Differential Roles of Coding and Non-Coding Transcripts in Obesity: Insights from RNA-Seq Analysis of Macaca Fascicularis Hepatocytes

Differential Roles of Coding and Non-Coding Transcripts in Obesity: Insights from RNA-Seq Analysis of Macaca Fascicularis Hepatocytes | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Differential Roles of Coding and Non-Coding Transcripts in Obesity: Insights from RNA-Seq Analysis of Macaca Fascicularis Hepatocytes Yu Liu, Ziting Wang, Linna Liu, Ya Tan, Wenling Zheng, Pengfei Zhang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6316157/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Dec, 2025 Read the published version in BMC Genomics → Version 1 posted 10 You are reading this latest preprint version Abstract Long non-coding RNAs (lncRNAs) can regulate metabolism-associated genes and cooperate to activate obesity-related pathways. However, the role of lncRNAs in obesity-related diabetes is not clear. To address this, we analyzed the hepatic transcriptomes of diabetic and non-diabetic Cynomolgus monkeys ( Macaca fascicularis ) using next-generation sequencing (NGS). Our findings demonstrate that coding and non-coding RNAs exhibit distinct patterns of expression, with coding mRNAs notably enriched for metabolic pathways and particularly lipid transport. At the same time, the expression of genes related to alcohol metabolism was suppressed in diabetic samples compared with normal samples. This study expands the understanding of the molecular underpinnings behind obesity and suggests possible avenues for precision treatment approaches that target metabolic diseases. T2DM Macaca fascicularis Enrichment analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Abnormal or excessive body fat storage is a symbol of obesity, a complex chronic metabolic condition that is frequently accompanied by insulin resistance, chronic inflammation, and other metabolic dysregulation [ 1 ]. Obesity is a serious concern for worldwide public health because it is caused by a combination of variables, including genetics, environment, lifestyle, and particularly the predominance of high-calorie foods and sedentary lifestyles[ 2 ]. In addition to being linked to an increased risk of cardiovascular disease, non-alcoholic fatty liver disease (NAFLD), and type 2 diabetes[ 3 ], obesity is also linked to an increased chance of developing some types of cancer. RNA sequencing (RNA-seq) has revealed important regulatory networks in obesity-related molecular mechanisms [ 4 ], including the interactions of coding RNAs and non-coding RNAs [ 5 ]. Despite progress made in these studies, the specific functions and differences between coding and non-coding regions in the development of obesity are not fully understood, which provides an important scientific basis for further research on the molecular mechanisms of obesity and precision treatment strategies. The cynomolgus monkey ( Macaca fascicularis ) [ 6 ] is a valuable non-human primate model for biomedical research as a human substitute. Due to their over 90% genomic similarity to humans, these primates are extensively studied to assess the safety and effectiveness of medications. They share similar immune system responses, organ activities, and metabolic pathways with humans, making the conversion of translational insights into potential treatment outcomes more reliable. Additionally, they are a practical option for long-term research because of their manageability, adaptability in laboratory settings, and relatively small size. Importantly, established ethical guidelines support using macaques fascicularis, ensuring that their involvement in research meets strict welfare standards[ 7 ]. Their biological relevance and practical utility combination underscores their value in advancing medication development and improving human health. Numerous non-coding genes which control organisms' glycogen levels, or govern metabolic activities are known to have a greater expression in the liver, all of which have an impact on this expression pattern. PPP1R3B , for instance, is a regulatory switch involved in the metabolism of glucose [ 8 ]. In liver cells, it controls the synthesis and reorganization of glycogen. It has a significant impact on blood sugar homeostasis and increases the activity of glycogen synthase by joining forces with protein phosphatase 1 (PP1). Similarly, in this study, we found through enrichment analysis that amongst the up-regulated genes in a model of diabetes, many genes related to lipid metabolism and fatty acid transport processes are enriched. We discovered that whereas a small number of related genes were concentrated in the non-coding areas of Macaca fascicularis , a high number of coding genes regulating lipid transport and metabolism were enriched. Suggesting, coding genes are primarily responsible for the obesity phenotype. This report represents the first large-scale transcriptome sequencing and gene analysis of obese Macaca fascicularis and may contribute to its applications in biomedical research and basic biology. This provides evidence for genetic regulation of the obesity phenotype. 2. Results 2.1 Study design and T2D diagnosis To overcome obstacles to comprehending the biological reactions of Macaca fascicularis , we sequenced the transcriptome of liver tissue from six Macaca fascicularis , both diabetic (n = 3) and non-diabetic (n = 3). In this study, all six samples were obtained from aged (> 17 years old) male monkeys that had been subjected to long-term dietary regimens (Fig. 1 A). Three monkeys were fed a high-fat diet (HFD), and were diagnosed with type 2 diabetes (T2D) in 2016 based on results from the Intravenous Glucose Tolerance Test (IVGTT). The other three monkeys served as controls, and together, these samples were used to investigate the metabolic and physiological effects associated with prolonged dietary interventions and type 2 diabetes progression. The diabetic group will be referred to as the HFD group and the normal diet-fed non-diabetic group as the normal control (NC) group. Based on the Intravenous Glucose Tolerance Test (IVGTT), the HFD group exhibited significantly elevated blood glucose levels one hour after glucose injection, indicating impaired glucose clearance and a reduced ability to metabolize glucose efficiently. This sustained high blood sugar response suggests the presence of insulin resistance, a hallmark of T2D. In contrast, the insulin levels in the HFD group were much lower compared to the NC group, reflecting a diminished insulin secretion response. The reduced insulin levels in the HFD group further support the notion of pancreatic β-cell dysfunction or insulin resistance, which is commonly associated with the development of type 2 diabetes. This contrast in glucose and insulin dynamics between the HFD and NC groups highlights the metabolic dysfunction induced by a high-fat diet and its contribution to diabetes pathogenesis. 2.2 Quality Control The Macaque genome assembly does not reach the same quality as the human genome or other model species, hence we validated the quality of our RNA-seq. In summary, the sequence alignment rate is high: Over 90% of the RNA-seq reads aligned to the reference genome (Fig. 2 A). Among the mapped reads, the portion of the reads that could be assigned to a coding gene is much higher than those reads that mapped to a non-coding gene (Fig. 2 A). This highlights the relatively high quality of transcriptome quantitation. As for the expression level, there was little difference between the HFD and NC group (Fig. 2 B-D). The density plots exhibit that the expression of most coding genes is concentrated in significant low-level areas, mainly distributed in the range of log2(FPKM + 1) from 0 to 5, with the first obvious peak appearing at About 2.5 FPKM. This shows that the vast majority of non-coding genes have low expression levels in the samples and are low-expressed genes. Coding genes, conversely had higher expression although the distribution becomes sparse for genes with an FPKM greater than 5. The median expression of coding genes was overall higher than that of non-coding genes (Fig. 2 B), suggesting that coding genes are more actively expressed. Especially in the two groups of HFD-coding and NC-coding, the box height is larger, indicating that the expression levels of coding genes are highly dispersed, and some genes show significantly high expression. In contrast, the expression of non-coding genes is more concentrated and distributed over a smaller range. From a comparison between groups, whether coding genes or non-coding genes, the median expression levels of the HFD group and the NC group are close, indicating that the overall gene expression did not change significantly. However, the number of outliers in non-coding genes was larger, especially in the HFD-noncoding group, which may suggest a specific regulatory effect of HFD on some non-coding genes. In addition, the existence of outliers implies that individual genes may have abnormally high expression levels, which are more significant in non-coding genes and may be related to certain regulatory functions or biological mechanisms. Together, these results indicate that the expression levels of coding genes are overall higher than those of non-coding genes, which may be related to their functional properties. Coding genes are often involved in complex biological processes such as gene expression regulation and, therefore need to be kept relatively high basal expression level to perform its function. Non-coding genes, conversely, often have lower overall levels of expression but can still have important biological functions. 2.2 Principal Component and Differential Expression Analysis We next performed Principal Component Analysis (PCA) to explore the differences in the data sets. Interestingly, coding transcripts alone could discriminate between HFD and NC groups, whilst lncRNAs in the samples tended to be mixed (Fig. 3 A, B). From the PCA plots, we find that the first two PCs contain approximately 50% of the information (Fig. 3 C). In two dimensions, there was a clear difference between the HFD and NC groups’ mRNA expression (Fig. 3 A). On the contrary, PCA of non-coding genes in the NC group and HFD group showed a mixture of the two groups along the first two PCs (Fig. 3 A). This suggests that non-coding genes are more variable, and differences between groups cannot be directly distinguished through these dimensions. Differential gene expression showed that 182 genes significantly increased, and the expression of 241 genes decreased (Fig. 3 C). The situation for non-coding RNAs was similar (Fig. 3 C). Overall, 4175 non-coding genes were not significantly different between NC and HFD (Fig. 3 C). However, 98 non-coding genes were significantly increased, and 115 genes increased in HFD. In general, PCA indicated differences between the NC and HFD groups, which could be identified by differential gene expression analysis. If the gene expression of the population can be clustered, that is, there are obvious differences between the populations, then we can analyze these differentially expressed genes. Heatmaps of differentially expressed genes clearly showed the differences in differential expression between genes in the coding region and genes in the non-coding region (Fig. 4 A, B). The number of differentially expressed coding genes exceeds that of genes in the non-coding region (Fig. 4 A). This shows that under different experimental conditions, the expression changes of coding region genes may be closely related to biological processes, disease development, and other factors. In contrast, noncoding genes, although they may also show differences under certain conditions, were less likely to be differentially regulated (Fig. 4 B). It should be noted that there is some expected variation between the different monkeys, and this was particularly acute in the lncRNAs (Fig. 4 B). This contrasts with the higher agreement of differentially regulated genes in the mRNAs (Fig. 4 A). 2. 3 Gene Ontology Enrichment analysis We next explored the functional consequences of the differentially regulated genes by looking at gene ontology (GO) enrichment. From the results of the GO enrichment analysis of mRNAs (Fig. 5 A), it was found that the most significantly up-regulated gene enrichment in the HFD group compared to the NC group was the process of ‘regulation of the lipid metabolism’. The next few that were significantly up-regulated were related to pathways such as lipid metabolism and transport. This suggests a clear link with obesity, as the top enriched genes were related to obesity. Terms such as monocarboxylic acid, and fatty acid transport [ 10 ]. In contrast, significant enrichment of non-coding regions resulted in only a single GO entry, which was the pathway regulating lipid metabolism(Fig. 5 B). This is likely due to a lack of good GO term annotations for lncRNAs, which tend to have less well-defined functions. Consequently, we turned to KEGG analysis on the DE lncRNAs, found many significantly enriched terms, including this time for non-coding RNAs. Compared with the results of GO enrichment analysis, KEGG enrichment analysis shows fewer lipid metabolism-related entries. However, KEGG pathways for iron metabolism abnormalities and lipid peroxidation were upregulated and may relate to the pathophysiology of diabetes. Indeed, both pathways are suggestive of ferroptosis, an iron-dependent type of cell death that has been implicated in human disease and senescence. Potentially, diabetes may impact cell metabolism and physiological activities through both mechanisms. Notably, we also discovered that the HFD group genes were enriched for fatty acid metabolism-related pathways, suggesting that lipid metabolism anomalies may be an unavoidable contributing component to the pathological process of diabetes via obesity. This indicates that in the pathogenesis of diabetes, changes in lipid metabolism may not be limited to the direct effects of fatty acid metabolism but involves more complex cell death and stress response mechanisms. Additionally, KEGG analysis of lncRNAs suggests that the HFD group had the greatest enrichment of the steroid production pathways. According to this, diabetes may have a significant impact on the pathophysiological process of the disease by influencing hormone synthesis and associated cell signaling through the control of steroid metabolism. Furthermore, KEGG enrichment analysis showed that several additional biological processes, such as carbon metabolism and mannose metabolism, were significantly enriched. In patients with diabetes, problems in energy metabolism, glucose balance, and lipid metabolism may be strongly linked to changes in these metabolic pathways [ 11 ]. The HFD group exhibited notable enrichment in the mannose metabolic pathway, suggesting that mannose, a crucial carbohydrate, may contribute to metabolic problems in diabetes. Diabetes patients' energy metabolism and pancreatic islet function may be impacted by abnormal mannose metabolism, indicating that this pathway could be a viable target for treatment. We next looked at GSEA, and among the up-regulated expression of genes in the coding regions of the HFD and NC groups, most of the significantly up-regulated genes were concentrated in the process of fat metabolism and fatty acid transport (Fig. 6 ). Besides, there are also some up-regulated genes enriched on the process of metal ion homeostasis. 2.3.2 network diagram From the GO enrichment network diagram of mRNA, the enrichment degree of genes in coding regions in different metabolic processes is small, but the most important enrichment pathway is lipid metabolism and its regulation, and several other sporadic fatty-acid-related pathways (Fig. 7 A). Most of the pathways are also obesity-related pathways, such as the transport of fatty acids. However, in the network diagram of non-coding regions, we found an enriched entry that was far larger than other pathways, which was lipid metabolism (Fig. 7 A). The second largest pathway was also a pathway regulated by lipid metabolism. Although there are some other sporadic small ones, they are far less significant than lipid metabolism. For example, the metabolism of alcohol and sterols, etc. In the KEGG network diagram (Fig. 7 B), we found that most genes are enriched in metabolic pathways, and a small number of branches are connected to fatty acid metabolism and degradation. Compared with the results of GO enrichment analysis, the lipid metabolism is roughly the same, but the enrichment results are more concentrated. In addition, some genes of this major metabolic node are connected to signaling pathways such as PPAP. The KEGG enrichment results in non-coding areas also show that most genes are enriched in metabolic pathways instead of lipid metabolism. Apart from that, there are also some genes linked to obesity-related items, including fatty acid metabolism, steroid biosynthesis, and carbon metabolism. This also confirms that genes in non-coding regions take some role in regulating obesity. 2.4 Other enriched pathways LncRNAs are weakly annotated in GO, hence, to gain some insight we looked at local cis-regulated coding genes(Fig. 8 A). on the basis that genes close to non-coding genes often display similar regulation. Hence, we analyzed both cis and trans-regulatory mechanisms influencing gene expression using KEGG and GO enrichment analysis. According to the analysis, most of the genes linked to obesity were found on different chromosomes, which implies that trans regulation is important in regulating the genes that contribute to the obesity phenotype. The intricate genetic makeup of obesity seems to be driven by trans regulation, which involves interactions between regulatory elements and target genes located on separate chromosomes. HFD samples showed a marked and significant down-regulation(Fig. 8 B). in the expression levels of many genes inside the coding regions as compared to the up-regulated genes. This pattern suggests a widespread inhibition of genetic activity in these regions, which probably adds to the HFD's effects on metabolism and physiology. We found that the expression of genes linked to alcohol metabolism was significantly down-regulated using GO enrichment analysis. There was inhibition of biological processes associated with the metabolism of alcohol, secondary alcohols, and organic hydroxyl molecules. According to these results, HFD may disrupt metabolic processes that are essential for preserving regular cellular and systemic functions, which could have a knock-on effect on health and the course of disease. 3. Discussion In summary, we used enrichment analysis of differentially expressed genes to explore gene pathways involved in HFD and obesity-mediated diabetes and identified lipid-related pathways in the livers of diabetic cynomolgus monkeys. In the GO and KEGG analysis, there were few gene annotations in non-coding regions, but overall, the differentially regulated genes pointed to several pathways involved in lipid metabolism. Furthermore, in individuals with diabetes, anomalies in the metabolism of carbohydrates, fatty acids, and amino acids are reflected in the enrichment of carbon metabolism [ 12 ]. These metabolic process alterations highlight the disease in the diabetic patient's systemic metabolic network, which might result in the development of pathological conditions like insulin resistance and diabetic complications [ 13 ]. All things considered, KEGG enrichment analysis showed notable alterations in several crucial metabolic pathways in the diabetic group, offering crucial hints for a thorough comprehension of the molecular mechanism underlying diabetes and the pursuit of novel therapeutic approaches. 4. Materials and Methods 4.1.1 Obesity Modeling Feed: 200 g of standard Macaca fascicularis formula feed (expanded pellet feed, 9:00–10:00), apple (150 g, 14:00–15:00) and 200 g high-fat diet (HFD feed, 16:00–17:00). All food was removed after 17:00 every afternoon, and the animals were fasted overnight. Animal feed intake was calculated by weighing the remaining amount of food removed after the feeding period. Energy intake was calculated based on daily feed intake in Kcal. Drinking water: Municipal tap water is filtered by reverse osmosis and provided for animals to drink freely. The animal drinking water is entrusted to a third party (Yunnan Tianlai Environmental Protection Technology Co., Ltd.) for regular inspections. The inspection indicators include microorganisms and environmental pollutants. Feed Energy(Kcal/g) Standard diet/pellet diet 3.81 Apple 0.52 High-fat diet 4.12 To improve the atmosphere and guarantee the welfare and mental well-being of animals, provide them with toys while they are in their cages. To increase the training impact, the animals are rewarded (with peanuts, etc.) after each training exercise during the animal adaption training. During the entire research period, each animal was kept in a stainless steel cage respectively. The environment temperature was maintained at 18–29 ℃, Besides the relative humidity was kept at approximately 30%-90%. Apart from that, the lab was ventilated at least 10 times per hour. A time-controlled lighting system (light hours from 7 a.m. to 7 p.m.) is used to provide a 12-hour light/12-hour dark day and night cycle. The cages were cleaned daily. 4.1.2 Diabetes indicator detection process: IVGTT (Intravenous Glucose Tolerance Test ): Use ketamine hydrochloride (10 mg/kg) to anesthetize the animal intramuscularly. The animals must be continually sedated throughout the test, and ketamine hydrochloride at doses of 5–10 mg/kg may be administered. A 0.5 g/kg (50% concentration) glucose solution is injected from the saphenous vein or peripheral vein within 30 seconds after the animal has been fasting for 12–16 hours. The following formula is used to determine the glucose dose: With the end of the glucose bolus as 0, use EDTA-K2 anticoagulant tubes to collect approximately 1.0 mL of blood from the vein 1 minute before and 1, 3, 5, 10, 20, 40, and 60 minutes after sugar administration. Plasma is separated for blood glucose and insulin testing. GLU: The Macaca fascicularis is immobilized and the blood collection site is cleaned with a disinfectant using aseptic technique to ensure that the risk of infection is reduced. Quickly obtain a blood sample using a finger stick using an appropriately sized needle, and test the animal's blood glucose using a standard blood glucose meter. 4.1.3 Diagnostic methods After feeding for some time, we diagnosed whether the animal is diabetic in the following way: a) Other signs were auxiliary indexes, and the venous blood glucose value in more than two consecutive laboratory tests met the diagnostic criteria. b) Fasting blood glucose values can be used alone for large-group screening. c) If fasting blood glucose at 7.0 mmol/L glucose regulation was impaired, an IVGTT examination was performed. d) HbA1c Can assist in the diagnosis of diabetes mellitus, the standard is HbA 1 c 6.5%. e) Rapid blood glucose meter test is suitable for large group screening and daily blood glucose monitoring, and the results are used as a reference for diagnosis. 4.2 Preprocessing of sequencing reads/quality control Before analysis, the accuracy and quality of the data must be ensured. In this case, the data should be cleaned, and remove the low-quality data with fastp [14]. 4.2.1 Sequencing Data Statistics This project completed a total transcript analysis of 6 samples and obtained a total of 70.607 Gb of Clean Data (sequencing data after quality control). The average data volume of Clean Data for each sample was 11.768 Gb, the Q30 base percentage was above 91.41%, and the GC content was 43.09–43.78%. 4.2.2 Reference genome Reference species: Macaca fascicularis (Cynomolgus monkey); Reference genome version: Macaca_fascicularis_6.0; The post-quality control sequencing data of each sample were compared with the specified reference genome. The comparison rates ranged from 91.857–93.69%, and the unique comparison rates ranged from 89.157–89.977%. Known gene annotation: https://ftp.ensembl.org/pub/release-109/gtf/macaca_fascicularis/Macaca_fascicularis.Macaca_fascicularis_6.0.109.gtf.gz; 4.3 Instruments Eukaryotic mRNA sequencing is based on the HiSeq platform, which sequences all mRNA transcribed from specific tissues or cells. The sequencing experiment used the Illumina TruseqTM RNA sample prep Kit method to construct the library according to the manufacturer's instructions. Total DNA was extracted using Thermo's TRIzol® Reagent. Additionally, Illumina provides the Novaseq 6000 SBS Kit v3-HS (200 cycles) for on-machine sequencing, the cBot Truseq PE Cluster Kit v3-cBot-HS for bridge amplification, and the TruSeq Stranded Total RNA with Ribo-Zero for library creation. Invitrogen provided the cDNA enzyme and Qubit4.0 was utilized for quantification. 4.4 Bioinformatic analysis For lncRNA transcriptome analysis with a reference genome, the sequence obtained by sequencing was first aligned to the genome using HISAT2 [15], and stringtie was used to splice and identify new genes and new transcripts [16]. Perform quality control, database annotation (GO, KEGG), expression quantification, and functional enrichment for genes and transcripts of mRNA and lncRNA. 4.4.2 Gene expression quantitation The degree of gene expression is reflected in the abundance of transcripts. The number of sequences (clean reads) mapped to the reference genome area in RNA-seq analysis was used to determine a gene's expression level. The read count is positively correlated with the gene's length and sequencing depth, and it is directly proportional to the gene's genuine expression level. FPKM (Fragments Per Kilobase of transcript per Million mapped reads) is the number of fragments per million of a certain gene's kilobase length. It also considers how gene length and sequencing depth affect the number of fragments. The gene expression level was calculated using the FPKM [17] method using the following formula: 4.5 Differential Expression Analysis The general distribution of differentially expressed genes can be deduced by using scatter plots and volcano plots to visualize the screened differentially expressed genes. In this section, R package DEseq2 and ggplot2 were used to do the differential expression analysis and visualization. 4.7 Gene Ontology and KEGG Enrichment Analysis A database called GO (Gene Ontology)[18] was developed by the Gene Ontology Consortium. Standardizing scientific nomenclature for genes and gene products across different databases is one of its objectives, along with describing and characterizing the activities of genes and proteins. Using the GO database, genes can be grouped according to the molecular roles they play, the biological processes they contribute to, and the components of cells. KEGG [19], enables the investigation of genes and expression data. KEGG, the primary public database on pathways, offers integrated metabolic pathway inquiries that cover the biodegradation of organic matter and the metabolism of carbohydrates, nucleosides, amino acids, and other substances. Both the enrichment analyses were done in R using the clusterProfiler package [20]. The input files were the results that we had in the previous DE analysis. 5. Conclusions In this study, we found that compared with the normal group, samples from the HFD group had significantly up-regulated genes in both coding and non-coding regions. They work together to open up the pathways of lipid metabolism and transport. Our final protein kinases were then enriched the most in domain statistics. This shows that the final gene changes the phenotype by regulating protein kinases and ultimately regulating lipids. Compared with previous studies that knocked down the coding region LOC157273 , increasing the expression of PPP1R3B , and ultimately activated glycogen phosphorylase, resulting in a decrease in blood sugar in the sample, this study used RNA-seq to conduct a full transcriptome enrichment analysis [ 21 ]. We further confirmed this through gene enrichment analysis, that is, a high-fat diet will lead to changes in the gene regulation of the samples. The combined interaction of coding and non-coding regions can lead to phenotypic changes. In conclusion, our results show that different metabolic pathways respond to dietary stressors using diverse pathways. Mainly, those Obesity-related pathways exhibit up-regulation in both coding and non-coding areas and are related to alcohol metabolism and fatty acid metabolism. These revelations provide a better knowledge of how metabolic pathways are coordinated at the molecular level by highlighting the intricacy and specificity of gene regulation in response to dietary variables. In subsequent research, this data will be a useful tool for determining whether regions of the macaque genome are functional. Abbreviations The following abbreviations are used in this manuscript: NC Normal Control HFD High Fat Diet PCA Principle Component Analysis DE Differential Expression KEGG Kyoto Encyclopedia of Genes and Genomes GO Gene Ontology T2D Type 2 Diabetes Declarations All manuscripts must contain the following sections under the heading 'Declarations'. Ethics approval and consent to participate Animal experiments were approved under the ethical guidelines, approval number HZ2023025. Consent for publication Not applicable Availability of data and materials The datasets generated and/or analysed during the current study are not publicly available due [REASON WHY DATA ARE NOT PUBLIC] but are available from the corresponding author on reasonable request. Competing interests The authors declare that they have nocompeting interests Funding No funding for this article Authors'contributions YL finshed the main manuscript. In addition, most of the analytical and visualization work was also carried out by YL. ZTW was responsible for data collection. Both authors made equal contributions and are recognized as co-first authors. LNL was primarily responsible for the initial investigation at the beginning of the animal feeding process. YT provided the experimental materials and animals and participated in the care and feeding of the experimental animals. Professor AH reviewed this manuscript and provided the most comments and edits. Regarding the corresponding authors, Professor WZW managed the project, while Professor LL and Professor WLZ also served as corresponding authors. Acknowledgements We sincerely appreciate Ms. XSZ and Mr. ANJ for their contributions to sample collection and acquisition throughout the project. References Hursting SD, Dunlap SM. Obesity, metabolic dysregulation, and cancer: A growing concern and an inflammatory (and microenvironmental) issue. Ann N Y Acad Sci. 2012;1271(1):82–7. Faghri P. Sedentary Lifestyle, Obesity, and Aging: Implication for Prevention. J Nutr Disord Ther. 2015;05(01):5–6. Xu Y, Lu J, Li M, Wang T, Wang K, Cao Q, Ding Y, Xiang Y, Wang S, Yang Q, Zhao X, Zhang X, Xu M, Wang W, Bi Y, Ning G. Diabetes in China part 1: epidemiology and risk factors. The Lancet Public Health. Volume 9. Elsevier Ltd; 2024. pp. e1089–97. 12 https://doi.org/10.1016/S2468-2667(24)00250-0 . Li X, Lin Z, Zhan X, Gao J, Sun L, Cao Y et al. RNA-seq analysis of the transcriptome of the liver of cynomolgus monkeys with type 2 diabetes. Gene [Internet]. 2018;651(February):118–25. Available from: https://doi.org/10.1016/j.gene.2018.02.010 Manning AK, Goustin AS, Kleinbrink EL, Thepsuwan P, Cai J, Ju D, et al. A Long Non-coding RNA, LOC157273, Is an Effector Transcript at the Chromosome 8p23.1-PPP1R3B Metabolic Traits and Type 2 Diabetes Risk Locus. Front Genet. 2020;11(July):1–13. Ebeling M, Küng E, See A, Broger C, Steiner G, Berrera M, et al. Genome-based analysis of the nonhuman primate Macaca fascicularis as a model for drug safety assessment. Genome Res. 2011;21(10):1746–56. Hau J, Schapiro SJ. The welfare of non-human primates. The welfare of laboratory animals. Dordrecht: Springer Netherlands; 2007. pp. 291–314. Mehta MB, Shewale SV, Sequeira RN, Millar JS, Hand NJ, Rader DJ. Hepatic protein phosphatase 1 regulatory subunit 3B (Ppp1r3b) promotes hepatic glycogen synthesis and thereby regulates fasting energy homeostasis. J Biol Chem [Internet]. 2017;292(25):10444–54. Available from: http://dx.doi.org/10.1074/jbc.M116.766329 Love MI, Anders S, Huber W. Genome Biol. 2014;15:550. Differential analysis of count data - the DESeq2 package [Internet]. Berk PD. Regulatable fatty acid transport mechanisms are central to the pathophysiology of obesity, fatty liver, and metabolic syndrome. Hepatology. 2008;48(5):1362–76. Havel PJ. (2004). Section IV: Lipid Modulators of Islet Function Update on Adipocyte Hormones Regulation of Energy Balance and Carbohydrate/Lipid Metabolism. In DIABETES (53, Issue 1). Shu Y, Yang F, Li W, et al. Changes and Significance of Central Carbon Metabolism before and after Metformin Treatment of Type 2 Diabetes Based on Mass Spectrometry. Discov Med. 2024;36(183):678–89. Wilcox G. (2005). Insulin and Insulin Resistance. Clin Biochem Rev (26). Chen S, Zhou Y, Chen Y, Gu J. Fastp: An ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. 2018;34(17):i884–90. Kim D, Paggi JM, Park C, et al. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat Biotechnol. 2019;37:907–15. Pertea M, Pertea G, Antonescu C, et al. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat Biotechnol. 2015;33:290–5. Zhao Y, Li MC, Konaté MM, Chen L, Das B, Karlovich C, et al. TPM, FPKM, or Normalized Counts? A Comparative Study of Quantification Measures for the Analysis of RNA-seq Data from the NCI Patient-Derived Models Repository. J Transl Med [Internet]. 2021;19(1):1. du Plessis L, Škunca N, Dessimoz C. The what, where, how and why of gene ontology-A primer for bioinformaticians. Brief Bioinform. 2011;12(6):723–35. Ogata H, Goto S, Fujibuchi W, Kanehisa M. Computation with the KEGG pathway database. BioSystems. 1998;47(1–2):119–28. Yu G, Wang LG, Han Y, He QY. ClusterProfiler: An R package for comparing biological themes among gene clusters. OMICS J Integr Biology. 2012;16(5):284–7. https://doi.org/10.1089/omi.2011.0118 . Slack FJ, Chinnaiyan AM. The Role of Non-coding RNAs in Oncology. Cell [Internet]. 2019;179(5):1033–55. Available from: https://doi.org/10.1016/j.cell.2019.10.017 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 22 Dec, 2025 Read the published version in BMC Genomics → Version 1 posted Editorial decision: Revision requested 01 Aug, 2025 Reviews received at journal 11 Jun, 2025 Reviewers agreed at journal 09 Jun, 2025 Reviews received at journal 01 Jun, 2025 Reviewers agreed at journal 11 May, 2025 Reviewers invited by journal 09 May, 2025 Editor assigned by journal 04 Apr, 2025 Editor invited by journal 03 Apr, 2025 Submission checks completed at journal 03 Apr, 2025 First submitted to journal 03 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6316157","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":455032234,"identity":"9170d7d4-7b4a-41a5-aed6-bf30e41a2e76","order_by":0,"name":"Yu Liu","email":"","orcid":"","institution":"Shenzhen Huayuan Biotechnology Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Liu","suffix":""},{"id":455032236,"identity":"cc34a526-31b6-4b90-9708-fb680e6350f9","order_by":1,"name":"Ziting Wang","email":"","orcid":"","institution":"Shenzhen Huayuan Biotechnology Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Ziting","middleName":"","lastName":"Wang","suffix":""},{"id":455032237,"identity":"2e3515c9-75ad-4623-9fbc-428247530da1","order_by":2,"name":"Linna Liu","email":"","orcid":"","institution":"Shenzhen Huayuan Biotechnology Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Linna","middleName":"","lastName":"Liu","suffix":""},{"id":455032238,"identity":"15d47d47-4c88-499a-8aec-ab4f7a866209","order_by":3,"name":"Ya Tan","email":"","orcid":"","institution":"Shenzhen Huayuan Biotechnology Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Ya","middleName":"","lastName":"Tan","suffix":""},{"id":455032239,"identity":"b065d65e-3f44-4d7f-908e-432db6b06995","order_by":4,"name":"Wenling Zheng","email":"","orcid":"","institution":"Shenzhen Huayuan Biotechnology Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Wenling","middleName":"","lastName":"Zheng","suffix":""},{"id":455032240,"identity":"f792620c-c457-467c-9f41-1d0d1c5eef87","order_by":5,"name":"Pengfei Zhang","email":"","orcid":"","institution":"Chinese Academy of Science","correspondingAuthor":false,"prefix":"","firstName":"Pengfei","middleName":"","lastName":"Zhang","suffix":""},{"id":455032241,"identity":"33a223e5-4971-413a-844f-ab871f155e45","order_by":6,"name":"Andrew Hutchins","email":"","orcid":"","institution":"Southern University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Andrew","middleName":"","lastName":"Hutchins","suffix":""},{"id":455032242,"identity":"efaa1118-e242-4947-95ae-41fc2f55190e","order_by":7,"name":"Leonard Lipovich","email":"","orcid":"","institution":"Shenzhen Huayuan Biotechnology Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Leonard","middleName":"","lastName":"Lipovich","suffix":""},{"id":455032243,"identity":"eed4ba4e-4b62-4cbb-863f-03e7e645633e","order_by":8,"name":"Weizhong Wang","email":"data:image/png;base64,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","orcid":"","institution":"Shenzhen Huayuan Biotechnology Co., Ltd","correspondingAuthor":true,"prefix":"","firstName":"Weizhong","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-03-27 02:23:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6316157/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6316157/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12864-025-12380-5","type":"published","date":"2025-12-22T15:57:09+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82828961,"identity":"fe342fcf-35c1-4c12-8859-123dfb18be25","added_by":"auto","created_at":"2025-05-15 16:43:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":408374,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInformation on the experiment animals used in this study, Table A is the basic information. Tables B and C show the results of the IVGTT assay and were used as evidence to diagnose T2D for the HFD group samples. Table B shows the results of blood sugar over different periods, while Table C shows insulin content in the blood.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6316157/v1/cb8021d8f1be806e4876d62d.png"},{"id":82828962,"identity":"d3a5028d-291e-4605-a1a6-48e08a075909","added_by":"auto","created_at":"2025-05-15 16:43:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":171863,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRNA-seq quality control data. (A) The percentage of the reads that map to the reference is displayed in Plot A. Green and orange indicate the percentage of reads that align to annotated coding and non-coding RNAs in the reference genome, respectively, while blue indicates the total number of reads that align anywhere to the reference genome in the RNA-seq sample. The percentage of reads that align to the reference genome is shown on the Y axis. (B) A box plot representing the expression levels based on the coding and non-coding RNAs of the HFD or NC groups. The log2 transformed FPKM Normalized expression level is shown on the Y axis. (C and D) Density plot distributions of the expression distributions for coding (panel C) and non-coding (panel D) RNAs. The log2 transformed FPKM expression level is represented on the X-axis. The density is on the Y-axis.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6316157/v1/20a1567883574e55c1969e74.png"},{"id":82828967,"identity":"caebf535-8873-48c6-bc07-cab24c9aecb0","added_by":"auto","created_at":"2025-05-15 16:43:06","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":367519,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrincipal component and differential expression analysis for coding and non-coding RNAs. (A) Scatter plots showing the first 2 principal components (PCs) for coding (left) and non-coding (right). Dots represent each sample, and the colors represent the same sample group. (B) PC loading plot, the PC loading is indicated on the axis labels. (C) Volcano plots, the two volcano plots show the differentially expressed genes. Differential expression compared with HFD group and NC group Macaque liver samples. Each dot represents the log2(fold-change) versus the adjusted p-value for coding mRNAs (left plot) and ncRNAs (right plot). A gene was considered differentially expressed if its absolute fold-change was greater than 1 and the adjusted p-value was less than 0.05. Differential expression was called using DESeq2 [9]. Specific genes are indicated on the plot.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6316157/v1/5c1b0aa829763fddc0549418.jpeg"},{"id":82828965,"identity":"8df75de7-bf66-48be-9067-5b17a4e45fb4","added_by":"auto","created_at":"2025-05-15 16:43:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":440476,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHeatmaps showing the expression patterns of differentially expressed genes in different experiments. (A) Differentially expressed mRNAs and (B) Differentially expressed lncRNAs. The color in the figure represents the gene expression level, using a gradient color bar from blue to red, where blue indicates low expression or small values, and red indicates high expression or large values (Z-score). The ordinate is a gene, and each row corresponds to a gene. The genes are hierarchically clustered according to their expression similarity in the sample, and genes with similar expression patterns are clustered together. The abscissa represents samples, and each column corresponds to a sample.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6316157/v1/f6c43ef758bc6fa42086211a.png"},{"id":82829992,"identity":"a01af4b0-30ce-462d-8692-fb806ead869a","added_by":"auto","created_at":"2025-05-15 17:07:06","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":381611,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGene Ontology analysis for differentially regulated mRNAs and lncRNAs. (A) GO analysis of DE genes, coding (left bar chart), and non-coding (right bar chart). The vertical axis shows significance (-log10(p-value)), while the horizontal axis shows significantly enriched GO biological process (BP) terms. (B) KEGG enrichment analysis is displayed as bar plots. Bar charts showing significantly enriched KEGG terms in coding mRNAs (left) and non-coding RNAs (right).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6316157/v1/7272e52e76fff11b3de9e259.jpeg"},{"id":82829990,"identity":"6a743154-cf71-4fe4-ab32-ab2d971121ee","added_by":"auto","created_at":"2025-05-15 17:07:06","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":152864,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGSEA analysis. The black bars mean different genes, the green line means the enrichment score and the heatmap exhibits the expression level, red means high (up) expression and blue means low (down) expression genes.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6316157/v1/c20d496465cee214a8d2b022.png"},{"id":82829141,"identity":"acea3b32-e6ab-4d08-82a7-cb5654ad09ad","added_by":"auto","created_at":"2025-05-15 16:51:06","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":605597,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNetwork-based GO analysis for coding and non-coding RNAs. (A) GO BP analysis, left network is coding transcripts, right network is non-coding. Each node in the network diagram shows the GO enrichment analysis results. The node size represents the number of genes in each GO term. Dark hues are indicative of significance, and the color signifies significance (e.g., the size of the p-value). The functional correlation or gene set overlap between the items is represented by the connections between nodes. The closer the functional pieces are to one another, the greater or thicker the connections. The larger yellow nodes, which suggest functional relevance, should be the focus of interpretation. (B) displays the network-based KEGG enrichment network analysis. The left network is coding mRNAs and the lncRNA is on the right. Each node represents a KEGG term, and the edges linked terms.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6316157/v1/968d8d483f9dabc372fc41d4.jpeg"},{"id":82829694,"identity":"b8a5fc76-c016-4144-bbef-7ec6facfd349","added_by":"auto","created_at":"2025-05-15 16:59:06","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":253747,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCis-gene regulation. (A) Bar plot for cis-regulated enrichment, which refers to genes within 10kb (100kb) upstream and downstream of a lncRNA. Generally, target genes transcribed in the promoter region in the same direction promote expression, and in the opposite direction, they inhibit expression. On the right plot, B is the downregulated genes GO enrichment analysis.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6316157/v1/4c50b458cb7be76b0b25082b.jpeg"},{"id":99172236,"identity":"806edfa9-dec4-4c02-a036-7b05ba91a11a","added_by":"auto","created_at":"2025-12-29 16:04:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5299800,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6316157/v1/cff931f6-a195-4d40-b7b6-13de6f1e2c9c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Differential Roles of Coding and Non-Coding Transcripts in Obesity: Insights from RNA-Seq Analysis of Macaca Fascicularis Hepatocytes","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAbnormal or excessive body fat storage is a symbol of obesity, a complex chronic metabolic condition that is frequently accompanied by insulin resistance, chronic inflammation, and other metabolic dysregulation [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Obesity is a serious concern for worldwide public health because it is caused by a combination of variables, including genetics, environment, lifestyle, and particularly the predominance of high-calorie foods and sedentary lifestyles[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In addition to being linked to an increased risk of cardiovascular disease, non-alcoholic fatty liver disease (NAFLD), and type 2 diabetes[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], obesity is also linked to an increased chance of developing some types of cancer.\u003c/p\u003e \u003cp\u003eRNA sequencing (RNA-seq) has revealed important regulatory networks in obesity-related molecular mechanisms [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], including the interactions of coding RNAs and non-coding RNAs [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Despite progress made in these studies, the specific functions and differences between coding and non-coding regions in the development of obesity are not fully understood, which provides an important scientific basis for further research on the molecular mechanisms of obesity and precision treatment strategies.\u003c/p\u003e \u003cp\u003eThe cynomolgus monkey (\u003cem\u003eMacaca fascicularis\u003c/em\u003e) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] is a valuable non-human primate model for biomedical research as a human substitute. Due to their over 90% genomic similarity to humans, these primates are extensively studied to assess the safety and effectiveness of medications. They share similar immune system responses, organ activities, and metabolic pathways with humans, making the conversion of translational insights into potential treatment outcomes more reliable. Additionally, they are a practical option for long-term research because of their manageability, adaptability in laboratory settings, and relatively small size. Importantly, established ethical guidelines support using macaques fascicularis, ensuring that their involvement in research meets strict welfare standards[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Their biological relevance and practical utility combination underscores their value in advancing medication development and improving human health.\u003c/p\u003e \u003cp\u003eNumerous non-coding genes which control organisms' glycogen levels, or govern metabolic activities are known to have a greater expression in the liver, all of which have an impact on this expression pattern. \u003cem\u003ePPP1R3B\u003c/em\u003e, for instance, is a regulatory switch involved in the metabolism of glucose [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In liver cells, it controls the synthesis and reorganization of glycogen. It has a significant impact on blood sugar homeostasis and increases the activity of glycogen synthase by joining forces with protein phosphatase 1 (PP1). Similarly, in this study, we found through enrichment analysis that amongst the up-regulated genes in a model of diabetes, many genes related to lipid metabolism and fatty acid transport processes are enriched. We discovered that whereas a small number of related genes were concentrated in the non-coding areas of \u003cem\u003eMacaca fascicularis\u003c/em\u003e, a high number of coding genes regulating lipid transport and metabolism were enriched. Suggesting, coding genes are primarily responsible for the obesity phenotype. This report represents the first large-scale transcriptome sequencing and gene analysis of obese \u003cem\u003eMacaca fascicularis\u003c/em\u003e and may contribute to its applications in biomedical research and basic biology. This provides evidence for genetic regulation of the obesity phenotype.\u003c/p\u003e"},{"header":"2. Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design and T2D diagnosis\u003c/h2\u003e \u003cp\u003eTo overcome obstacles to comprehending the biological reactions of \u003cem\u003eMacaca fascicularis\u003c/em\u003e, we sequenced the transcriptome of liver tissue from six \u003cem\u003eMacaca fascicularis\u003c/em\u003e, both diabetic (n\u0026thinsp;=\u0026thinsp;3) and non-diabetic (n\u0026thinsp;=\u0026thinsp;3). In this study, all six samples were obtained from aged (\u0026gt;\u0026thinsp;17 years old) male monkeys that had been subjected to long-term dietary regimens (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Three monkeys were fed a high-fat diet (HFD), and were diagnosed with type 2 diabetes (T2D) in 2016 based on results from the Intravenous Glucose Tolerance Test (IVGTT). The other three monkeys served as controls, and together, these samples were used to investigate the metabolic and physiological effects associated with prolonged dietary interventions and type 2 diabetes progression. The diabetic group will be referred to as the HFD group and the normal diet-fed non-diabetic group as the normal control (NC) group.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBased on the Intravenous Glucose Tolerance Test (IVGTT), the HFD group exhibited significantly elevated blood glucose levels one hour after glucose injection, indicating impaired glucose clearance and a reduced ability to metabolize glucose efficiently. This sustained high blood sugar response suggests the presence of insulin resistance, a hallmark of T2D. In contrast, the insulin levels in the HFD group were much lower compared to the NC group, reflecting a diminished insulin secretion response. The reduced insulin levels in the HFD group further support the notion of pancreatic β-cell dysfunction or insulin resistance, which is commonly associated with the development of type 2 diabetes. This contrast in glucose and insulin dynamics between the HFD and NC groups highlights the metabolic dysfunction induced by a high-fat diet and its contribution to diabetes pathogenesis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Quality Control\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe Macaque genome assembly does not reach the same quality as the human genome or other model species, hence we validated the quality of our RNA-seq.\u0026nbsp;In summary, the sequence alignment rate is high: Over 90% of the RNA-seq reads aligned to the reference genome (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Among the mapped reads, the portion of the reads that could be assigned to a coding gene is much higher than those reads that mapped to a non-coding gene (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). This highlights the relatively high quality of transcriptome quantitation. As for the expression level, there was little difference between the HFD and NC group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB-D). The density plots exhibit that the expression of most coding genes is concentrated in significant low-level areas, mainly distributed in the range of log2(FPKM\u0026thinsp;+\u0026thinsp;1) from 0 to 5, with the first obvious peak appearing at About 2.5 FPKM. This shows that the vast majority of non-coding genes have low expression levels in the samples and are low-expressed genes. Coding genes, conversely had higher expression although the distribution becomes sparse for genes with an FPKM greater than 5. The median expression of coding genes was overall higher than that of non-coding genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), suggesting that coding genes are more actively expressed. Especially in the two groups of HFD-coding and NC-coding, the box height is larger, indicating that the expression levels of coding genes are highly dispersed, and some genes show significantly high expression. In contrast, the expression of non-coding genes is more concentrated and distributed over a smaller range.\u003c/p\u003e \u003cp\u003eFrom a comparison between groups, whether coding genes or non-coding genes, the median expression levels of the HFD group and the NC group are close, indicating that the overall gene expression did not change significantly. However, the number of outliers in non-coding genes was larger, especially in the HFD-noncoding group, which may suggest a specific regulatory effect of HFD on some non-coding genes. In addition, the existence of outliers implies that individual genes may have abnormally high expression levels, which are more significant in non-coding genes and may be related to certain regulatory functions or biological mechanisms.\u003c/p\u003e \u003cp\u003eTogether, these results indicate that the expression levels of coding genes are overall higher than those of non-coding genes, which may be related to their functional properties. Coding genes are often involved in complex biological processes such as gene expression regulation and, therefore need to be kept relatively high basal expression level to perform its function. Non-coding genes, conversely, often have lower overall levels of expression but can still have important biological functions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Principal Component and Differential Expression Analysis\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe next performed Principal Component Analysis (PCA) to explore the differences in the data sets. Interestingly, coding transcripts alone could discriminate between HFD and NC groups, whilst lncRNAs in the samples tended to be mixed (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, B). From the PCA plots, we find that the first two PCs contain approximately 50% of the information (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). In two dimensions, there was a clear difference between the HFD and NC groups\u0026rsquo; mRNA expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). On the contrary, PCA of non-coding genes in the NC group and HFD group showed a mixture of the two groups along the first two PCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). This suggests that non-coding genes are more variable, and differences between groups cannot be directly distinguished through these dimensions.\u003c/p\u003e \u003cp\u003eDifferential gene expression showed that 182 genes significantly increased, and the expression of 241 genes decreased (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). The situation for non-coding RNAs was similar (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Overall, 4175 non-coding genes were not significantly different between NC and HFD (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). However, 98 non-coding genes were significantly increased, and 115 genes increased in HFD. In general, PCA indicated differences between the NC and HFD groups, which could be identified by differential gene expression analysis. If the gene expression of the population can be clustered, that is, there are obvious differences between the populations, then we can analyze these differentially expressed genes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHeatmaps of differentially expressed genes clearly showed the differences in differential expression between genes in the coding region and genes in the non-coding region (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, B). The number of differentially expressed coding genes exceeds that of genes in the non-coding region (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). This shows that under different experimental conditions, the expression changes of coding region genes may be closely related to biological processes, disease development, and other factors. In contrast, noncoding genes, although they may also show differences under certain conditions, were less likely to be differentially regulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). It should be noted that there is some expected variation between the different monkeys, and this was particularly acute in the lncRNAs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). This contrasts with the higher agreement of differentially regulated genes in the mRNAs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e2. 3 Gene Ontology Enrichment analysis\u003c/h3\u003e\n\u003cp\u003e \u003c/p\u003e \u003cp\u003eWe next explored the functional consequences of the differentially regulated genes by looking at gene ontology (GO) enrichment. From the results of the GO enrichment analysis of mRNAs (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA), it was found that the most significantly up-regulated gene enrichment in the HFD group compared to the NC group was the process of \u0026lsquo;regulation of the lipid metabolism\u0026rsquo;. The next few that were significantly up-regulated were related to pathways such as lipid metabolism and transport. This suggests a clear link with obesity, as the top enriched genes were related to obesity. Terms such as monocarboxylic acid, and fatty acid transport [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn contrast, significant enrichment of non-coding regions resulted in only a single GO entry, which was the pathway regulating lipid metabolism(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). This is likely due to a lack of good GO term annotations for lncRNAs, which tend to have less well-defined functions. Consequently, we turned to KEGG analysis on the DE lncRNAs, found many significantly enriched terms, including this time for non-coding RNAs. Compared with the results of GO enrichment analysis, KEGG enrichment analysis shows fewer lipid metabolism-related entries. However, KEGG pathways for iron metabolism abnormalities and lipid peroxidation were upregulated and may relate to the pathophysiology of diabetes. Indeed, both pathways are suggestive of ferroptosis, an iron-dependent type of cell death that has been implicated in human disease and senescence. Potentially, diabetes may impact cell metabolism and physiological activities through both mechanisms. Notably, we also discovered that the HFD group genes were enriched for fatty acid metabolism-related pathways, suggesting that lipid metabolism anomalies may be an unavoidable contributing component to the pathological process of diabetes via obesity. This indicates that in the pathogenesis of diabetes, changes in lipid metabolism may not be limited to the direct effects of fatty acid metabolism but involves more complex cell death and stress response mechanisms.\u003c/p\u003e \u003cp\u003eAdditionally, KEGG analysis of lncRNAs suggests that the HFD group had the greatest enrichment of the steroid production pathways. According to this, diabetes may have a significant impact on the pathophysiological process of the disease by influencing hormone synthesis and associated cell signaling through the control of steroid metabolism. Furthermore, KEGG enrichment analysis showed that several additional biological processes, such as carbon metabolism and mannose metabolism, were significantly enriched. In patients with diabetes, problems in energy metabolism, glucose balance, and lipid metabolism may be strongly linked to changes in these metabolic pathways [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The HFD group exhibited notable enrichment in the mannose metabolic pathway, suggesting that mannose, a crucial carbohydrate, may contribute to metabolic problems in diabetes. Diabetes patients' energy metabolism and pancreatic islet function may be impacted by abnormal mannose metabolism, indicating that this pathway could be a viable target for treatment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe next looked at GSEA, and among the up-regulated expression of genes in the coding regions of the HFD and NC groups, most of the significantly up-regulated genes were concentrated in the process of fat metabolism and fatty acid transport (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Besides, there are also some up-regulated genes enriched on the process of metal ion homeostasis.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.3.2 network diagram\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFrom the GO enrichment network diagram of mRNA, the enrichment degree of genes in coding regions in different metabolic processes is small, but the most important enrichment pathway is lipid metabolism and its regulation, and several other sporadic fatty-acid-related pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). Most of the pathways are also obesity-related pathways, such as the transport of fatty acids. However, in the network diagram of non-coding regions, we found an enriched entry that was far larger than other pathways, which was lipid metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). The second largest pathway was also a pathway regulated by lipid metabolism. Although there are some other sporadic small ones, they are far less significant than lipid metabolism. For example, the metabolism of alcohol and sterols, etc. In the KEGG network diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB), we found that most genes are enriched in metabolic pathways, and a small number of branches are connected to fatty acid metabolism and degradation. Compared with the results of GO enrichment analysis, the lipid metabolism is roughly the same, but the enrichment results are more concentrated. In addition, some genes of this major metabolic node are connected to signaling pathways such as PPAP. The KEGG enrichment results in non-coding areas also show that most genes are enriched in metabolic pathways instead of lipid metabolism. Apart from that, there are also some genes linked to obesity-related items, including fatty acid metabolism, steroid biosynthesis, and carbon metabolism. This also confirms that genes in non-coding regions take some role in regulating obesity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Other enriched pathways\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eLncRNAs are weakly annotated in GO, hence, to gain some insight we looked at local cis-regulated coding genes(Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). on the basis that genes close to non-coding genes often display similar regulation. Hence, we analyzed both cis and trans-regulatory mechanisms influencing gene expression using KEGG and GO enrichment analysis. According to the analysis, most of the genes linked to obesity were found on different chromosomes, which implies that trans regulation is important in regulating the genes that contribute to the obesity phenotype. The intricate genetic makeup of obesity seems to be driven by trans regulation, which involves interactions between regulatory elements and target genes located on separate chromosomes.\u003c/p\u003e \u003cp\u003eHFD samples showed a marked and significant down-regulation(Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). in the expression levels of many genes inside the coding regions as compared to the up-regulated genes. This pattern suggests a widespread inhibition of genetic activity in these regions, which probably adds to the HFD's effects on metabolism and physiology. We found that the expression of genes linked to alcohol metabolism was significantly down-regulated using GO enrichment analysis. There was inhibition of biological processes associated with the metabolism of alcohol, secondary alcohols, and organic hydroxyl molecules. According to these results, HFD may disrupt metabolic processes that are essential for preserving regular cellular and systemic functions, which could have a knock-on effect on health and the course of disease.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Discussion","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn summary, we used enrichment analysis of differentially expressed genes to explore gene pathways involved in HFD and obesity-mediated diabetes and identified lipid-related pathways in the livers of diabetic cynomolgus monkeys. In the GO and KEGG analysis, there were few gene annotations in non-coding regions, but overall, the differentially regulated genes pointed to several pathways involved in lipid metabolism.\u003c/p\u003e \u003cp\u003eFurthermore, in individuals with diabetes, anomalies in the metabolism of carbohydrates, fatty acids, and amino acids are reflected in the enrichment of carbon metabolism [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. These metabolic process alterations highlight the disease in the diabetic patient's systemic metabolic network, which might result in the development of pathological conditions like insulin resistance and diabetic complications [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. All things considered, KEGG enrichment analysis showed notable alterations in several crucial metabolic pathways in the diabetic group, offering crucial hints for a thorough comprehension of the molecular mechanism underlying diabetes and the pursuit of novel therapeutic approaches.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"4. Materials and Methods","content":"\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003e4.1.1 Obesity Modeling\u003c/h2\u003e\n \u003cdiv\u003e\n \u003cp\u003eFeed: 200 g of standard \u003cem\u003eMacaca fascicularis\u003c/em\u003e formula feed (expanded pellet feed, 9:00\u0026ndash;10:00), apple (150 g, 14:00\u0026ndash;15:00) and 200 g high-fat diet (HFD feed, 16:00\u0026ndash;17:00).\u003c/p\u003e\n \u003cp\u003eAll food was removed after 17:00 every afternoon, and the animals were fasted overnight. Animal feed intake was calculated by weighing the remaining amount of food removed after the feeding period. Energy intake was calculated based on daily feed intake in Kcal.\u003c/p\u003e\n \u003cp\u003eDrinking water: Municipal tap water is filtered by reverse osmosis and provided for animals to drink freely. The animal drinking water is entrusted to a third party (Yunnan Tianlai Environmental Protection Technology Co., Ltd.) for regular inspections. The inspection indicators include microorganisms and environmental pollutants.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tabb\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFeed\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEnergy(Kcal/g)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStandard diet/pellet diet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eApple\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh-fat diet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eTo improve the atmosphere and guarantee the welfare and mental well-being of animals, provide them with toys while they are in their cages. To increase the training impact, the animals are rewarded (with peanuts, etc.) after each training exercise during the animal adaption training.\u003c/p\u003e\n \u003cp\u003eDuring the entire research period, each animal was kept in a stainless steel cage respectively. The environment temperature was maintained at 18\u0026ndash;29 ℃, Besides the relative humidity was kept at approximately 30%-90%. Apart from that, the lab was ventilated at least 10 times per hour. A time-controlled lighting system (light hours from 7 a.m. to 7 p.m.) is used to provide a 12-hour light/12-hour dark day and night cycle. The cages were cleaned daily.\u003c/p\u003e\n \u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003e4.1.2 Diabetes indicator detection process:\u003c/h2\u003e\n \u003cdiv\u003e\n \u003cp\u003eIVGTT (Intravenous Glucose Tolerance Test ): Use ketamine hydrochloride (10 mg/kg) to anesthetize the animal intramuscularly. The animals must be continually sedated throughout the test, and ketamine hydrochloride at doses of 5\u0026ndash;10 mg/kg may be administered. A 0.5 g/kg (50% concentration) glucose solution is injected from the saphenous vein or peripheral vein within 30 seconds after the animal has been fasting for 12\u0026ndash;16 hours. The following formula is used to determine the glucose dose:\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eWith the end of the glucose bolus as 0, use EDTA-K2 anticoagulant tubes to collect approximately 1.0 mL of blood from the vein 1 minute before and 1, 3, 5, 10, 20, 40, and 60 minutes after sugar administration. Plasma is separated for blood glucose and insulin testing.\u003c/p\u003e\n \u003cp\u003eGLU: The Macaca fascicularis is immobilized and the blood collection site is cleaned with a disinfectant using aseptic technique to ensure that the risk of infection is reduced. Quickly obtain a blood sample using a finger stick using an appropriately sized needle, and test the animal\u0026apos;s blood glucose using a standard blood glucose meter.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003e4.1.3 Diagnostic methods\u003c/h2\u003e\n \u003cdiv\u003e\n \u003cp\u003eAfter feeding for some time, we diagnosed whether the animal is diabetic in the following way:\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003ea) Other signs were auxiliary indexes, and the venous blood glucose value in more than two consecutive laboratory tests met the diagnostic criteria.\u003c/p\u003e\n \u003cp\u003eb) Fasting blood glucose values can be used alone for large-group screening.\u003c/p\u003e\n \u003cp\u003ec) If fasting blood glucose at 7.0 mmol/L glucose regulation was impaired, an IVGTT examination was performed.\u003c/p\u003e\n \u003cp\u003ed) HbA1c Can assist in the diagnosis of diabetes mellitus, the standard is HbA 1 c 6.5%.\u003c/p\u003e\n \u003cp\u003ee) Rapid blood glucose meter test is suitable for large group screening and daily blood glucose monitoring, and the results are used as a reference for diagnosis.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\"\u003e\n \u003ch2\u003e4.2 Preprocessing of sequencing reads/quality control\u003c/h2\u003e\n \u003cdiv\u003e\n \u003cp\u003eBefore analysis, the accuracy and quality of the data must be ensured. In this case, the data should be cleaned, and remove the low-quality data with fastp [14].\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec15\"\u003e\n \u003ch2\u003e4.2.1 Sequencing Data Statistics\u003c/h2\u003e\n \u003cdiv\u003e\n \u003cp\u003eThis project completed a total transcript analysis of 6 samples and obtained a total of 70.607 Gb of Clean Data (sequencing data after quality control). The average data volume of Clean Data for each sample was 11.768 Gb, the Q30 base percentage was above 91.41%, and the GC content was 43.09\u0026ndash;43.78%.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec16\"\u003e\n \u003ch2\u003e4.2.2 Reference genome\u003c/h2\u003e\n \u003cdiv\u003e\n \u003cp\u003eReference species: Macaca fascicularis (Cynomolgus monkey); Reference genome version: Macaca_fascicularis_6.0;\u003c/p\u003e\n \u003cp\u003eThe post-quality control sequencing data of each sample were compared with the specified reference genome. The comparison rates ranged from 91.857\u0026ndash;93.69%, and the unique comparison rates ranged from 89.157\u0026ndash;89.977%.\u003c/p\u003e\n \u003cp\u003eKnown gene annotation:\u003c/p\u003e\n \u003cp\u003ehttps://ftp.ensembl.org/pub/release-109/gtf/macaca_fascicularis/Macaca_fascicularis.Macaca_fascicularis_6.0.109.gtf.gz;\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\"\u003e\n \u003ch2\u003e4.3 Instruments\u003c/h2\u003e\n \u003cdiv\u003e\n \u003cp\u003eEukaryotic mRNA sequencing is based on the HiSeq platform, which sequences all mRNA transcribed from specific tissues or cells. The sequencing experiment used the Illumina TruseqTM RNA sample prep Kit method to construct the library according to the manufacturer\u0026apos;s instructions.\u003c/p\u003e\n \u003cp\u003eTotal DNA was extracted using Thermo\u0026apos;s TRIzol\u0026reg; Reagent. Additionally, Illumina provides the Novaseq 6000 SBS Kit v3-HS (200 cycles) for on-machine sequencing, the cBot Truseq PE Cluster Kit v3-cBot-HS for bridge amplification, and the TruSeq Stranded Total RNA with Ribo-Zero for library creation. Invitrogen provided the cDNA enzyme and Qubit4.0 was utilized for quantification.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\"\u003e\n \u003ch2\u003e4.4 Bioinformatic analysis\u003c/h2\u003e\n \u003cdiv\u003e\n \u003cp\u003eFor lncRNA transcriptome analysis with a reference genome, the sequence obtained by sequencing was first aligned to the genome using HISAT2 [15], and stringtie was used to splice and identify new genes and new transcripts [16]. Perform quality control, database annotation (GO, KEGG), expression quantification, and functional enrichment for genes and transcripts of mRNA and lncRNA.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec19\"\u003e\n \u003ch2\u003e4.4.2 Gene expression quantitation\u003c/h2\u003e\n \u003cdiv\u003e\n \u003cp\u003eThe degree of gene expression is reflected in the abundance of transcripts. The number of sequences (clean reads) mapped to the reference genome area in RNA-seq analysis was used to determine a gene\u0026apos;s expression level.\u003c/p\u003e\n \u003cp\u003eThe read count is positively correlated with the gene\u0026apos;s length and sequencing depth, and it is directly proportional to the gene\u0026apos;s genuine expression level. FPKM (Fragments Per Kilobase of transcript per Million mapped reads) is the number of fragments per million of a certain gene\u0026apos;s kilobase length. It also considers how gene length and sequencing depth affect the number of fragments. The gene expression level was calculated using the FPKM [17] method using the following formula:\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/69519_bce2c0439cd956a6/69519_custom_files/img1747326775.png\"\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\"\u003e\n \u003ch2\u003e4.5 Differential Expression Analysis\u003c/h2\u003e\n \u003cdiv\u003e\n \u003cp\u003eThe general distribution of differentially expressed genes can be deduced by using scatter plots and volcano plots to visualize the screened differentially expressed genes. In this section, R package DEseq2 and ggplot2 were used to do the differential expression analysis and visualization.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\"\u003e\n \u003ch2\u003e4.7 Gene Ontology and KEGG Enrichment Analysis\u003c/h2\u003e\n \u003cdiv\u003e\n \u003cp\u003eA database called GO (Gene Ontology)[18] was developed by the Gene Ontology Consortium. Standardizing scientific nomenclature for genes and gene products across different databases is one of its objectives, along with describing and characterizing the activities of genes and proteins. Using the GO database, genes can be grouped according to the molecular roles they play, the biological processes they contribute to, and the components of cells.\u003c/p\u003e\n \u003cp\u003eKEGG [19], enables the investigation of genes and expression data. KEGG, the primary public database on pathways, offers integrated metabolic pathway inquiries that cover the biodegradation of organic matter and the metabolism of carbohydrates, nucleosides, amino acids, and other substances.\u003c/p\u003e\n \u003cp\u003eBoth the enrichment analyses were done in R using the clusterProfiler package [20]. The input files were the results that we had in the previous DE analysis.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn this study, we found that compared with the normal group, samples from the HFD group had significantly up-regulated genes in both coding and non-coding regions. They work together to open up the pathways of lipid metabolism and transport. Our final protein kinases were then enriched the most in domain statistics. This shows that the final gene changes the phenotype by regulating protein kinases and ultimately regulating lipids. Compared with previous studies that knocked down the coding region \u003cem\u003eLOC157273\u003c/em\u003e, increasing the expression of \u003cem\u003ePPP1R3B\u003c/em\u003e, and ultimately activated glycogen phosphorylase, resulting in a decrease in blood sugar in the sample, this study used RNA-seq to conduct a full transcriptome enrichment analysis [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. We further confirmed this through gene enrichment analysis, that is, a high-fat diet will lead to changes in the gene regulation of the samples. The combined interaction of coding and non-coding regions can lead to phenotypic changes.\u003c/p\u003e \u003cp\u003eIn conclusion, our results show that different metabolic pathways respond to dietary stressors using diverse pathways. Mainly, those Obesity-related pathways exhibit up-regulation in both coding and non-coding areas and are related to alcohol metabolism and fatty acid metabolism. These revelations provide a better knowledge of how metabolic pathways are coordinated at the molecular level by highlighting the intricacy and specificity of gene regulation in response to dietary variables. In subsequent research, this data will be a useful tool for determining whether regions of the macaque genome are functional.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eThe following abbreviations are used in this manuscript:\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"524\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eNC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eNormal Control\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eHFD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eHigh Fat Diet\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003ePCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003ePrinciple Component Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eDE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eDifferential Expression\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eKyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eGO\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eGene Ontology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eT2D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eType 2 Diabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003eAll manuscripts must contain the following sections under the heading \u0026apos;Declarations\u0026apos;.\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eAnimal experiments were approved under the ethical guidelines, approval number \u0026nbsp;HZ2023025.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are not publicly available due [REASON WHY DATA ARE NOT PUBLIC] but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have nocompeting interests\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eNo funding for this article\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos;contributions\u003c/p\u003e\n\u003cp\u003eYL finshed the main manuscript. In addition, most of the analytical and visualization work was also carried out by YL.\u003c/p\u003e\n\u003cp\u003eZTW was responsible for data collection. Both authors made equal contributions and are recognized as co-first authors.\u003c/p\u003e\n\u003cp\u003eLNL was primarily responsible for the initial investigation at the beginning of the animal feeding process.\u003c/p\u003e\n\u003cp\u003eYT provided the experimental materials and animals and participated in the care and feeding of the experimental animals.\u003c/p\u003e\n\u003cp\u003eProfessor AH reviewed this manuscript and provided the most comments and edits.\u003c/p\u003e\n\u003cp\u003eRegarding the corresponding authors, Professor WZW managed the project, while Professor LL and Professor WLZ also served as corresponding authors.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eWe sincerely appreciate Ms. XSZ and Mr. ANJ for their contributions to sample collection and acquisition throughout the project.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHursting SD, Dunlap SM. Obesity, metabolic dysregulation, and cancer: A growing concern and an inflammatory (and microenvironmental) issue. Ann N Y Acad Sci. 2012;1271(1):82\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFaghri P. Sedentary Lifestyle, Obesity, and Aging: Implication for Prevention. J Nutr Disord Ther. 2015;05(01):5\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu Y, Lu J, Li M, Wang T, Wang K, Cao Q, Ding Y, Xiang Y, Wang S, Yang Q, Zhao X, Zhang X, Xu M, Wang W, Bi Y, Ning G. Diabetes in China part 1: epidemiology and risk factors. The Lancet Public Health. Volume 9. Elsevier Ltd; 2024. pp. e1089\u0026ndash;97. 12\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S2468-2667(24)00250-0\u003c/span\u003e\u003cspan address=\"10.1016/S2468-2667(24)00250-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi X, Lin Z, Zhan X, Gao J, Sun L, Cao Y et al. RNA-seq analysis of the transcriptome of the liver of cynomolgus monkeys with type 2 diabetes. Gene [Internet]. 2018;651(February):118\u0026ndash;25. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.gene.2018.02.010\u003c/span\u003e\u003cspan address=\"10.1016/j.gene.2018.02.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eManning AK, Goustin AS, Kleinbrink EL, Thepsuwan P, Cai J, Ju D, et al. A Long Non-coding RNA, LOC157273, Is an Effector Transcript at the Chromosome 8p23.1-PPP1R3B Metabolic Traits and Type 2 Diabetes Risk Locus. Front Genet. 2020;11(July):1\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEbeling M, K\u0026uuml;ng E, See A, Broger C, Steiner G, Berrera M, et al. Genome-based analysis of the nonhuman primate Macaca fascicularis as a model for drug safety assessment. Genome Res. 2011;21(10):1746\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHau J, Schapiro SJ. The welfare of non-human primates. The welfare of laboratory animals. Dordrecht: Springer Netherlands; 2007. pp. 291\u0026ndash;314.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMehta MB, Shewale SV, Sequeira RN, Millar JS, Hand NJ, Rader DJ. Hepatic protein phosphatase 1 regulatory subunit 3B (Ppp1r3b) promotes hepatic glycogen synthesis and thereby regulates fasting energy homeostasis. J Biol Chem [Internet]. 2017;292(25):10444\u0026ndash;54. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1074/jbc.M116.766329\u003c/span\u003e\u003cspan address=\"10.1074/jbc.M116.766329\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLove MI, Anders S, Huber W. Genome Biol. 2014;15:550. Differential analysis of count data - the DESeq2 package [Internet].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerk PD. Regulatable fatty acid transport mechanisms are central to the pathophysiology of obesity, fatty liver, and metabolic syndrome. Hepatology. 2008;48(5):1362\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHavel PJ. (2004). Section IV: Lipid Modulators of Islet Function Update on Adipocyte Hormones Regulation of Energy Balance and Carbohydrate/Lipid Metabolism. In DIABETES (53, Issue 1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShu Y, Yang F, Li W, et al. Changes and Significance of Central Carbon Metabolism before and after Metformin Treatment of Type 2 Diabetes Based on Mass Spectrometry. Discov Med. 2024;36(183):678\u0026ndash;89.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilcox G. (2005). Insulin and Insulin Resistance. Clin Biochem Rev (26).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen S, Zhou Y, Chen Y, Gu J. Fastp: An ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. 2018;34(17):i884\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim D, Paggi JM, Park C, et al. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat Biotechnol. 2019;37:907\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePertea M, Pertea G, Antonescu C, et al. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat Biotechnol. 2015;33:290\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao Y, Li MC, Konat\u0026eacute; MM, Chen L, Das B, Karlovich C, et al. TPM, FPKM, or Normalized Counts? A Comparative Study of Quantification Measures for the Analysis of RNA-seq Data from the NCI Patient-Derived Models Repository. J Transl Med [Internet]. 2021;19(1):1.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003edu Plessis L, Škunca N, Dessimoz C. The what, where, how and why of gene ontology-A primer for bioinformaticians. Brief Bioinform. 2011;12(6):723\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOgata H, Goto S, Fujibuchi W, Kanehisa M. Computation with the KEGG pathway database. BioSystems. 1998;47(1\u0026ndash;2):119\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu G, Wang LG, Han Y, He QY. ClusterProfiler: An R package for comparing biological themes among gene clusters. OMICS J Integr Biology. 2012;16(5):284\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1089/omi.2011.0118\u003c/span\u003e\u003cspan address=\"10.1089/omi.2011.0118\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSlack FJ, Chinnaiyan AM. The Role of Non-coding RNAs in Oncology. Cell [Internet]. 2019;179(5):1033\u0026ndash;55. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cell.2019.10.017\u003c/span\u003e\u003cspan address=\"10.1016/j.cell.2019.10.017\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"T2DM, Macaca fascicularis, Enrichment analysis","lastPublishedDoi":"10.21203/rs.3.rs-6316157/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6316157/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLong non-coding RNAs (lncRNAs) can regulate metabolism-associated genes and cooperate to activate obesity-related pathways. However, the role of lncRNAs in obesity-related diabetes is not clear. To address this, we analyzed the hepatic transcriptomes of diabetic and non-diabetic Cynomolgus monkeys (\u003cem\u003eMacaca fascicularis\u003c/em\u003e) using next-generation sequencing (NGS). Our findings demonstrate that coding and non-coding RNAs exhibit distinct patterns of expression, with coding mRNAs notably enriched for metabolic pathways and particularly lipid transport. At the same time, the expression of genes related to alcohol metabolism was suppressed in diabetic samples compared with normal samples. This study expands the understanding of the molecular underpinnings behind obesity and suggests possible avenues for precision treatment approaches that target metabolic diseases.\u003c/p\u003e","manuscriptTitle":"Differential Roles of Coding and Non-Coding Transcripts in Obesity: Insights from RNA-Seq Analysis of Macaca Fascicularis Hepatocytes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-15 16:43:01","doi":"10.21203/rs.3.rs-6316157/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-01T12:02:11+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-12T01:54:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"86633183152939798649195665492766575893","date":"2025-06-10T03:01:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-01T23:52:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"228094200791512981495258569461903129716","date":"2025-05-11T15:25:34+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-09T12:34:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-04T08:16:12+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-04-03T18:00:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-03T13:28:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Genomics","date":"2025-04-03T13:27:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e38b47bf-f311-46a6-9b70-1ac180fa290b","owner":[],"postedDate":"May 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-29T15:59:20+00:00","versionOfRecord":{"articleIdentity":"rs-6316157","link":"https://doi.org/10.1186/s12864-025-12380-5","journal":{"identity":"bmc-genomics","isVorOnly":false,"title":"BMC Genomics"},"publishedOn":"2025-12-22 15:57:09","publishedOnDateReadable":"December 22nd, 2025"},"versionCreatedAt":"2025-05-15 16:43:01","video":"","vorDoi":"10.1186/s12864-025-12380-5","vorDoiUrl":"https://doi.org/10.1186/s12864-025-12380-5","workflowStages":[]},"version":"v1","identity":"rs-6316157","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6316157","identity":"rs-6316157","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}