibrosis. These include Dyrk1a (Bertrand et al., 2025), Dscr1/Rcan1 (Peiris et al., 2016; Peiris et al., 2012), AtpJ (Ren et al., 2024), Atp5o (Rönn et al., 2009), Nrip1 (Tsagkaraki et al., 2023), Tiam1 (Kowluru et al., 2014; Syed et al., 2010), Prdm15 (Mzoughi et al., 2020), Ripk4 (Zhang et al., 2024), Znf295 (Mirhafez et al., 2016), Hmgn1 (Nanduri et al., 2022), Cbr1 (Bell et al., 2021), Bach1 (Jin et al., 2023), Fam3b (Wei et al., 2025), Ets2 (Birsoy et al., 2011), Adamts5 (Bauters et al., 2016; Bauters et al., 2018), Usp16 (Gan et al., 2023), Runx1 (Kilbey et al., 2017), Sim2 (Wall et al., 2023), and the interferon receptor gene locus (Ifnar2, Il10rb, Ifnar1, Ifngr2) (Sullivan et al., 2016; Waugh et al., 2023). Interestingly, some of these triplicated genes show sexually dimorphic expression in gWAT (Il10rb, Cbr1, Ets2, Nrip1), iWAT (Il10rb, Runx1, Adamts5, Tiam1, Rcan1, Ripk4, Bach1, Nrip1, Dyrk1a, Fam3b), BAT (Cbr1, Usp16, Ets2, Atp5o, Nrip1), Liver (Tiam1, Rcan1, Bach1, Prdm15), skeletal muscle (Adamts5, Ifnar2, Rcan1, Ets2, Atp5o, Nrip1), and hypothalamus (Ripk4, Sim2); these sex-biased expression patterns may contribute to the sex differences in Dp16 metabolic phenotypes. In striking contrast to other DS phenotypes, normalizing the expression dosage of the interferon receptor gene locus does not reverse the hepatic lipid metabolism profile (Dunn et al., 2026). None of the other triplicated candidate genes were studied in the context of trisomy; thus, it is unknown whether normalizing these triplicated genes–individually or in combination–could reverse some or all of the metabolic phenotypes described. Systematic genetic dissection of dosage-sensitive genes in the context of trisomy—an approach successfully used for other DS phenotypes (Lana-Elola et al., 2016; Jiang et al., 2015; Sloan et al., 2023; Ahumada Saavedra et al., 2025)–are required to establish their necessity and sufficiency in promoting metabolic dysfunction. Given the complex metabolic phenotypes of Dp16, we anticipate that multiple dosage-sensitive genes are likely to work additively or synergistically to disrupt systemic glucose and lipid metabolism.

Several limitations of the study, however, are noted. First, while Dp16 mouse model contains ~58% of the Hsa21 gene orthologs (Li et al., 2007; Lana-Elola et al., 2016), it does not contain the full complement of Hsa21 gene orthologs. Although no single mouse model fully recapitulates all DS phenotypes (Herault et al., 2017), in light of the impact and complex combinatorial effects of the triplicated genes on phenotypic outcomes and tissue transcriptomes (Duchon et al., 2021; Pereira et al., 2009), it is worthwhile in future studies to comprehensively examine the metabolic phenotypes of the triple compound model (Dp16;Dp10;Dp17) carrying the full complement of the Hsa21 gene orthologs (Yu et al., 2010). The large-scale breeding, cost, and labor associated with generating the compound mice have been a major challenge; however, progress has recently been made to overcome this hurdle (Li et al., 2021). Secondly, Dp16 is a segmental duplication model and not a trisomic model with an independently segregating chromosome. Recent studies have suggested that the presence of an extra chromosome (i.e. trisomy) can affect phenotypes and disomic gene expressions beyond the gene dosage effect of triplicated genes (Xing et al., 2023). Disentangling dosage-dependent versus chromosome-dependent effects will be an important future direction. Thirdly, although we observed major perturbations across tissue transcriptomes, not all changes in mRNAs would translate into corresponding changes in protein levels, and vice versa (Liu et al., 2016; Liu et al., 2017). Future studies incorporating proteomics data would enhance and complement our transcriptomic results. Fourthly, while our studies were ongoing, Tolu and co-workers also reported the glucose intolerance and insulin resistance phenotype in Dp16 mice (Tolu et al., 2025). The authors also showed reduced insulin content in the pancreatic β-cell without changes in β-cell mass. Notably, in our study, we did not measure pancreatic insulin content. We also did not include transcriptomic data from the pancreas, as none of the RNA samples passed the quality control needed for RNA sequencing.

In summary, our data show that the triplication of Hsa21 gene orthologs severely disrupts metabolic homeostasis through concerted perturbations in transcriptome, metabolome, tissue remodeling, mitochondrial function, and glucose and lipid metabolism. Our findings provide physiological contexts for ongoing studies aiming to understand the vulnerability of DS population to developing metabolic disorders. The striking and extensive sex differences uncovered here argue that metabolic studies and therapeutic strategies in DS should account for sex as an important biological variable. Our study highlighted both shared and sex-specific mechanisms of metabolic dysfunction in a DS mouse model, and the impact of gene dosage imbalance on altering whole-body metabolism. The wealth of molecular, biochemical, and physiological data help lay the crucial groundwork for genetic dissection of dosage-sensitive genes causally linked to metabolic dysfunction, and to inform efforts at identifying actionable therapeutic targets that can mitigate one or more aspects of metabolic deficits seen in individuals with DS.

Materials and methods

Mouse model

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Dp(16)1Yey/+ (abbreviated Dp16) mice and wild-type (WT) littermate controls, on C57BL/6 J genetic background, were obtained from the Jackson Laboratory (Strain # 013530). Mice were fed a standard chow (Envigo; 2018 SX) or a HFD (60% kcal derived from fat, #D12492, Research Diets, New Brunswick, NJ). Mice were housed in polyethylene terephthalate (PET) cages on a 12 hr:12 hr light-dark photocycle (lights on at 6 am, lights off at 6 pm) with ad libitum access to water and food. For the HFD-fed group, HFD was provided beginning for 26–34 weeks. At termination of the study, all mice were fasted for 2 hr and euthanized. The age of mice at the time of tissue harvest: male and female mice fed a standard chow were 27.5 weeks old; male mice fed an HFD were 50 weeks old (on HFD for 34.5 weeks); female mice fed an HFD were 45 weeks old (on HFD for 26 weeks). Tissues were collected, snap-frozen in liquid nitrogen, and kept at -80 °C until analysis.

All mouse protocols were approved by the Institutional Animal Care and Use Committee of the Johns Hopkins University School of Medicine (animal protocol # MO22M367). All animal experiments were conducted in accordance with the National Institute of Health guidelines and followed the standards established by the Animal Welfare Acts.

Body composition analysis

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Body composition analyses for total fat mass, lean mass, and water content were determined using a quantitative magnetic resonance instrument (Echo-MRI-100, Echo Medical Systems, Waco, TX) at the Mouse Phenotyping Core facility at Johns Hopkins University School of Medicine.

Indirect calorimetry

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Chow- or HFD-fed Dp16 male and female mice and WT littermates were used for simultaneous assessments of daily body weight change, food intake (corrected for spillage), physical activity, and whole-body metabolic profile in an open flow indirect calorimeter (Comprehensive Laboratory Animal Monitoring System, CLAMS; Columbus Instruments, Columbus, OH) as previously described (Sarver et al., 2020). In brief, data were collected for three days to confirm mice were acclimatized to the calorimetry chambers (indicated by stable body weights, food intakes, and diurnal metabolic patterns), then data were analyzed for the subsequent three days. Mice were observed with ad libitum access to food, throughout the fasting process, and in response to refeeding. Rates of oxygen consumption (V˙O2; mL·kg–1·hr–1) and carbon dioxide production (V˙CO2; mL·kg-1·hr–1) in each chamber were measured every 24 min. Respiratory exchange ratio (RER = V˙CO2/V˙O2) was calculated by CLAMS software (version 5.18) to estimate relative oxidation of carbohydrates (RER = 1.0) versus fats (RER = 0.7), not accounting for protein oxidation. Energy expenditure (EE) was calculated as EE = V˙O2× [3.815 + (1.232×RER)] and normalized to lean mass. We also performed ANCOVA analysis on EE using body weight as a covariate (Tschöp et al., 2012). Physical activities (total and ambulatory) were measured by infrared beam breaks in the metabolic chamber. Average metabolic values and summed intake and activity values were calculated per subject and averaged across subjects for statistical analysis by Student’s t-test.

Body temperature

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Deep colonic temperature was measured by inserting a lubricated (Medline, water soluble lubricating jelly, MDS032280) probe (Physitemp, BAT-12 Microprobe Thermometer) into the anus of mice at a depth of 2 cm. Stable numbers were recorded in both the dark and light cycle for each mouse.

Fecal bomb calorimetry and assessment of fecal parameters

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Fecal pellet frequency and average fecal pellet weight were monitored by housing each mouse singly in clean cages and counting the number of fecal pellets and recording their weight at the end of a 24 hr period. Fecal pellets were shipped to the University of Michigan Animal Phenotyping Core for fecal bomb calorimetry. Briefly, fecal samples were dried overnight at 50 °C prior to weighing and grinding them to powder. Each sample was mixed with wheat flour (90% wheat flour, 10% sample) and formed into 1.0 g pellet, which was then secured into the firing platform and surrounded by 100% oxygen. The bomb was lowered into a water reservoir and ignited to release heat into the surrounding water. Together these data were used to calculate fecal pellet frequency (bowel movements/day), average fecal pellet weight (g/bowel movement), fecal energy (cal/g feces), and total fecal energy (kcal/day).

Glucose, insulin, and lipid tolerance tests

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All tolerance tests were conducted as previously described (Rodriguez et al., 2016; Lei and Wong, 2019; Tan et al., 2016). For glucose tolerance tests (GTTs), mice were fasted for 6 hr before glucose injection. Glucose (Sigma, St. Louis, MO) was reconstituted in saline (0.9 g NaCl/L) to a final concentration of 1 g/10 mL (for the chow-fed mice) or 2 g/10 mL (for the HFD-fed mice), sterile-filtered, and injected intraperitoneally (i.p.) at 1 mg/g body weight (i.e. 10 μL/g body weight for chow-fed mice or 5 μL/g body weight of HFD-fed mice). Blood glucose was measured at 0, 15, 30, 60, and 120 min after glucose injection using a glucometer (NovaMax Plus, Billerica, MA). For ITTs, food was removed 2 hr before insulin injection. 6.5 μL of insulin stock (4 mg/mL; Gibco) was diluted in 10 mL of saline, sterile-filtered, and injected i.p. at 0.75 U/kg body weight (i.e. 10 μL/g body weight). Blood glucose was measured at 0, 15, 30, 60, and 90 min after insulin injection using a glucometer (NovaMax Plus). For lipid tolerance tests (LTTs), mice were fasted for 12 hr and then injected i.p. with 20% emulsified Intralipid (soybean oil; Sigma; 10 μL/g of body weight). Sera were collected via tail bleed using a Microvette CB 300 (Sarstedt) at 0, 1, 2, 3, and 4 hr post-injection. Serum TG levels were quantified using kits from Infinity Triglycerides (Thermo Fisher Scientific).

Fasting glucose, insulin, and lipid profile

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Mice were fasted overnight (~16 hr), beginning at 1 hr before the dark cycle (around 5 pm). Clean cages were provided before food withdrawal. Overnight fasting blood glucose levels from tail bleed were measured using a glucometer. Serum was collected at the 16 hr fast (around 10 am in the morning) for insulin ELISA, as well as for the quantification of TG, cholesterol, NEFAs, and β-hydroxybutyrate concentrations.

Blood and tissue chemistry analysis

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Tail vein blood samples were allowed to clot on ice and then centrifuged for 10 min at 10,000 × g. Serum samples were stored at –80 °C until analyzed. Serum TGs and cholesterol were measured according to manufacturer’s instructions using an Infinity kit (Thermo Fisher Scientific, Middletown, VA). NEFAs were measured using a Wako kit (Wako Chemicals, Richmond, VA). Serum β-hydroxybutyrate (ketone) concentrations were measured with a StanBio Liquicolor kit (StanBio Laboratory, Boerne, TX). Serum insulin (Crystal Chem, 90080), T3 (Calbiotech, T3043T-100), testosterone (Cayman, 582701), estradiol (Cayman, 501890), corticosterone (Cayman, 501320), and alanine aminotransferase (ALT; abcam, ab282882) levels were measured using commercial kits according to manufacturer’s instructions.

Hydroxyproline assay (Sigma Aldrich, MAK569) was used to quantify total collagen content in liver, hypothalamus, and adipose tissues according to the manufacturer’s instructions, with specific optimizations for each tissue type. Tissues were homogenized in deionized water using a bead mill homogenizer. Liver, iWAT, and gWAT were homogenized to a concentration of 0.1 mg tissue/µL. Due to the small and variable tissue mass, hypothalami were homogenized in a fixed volume of 110–120 µL to yield sufficient volume for processing. All samples were hydrolyzed with an equal volume of HCl at 120 °C for 3 hr. The volume of hydrolyzed tissue samples plated for the dehydration step was optimized for each tissue type and experimental group to ensure all measurements fell within the linear range of the standard curve. Hydroxyproline content was calculated based on a standard curve and normalized to tissue weight.

Lipid peroxidation levels (marker of oxidative stress) in liver, hypothalamus, and adipose tissues were assessed by the quantification of malondialdehyde (MDA) levels via the Thiobarbituric Acid Reactive Substances (TBARS) assay (Cayman Chemical, 700870) according to the manufacturer’s instructions.

Serum lipoprotein-triglyceride and cholesterol analysis by FPLC

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Food was removed for ~2 hr (in the light cycle) prior to blood collection. Sera collected from mice were pooled (n=10–14/group) and sent to the Mouse Metabolism Core at Baylor College of Medicine for fast protein liquid chromatography (FPLC) separation. A total of 45 fractions were collected, and TG and cholesterol in each fraction were quantified.

Extraction of hepatic lipids

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Lipid extraction from frozen liver samples was performed using a modified Folch method (Folch et al., 1957). Briefly, approximately 25 mg of frozen liver tissue was weighed and homogenized in 400 µL of cold sucrose buffer (250 mM sucrose, 10 mM Tris, 1 mM EDTA) using a bead beater (FastPrep-24, MP Biomedical). The samples underwent three rounds of 20 s bead beating cycles. Lipids were then extracted from the liver homogenate by adding 1.5 mL of a chloroform:methanol (2:1, v/v) solution to the mixture. The sample was vortexed thoroughly and centrifuged at 1700 rpm for 5 min at 4℃ to separate the phases. The lower chloroform phase, containing lipids, was carefully transferred to a new tube and split equally into two separate tubes. Each tube was then dried in a speed vacuum to dehydrate samples. The dried lipid extracts were reconstituted in 50 µL of chloroform for lipid analysis and quantification by thin-layer chromatography (TLC) or in 50 µL of tert-butanol:methanol:TritonX-100 solution (3:1:1, v/v/v) for cholesterol quantification. Reconstituted samples were either used immediately or stored at –80 °C until analysis.

Separation of lipid classes and quantification by thin-layer chromatography

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TLC was performed as described by Ring et al., 2002 to separate specific classes of lipids. To separate and quantify lipid classes from liver tissue, silica gel (Analtech, Preadsorbent Silica Gel G UNIPLATES Channeled, 20×20 cm, 250 µm, Cat #P31911) plates were pre-washed in methanol, air-dried, and then subsequently equilibrated by pre-washing in hexane:diethyl ether:acetic acid (H:D:A; 80:20:1, v/v/v) solvent system. The plates were allowed to air-dry completely before lipid spotting. Lipid extracts (2 µL) and lipid standards (2 µL, 5 mg/mL in chloroform, Millipore Sigma, Cat# 1787-1AMP, Supelco) were spotted onto the plates, and lipids were separated in H:D:A (80:20:1, v/v/v) to resolve triacylglycerols (TAG) and DAGs. After development, the plates were air-dried and exposed to iodine vapor in a pre-equilibrated tank overnight to visualize lipid spots. For quantification, the plates were imaged, and lipid spots were analyzed using ImageJ software (https://imagej.net/) to determine the intensity of each lipid spot relative to the known lipid standards and normalized to tissue weight.

Hepatic cholesterol quantification

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Following tissue extraction, total cholesterol content was quantified using the Infinity Cholesterol Reagent kit (Thermo Fisher Scientific, Middletown, VA) according to the manufacturer’s instructions. Quantified cholesterol values were normalized to tissue weight.

Untargeted serum and liver metabolomic analyses

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Serum and liver metabolites were extracted and subjected to LC-MS/MS detection on a Q Exactive HF-X Quadrupole-Orbitrap mass spectrometer system at Novogene (Sacramento, CA; n=6 mice per genotype per sex). Data were processed using Novogene in-house analysis pipeline. In brief, the raw mass spectrometry data were first converted to mzXML format using ProteoWizard (Chambers et al., 2012). Peak extraction, alignment, and retention time correction were then performed with XCMS software (Smith et al., 2006). The total peak area within each sample was normalized, and peaks with a missing rate greater than 50% across sample groups were filtered out. The corrected and filtered results were matched with the Novogene local database to obtain metabolite identification information. A total of 4182 metabolites were identified from the 48 samples. Multivariate statistical analysis was conducted on the metabolites, including Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA). PLS-DA is a supervised discriminant analysis statistical method. This method uses partial least squares regression (Boulesteix and Strimmer, 2007) to establish the relationship model between the relative quantitative value of metabolites and the sample category to realize the prediction of the sample category. The PLS-DA model of each comparison group was established, and the model evaluation parameters (R2, Q2) obtained by 7-cycle cross-validation. Differential metabolites were screened according to the criteria: VIP >1.0, fold change (FC)>1.2 or FC <0.833 and p-value <0.05. VIP refers to the variable importance in the projection of the first principal component of the PLS-DA model, and the VIP value represents the contribution of the metabolites to the grouping. KEGG enrichment (FDR correction by Benjamini and Hochberg method) and GSEA analysis was performed on KEGG entries based on the changes in quantitative values of metabolites. All metabolomics data, raw spectral files, and details of experimental protocol and data analyses have been deposited in a public repository, the Metabolomics Workbench (Sud et al., 2016).

Mitochondrial respirometry

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Respirometry was conducted on frozen tissue samples to assay for mitochondrial activity as described previously (Acin-Perez et al., 2020; Sarver et al., 2024). Briefly, liver and BAT were dissected, snapped frozen in liquid nitrogen, and stored at –80 °C for later analysis. Samples were thawed in MAS buffer (70 mM sucrose, 220 mM mannitol, 5 mM KH2PO4, 5 mM MgCl2, 1 mM EGTA, 2 mM HEPES pH 7.4), finely minced with scissors, and then homogenized with a glass Dounce homogenizer. The resulting homogenate was spun at 1000 × g for 10 min at 4 °C. The supernatant was collected and immediately used for protein quantification by BCA assay (Thermo Fisher Scientific, 23225). Each well of the Seahorse microplate was loaded with 4 µg (BAT) or 8 µg (liver) homogenate protein. Each biological replicate consists of three technical replicates. Samples from all tissues were treated separately with NADH (1 mM) as a complex I substrate or Succinate (a complex II substrate, 5 mM) in the presence of rotenone (a complex I inhibitor, 2 µM), then with the inhibitors rotenone (2 µM) and Antimycin A (4 µM), followed by TMPD (0.45 mM) and Ascorbate (1 mM) to activate complex IV, and finally treated with Azide (40 mM) to assess non-mitochondrial respiration. All mitochondrial respiration data were normalized to mitochondrial content, quantified using MitoTracker Deep Red (MTDR, Thermo Fisher, M22426) as described (Acin-Perez et al., 2020; Sarver et al., 2024). Briefly, lysates were incubated with MTDR (1 µM) for 10 min at 37 °C, then centrifuged at 2000 × g for 5 min at 4 °C. The supernatant was carefully removed and replaced with 1 x MAS solution and fluorescence was read with excitation and emission wavelengths of 625 and 670 nm, respectively. To minimize non-specific background signal contribution, control wells were loaded with MTDR and 1 x MAS and subtracted from all sample values.

RNA-sequencing and bioinformatics analysis

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Bulk RNA sequencing of Dp16 (n=6) and WT (n=6) mouse liver, gWAT, iWAT, BAT, skeletal muscle (gastrocnemius), pancreas, and hypothalamus were performed by Novogene (Sacramento, California, USA) on a NovaSeq X Plus platform and pair-end reads (2×150 bp) were generated, with 6 G raw data per sample. One gWAT sample from the Dp16 female mice failed the initial quality control test and was excluded from subsequent RNA sequencing. Sequencing data was analyzed using the standard Novogene Analysis Pipeline. Sequencing reads were aligned to Mus musculus reference genome (GRCm39/mm39). Data analysis was performed using a combination of programs, including Fastp, Hisat2, and FeatureCounts. Differential expressions were determined through DESeq2. The resulting p-values were adjusted using the Benjamini and Hochberg’s approach for controlling the false discovery rate. Genes with an adjusted p-value <0.05 and log2(FC)>0.5 were assigned as differentially expressed. GO, KEGG, and Reactome (http://www.reactome.org) enrichment were implemented by ClusterProfiler. All volcano plots and heat maps were generated in Graphpad Prism 10 software. All statistics were performed on log transformed data. All heat maps were generated from column z-score transformed data. The z-score of each column was determined by taking the column average, subtracting each sample’s individual expression value by said average then dividing that difference by the column standard deviation. Z-score = (value – column average)/column standard deviation. High-throughput sequencing data from this study have been submitted to the NCBI Sequence Read Archive (SRA) under accession number # PRJNA1160420.

Statistical analyses

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All results are expressed as mean ± SEM. Statistical analysis was performed with GraphPad Prism 10 software (GraphPad Software, San Diego, CA). Data were analyzed with two-tailed Student’s t-tests or by repeated measures ANOVA. For two-way ANOVA, we performed Sidek or Bonferroni post hoc tests. p<0.05 was considered statistically significant.

Data availability

All RNA-seq data have been deposited in NCBI Sequence Read Archive (SRA), with the accession # PRJNA1160420. The metabolomics data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench (https://www.metabolomicsworkbench.org) where it has been assigned Study ID (ST004905, ST004915, ST004916, and ST004918). The data can be accessed directly via it's Project DOI: https://doi.org/10.21228/M8SZ8Q.

The following data sets were generated
    1. Chen F
    2. Saqib M
    3. Nguyen CM
    4. Sarver DC
    5. Yu YE
    6. Aja S
    7. Seldin MM
    8. Wong GW
    (2024) NCBI BioProject
    ID PRJNA1160420. RNA-seq from multiple tissues from DP16 vs WT mice.
    https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA1160420
    1. Wong GW
    (2026) National Metabolomics Data Repository
    ID ST004915. Metabolomics analysis of serum samples from female wild-type (WT) and Dp16 Down syndrome mice.
    https://www.metabolomicsworkbench.org/data/DRCCMetadata.php?Mode=Study&StudyID=ST004915
    1. Wong GW
    (2026) National Metabolomics Data Repository
    ID ST004916. Metabolomics analysis of serum samples from male wild-type (WT) and Dp16 Down syndrome mice.
    https://www.metabolomicsworkbench.org/data/DRCCMetadata.php?Mode=Study&StudyID=ST004916
    1. Wong GW
    (2026) National Metabolomics Data Repository
    ID ST004918. Metabolomics analysis of liver samples from female wild-type (WT) and Dp16 Down syndrome mice.
    https://www.metabolomicsworkbench.org/data/DRCCMetadata.php?Mode=Study&StudyID=ST004918
    1. Wong GW
    (2026) National Metabolomics Data Repository
    ID ST004905. Metabolomics analysis of liver samples from male wild-type (WT) and Dp16 Down syndrome mice.
    https://www.metabolomicsworkbench.org/data/DRCCMetadata.php?Mode=Study&StudyID=ST004905
    1. Wong GW
    (2026) Metabolics Workbench
    Gene dosage imbalance disrupts systemic metabolism in the Dp16 Down syndrome mouse model.
    https://doi.org/10.21228/M8SZ8Q

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    https://doi.org/10.1002/mrdd.20157

Article and author information

Author details

  1. Fangluo Chen

    1. Department of Physiology, Pharmacology and Therapeutics, Johns Hopkins University, School of Medicine, Baltimore, United States
    2. Center for Metabolism and Obesity Research, Johns Hopkins University, School of Medicine, Baltimore, United States
    Contribution
    Conceptualization, Formal analysis, Investigation, Visualization, Writing – review and editing
    Contributed equally with
    Muzna Saqib
    Competing interests
    No competing interests declared

  2. Muzna Saqib

    1. Department of Physiology, Pharmacology and Therapeutics, Johns Hopkins University, School of Medicine, Baltimore, United States
    2. Center for Metabolism and Obesity Research, Johns Hopkins University, School of Medicine, Baltimore, United States
    Contribution
    Conceptualization, Formal analysis, Investigation, Visualization, Writing – review and editing
    Contributed equally with
    Fangluo Chen
    Competing interests
    No competing interests declared

  3. Christy M Nguyen

    1. Department of Biological Chemistry, University of California, Irvine, Irvine, United States
    2. Center for Epigenetics and Metabolism, University of California Irvine, Irvine, United States
    Contribution
    Formal analysis, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared

  4. Dylan C Sarver

    1. Department of Physiology, Pharmacology and Therapeutics, Johns Hopkins University, School of Medicine, Baltimore, United States
    2. Center for Metabolism and Obesity Research, Johns Hopkins University, School of Medicine, Baltimore, United States
    Contribution
    Formal analysis, Visualization, Writing – review and editing
    Competing interests
    No competing interests declared

  5. Y Eugene Yu

    1. The Children's Guild Foundation Down Syndrome Research Program, Department of Cancer Genetics and Genomics, Roswell Park Comprehensive Cancer Center, Buffalo, United States
    2. Genetics, Genomics and Bioinformatics Program, State University of New York at Buffalo, Buffalo, United States
    Contribution
    Methodology, Writing – review and editing
    Competing interests
    No competing interests declared

  6. Susan Aja

    1. Center for Metabolism and Obesity Research, Johns Hopkins University, School of Medicine, Baltimore, United States
    2. Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, United States
    Contribution
    Formal analysis, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared

  7. Marcus M Seldin

    1. Department of Biological Chemistry, University of California, Irvine, Irvine, United States
    2. Center for Epigenetics and Metabolism, University of California Irvine, Irvine, United States
    Contribution
    Formal analysis, Supervision, Visualization, Methodology, Writing – review and editing
    Competing interests
    Reviewing editor, eLife
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8026-4759

  8. G William Wong

    1. Department of Physiology, Pharmacology and Therapeutics, Johns Hopkins University, School of Medicine, Baltimore, United States
    2. Center for Metabolism and Obesity Research, Johns Hopkins University, School of Medicine, Baltimore, United States
    Contribution
    Conceptualization, Formal analysis, Funding acquisition, Investigation, Visualization, Writing – original draft, Project administration
    For correspondence
    gwwong@jhmi.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5286-6506

Funding

National Institute of Diabetes and Digestive and Kidney Diseases (DK084171)

  • G William Wong

National Heart Lung and Blood Institute (HL138193)

  • Marcus M Seldin

National Institute of Diabetes and Digestive and Kidney Diseases (DK130640)

  • Marcus M Seldin

Eunice Kennedy Shriver National Institute of Child Health and Human Development (HD109750)

  • Y Eugene Yu

National Institute on Deafness and Other Communication Disorders (DC019735)

  • Y Eugene Yu

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

The work was funded, in part, by grants from the National Institute of Health (DK084171 to GWW, HL138193 and DK130640 to MMS, HD109750 and DC019735 to YEY). MS and DCS were supported by an NIH T32 training grant (HL007534). The FPLC/serum analyses were conducted by the Mouse metabolic Phenotyping Center (MMPC) at Baylor College of medicine, funded by NIH grants DK114356 and UM1HG006348. The fecal bomb calorimetry analysis was performed at the University of Michigan Animal Phenotyping Core, supported by center grants 1U2CDK135066-01 (Mi-MPMOD) and DK020572 (MDRC). The Metabolomics Workbench/National Metabolomics Data Repository (NMDR) is supported by the NIH (U2C-DK119886), Common Fund Data Ecosystem (CFDE) (3OT2OD030544) and Metabolomics Consortium Coordinating Center (M3C) (1U2C-DK119889)

Ethics

All mouse protocols were approved by the Institutional Animal Care and Use Committee of the Johns Hopkins University School of Medicine (animal protocol # MO22M367). All animal experiments were conducted in accordance with the National Institute of Health guidelines and followed the standards established by the Animal Welfare Acts.

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You can cite all versions using the DOI https://doi.org/10.7554/eLife.110476. This DOI represents all versions, and will always resolve to the latest one.

Copyright

© 2026, Chen, Saqib et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Fangluo Chen
  2. Muzna Saqib
  3. Christy M Nguyen
  4. Dylan C Sarver
  5. Y Eugene Yu
  6. Susan Aja
  7. Marcus M Seldin
  8. G William Wong
(2026)
Gene dosage imbalance disrupts systemic metabolism in the Dp16 Down syndrome mouse model
eLife 15:RP110476.
https://doi.org/10.7554/eLife.110476.3

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