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Impact of DNA methylation on digestive and metabolic gene expression in red pandas (Ailurus fulgens) during the transition from milk to bamboo diet

BMC Genomics volume 26, Article number: 404 (2025) Cite this article

This article has been updated

Abstract

Background

DNA methylation plays a crucial role in species development and environmental adaptation. In mammals, there are significant dietary changes from infancy to adulthood. Notably, the red panda transitions from milk consumption as juveniles to a bamboo-based diet as adults, with significant alterations in food characteristics and nutritional content. However, the regulatory role of DNA methylation in this process remains unclear. In this study, we investigate the regulatory role of DNA methylation on the expression of digestive and metabolic genes in the liver and pancreas during the red panda’s dietary transition from suckling stage to adulthood.

Results

Our findings reveal significant differences in DNA methylation patterns before and after dietary transition, highlighting the specific alterations in the methylation profiles of genes involved in lipid, carbohydrate, and amino acid metabolism. We found that perilipin-4 (PLIN4) is hypomethylated and highly expressed in the liver of adult red pandas, facilitating lipid droplet formation and storage, crucial for adapting to the low-fat content in bamboo. In contrast, genes like lipoprotein lipase (LPL), crucial for lipid breakdown, exhibited hypermethylated with low-expression patterns, reflecting a reduced lipid metabolism capacity in adults. Carbohydrate metabolism-related genes like ADH4 and FAM3C are hypomethylated and highly expressed in adults, enhancing glycogen production and glucose utilization. Genes involved in protein metabolism like CTSZ and GLDC, exhibit hypomethylated with high-expression and hypermethylated with low-expression patterns in the pancreas of adults, respectively, contributing to protein metabolism balance post-weaning.

Conclusion

This study reveals the regulatory role of DNA methylation in the dietary transition of red pandas from milk to bamboo and provides methylation evidence for the molecular regulation of adaptive expression of digestive and metabolic genes in red pandas with specialized diets.

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Introduction

Dietary adjustments have significant impacts on the growth and development of animals [1,2,3,4]. Throughout the postnatal developmental period from birth to adulthood, mammals undergo changes in their nutritional environment that accompany their growth and development. The primary source of energy shifts from glucose in the umbilical cord blood during the fetal stage to lipids in maternal milk during infancy, ultimately transitioning to meat or carbohydrates in solid foods during adulthood. Numerous studies have unequivocally demonstrated that modifications in the nutritional milieu can exert a profound impact on animal growth and development. Particularly during early life stages, inadequate nutritional conditions can not only induce lasting alterations in the body but also markedly increase the risk of developing chronic diseases later in life [5]. During animal development, changes in diet inevitably lead to adaptive alterations in the expression of genes related to digestion and metabolism, yet the regulatory mechanisms underlying these changes remain largely unclear.

DNA methylation is a heritable epigenetic modification that regulates gene expression without altering the DNA sequence [6]. It plays a pivotal role in biological processes such as gene regulation, sex determination, reproduction, development, diseases, and aging, ensuring precise patterns of gene expression and cell fate, thereby constructing and maintaining the structure and function of different organs, which is essential for normal development and health in organisms [7]. Furthermore, DNA methylation is a dynamic process that changes under the influence of internal genetic encoding and external environmental factors [8]. Numerous studies have shown that DNA methylation is different under the influence of various factors including genetics, environment, and lifestyle [9,10,11,12,13,14,15], and thus also suggest its important roles in these biological processes. For instance, early-life DNA methylation modification may play a key role in preventing childhood and adult obesity through exclusive breastfeeding [16]. Therefore, during growth and development, dietary habits and nutritional environment significantly influence DNA methylation.

The red panda (Ailurus fulgens) is an endangered semi-arboreal mammal, typically inhabiting temperate broadleaf forests and subalpine forests with bamboo understories [17, 18]. Adult red pandas exhibit a certain degree of food preference. They primarily consume bamboo leaves and shoots, which constitute over 90% of their diet [19,20,21,22,23]. Although red pandas belong to the order Carnivora, they retain digestive system characteristics typical of carnivores, however, they have adapted to consuming bamboo as a typical example of adaptive evolution. They have evolved a pseudo thumb for gripping bamboo, similar to the giant panda, which also feeds predominantly on bamboo [24]. Unlike other carnivores, red and giant pandas need to adapt to a drastic transition from highly nutritious mother’s milk to a diet primarily consisting of low-nutrient, high-fiber bamboo after weaning. Ma et al. investigated the regulatory role of DNA methylation in the dietary transition of giant pandas from milk consumption in infancy to bamboo consumption in adulthood. They found that methylation plays a crucial regulatory role in the expression of key genes related to digestion and metabolism during the dietary shift in giant pandas, exhibiting adaptive characteristics to food changes [25]. However, whether this methylation modification has a universal interspecies characteristic among mammals or a similar regulatory role in red pandas remains to be further explored. The liver and pancreas are major digestive organs that play a key role in the digestion and metabolism of the organism. In our previous study, we investigated the expression change of digestive and metabolic genes of red pandas and six other mammalian species from juvenile to adult [26]. We found that the expression changes of digestive and metabolic genes in red pandas differ significantly from those observed in the carnivorous ferret, but are similar to those in other herbivorous animals, reflecting adaptations associated with dietary nutrient limitations in adulthood. However, the role of DNA methylation in the expression of digestive and metabolic genes in the red panda remains unclear. Therefore, in this study, we explore the regulatory mechanisms of digestive and metabolic gene expression changes during the dietary transition in the growth and development of the red panda, focusing on the role of DNA methylation in them. Our study will provide new insights into the role of epigenetic regulation during the growth and development of the red panda.

Materials and methods

Sample collection

To investigate the regulatory mechanisms of digestive and metabolic gene expression changes during the dietary transition in the growth and development of the red panda, we selected the liver and pancreas as the primary study tissues. Three experimental groups were established based on feeding habits and developmental stages: the No-feeding group, the Suckling group, and the Adult group. Specifically, the no-feeding group consisted of newborn red pandas that died before feeding; the suckling group consisted of juvenile red pandas that were still nursing from their mothers; and the adult group consisted of adult red pandas that had been weaned and primarily consumed bamboo, including bamboo leaves and shoots, which make up over 90% of their diet, with occasional fruits provided as supplements.

The tissue samples used in this study were provided by the Chengdu Research Base of Giant Panda Breeding (Table 1 and Table S1), and were approved by the Ethics Committee of the College of Life Science, Sichuan University (Approval No: 20190506001). All samples were collected from red pandas that had died naturally or accidentally at the Chengdu Research Base, which were unrelated to the experimental design of this study. Once the animal’s death was noticed by the keepers, a veterinarian immediately performed a necropsy and sample collection, typically within 30 min of death. Only liver and pancreas tissues that were anatomically normal, free of pathological changes, and confirmed by the veterinarian were included in the study. All collected tissue samples were immediately flash-frozen in liquid nitrogen and stored at -80℃.

In total, thirteen liver and ten pancreas samples were collected. For the no-feeding group, due to the well-managed breeding program and environment, neonatal mortality was very low, only two liver and two pancreas samples were obtained. These four samples were obtained from three newborn red pandas that died accidentally before being fed. The causes of death included obstructed labor by the mother and physical injuries, such as being crushed or bitten by the mother. Furthermore, due to the small size of the no-feeding infants, the available tissue was limited, and we were only able to perform transcriptomic sequencing, with no available tissue for whole-genome methylation sequencing. In the suckling group, five liver and four pancreas samples were obtained from five suckling juvenile red pandas. These individuals died from causes including accidental injuries (e.g., bites from the mother) or inadequate maternal care. Due to limited tissue quantities, two liver (RD-L and RE-L) and one pancreas (RE-P) samples from the suckling group were subjected to both transcriptomic and whole-genome methylation sequencing, while the remaining samples were only analyzed by one of the transcriptomic and whole-genome methylation sequencing. In the adult group, six liver and four pancreas samples were obtained from seven adult red pandas that had been weaned and primarily fed bamboo. These individuals died from causes unrelated to the liver or pancreas, including accidental injuries (e.g., fighting) or diseases not affecting the digestive system, with no pathological changes observed in the liver or pancreas. Due to insufficient tissue in many of the samples, two liver (RJ-L and RK-L) and three pancreas (RJ-P, RK-P and RL-P) samples from the adult group underwent both transcriptomic and whole-genome methylation sequencing, while the remaining samples were subjected to one sequencing method.

To minimize potential confounding factors, we also considered several variables, including age, sex, dietary intake, cause of death, environmental factors, and genetic variation. Specifically, we selected animals at similar developmental stages and ensured that the sex ratio was balanced across all groups. The food fed within each group was the same to ensure stability and consistency of dietary composition. All red pandas were housed in the same environment to eliminate the impact of environmental variables on the results. The selected samples came from individuals that died naturally or accidentally, with causes of death unrelated to the digestive system. Additionally, all animals were selected from the same population, minimizing genetic variation and its potential impact on the results. Therefore, we believe that the primary driver of the observed changes in gene expression and DNA methylation in this study was the developmental stages with special dietary transition.

In addition, the transcriptomic data used in this study was derived from our previous research [26]. In that study, we compared the characteristics of change in the expression of digestive and metabolic genes from the juvenile to adult stages in red pandas and six other mammalian species. These transcriptomic data will serve as the foundation for further analysis in this study, which aims to explore the role of DNA methylation regulation in the expression changes of digestive and metabolic genes in the red panda.

Analysis of gene expression data

Gene expression data for red panda’s liver and pancreas during the postnatal no-feeding, suckling, and adult phases were obtained from our previous research [26]. Detailed sample information is provided in Table S1. All subsequent analyses involving R packages were conducted using R version 4.3. To evaluate the grouping relationships and clustering patterns among all samples, we first normalized the raw gene expression data using the TMM algorithm from the R package edgeR (version 3.42.4) [27] and then applied a log2 transformation. Principal component analysis (PCA) was conducted with the prcomp function from the R package stats (version 4.3.1), and the results were visualized using the ggplot function from the ggplot2 package [28]. Spearman’s rank correlation coefficients for sample clustering were calculated with the cor function from the stats package, and heatmaps were produced using the heatmap.2 function in the gplots package.

To investigate gene expression alterations during various developmental stages, we utilized the filterByExpr function from the edgeR package to remove low-expression genes from the liver and pancreas expression matrices [27]. This function retains genes with 10 or more sequence counts in the smallest group sample size. Considering technical variations, sequencing depth, and gene length, normalization was performed using the TMM method in edgeR [27]. We then analyzed gene expression changes between the no-feeding and suckling stages, and between the suckling and adult stages, using the normalized gene expression matrices. Genes with a Benjamini-Hochberg FDR ≤ 0.05 and an absolute log2FC ≥ 1 were considered significantly differentially expressed.

For functional enrichment analysis of these differentially expressed genes (DEGs), we employed the enricher function from the clusterProfiler package to analyze Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways [29]. GO annotations for all red panda genes were acquired via EggNOGv5.0 (http://eggnog5.embl.de/#/app/home), and KEGG pathway data were retrieved from the KEGG pathway database (https://www.genome.jp/kegg/pathway.html). GO terms or KEGG pathways with a pvalue cutoff of less than 0.05 were considered significantly enriched.

DNA extraction, library preparation, and whole genome bisulfite sequencing

For the whole-genome methylation sequencing of red panda samples, DNA was extracted using the Qiagen DNeasy Blood & Tissue Kit. The integrity of the extracted DNA was verified via agarose gel electrophoresis to ensure it was free from contamination. An uncontaminated DNA sample, measuring 1 µL, was evaluated for the OD260/280 ratio, followed by precise DNA concentration measurement with the Qubit system. After the samples passed quality control, 100 ng of genomic DNA and 0.5 ng of unmethylated lambda DNA were mixed, and then fragmented into 200–400 bp segments using the Covaris S220 ultrasonic sonicator (Covaris, USA). After fragmentation, unmethylated cytosines were converted to uracils using the EZ DNA Methylation-Gold™ Kit (Zymo Research), followed by adapter ligation, fragment selection, and PCR amplification to complete the library construction. The library quality was assessed using the Agilent 5400 System (Agilent, USA), and quantified by qPCR. The library concentration had to be greater than 1.5 nM. Libraries that met the quality criteria were clustered using the TruSeq PE Cluster Kit v3-cBot-HS (Illumina) and sequenced on the Illumina NovaSeq 6000 platform, producing 150 bp paired-end reads. These sequences have been uploaded to the SRA database, with project ID PRJNA1139133.

Quality control and genome alignment of DNA methylation sequencing data

Raw DNA methylation sequencing data were subjected to quality control using Trim Galore (version 0.6.1, https://github.com/FelixKrueger/TrimGalore). This involved removing adapter sequences, filtering out reads with quality scores below 20, and discarding reads shorter than 20 base pairs. FastQC (version 0.11.9) was employed both before and after the trimming process to assess the sequencing quality. The pre-trimming FastQC evaluation ensured that the raw sequencing data met basic quality requirements, while the post-trimming FastQC assessment verified the effectiveness of the trimming process and confirmed that all data used in subsequent analyses met quality standards. Following quality control, reads were aligned to the red panda reference genome (ASM200746v1_HiC), obtained from DNA Zoo (https://www.dnazoo.org/). This reference genome was reassembled using the 3D-DNA pipeline [30] and curated using Juicebox Assembly Tools [31] based on the draft assembly ASM200746v1 (GCA_002007465.1) [32]. The alignment was performed using Bismark (version 0.20.0) with the Bowtie2 parameters L,0,-0.2 [33]. PCR duplicates were removed using the deduplicate_bismark script provided by Bismark. Methylation status at each genomic position was extracted using the bismark_methylation_extractor script. All WGBS samples demonstrated alignment rates exceeding 70%, making them suitable for further analysis (Table 1).

DNA methylation profiling

To investigate the methylation patterns of the red panda genome, we evaluated the methylation levels at CG, CHG, and CHH contexts, excluding sites with less than tenfold coverage. The methylation level at a cytosine site is calculated as the ratio of methylated cytosines (mC) to the total cytosines: Methylation level of C site = mC / (mC + C), where mC represents the count of methylated cytosines and C represents unmethylated cytosines. For methylation analysis, the genome was divided into 10 kb bins, and the methylation level for each bin was calculated as: Bin Methylation level = ∑ mC / ∑ (mC + C). Violin plots were then generated to show the overall methylation level distributions for liver and pancreas samples. Additionally, principal component analysis and Spearman correlation distance clustering were performed on the genomic bins for each sample using the prcomp and cor functions from the R stats package. Annotation information was extracted from the red panda GTF files, and different functional regions of the genes were identified using the genes, transcripts, promoters, intronsByTranscript, and exonsBy functions in the R package GenomicFeatures [34]. We analyzed methylation levels in various gene functional regions, including promoters (1000 bp upstream of the transcription start site), gene bodies, exons, and introns. To examine methylation level trends, we divided the gene regions and their 2000 bp upstream and downstream flanking regions into 20 bins, calculated the average methylation level for each bin, and plotted the trend of methylation changes.

Identification of genes with differential methylation

Differentially methylated regions (DMRs) represent genomic areas showing methylation differences between different sample groups. Using the R package DSS, we identified DMRs between adult and suckling samples [35,36,37], with parameters set to a 500 bp window size and a p.threshold of 0.05. DMRs were required to contain at least three CpG sites and a methylation level difference greater than 0.1. Gene bodies were defined based on “gene” annotations in the reference genome file. Conversion and positional intersection between gene bodies and DMRs were carried out using the Granges and findOverlaps functions from the GenomicRanges package [34, 38]. A region was classified as a differentially methylated gene body if it overlapped with more than half of a DMR. Promoter regions, defined as the 1000 bp upstream of the transcription start site (TSS) with at least two CpG sites and coverage over five, were also analyzed for differential methylation [39]. The methylation level of promoter regions was calculated as: Promoter Methylation level = ΣmC / Σ(mC + C), where mC represents reads at methylated CpG sites within the promoter. We compared adult and suckling samples using the Wilcoxon rank sum test to determine the significance of methylation differences in promoters, with significant thresholds set at P < 0.05 and a log2 fold change (FC) greater than one. Genes with significant promoter methylation differences were identified as differentially methylated. For enrichment analysis of these differentially methylated genes, the enricher function from the R package clusterProfiler was used to analyze Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways.

Integrative analysis of differentially expressed and methylated genes

DNA methylation plays a crucial role in gene expression regulation, especially in the promoter and gene body regions [40]. In this study, we focused on the relationship between methylation levels in these regions and gene expression, aiming to investigate how DNA methylation regulates the expression of digestive and metabolic genes in the liver and pancreas during the dietary transition of red pandas from a milk-based to a bamboo-based diet. To evaluate the correlation between methylation levels in the promoter and gene body regions and their corresponding gene expression levels, we first extracted significant differentially methylated promoter genes or gene body genes. We then extracted the corresponding gene expression FC for these differentially methylated genes. Then, we applied a log2 transformation to both the methylation level FC and expression level FC to ensure more accurate correlation analysis. We then calculated the Spearman correlation coefficients between promoter methylation and gene expression, as well as between gene-body methylation and gene expression, based on the transformed data. A two-tailed t-test was used to assess the statistical significance of these correlations.

To further investigate the interactions between genes that exhibited a negative correlation between promoter region differential methylation and gene differential expression, as well as other DEGs, we performed protein-protein interaction (PPI) network analysis using the STRING database (version 12.0) (https://string-db.org), applying a minimum interaction confidence score of 0.4, while all other parameters were set to default. The resulting interaction networks were visualized using Cytoscape v3.9.1, where the degree of gene interaction was calculated using default settings. To identify hub genes, we extracted a subnet consisting of negatively correlated genes along with their interacting DEGs, and ranked them based on their degree (number of interactions), with higher-degree genes representing key interaction hubs.

Results

Overview of gene expression profiles

To investigate the differences in gene expression patterns between liver and pancreas samples at several important stages associated with postnatal dietary changes in red pandas, we performed Spearman correlation distance clustering and principal component analysis (Fig. 1). The results of Spearman correlation distance clustering revealed distinct tissue-specific expression patterns between liver and pancreas samples, leading to the segregation of samples into different groups. Specifically, the clustering pattern of liver samples revealed two main branches: one corresponding to no-feeding cubs and the other containing both suckling cubs and adult individuals. This suggests significant differences in liver gene expression patterns between suckling and adult red pandas compared to no-feeding cubs. Additionally, it has smaller expression differences in livers between suckling and adult red pandas, suggesting the liver function maturing rapidly during the suckling period. The clustering pattern of pancreas samples was also divided into two major branches, with no-feeding and suckling cubs contrasting with adult individuals. This pattern indicates that the pancreatic gene expression in adult red pandas differs more significantly from no-feeding and suckling cubs. The differentiation between no-feeding and suckling cubs and adult individuals was less pronounced in the liver compared to the pancreas, suggesting that the development and maturation timelines of the liver and pancreas are not consistent. However, both tissues still clearly distinguished no-feeding and suckling cubs from adult individuals. These observations are further supported by the results obtained from principal component analysis.

Fig. 1
figure 1

Clustering analysis and PCA of RNA-seq samples. (a) Clustering analysis of log-transformed normalized expression levels among liver and pancreas samples. Distance between samples is measured by Spearman’s rank correlation coefficient. (b) PCA of log-transformed normalized expression levels among liver and pancreas samples. Different colors represent organs, while different shapes indicate developmental stages.

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To investigate the gene expression changes in the digestive and metabolic organs after birth, we comparatively analyzed gene expression profiles of liver and pancreas from red pandas at three developmental stages with special dietary transition: no-feeding, suckling, and adulthood. In the liver tissue, compared to the no-feeding group, we identified 319 differentially expressed genes (DEGs) in the suckling group, with 220 genes up-regulated and 99 genes down-regulated; while in the comparison between the adult and suckling groups, a total of 339 DEGs were found, including 251 up-regulated and 88 down-regulated genes (Fig. S1). The distribution patterns of DEGs at different stages in liver tissue suggests that the activation of numerous lipid metabolism-related genes under the stimulation of breast milk provides energy for the rapid growth of young red pandas in early stages, while there is a significant change in the energy metabolism after weaning. In the analysis of pancreas tissue, only 32 DEGs were identified between the suckling and no-feeding groups, including 22 up-regulated and 10 down-regulated genes; in contrast, 1,785 DEGs were identified between the adult and suckling groups, with 703 upregulated and 1082 downregulated genes (Fig. S1). The distribution patterns of DEGs at different stages in pancreas tissue suggest that the pancreatic genes may be fully activated at birth in red pandas for the utilization of key proteins needed for growth and development, and there is a significant change in protein utilization after weaning. The proportional patterns of DEGs in the comparative analysis of different stages of the two organs further illustrate that the development and maturation timings of the two organs are inconsistent.

Differential expression gene enrichment analysis

To understand the functional changes in the liver and pancreas of red pandas from infancy to adulthood, we conducted GO and KEGG enrichment analysis on DEGs across three developmental stages. We primarily focused on pathways related to digestion, absorption, and nutrient metabolism (Table S2-S3).

Carbohydrate metabolism is a fundamental and crucial biochemical process that ensures continuous energy supply to the organism and provides abundant raw materials for the biosynthesis pathways of other substances [41]. We found that glycolysis/gluconeogenesis was significantly enriched in the upregulated genes of the liver in the adult group compared to the suckling group. Compared to the suckling group, processes of carbohydrate biosynthetic, glucose metabolic, glycolysis/gluconeogenesis, and other carbohydrate metabolic related pathways were significantly enriched in the upregulated genes of the pancreas in the adult group. The upregulation of genes related to these processes and pathways facilitates the efficient utilization of available carbohydrates in bamboo, thereby enhancing the utilization of limited nutrients in bamboo by red pandas.

Liver plays a critical role in processes encompassing digestion, absorption, synthesis, and transportation of lipids [42, 43]. Meanwhile, the pancreas serves as an essential exocrine gland capable of secreting lipase to regulate lipid metabolism. Enrichment analysis revealed that genes associated with regulation of cholesterol storage and genes related to cholesterol metabolism were highly expressed in the liver tissues of both the no-feeding and suckling groups but decreased in expression in the adult group. Processes such as acylglycerol biosynthesis and metabolism, cholesterol transport and efflux, along with genes associated with the PPAR signaling pathway were highly expressed in the liver tissues of the suckling group but downregulated in the adult group. Conversely, genes related to the PPAR signaling pathway were significantly upregulated in the adult pancreatic samples. In comparison to the suckling group pancreatic samples, processes such as positive regulation of triglyceride biosynthesis process were significantly upregulated in the adult group. Furthermore, compared to the suckling group, genes highly expressed in the liver and pancreatic samples of adult red pandas were significantly enriched in pathways such as arachidonic acid metabolism, linoleic acid metabolism, metabolism of xenobiotics by cytochrome P450 and other entries.

The synthesis and metabolism of amino acids and proteins are crucial for maintaining life. Among the protein metabolism-related terms, genes associated with negative regulation of peptidase activity and negative regulation of endopeptidase activity were upregulated in the liver tissue of the suckling group but downregulated in the adult group. Compared to the adult group, genes highly expressed in the pancreatic samples of the suckling group were significantly enriched in pathways such as protein digestion and absorption, and glycine, serine and threonine metabolism. Compared to the no-feeding group, genes highly expressed in the liver of the suckling group were significantly enriched in processes such as serine-type peptidase activity, histidine metabolism, among others. In contrast, compared to the suckling group, genes highly expressed in the liver of the adult group were significantly enriched in terms such as exopeptidase activity, and pathways including protein digestion and absorption. Moreover, genes significantly enriched in multiple amino acid metabolism pathways and arginine biosynthesis were highly expressed in the pancreatic samples of the adult group compared to the suckling group.

Overview of DNA methylation landscape

In order to analyze the DNA methylation patterns in the liver and pancreas of red pandas after birth, we performed whole-genome methylation sequencing on nine liver and six pancreas samples from two postnatal stages (infancy and adulthood). After quality control of the data, each sample generated 100-220G of clean bases, and the maximum genome depth of mapped reads exceeded 20 times the reference genome (Table 1). The conversion rates of sodium bisulfite were all above 99% for each sample. We aligned the paired-end reads to the red panda reference genome and found that the final efficiency of BS-seq read alignment ranged from 70.40 to 79.30%.

Table 1 Summary of WGBS samples in this study
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We performed a statistical analysis on the number of C sites in each sample, revealing that over 96% of all C sites had a coverage of more than one read, and over 84% had a coverage exceeding five reads (Table S4). Additionally, we partitioned the genome into 10,000 bp intervals and calculated the methylation levels for each bin based on different types of C sites in each sample (Fig. 2). The findings indicated that the methylation levels of C sites were consistent among liver and pancreas samples of red pandas. On average, CG-type sites exhibited significantly higher methylation levels compared to CHG and CHH-type sites, with most regions showing methylation levels above 0.5. This suggests that CpG sites are the primary locations of methylation in the red panda genome. Methylation levels at different genomic locations serve different functions. Given that methylation predominantly occurs at CG motifs, we calculated the average methylation levels of different genomic regions (promoter regions, gene bodies, exons, and introns) based on CpG site types at the whole-genome level. The results showed that the methylation levels were relatively similar across liver and pancreas samples of red pandas, though there were some differences among various functional regions. Specifically, the promoter region had significantly lower methylation levels compared to the gene body, exons, and introns (Fig. 3a). Promoter region hypomethylation is generally associated with transcriptional activation, as DNA methylation inhibits the binding of transcription factors and RNA polymerase, leading to decreased gene expression [48]. This low methylation state in the promoter region may provide a more open chromatin conformation, allowing essential regulatory proteins to access DNA, thus facilitating transcription initiation. To visually illustrate the trend of methylation level changes, we divided the gene body and its upstream and downstream regions into 20 bins and plotted the methylation level trends for each bin (Fig. 3b). The results showed that the methylation level trends were quite similar across different liver and pancreas samples. In the upstream 2 kb region of the gene (proximal to the promoter), the methylation level gradually decreased, which is associated with an open chromatin state and the preparation for transcription initiation. Conversely, in the downstream 2 kb region, the methylation level slightly decreased and then remained stable. Such a pattern is consistent with findings from other animals [44, 45].

Fig. 2
figure 2

Genome-wide distribution of CG methylation levels in liver and pancreas samples. (a) Distribution of CG methylation levels in liver samples. (b) Distribution of CG methylation levels in pancreas samples. The X-axis represents individual samples from two developmental stages, while the Y-axis represents methylation levels. Every 10 kb is taken as a bin. The width of each violin plot reflects the number of bins at a given methylation level.

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Fig. 3
figure 3

DNA methylation patterns in liver and pancreas samples. (a) Mean CG methylation levels across different gene elements in liver samples. (b) Mean CG methylation levels across different gene elements in pancreas samples. (c) Dynamic changes in methylation levels across upstream and downstream regions of genes in liver samples. (d) Dynamic changes in methylation levels across upstream and downstream regions of genes in pancreas samples. For all panels, the X-axis represents different genomic regions, while the Y-axis represents methylation levels. Different colors correspond to different samples.

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To assess the rationality of grouping, we conducted principal component analysis and Spearman correlation distance clustering analysis based on the methylation levels of CG sites across all samples (Fig. 4). The PCA and clustering results revealed significant tissue specificity between liver and pancreas samples, with distinct separation of samples from the suckling and adult groups along PC1 and PC2 within different tissues.

Fig. 4
figure 4

Clustering analyses and PCA of WGBS samples. (a) Clustering of liver and pancreas samples based on CG methylation levels. Distance between samples is measured by Spearman’s rank correlation coefficient. (b) PCA analyses of the CG methylation levels for liver and pancreas samples. Groups are represented by different colors.

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Identification of differentially methylated genes

We separately compared the liver and pancreas samples of the adult group with those of the suckling group to identify differentially methylated regions (DMRs). In the liver, we identified a total of 12,711 DMRs (8,892 hypermethylated and 3,819 hypomethylated) between the adult and suckling groups. In the pancreas, we found 3,913 DMRs (1,857 hypermethylated and 2,056 hypomethylated) between the adult and suckling groups. These results indicate significant changes in methylation patterns in the liver and pancreas before and after weaning in red pandas, with more pronounced changes observed in the liver.

Genomic regions that overlap with genes, particularly within gene bodies, often play crucial roles in biological processes [46, 47]. We defined genes with more than 50% overlap with DMRs as gene body differentially methylated genes. Compared to the suckling group, we identified 2,890 gene body differentially methylated genes in the liver (comprising 2,131 genes with hypermethylated DMRs and 759 genes with hypomethylated DMRs) and 799 gene body differentially methylated genes in the pancreas of the adult group (including 479 genes with hypermethylated DMRs and 320 genes with hypomethylated DMRs) (Fig. 5a). Some genes exhibited both hypermethylated and hypomethylated DMRs, reflecting the complexity of methylation distribution. Therefore, we proceeded with further analysis focusing on genes with exclusively hypermethylated or hypomethylated DMRs in different comparisons.

We performed KEGG and GO enrichment analyses for genes with high and low methylation levels in gene bodies. In the liver of adult group, hypermethylated genes were mainly enriched in pathways related to cell and organ development, such as embryonic organ development, organ morphogenesis, and digestive system development. This is crucial for the postnatal development of the liver in suckling red panda cubs. On the other hand, hypomethylated genes in the liver of adult group were primarily enriched in metabolic pathways, covering fatty acid, cholesterol, and glucose metabolism, as well as amino acid and protein metabolism. Additionally, we observed that both hypermethylated and hypomethylated genes in the pancreas of adult group were enriched to varying degrees in pathways related to nutrient metabolism. This may suggest the complexity of pancreatic epigenetics, particularly involving its digestive and metabolic functions.

Current research indicates that gene promoter regions play a key role in regulating gene expression, and methylation of the promoter region often results in silencing of gene expression [48]. To explore the correlation between changes in DNA methylation patterns before and after weaning in red pandas and the regulation of gene expression, we investigated the dynamic changes in methylation levels in each gene promoter region. In comparisons between adult and suckling groups, we identified differentially methylated promoter genes in the liver and pancreas of red pandas, with 1,384 genes (including 1,141 hypermethylated and 243 hypomethylated promoter genes) in the liver and 499 genes (including 398 hypermethylated and 101 hypomethylated promoter genes) in the pancreas (Fig. 5a, Fig. S2 and Fig. S3).

Fig. 5
figure 5

Differentially methylated genes (DMGs) in Adult vs. Suckling Groups. (a) Differentially methylated genes (DMGs) in gene bodies and promoter regions between Adult and Suckling groups. Red and blue bars indicate hyper- and hypo-methylated DMGs, respectively. (b) Venn diagram showing the overlap between promoter DMGs and DEGs in liver samples. (c) Venn diagram showing the overlap between promoter DMGs and DEGs in pancreas samples.

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We performed KEGG and GO enrichment analyses for genes with high and low methylation in the promoter regions. In the liver of adult group, hypermethylated genes were mainly enriched in pathways related to cell and organ development, such as negative regulation of cell development and limb development. Hypomethylated genes in the liver of adult group were primarily enriched in metabolic pathways, including positive regulation of catabolic processes, fatty acid catabolic processes, positive regulation of proteasomal ubiquitin-dependent protein catabolic processes, and glucose homeostasis. Additionally, similar to the liver, in the pancreas of adult group, hypermethylated genes were predominantly enriched in entries related to pancreatic development, while both hypermethylated and hypomethylated genes showed enrichment in pathways related to nutrient metabolism.

Integrative analysis and visualization of key genes

To elucidate the relationship between methylation status in the promoter and gene body regions and gene transcription, we integrated genes with differential methylation in these regions with transcriptome data for correlation analysis, calculating Spearman correlation coefficients (Fig. 6). A significant negative correlation was observed between methylation and gene expression in both the liver and pancreas promoter regions, whereas the gene body region showed mixed results, with some regions exhibiting significant negative correlations and others not reaching statistical significance. Specifically, significant negative correlations were found in the promoter regions of both the liver (r = -0.045, p = 0.0051) and pancreas (r = -0.0708, p = 0.0021), while in the gene body regions of the liver (r = -0.0363, p = 0.0771) and pancreas (r = -0.209, p < 0.0001), the correlations were less consistent, with only the pancreas gene body region showing a significant negative correlation. These results further support the crucial role of methylation in regulating gene expression, particularly in the promoter regions. Although it is well-established that methylation in the promoter region can lead to gene silencing, the functional role of methylation in the gene body region remains unclear. Therefore, despite observing some correlations in the gene body regions, the significance of the promoter regions is more pronounced, and we have focused on the methylation regulation in the promoter regions in the subsequent analyses.

Fig. 6
figure 6

Spearman correlation analysis of methylation levels with gene expression in gene-body and promoter regions. (a) Spearman correlation analysis of methylation levels and gene expression in the gene-body region of liver samples. (b) Spearman correlation analysis of methylation levels and gene expression in the promoter region of liver samples. (c) Spearman correlation analysis of methylation levels and gene expression in the gene-body region of pancreas samples. (d) Spearman correlation analysis of methylation levels and gene expression in the promoter region of pancreas samples. Red dots indicate genes with negative correlation between differential methylation level and differential expression, while blue dots indicate genes with positive correlation between differential methylation level and differential expression. r represents the correlation coefficient, and P indicates the significance level.

Full size image

In the liver, a total of 13 genes were identified from the comparison between the adult and suckling groups, showing a negative correlation between promoter methylation and gene expression (Fig. 5b). Among these, 4 genes exhibited low methylation in the promoter region and high expression, while 9 genes showed high methylation in the promoter region and low expression. In the pancreas, a total of 28 genes were identified from the comparison between the adult and suckling groups, showing a negative correlation between promoter methylation and gene expression (Fig. 5c). Among these, 7 genes exhibited low methylation in the promoter region and high expression, while 21 genes showed high methylation in the promoter region and low expression.

Through Cytoscape visualization, we identified the key interactions between differentially methylated promoter genes and differentially expressed genes involved in the dietary transition (Fig. S4 and Fig. S5). In the liver, we identified four hub genes (LPL, THY1, NRP2, WNT2) among the differentially methylated genes. Notably, LPL plays a crucial role in lipid metabolism. Additionally, in the pancreas, we identified key genes like PAX6, GLDC, HAS2, and CTSZ. PAX6 plays an important role in pancreatic islet cells. GLDC is involved in amino acid metabolism regulation, while CTSZ plays a crucial role in protein metabolism, reflecting the metabolic adjustments in adulthood.

Discussion

Unlike most mammals, red pandas have undergone a significant dietary shift during evolution, transitioning from a meat-based diet rich in fats and proteins to a bamboo-based diet low in fats and proteins [49]. During infancy, individuals rely on maternal milk, which is rich in proteins, lipids, lactose, oligosaccharides, vitamins, and trace elements (such as vitamin D and metal ions) [50, 51]. In contrast, adults primarily consume bamboo, which has a significantly different nutritional composition, particularly with lower fat and protein content. Therefore, it is of great significance to explore the underlying adaptive mechanisms that govern this drastic dietary shift before and after weaning in red pandas. In this context, DNA methylation sequencing offers an insightful approach to detecting the methylation levels on cytosine bases (C bases) across the genome. This study utilizes DNA methylation sequencing in combination with gene expression data to explore the potential associations between DNA methylation modifications and the expression of key genes involved in nutrient metabolism in the liver and pancreas of both suckling and adult red pandas. The focus is on understanding how these changes in DNA methylation may correlate with alterations in lipid, carbohydrate, and protein metabolism, which are essential for adapting to the dramatic dietary transition from milk to bamboo.

Changes in lipid metabolism

The transition from suckling to weaning in red pandas is characterized by significant changes in nutritional composition. Towards the end of suckling, breast milk gradually gives way to solid foods, which have higher carbohydrate content and lower fat and protein content. Compared to the suckling group, the adult group exhibits increased lipid droplet synthesis, elevated triglyceride storage, and reduced lipid metabolism. Results from the integrated analysis of transcriptome and DNA methylation sequencing suggest that perilipin-4 (PLIN4) is hypomethylated and highly expressed, while lipoprotein lipase (LPL) is hypermethylated and downregulated in the liver tissues of adult red pandas. PLIN4, a gene associated with lipid droplets, plays a crucial role in regulating the formation and metabolism of lipid droplets [52]. Lipid droplets (LDs) are small intracellular fat bodies that store and metabolize neutral lipids such as triglycerides and cholesterol esters. The storage of neutral lipids in LDs is essential for protecting cells from lipotoxicity due to excessive lipid accumulation in cell membranes. LDs are coated with various proteins, including perilipins and other structural proteins, lipid synthetic enzymes, lipases, and membrane transport proteins, and can interact with other organelles through protein-mediated membrane contact sites. Perilipins on the surface of lipid droplets play crucial roles in lipid droplet formation, lipid metabolism, and release [53,54,55,56]. Among them, PLIN4 has been reported to be involved in cardiac lipid accumulation [53]. The promoter region of PLIN4 contains conserved functional peroxisome proliferator-activated receptor (PPAR) response elements (PPREs) [57]. PPARs are nuclear receptors with ligand-activated transcription factor functions, consisting of three isoforms: PPARα, PPARβ/δ, and PPARγ, which regulate many aspects of lipid metabolism [58]. PPARα regulates energy balance, activation of PPARγ leads to insulin sensitization and promotes glucose metabolism, while activation of PPARβ/δ promotes fatty acid metabolism [59,60,61]. Lipoprotein lipase (LPL) is an enzyme that hydrolyzes triglycerides in blood and tissues into free fatty acids (FFAs) and glycerol for tissue utilization [62,63,64,65]. It releases fatty acids from triglyceride-rich lipoproteins such as VLDL and chylomicrons and can activate PPARα [59]. Our findings indicate that in the liver of adult red pandas compared to the suckling group, there is an increase in the process of storing triglycerides and other lipids in lipid droplets, accompanied by a decrease in the ability to metabolize triglycerides due to the low-methylation high-expression of PLIN4 and high -methylation low-expression of LPL. This reduction in triglyceride metabolism leads to decreased activation of PPARα, consistent with our transcriptome results.

Adult red pandas exhibit enhanced metabolism of plant secondary metabolites (PSMs), a key adaptation to their specialized diet. The results of the combined analysis of transcriptome and DNA methylation sequencing reveal that alcohol dehydrogenase 4 (ADH4) is hypomethylated and highly expressed in liver tissues of adult red pandas. ADH4 is involved not only in ethanol metabolism but also in the metabolism of fatty acids [66, 67]. Bamboo, the primary diet of adult red pandas, contains a specific ratio of non-polar lipids (NPL), glycolipids (GL), and phospholipids (PL) — approximately 17:27:56 — with predominant fatty acids such as palmitic acid, linoleic acid, and linolenic acid [68, 69]. These fatty acids, although varying across different parts of bamboo, play significant roles in the metabolic adjustments of the red panda. One important metabolite of fatty acid metabolism is 20-carboxyeicosatetraenoic acid (20-COOH-AA), which is synthesized from 20-hydroxyeicosatetraenoic acid (20-HETE) by cytochrome P450 (CYP) enzymes. Mouse ADH4 oxidizes 20-HETE, producing intermediate aldehydes and the final product 20-COOH-AA. The accumulation of 20-COOH-AA in extracellular fluid may modulate vascular regulatory signaling pathways [70], which aligns with our findings from the transcriptome analysis, showing enriched pathways related to the metabolism of arachidonic acid, linoleic acid, and cytochrome P450-mediated metabolism of exogenous substances. These processes are thought to contribute to the maintenance of cardiovascular health [71,72,73]. Additionally, cytochrome P450-related genes play a role in metabolizing PSMs in bamboo, potentially alleviating any negative impacts from the extensive consumption of bamboo [74,75,76].

Another crucial aspect of red panda metabolism is the handling of vitamin A. Vitamin A, or retinol, is an essential micronutrient that supports normal vision, immune function, and cellular integrity [77]. Since red pandas cannot synthesize vitamin A, they must obtain it through their diet. Despite bamboo’s relatively low content of vitamin A compared to breast milk, red pandas are able to store and utilize vitamin A efficiently, with adult red pandas showing increased storage of this vitamin. The combined transcriptome and DNA methylation sequencing results revealed that dehydrogenase/reductase 3 (DHRS3), an enzyme involved in vitamin A anabolism, is hypomethylated and highly expressed in adult red pandas. DHRS3 reduces retinaldehyde to retinol in lipid droplets, where it is further converted to retinyl esters and stored for future use [78,79,80]. This mechanism ensures that red pandas can access vitamin A despite the dietary variations in bamboo, supporting their nutritional needs throughout different seasons [81].

Changes in carbohydrate metabolism

After weaning, the diet of red panda transitions from high-fat milk to high-fiber, low-fat bamboo. Bamboo is a carbohydrate-rich food, and its leaves contain various monosaccharide and polysaccharide components, including xylose, arabinose, glucose, galactose, and rhamnose [21, 68, 69, 82]. Given this dietary shift, carbohydrates serve as the primary energy source for adult red pandas. Our findings indicate that, compared to the suckling group, the adult group exhibits significantly higher expression of genes related to carbohydrate metabolism, suggesting an enhanced capacity for carbohydrate utilization. Among these, alcohol dehydrogenase 4 (ADH4), a key glycolysis gene, was highly expressed, and its promoter region was notably hypomethylated. In glycolysis, ADH4 can oxidize ethanol to aldehydes and ketones, while reducing NAD to NADH, thus linking anaerobic and aerobic respiration to optimize the utilization of carbohydrate carbons [83, 84]. The hypomethylation and high expression of ADH4 in adult red pandas may enhance their ability to efficiently utilize the complex carbohydrates provided by bamboo. In addition, integrated transcriptomic and DNA methylation sequencing analysis revealed that FAM3C, a signaling molecule involved in metabolism regulation, was hypomethylated and highly expressed, while paired box 6 (PAX6) was hypermethylated and lowly expressed in the adult group. FAM3C, a member of the family of protein sequence similarity 3 (FAM3), can suppress the expression of gluconeogenesis genes and glucose production in liver cells independently of insulin [85, 86]. By downregulating gluconeogenesis, FAM3C ensures that the metabolic focus remains on utilizing available dietary carbohydrates rather than expending energy to produce glucose from other substrates. By enhancing PI3K/Akt signaling pathway, FAM3C supports the conversion of excess glucose into glycogen for storage in the liver and muscle tissues [85]. This is particularly important for maintaining glucose homeostasis and providing a steady energy supply during periods of fasting or low food availability. PAX6 plays a significant role in pancreatic islet cells by regulating the expression of insulin gene through interactions with other transcription factors and response to glucose levels [87,88,89,90]. In adult red pandas, the differential expression and methylation of genes related to carbohydrate metabolism likely contribute to their efficient use of the available carbohydrates in bamboo, optimizing the limited nutritional resources in their diet. Interestingly, this adaptive strategy in red pandas is consistent with that of giant pandas, supporting efficient carbohydrate utilization in their specialized diets [25].

Changes in protein metabolism

The synthesis and metabolism of amino acids and proteins are crucial for maintaining life. Cathepsin Z (CTSZ), a member of the papain family of cysteine proteases, is a major component of the lysosomal proteolytic system [91]. It plays an essential role in the degradation and recycling of proteins, both intra- and extracellularly. The results of the combined analysis of transcriptome and DNA methylation sequencing show that CTSZ in pancreatic tissue exhibits low methylation and high expression. This hypomethylation and increased expression of CTSZ in adult red pandas contribute to the efficient recycling and utilization of intracellular proteins under conditions of limited protein and amino acid availability. This process helps release free amino acids, which are crucial for the synthesis of endogenous proteins and the maintenance of normal physiological functions in red pandas.

Glycine decarboxylase (GLDC) is an enzyme involved in glycine metabolism and plays a significant role in one-carbon metabolism and mitochondrial function [92, 93]. In humans, GLDC catalyzes the first step of glycine degradation as part of the glycine cleavage system (GCS). Additionally, GLDC is involved in regulating protein lipidation and influencing mitochondrial metabolism and cell proliferation [94]. Our result showed that in pancreatic tissue, GLDC exhibits high methylation and low expression in adult red pandas. After weaning, the increased methylation of the GLDC gene leads to a reduction in its expression, which may suppress glycine degradation. This mechanism could contribute to the maintenance of glycine levels, thereby facilitating adaptation to a low-amino acid bamboo diet.

Conclusion

This study highlights potential associations between DNA methylation modifications and dietary adaptation in red pandas during the transition from milk to bamboo-based diets. Our findings suggest that hypomethylation and high expression of PLIN4 in adult red pandas could be linked to enhanced lipid storage, while hypermethylation and suppressed expression of LPL may be associated with reduced lipid breakdown, possibly adapting to the low-fat content of bamboo. Additionally, changes in DNA methylation of carbohydrate metabolism genes like ADH4 and FAM3C may correlate with enhanced glucose management in adult red pandas, which is essential for a high-carbohydrate diet. Alterations in protein metabolism, through methylation changes in genes such as CTSZ and GLDC, could contribute to optimizing amino acid processing. These epigenetic modifications reflect potential molecular strategies that red pandas might employ to navigate the nutritional challenges of their bamboo-based diet. However, we emphasize that while these correlations are observed, further studies, particularly those with larger sample sizes and experimental validation of methylation regulation with technical possibilities in the future, are needed to substantiate causality. We acknowledge that several potential confounding factors, including age, sex, environmental influences, and genetic variation, could have subtly affected the results. Despite careful consideration of these factors, the small sample size due to the challenges of studies with endangered species remains a limitation of our study. Future research with a larger number of samples and more robust statistical approaches, such as multivariate regression, would allow for a more detailed exploration of how these variables contribute to the observed changes in gene expression and DNA methylation. Our findings contribute to a broader understanding of how obligate dietary specialist manage their nutrient metabolism, which can inform conservation strategies and studies on other species with unique dietary needs.

Data availability

The high-throughput sequencing data from this study have been submitted to the NCBI with the project accession PRJNA1139133.

Change history

  • 02 May 2025

    There was an error in the department information of Affiliation 1. The article has been updated to rectify the error.

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Acknowledgements

We sincerely appreciate the reviewers and editors for their insightful comments and helpful suggestions. We thank all laboratory members for their constructive advice and discussions.

Funding

The research was funded by the National Natural Science Foundation of China (31770574) and the Natural Science Foundation of Sichuan Province (No. 2022NSFSC0121).

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Author notes

  1. Lei Chen and Liang Zhang contributed equally to this work and share first authorship.

Authors and Affiliations

  1. Key Laboratory of Bio-resources and Eco-environment, Ministry of Education, College of Life Science, Sichuan University, Chengdu, 610064, China

    Lei Chen, Yanni Zhao, Jingheng Wang, Zhoulong Chen, Yongqi Zhao & Xiuyue Zhang

  2. Sichuan Key Laboratory for Conservation Biology of Endangered Wildlife, Chengdu Research Base of Giant Panda Breeding, 1375 Panda Road, Northern Suburb, Chengdu, 610081, China

    Liang Zhang & Fujun Shen

  3. China Conservation and Research Center for the Giant Panda, Dujiangyan, 611800, China

    Ming He & Honglin Wu

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  1. Lei Chen
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Contributions

L.C. and L.Z. coordinated and performed the research. L.C., L.Z., Y.Z., M.H., H.W., J.W., Z.C. and Y.Z. analyzed the data, wrote the manuscript and prepared all Figures. L.Z. and F.S. provided the samples of red pandas. X.Z. conceived and designed the study. All authors contributed to manuscript revision, read, and approved the submitted version.

Corresponding author

Correspondence to Xiuyue Zhang.

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This study was approved by the Ethics Committee of College of Life Science, Sichuan University, China (Grant No: 20190506001). All methods were performed in accordance with the relevant guidelines and regulations.

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The authors declare no competing interests.

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Chen, L., Zhang, L., Zhao, Y. et al. Impact of DNA methylation on digestive and metabolic gene expression in red pandas (Ailurus fulgens) during the transition from milk to bamboo diet. BMC Genomics 26, 404 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12864-025-11606-w

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Keywords

BMC Genomics

ISSN: 1471-2164

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