Multi-Omics Profiling Identifies Biomarker Candidates and Pathogenic Divergence Between Post-Transplant Diabetes Mellitus and Type 2 Diabetes

Multi-Omics Profiling Identifies Biomarker Candidates and Pathogenic Divergence Between Post-Transplant Diabetes Mellitus and Type 2 Diabetes | 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 Multi-Omics Profiling Identifies Biomarker Candidates and Pathogenic Divergence Between Post-Transplant Diabetes Mellitus and Type 2 Diabetes Yuan Liu, Zichao Cao, peizhen Wen, Ruonan Gao, Cheng Qiu, Rui Wang, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7682951/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Post-transplant diabetes mellitus (PTDM) is a significant complication following liver transplantation, primarily induced by immunosuppressive agents like tacrolimus. While PTDM shares some clinical features with type 2 diabetes (T2D), its distinct pathogenesis remains poorly understood. Methods We developed mouse models of PTDM and T2D using tacrolimus, streptozotocin (STZ), and a high-fat diet to simulate disease conditions. Integrated whole-transcriptomics and metabolomics analyses were performed on liver, pancreatic, and adipose tissue to map disease-specific molecular landscapes and nominate candidate biomarkers. Results Despite higher body weight, PTDM mice exhibited lower blood glucose and improved insulin tolerance compared to T2D mice. Multi-omics analyses revealed PTDM-specific activation of the MAPK pathway, marked Treg cell infiltration in in the liver and pancreas, and dysregulation of lincRNA-circRNA networks. Metabolomics identified altered metabolites including 2,2-dimethylsuccinic acid, indicating mitochondrial dysfunction. Most notably, integrative analysis nominated 2510002D24Rik (also known as Pants) — a previously uncharacterized, liver-restricted gene — as a hub coordinating immune-metabolic crosstalk, positioning it as a lead candidate biomarker for PTDM. Conclusions Our multi-omics approach uncovers distinct molecular signatures that differentiate PTDM from T2D, providing novel therapeutic interventions for PTDM. post-transplant diabetes mellitus type 2 diabetes tacrolimus streptozotocin omics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Solid organ transplantation, a life-saving medical procedure for individuals with end-stage organ failure, has revolutionized the landscape of modern medicine. Among the challenges faced by transplant recipients, post-transplant diabetes mellitus (PTDM) emerges as a significant metabolic complication that affects their long-term health and quality of life 1 – 4 . PTDM can occur following various types of organ transplantation, including liver transplantation, and its incidence is estimated to range from 10% to 40% 5, 6 . The cornerstone of successful organ transplantation lies in the use of immunosuppressive regimens that prevent the recipient's immune system from rejecting the transplanted organ. However, within this delicate balance between immune tolerance and graft rejection, lies an unforeseen consequence – the emergence of PTDM. Among the immunosuppressive agents, tacrolimus, a calcineurin inhibitor, has been particularly implicated as a potential risk factor for PTDM 7 , 8 . Remarkably, whether PTDM should be classified as a subset of type 2 diabetes (T2D) or considered a distinct entity has fueled debates among researchers and clinicians for over three decades 6 , 9 , 10 . PTDM has many characteristics in common with type 2 diabetes mellitus, such as insulin resistance and obesity 11 . however, the underlying mechanisms and differences between PTDM and T2D remain poorly understood. Recent studies have illuminated the existence of intricate crosstalk among insulin-sensitive tissues, including the liver, white adipose tissue, and the pancreas. This cross-tissue communication is pivotal in precisely regulating insulin release and glucose metabolism 11 , 12 . Recognizing the interconnectedness of these tissues and their potential role in the development of PTDM and T2D, our study endeavors to address the lingering questions regarding the differences and connections between these two metabolic disorders. By employing state-of-the-art omics technologies, we aim to delve deep into the molecular and metabolic landscape of PTDM, focusing on understanding its distinctive features compared to T2D, across the liver, pancreas, and adipose tissue. Through this research, we aim to provide a deeper understanding of PTDM, opening new avenues for its prevention and management and, ultimately, improving the long-term outcomes and quality of life for transplant recipients. Materials and methods Animal treatments Male C57BL/6 mice, aged 6 weeks, were procured from the Shanghai Laboratory Animal Center affiliated with the Chinese Academy of Sciences. These mice were accommodated in a specific pathogen-free animal facility, maintaining a controlled environment at 20°C with a 12-hour light-dark cycle. They were fed a high-fat diet. Mice were randomly assigned to groups to generate the required numbers based on cage and standardised protocols used to minimise nuisance variables. One group received daily intraperitoneal injections of TAC (2.0 mg/kg/d), sourced from Astellas Ireland Co., Ltd. (Killorglin County), while the other group received streptozotocin (STZ, 45 mg/kg/d) injections from Sigma-Aldrich. Throughout the study, regular records were kept of body weight, as well as food and water intake levels, with measurements taken every 3 days. Blood samples were collected after an overnight fast, and biochemical parameters were assessed using a BioVision system every 2 weeks. Plasma insulin levels were determined using an ELISA Kit (EZRMI-13K, Sigma-Aldrich). The blood trough concentration of TAC was measured using a PRO-trac II TAC ELISA Kit (DiaSorin Inc.) at the end of the fourth week following the initial treatment. To evaluate glucose tolerance (GTT) and insulin sensitivity (ITT), standard protocols were followed 13 . At the end of the study, the animals were euthanized in a fasting state, and both peripheral blood and tissues were collected. All experiments involving animals were conducted according to the ethical policies (Approval no.2024AWS258). Liver glycogen, hematoxylin and eosin (H&E), periodic acid Schiff (PAS) and Oil Red O Mice liver tissues (10 mg) were suspended in 100 μL of assay buffer and then homogenized with a pestle on ice. The samples were centrifuged for 2 to 5 minutes at top speed at 4°C in a cold microcentrifuge to remove any insoluble material. The supernatant was collected and transferred to a clean tube. The samples were kept on ice before measurement. The concentration of glucose was measured by using a glucose assay kit (ab65333; Abcam). Liver samples were collected and promptly fixed in 10% neutral-buffered formalin for histopathologic examination. The samples were trimmed, dehydrated and embedded in paraffin wax. Afterward, sections at 4 µm for H&E and PAS (both from Sigma-Aldrich) were used for staining. The frozen sections of liver tissue were fixed in 4% paraformaldehyde for 15 min after being reheated and dried. Oil red O was added to the slides and stained for 8–10 min, and rinsed with tap water, differentiated with 75% ethanol and re-stained with hematoxylin. Sealed sections were observed and images were recorded by optical microscope (Nikon Eclipse 50i, Tokyo, Japan). Flow cytometry Single-cell suspensions were prepared and stained with live/dead dye (Invitrogen) and anti-mouse CD16/32 antibody (BioLegend) for 15 min at 4°C to block the nonspecific binding. Immune cell subsets were characterized using antibodies against CD3, CD4, and CD45 (all BioLegend) as published. And then incubated with indicated antibodies at 1:200 dilution in FACS buffer for 30 min at 4°C in the dark. For intranuclear staining, anti-FoxP3 antibodies were purchased from BioLegend. A transcription factor buffer set (BD Pharmingen) was used to fix fixing surface markers and permeabilize cells before FoxP3 staining. Detailed flow cytometry antibodies are listed in Supplementary Table 1 . All samples were acquired on an LSRII cytometer (BD Biosciences) and analyzed using FlowJo software (BD Biosciences). RNA sequencing (RNA-Seq) Total RNAs were extracted from tissues using TRIzol Reagent (Life Technologies). More than three biological replicates were performed for each studied condition. TruSeq Stranded Total Sample Preparation Kit (Illumina, San Diego, CA) was used for sequencing library construction. Libraries were fed onto HiSeq machines according to the activity and expected data volume. The reference genome version is GRCm38.p6 (http://asia.ensembl.org/Mus_musculus/Info/Index) and differentially expressed gene levels were measured as Fragments per kilobase of transcript per Million mapped reads (FPKM) by using DESeq2, except circRNA, which was used DESeq. Metabolomic profiling Liver and pancreas samples were used for non-targeted metabolomics (BIOTREE, Shanghai, China). The extracted metabolites were transferred to a fresh glass vial for liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. Ultra-high performance liquid chromatography (UHPLC) separation was carried out using a 1290 Infinity series UHPLC System (Agilent Technologies, Palo Alto, CA), equipped with a UPLC BEH Amide column. The TripleTOF 6600 mass spectrometry (AB Sciex, Foster City, California) was used to acquire tandem mass spectrometry (MS/MS) spectra on an information-dependent basis during LC-MS/MS experiment. The raw data were converted to the mzXML format using ProteoWizard and processed with an in-house program, which was developed using R and based on XCMS, for peak detection, extraction, alignment, and integration. Then an in-house MS2 database (BiotreeDB) was applied to metabolite annotation. The ionization source is electrospray ionization, and there are two ionization modes: positive ion mode (POS) and negative ion mode (NEG). Statistical analysis The statistical significance of differences between the 2 groups was determined with the Student’s t-test or Mann-Whitney U test. Correlations between parameters were assessed with the Pearson or Spearman correlation analysis. The filtering thresholds of difference analysis of gene expression were |log2FC| >= 1 and false discovery rate (FDR) < 0.05. The screening criteria for differential metabolites were variable importance for the projection (VIP) greater than 1 and P-value less than 0.05. When combined with transcriptomics, we chose the condition with a P-value less than 0.05 and β more than 0.2 after 1000 permutation. Most analyses were carried out by RStudio and GraphPad software. A value of P < 0.05 was considered statistically significant. * P < 0.05; ** P < 0.01. Results The characteristic comparison between PTDM and T2D mouse model Compared with the T2D group, the PTDM group showed higher body weight and lower fasting glucose ( Figure 1A-B ). Besides, the PTDM group showed higher plasma insulin levels (Figure 1F ). The PTDM group had higher alanine aminotransferase and aspartate aminotransferase, while GSP was not significant (Figure 1C-E ). Interestingly, the PTDM group showed lower glucose levels after insulin injection ( Figure 1G ), as well as smaller areas under the ITT curves ( Figure 1H ). In GTT, the PTDM group showed lower glucose levels ( Figure 1I ) and smaller areas under the GTT curves after injection of insulin ( Figure 1J ) compared with the T2D group. To further validate our findings that the PTDM group had lower glucose than the T2D group, we assessed the glucose and triglyceride content in liver and adipose tissues using PAS and Oil red O staining, respectively ( Figure 1K-R) . Consistently, our results indicated lower liver glycogen content and reduced fat content in the PTDM group, aligning with the outcomes of our biochemical experiments. These findings collectively contribute to understanding the intricate interplay between gene expression and metabolite profiles, especially the liver, which plays a vital role in PTDM development. Transcriptomics analysis among the liver, pancreas and adipose tissue Difference analysis showed that 149 genes upregulated (FDR1) and 236 genes downregulated in liver (FDR<0.05, log2(FC)< -1) ( Figure 2A) . Difference analysis showed that 94 genes upregulated (FDR1) and 350 genes downregulated in the pancreas (FDR<0.05, log2(FC)< -1) ( Figure 2B) . Difference analysis showed that 130 genes upregulated (FDR1) and 490 genes downregulated in Adipose (FDR<0.05, log2(FC)< -1) ( Figure 2C) . The Venn diagram clearly illustrates a shared upregulation of five genes—serpinh1, slc38a3, prepl, mocs2, and plekha7—in all three tissues: liver, pancreas, and adipose tissue ( Figure 2D, Supplementary Table 2). Conversely, 31 genes, such as syt13, nfasc, ngp, and ighg1, exhibited a consistent downregulation across these same tissues. ( Figure 2E, Supplementary Table 3). Figure 2F showed that ighg1 and ngp genes expressed higher in the T2D than PTDM in the pancreas, while slc38a3 expressed higher in the PTDM than T2D in the liver, and serpinh1 expressed higher in the PTDM than T2D in the adipose tissue. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses unveiled substantial alterations in various pathways across different tissues. In the liver ( Figure 2G ), noteworthy changes were observed in the MAPK pathway, EGFR pathway, and leukocyte migration. Similarly, in adipose tissue ( Figure 2I ), we identified notable modifications in the MAPK signaling pathway, leukocyte migration, and Rap1 pathways. Within the pancreas ( Figure 2H ), there were significant shifts in the differentiation of Th17, Th1, and Th2 cells and T cell activation. As the TCR signalling pathway had strong association with PTDM, we sought to measure the content of T cells, and the FACS showed CD4+FOXP3+T significantly increased in the liver and pancreas respectively in the PTDM group compared to the T2D group ( Figure 2J-L) . Weighted gene co-expression network analysis (WGCNA) showed the module correlated with glucose levels WGCNA was employed to identify the module most relevant to glucose metabolism, as depicted in Figure 3A . Modules with interconnected characteristics were selected based on a combination of network analysis, correlation coefficients, and hierarchical clustering. Notably, the lightcyan module emerged as highly correlated with blood glucose levels ( Figure 3A ). This module encompasses genes such as treh, acsm4, cyp2c65, cyp2c66, cyp2d11, and cyp4a12b, with detailed information in Supplementary Table 4. Of particular significance, treh and acsm4 are genes associated with glycolysis. Furthermore, we conducted a comparative analysis between the genes within the lightcyan module and those identified in the differential analysis of liver, pancreas, and adipose tissue. Intriguingly, approximately 7.5%, 0.5%, and 2% of these genes were found to be shared among the liver, pancreas, and adipose tissue, respectively. This observation suggests that the liver may play a predominant role in the development of PTDM. The results of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses further accentuated the importance of the lightcyan module. These analyses indicated significant alterations in pathways related to steroid hormone biosynthesis, insulin secretion, and oxidoreductase activity ( Figure 3B-E) . Comparison of ceRNA networks between PTDM and T2D in liver and pancreas tissues Furthermore, we constructed a network encompassing mRNAs, circRNAs, and lncRNAs, which serve as targets for key miRNAs and exhibit strong correlations within the ceRNA network. Specifically, within the lincRNA-associated ceRNA network, we identified 4 crucial miRNAs (mmu-miR-369-3p, mmu-miR-494-3p, mmu-miR-495-3p, mmu-miR-6240) and 4 lncRNAs (chr11:67687120-67688930, ENSMUST00000236340, XR_871293.4, XR_877383.3) in a subnetwork that play pivotal roles in shaping PTDM in the liver ( Figure 4A) , the details were in Supplementary Table 5. In the circRNA-associated ceRNA network, we identified 6 significant miRNAs (mmu-miR-369-3p, mmu-miR-376b-3p, mmu-miR-409-3p, mmu-miR-411-5p, mmu-miR-494-3p, mmu-miR-495-3p) and 1 circRNA (15_3457930_3551685) within a subnetwork that influencing the development of PTDM in the liver ( Figure 4B) , the details were in Supplementary Table 6. Moving to the pancreas, in the lincRNA-associated ceRNA network, we pinpointed 2 miRNAs (mmu-miR-196b-5p, mmu-miR-410-3p) and 6 lncRNAs (chr2:174710024-174710821, chr8:15030633-15033329, ENSMUST00000185694, ENSMUST00000226736, XR_001780381.3, XR_003951655.1) in a subnetwork that shaping the development of PTDM ( Figure 4C) , the details were in Supplementary Table 7. Similarly, in the circRNA-associated ceRNA network of the pancreas, we identified 2 miRNAs (mmu-miR-196b-5p, mmu-miR-410-3p) and 2 circRNAs (17_51803431_51809287, 19_6341903_6343176) within a subnetwork that serve as crucial regulators influencing the development of PTDM ( Figure 4D) , the details were in Supplementary Table 8. Difference Analysis of Metabolomics both in the liver and pancreas To assess the alterations in metabolomics within solid organs, we conducted a comparative analysis between the liver and pancreas. Figures 5A-B visually depict the discrimination between the PTDM and T2D groups based on metabolite profiles, as determined by orthogonal partial least squares discriminant analysis (OPLA-DA). In the negative ionization mode, several metabolites exhibited significant differences between the PTDM and T2D groups in both the liver and pancreas. In the liver ( Figure 5C, 5E, 5G ), metabolites such as 2,2-dimethylsuccinic acid, D-2,3-Dihydroxypropanoic acid, Succinic acid semialdehyde, D-Ribose, Methylsuccinic acid, (R)-lipoic acid, Resveratrol, Pyrrole-2-carboxylic acid, Aldehydo-D-xylose, and Nicotinic acid were notably changed in the PTDM group compared to the T2D group. Similarly, in the pancreas ( Figure 5D, 5F, 5H ), metabolites including 2,2-dimethylsuccinic acid, L-Serine, L-Leucine, N-Acetylserine, and Ketoleucine displayed a significant change in the PTDM group compared to the T2D group. In the positive ionization mode, distinct metabolites were found to be significantly decreased in the PTDM group as compared to the T2D group. In the liver, metabolites such as Pinostrobin chalcone and PC(18:1(11Z)/P-16:0) exhibited notable increases. Similarly, in the pancreas, metabolites including Pinostrobin chalcone, Caffeine, D-Galactose, and PC(22:4(7Z,10Z,13Z,16Z)/15:0) were significantly decreased in the PTDM group compared to the T2D group (Supplementary Table 9). These findings underscore the significant metabolic differences between the PTDM and T2D groups within the liver and pancreas, shedding light on the unique metabolic signatures associated with PTDM. Combination analysis of transcriptomics and metabolomics Through a comprehensive analysis combining transcriptomics and metabolomics following differential analysis with permutation test, we observed intriguing correlations involving 2510002D24Rik. In the liver ( Figure 6A ), this gene was found to be correlated with changes in Resveratrol, Aldehydo-D-xylose, 3-Hydroxybutyric acid, 5-Aminoimidazole-4-carboxamide, Leucinic acid and (R)-lipoic acid. Figure 6B showed that 2510002D24Rik was significantly increased in the PTDM group compared to the T2D group. Figure 6C-H provides a detailed visualization of the correlations between the above metabolites and 2510002D24Rik in the liver, while we did not see a correlation of 2510002D24Rik and metabolites in the pancreas. Discussion Liver transplantation is a life-saving procedure for patients with end-stage liver disease 14 – 16 . However, the development of post-transplant diabetes mellitus (PTDM) significantly impacts the long-term survival and quality of life of transplant recipients 1 , 17 , 18 . This study aimed to unravel the intricate mechanisms underlying PTDM, primarily associated with the immunosuppressive agent tacrolimus, and draw meaningful comparisons with type 2 diabetes (T2D) using murine models. Our integrated whole-transcriptomic and metabolomic analyses provided valuable insights into the molecular and metabolic alterations occurring in liver and pancreatic tissues, shedding light on the distinct features of PTDM compared to T2D. Our findings highlight several vital observations that deepen our understanding of PTDM. PTDM mice displayed a unique metabolic phenotype characterized by higher body weight, lower blood glucose levels, and improved insulin tolerance compared to T2D mice. These differences suggest that the pathophysiology of PTDM may involve distinct molecular mechanisms compared to classical T2D. The MAPK pathway and leukocyte migration emerged as significantly altered processes in the liver of PTDM mice, potentially implicating these pathways in the development of PTDM. These findings align with previous studies that have linked MAPK signaling to glucose homeostasis and insulin resistance 19 – 21 . Interestingly, we also observed changes in the MAPK and leukocyte migration pathways in adipose tissue. In the pancreas, our analyses revealed marked changes in the differentiation of Th17, Th1, and Th2 cells, T cell activation and increased CD4 + FOXP3 + Treg cells in PTDM. These immune-related alterations are intriguing and may indicate a role for immune dysregulation in developing PTDM. Tacrolimus, a potent immunosuppressive agent commonly used in transplant recipients, is known to decrease the number of circulating Tregs in transplanted patients; the discordance with our results might be because the amounts of Tregs decreased more significantly in T2D 15 , 22 – 24 . Furthermore, our ceRNA network analysis uncovered significant changes in long intergenic non-coding RNA (lincRNA) an circular RNA (circRNA) networks in the liver and pancreas of PTDM mice. These non-coding RNAs have been increasingly recognized as important regulators of gene expression and could potentially play a role in the development of PTDM or other complications 25 . Future studies should explore the functional significance of these RNA networks in PTDM pathogenesis. Our WGCNA revealed that a small subset of genes from the glucose module were shared among the liver, pancreas, and adipose tissue 26 . This cross-tissue gene expression pattern suggests the presence of interconnected regulatory networks that coordinate metabolic processes throughout the body. Intriguingly, approximately 7.5%, 0.5%, and 2% of these genes were found to be shared among the liver, pancreas, and adipose tissue, respectively. This observation suggests that the liver may play a predominant role in developing PTDM 5 . Importantly, our metabolomic analysis identified specific metabolites that were significantly altered in both liver and pancreas tissues of PTDM mice compared to T2D. Notably, Resveratrol, Aldehydo-D-xylose, 3-Hydroxybutyric acid, 5-Aminoimidazole-4-carboxamide, Leucinic acid, and (R)-lipoic acid exhibited significant changes, which were regulated by 2510002D24Rik in the liver. These metabolites may serve as potential biomarkers or therapeutic targets for PTDM 27 . Additionally, it has been demonstrated that 5-Aminoimidazole-4-carboxamide plays a similar role to metformin in inhibiting glucose phosphorylation and glycolysis in rat hepatocytes 28 . Further investigations into their roles in glucose metabolism and insulin sensitivity are warranted. Histological staining of liver tissue revealed lower glycogen and lipid content in PTDM mice, suggesting altered hepatic metabolism. Using PAS staining and Oil red O staining, our results indicated lower liver glycogen content and reduced fat content in the PTDM group, aligning with the outcomes of our biochemical experiments. Conclusion In conclusion, our study provides valuable insights into the pathogenesis of PTDM, offering a comprehensive view of the molecular and metabolic changes that distinguish it from T2D. The unique clinical and pathological characteristics of PTDM, including its distinct metabolic phenotype, immune-related alterations, and non-coding RNA networks, underscore the need for tailored approaches to its prevention and treatment. The shared molecular pathways between PTDM and T2D suggest common therapeutic targets, while the specific metabolites identified in PTDM mice present potential biomarkers and therapeutic candidates. Future research should aim to validate these findings in clinical settings and explore the functional roles of the identified molecular players, ultimately paving the way for improved outcomes and quality of life for liver transplant recipients at risk of PTDM. Declarations Ethics approval All experiments involving animals were conducted according to the ethical policies and procedures approved by the ethics committee of Shanghai General Hospital (Approval no.2024AWS258). Consent for publication All authors have reviewed the manuscript and provided their consent for submission. Data availability Data is provided within the manuscript or supplementary information files. Competing interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding This work was supported by The National Natural Science Foundation of China (No. 82400774). Authors’ contributions Y.L. was involved in the study design, conducted the experiments, and drafted the paper: P.Z.W., R.N.G., Z.C.C. and C.Q. performed bioinformatics analysis and performed the experiments; R.W., B.J.S., W.B.A., W.J.S., Y.X., H.Y.W., P.H.W. and T.Z. commented on the study; X.P., J.W.F., and Z.H.P. designed, supervised the study and revised the manuscript. Acknowledgements We are grateful to the staff of Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine. Author details 1 Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China. 2 Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China. 3 Department of General Surgery, Changzheng Hospital, Navy Medical University, Shanghai, 200003, China. 4 Department of Endocrinology, Fujian Medical University Union Hospital. Fuzhou, 350001, China. 5 Organ Transplantation Institute of Xiamen University, Fujian Provincial Key Laboratory of Organ and Tissue Regeneration, School of Medicine, Xiamen University, Xiamen 361000, China. 6 Department of General Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450000, Henan, China. References Wang, R., Zhang, Y., Fan, J. Risk Factors for New-Onset Diabetes Mellitus After Heart Transplantation: A Nomogram Approach. Transplant. Proc. 54 (2022) 762-768. Halden, T.A.S., Kvitne, K.E., Midtvedt, K. Efficacy and Safety of Empagliflozin in Renal Transplant Recipients With Posttransplant Diabetes Mellitus. Diabetes Care 42 (2019) 1067-1074. Newman, J.D., Schlendorf, K.H., Cox, Z.L., Rangaswami, J., Lindenfeld, J., Fong, M.W., et al. Post-transplant diabetes mellitus following heart transplantation. J. Heart Lung Transplant. 41 (2022) 1537-1546. Eleftheriadis, G., Naik, M.G., Osmanodja, B., Koch, N., Kiencke, P., Oefner, P.J., et al. Continuous glucose monitoring for the prediction of posttransplant diabetes mellitus and impaired glucose tolerance on day 90 after kidney transplantation. Am. J. Transplant. (2024) [Epub ahead of print]. Jenssen, T., Hartmann, A. Post-transplant diabetes mellitus in patients with solid organ transplants. Nat. Rev. Endocrinol. 15 (2019) 172-188. Shaked, A., Loza, B.-L., Van Loon, E., Callemeyn, J., Lerut, E., Lamarthée, B., et al. Donor and recipient polygenic risk scores influence the risk of post-transplant diabetes. Nat. Med. 28 (2022) 1412–1422. Song, J.L., Li, M., Yan, L.N., Wang, Q., Zeng, Y., Wen, T.F., et al. Higher tacrolimus blood concentration is related to increased risk of post-transplantation diabetes mellitus after living donor liver transplantation. Int. J. Surg. 51 (2018) 17-23. Ko, E.J., Shin, Y.J., Cui, S., Li, Q., Lee, J.Y., Kim, H.J., et al. Effect of dual inhibition of DPP4 and SGLT2 on tacrolimus-induced diabetes mellitus and nephrotoxicity in a rat model. Am. J. Transplant. 22 (2022) 1537-1549. Rodriguez-Rodriguez, A.E., Porrini, E., Torres, A. Beta-Cell Dysfunction Induced by Tacrolimus: A Way to Explain Type 2 Diabetes? Int. J. Mol. Sci. 22 (2021) 10311. Kang, E.S., Kim, M.S., Kim, C.H., Han, S.J., Hur, K.Y., Ahn, C.W., et al. Association of common type 2 diabetes risk gene variants and posttransplantation diabetes mellitus in renal allograft recipients in Korea. Transplantation 88 (2009) 693-698. Sharif, A., Cohney, S. Post-transplantation diabetes-state of the art. Lancet Diabetes Endocrinol. 4 (2016) 337-349. Kleinridders, A., Ferris, H.A., Cai, W., Kahn, C.R. Insulin action in brain regulates systemic metabolism and brain function. Diabetes 63 (2014) 2232-2243. Qu, Y.L., Deng, C.H., Luo, Q., Hu, Y.R., Yin, W.J., Wang, F.B., et al. Arid1a regulates insulin sensitivity and lipid metabolism. EBioMedicine 42 (2019) 481-493. 481-493. Shi, B., Liu, Y., Liu, D., Wang, Z., Jiang, W., She, X., et al. Genotype-guided model significantly improves accuracy of tacrolimus initial dosing after liver transplantation. EClinicalMedicine 55 (2023) 101752. Liu, Y., Wang, R., Wen, P., Shi, B., Xu, J., Huang, L., et al. Genetic factors underlying tacrolimus intolerance after liver transplantation. Front. Immunol. 13 (2022) 944442. Huang, L., Assiri, A.A., Wen, P., Shi, B., Liu, Y., Wang, M., et al. The CYP3A5 genotypes of both liver transplant recipients and donors influence the time-dependent recovery of tacrolimus clearance during the early stage following transplantation. Clin. Transl. Med. 11 (2021) e542. Bhat, V., Tazari, M., Watt, K.D., Chaiteerakij, R., Torbenson, M.S., Borad, M.J., et al. New-Onset Diabetes and Preexisting Diabetes Are Associated With Comparable Reduction in Long-Term Survival After Liver Transplant: A Machine Learning Approach. Mayo Clin. Proc. 93 (2018) 1794-1802. Shivaswamy, V., Boerner, B., Larsen, J. Post-Transplant Diabetes Mellitus: Causes, Treatment, and Impact on Outcomes. Endocr. Rev. 37 (2016) 37-61. Zhu, R., Chen, B., Bai, Y., Miao, T., Rui, L., Zhang, H., et al. Lycopene in protection against obesity and diabetes: A mechanistic review. Pharmacol. Res. 159 (2020) 104966. Fang, Z., Xu, H., Duan, J., Li, H., Li, Y., Zhu, Z., et al. Short-term tamoxifen administration improves hepatic steatosis and glucose intolerance through JNK/MAPK in mice. Signal Transduct. Target. Ther. 8 (2023) 94. Koh, A., Molinaro, A., Ståhlman, M., Khan, M.T., Schmidt, C., Mannerås-Holm, L., et al. Microbially Produced Imidazole Propionate Impairs Insulin Signaling through mTORC1. Cell 175 (2018) 947-961.e17. Cantarovich, D., Kervella, D., Karam, G., Giral-Classe, M., Hourmant, M., Dantal, J., et al. Tacrolimus- versus sirolimus-based immunosuppression after simultaneous pancreas and kidney transplantation: 5-year results of a randomized trial. Am. J. Transplant. 20 (2020) 1679-1690. Liu, Y., Zhang, C., Li, L., Wu, Y., Zhao, W., Liu, K., et al. Genome-Wide Association Study of Tacrolimus Pharmacokinetics Identifies Novel Single Nucleotide Polymorphisms in the Convalescence and Stabilization Periods of Post-transplant Liver Function. Front. Genet. 10 (2019) 528. Whitehouse, G., Gray, E., Mastoridis, S., Merritt, E., Kodela, E., Yang, J.H.M., et al. IL-2 therapy restores regulatory T-cell dysfunction induced by calcineurin inhibitors. Proc. Natl. Acad. Sci. U. S. A. 114 (2017) 7083-7088. Su, R., Wei, X., Wei, Q., Li, Y., Wang, M., Li, X., et al. Extrahepatic organs in the development of non-alcoholic fatty liver disease in liver transplant patients. Hepatobiliary Surg. Nutr. 11 (2022) 400-411. Zhong, H., Liu, S., Cao, F., Zhao, Y., Zhou, J., Tang, F., et al. Dissecting Tumor Antigens and Immune Subtypes of Glioma to Develop mRNA Vaccine. Front. Immunol. 12 (2021) 709986. Wang, M., Xu, J., Yang, N., Chen, J., Zhou, T., Li, Z., et al. Insight Into the Metabolomic Characteristics of Post-Transplant Diabetes Mellitus by the Integrated LC-MS and GC-MS Approach- Preliminary Study. Front. Endocrinol. 12 (2021) 807318. Guigas, B., Bertrand, L., Taleux, N., Foretz, M., Wiernsperger, N., Vertommen, D., et al. 5-Aminoimidazole-4-carboxamide-1-beta-D-ribofuranoside and metformin inhibit hepatic glucose phosphorylation by an AMP-activated protein kinase-independent effect on glucokinase translocation. Diabetes 55 (2006) 865-874. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable4.csv SupplementaryTable5.csv SupplementaryTable6.csv SupplementaryTable1.docx SupplementaryTable9.xlsx Supplementarytable2Upgene.xlsx SupplementaryTable8.csv Supplementarytable3Downgene.xlsx SupplementaryTable7.csv Cite Share Download PDF Status: Posted Version 1 posted 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-7682951","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":526826597,"identity":"535a71be-2695-4a91-8bbf-2cb91d4f4f94","order_by":0,"name":"Yuan Liu","email":"","orcid":"","institution":"Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China","correspondingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Liu","suffix":""},{"id":526826599,"identity":"16a5d870-87b8-4f03-b1c6-75368c404b4c","order_by":1,"name":"Zichao Cao","email":"","orcid":"","institution":"Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China","correspondingAuthor":false,"prefix":"","firstName":"Zichao","middleName":"","lastName":"Cao","suffix":""},{"id":526826600,"identity":"83e65c54-e98a-4b66-b404-401df0c2b459","order_by":2,"name":"peizhen Wen","email":"","orcid":"","institution":"Department of General Surgery, Changzheng Hospital, Navy Medical University, Shanghai, 200003, China.","correspondingAuthor":false,"prefix":"","firstName":"peizhen","middleName":"","lastName":"Wen","suffix":""},{"id":526826601,"identity":"72402689-4abf-4f8b-ac9e-934d686eb382","order_by":3,"name":"Ruonan Gao","email":"","orcid":"","institution":"Department of Endocrinology, Fujian Medical University Union Hospital. Fuzhou, 350001, China.","correspondingAuthor":false,"prefix":"","firstName":"Ruonan","middleName":"","lastName":"Gao","suffix":""},{"id":526826602,"identity":"36d4a36a-a8d1-41be-98cf-91889569a835","order_by":4,"name":"Cheng Qiu","email":"","orcid":"","institution":"Organ Transplantation Institute of Xiamen University, Fujian Provincial Key Laboratory of Organ and Tissue Regeneration, School of Medicine, Xiamen University, Xiamen 361000, China.","correspondingAuthor":false,"prefix":"","firstName":"Cheng","middleName":"","lastName":"Qiu","suffix":""},{"id":526826603,"identity":"4c790392-259d-4d70-af23-35aa67a5087a","order_by":5,"name":"Rui Wang","email":"","orcid":"","institution":"Organ Transplantation Institute of Xiamen University, Fujian Provincial Key Laboratory of Organ and Tissue Regeneration, School of Medicine, Xiamen University, Xiamen 361000, China.","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Wang","suffix":""},{"id":526826604,"identity":"608a005b-93d0-4a27-a829-23eeb5c7ce0d","order_by":6,"name":"Baojie Shi","email":"","orcid":"","institution":"Organ Transplantation Institute of Xiamen University, Fujian Provincial Key Laboratory of Organ and Tissue Regeneration, School of Medicine, Xiamen University, Xiamen 361000, China.","correspondingAuthor":false,"prefix":"","firstName":"Baojie","middleName":"","lastName":"Shi","suffix":""},{"id":526826606,"identity":"d0405b6a-be95-4c25-9f5c-c665de41427f","order_by":7,"name":"Wenbin An","email":"","orcid":"","institution":"Organ Transplantation Institute of Xiamen University, Fujian Provincial Key Laboratory of Organ and Tissue Regeneration, School of Medicine, Xiamen University, Xiamen 361000, China.","correspondingAuthor":false,"prefix":"","firstName":"Wenbin","middleName":"","lastName":"An","suffix":""},{"id":526826608,"identity":"73ad23dd-7cdf-486e-b9f3-52cc1bcf2e65","order_by":8,"name":"Wenjie Shi","email":"","orcid":"","institution":"Department of Endocrinology, Fujian Medical University Union Hospital. Fuzhou, 350001, China.","correspondingAuthor":false,"prefix":"","firstName":"Wenjie","middleName":"","lastName":"Shi","suffix":""},{"id":526826610,"identity":"c4815bc3-7299-43b0-b34a-e6025b67e09d","order_by":9,"name":"Yan Xia","email":"","orcid":"","institution":"Department of Endocrinology, Fujian Medical University Union Hospital. Fuzhou, 350001, China.","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Xia","suffix":""},{"id":526826615,"identity":"8b243175-f0da-4663-8163-c372f8c9f6f7","order_by":10,"name":"Haoyu Wang","email":"","orcid":"","institution":"Department of Endocrinology, Fujian Medical University Union Hospital. Fuzhou, 350001, China.","correspondingAuthor":false,"prefix":"","firstName":"Haoyu","middleName":"","lastName":"Wang","suffix":""},{"id":526826617,"identity":"8d268915-791e-4283-b62d-c9c2d79edb77","order_by":11,"name":"Peihao Wen","email":"","orcid":"","institution":"Department of General Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450000, Henan, China.","correspondingAuthor":false,"prefix":"","firstName":"Peihao","middleName":"","lastName":"Wen","suffix":""},{"id":526826619,"identity":"91568d50-5e17-4588-bd12-3d5de1afc92a","order_by":12,"name":"Tao Zhang","email":"","orcid":"","institution":"Organ Transplantation Institute of Xiamen University, Fujian Provincial Key Laboratory of Organ and Tissue Regeneration, School of Medicine, Xiamen University, Xiamen 361000, China.","correspondingAuthor":false,"prefix":"","firstName":"Tao","middleName":"","lastName":"Zhang","suffix":""},{"id":526826621,"identity":"f68477b5-1f95-4bc8-9a78-085947befd09","order_by":13,"name":"Xiao Peng","email":"","orcid":"","institution":"Organ Transplantation Institute of Xiamen University, Fujian Provincial Key Laboratory of Organ and Tissue Regeneration, School of Medicine, Xiamen University, Xiamen 361000, China.","correspondingAuthor":false,"prefix":"","firstName":"Xiao","middleName":"","lastName":"Peng","suffix":""},{"id":526826623,"identity":"17166d62-5f41-45a2-b095-22d8f48d5127","order_by":14,"name":"Junwei Fan","email":"","orcid":"","institution":"Department of Endocrinology, Fujian Medical University Union Hospital. Fuzhou, 350001, China.","correspondingAuthor":false,"prefix":"","firstName":"Junwei","middleName":"","lastName":"Fan","suffix":""},{"id":526826625,"identity":"04a5823c-6435-4a49-814d-8ef62d2a3bba","order_by":15,"name":"Zhihai Peng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIiWNgGAWjYLACxoYDDGzMzMc/fKiQkJMnXgs7WxrjjDMWxoYNxGph4OcxY+Ztq0hkOEBAtTl77+GXP3fckedj5jF7OHOeRAJjA/PDRzfwaLHsOZdmIXnmmWEbM1u5wcdtEnnsDGzGxjl4tBjcyDEzMGw7zNjGzLxBcuY2iWLGBh42abxa7r8xM0hsO2zfxsxgIM07RyKx4QAhLTd4jB8cbDuc2MbMYibN20CEFsueHDPGxrbDyUC/JBvOOCZhbNhMwC/m7GeMP/5sO2w7v//wwQcfaurk5NmbHz7G6zAGBjYJVCFmPMqhWpg/EFAzCkbBKBgFIx0AAIJLTtki4AHBAAAAAElFTkSuQmCC","orcid":"","institution":"Organ Transplantation Institute of Xiamen University, Fujian Provincial Key Laboratory of Organ and Tissue Regeneration, School of Medicine, Xiamen University, Xiamen 361000, China.","correspondingAuthor":true,"prefix":"","firstName":"Zhihai","middleName":"","lastName":"Peng","suffix":""}],"badges":[],"createdAt":"2025-09-22 23:09:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7682951/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7682951/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93215982,"identity":"ad653254-c792-42ef-8aa5-340643e7362c","added_by":"auto","created_at":"2025-10-10 09:51:57","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":66662095,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.docx","url":"https://assets-eu.researchsquare.com/files/rs-7682951/v1/9058413b6cd6bc4fc267137b.docx"},{"id":93215960,"identity":"6f7fcaa9-e7b2-4682-b7c7-d722b697e0ac","added_by":"auto","created_at":"2025-10-10 09:51:55","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":14795,"visible":true,"origin":"","legend":"","description":"","filename":"6bec1aea9ff94f45a06c1608b92e3523.json","url":"https://assets-eu.researchsquare.com/files/rs-7682951/v1/cfd82e831616a54ba57268d5.json"},{"id":93215949,"identity":"e6e16940-5394-44d1-9808-c4cca13bd7ce","added_by":"auto","created_at":"2025-10-10 09:51:53","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":16293,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7682951/v1/f117e4aa6e94c2ef2e09397a.docx"},{"id":93215942,"identity":"7401b780-218d-49e9-ad25-9bb95c603a63","added_by":"auto","created_at":"2025-10-10 09:51:50","extension":"csv","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1976,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable4.csv","url":"https://assets-eu.researchsquare.com/files/rs-7682951/v1/b1a2f3a2690f8eedaac08359.csv"},{"id":93215940,"identity":"016ed806-0c80-4e38-8768-6a657ddb5bd7","added_by":"auto","created_at":"2025-10-10 09:51:50","extension":"csv","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7875,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable5.csv","url":"https://assets-eu.researchsquare.com/files/rs-7682951/v1/6768c3798d37984370270066.csv"},{"id":93215964,"identity":"5280562e-147c-43ed-97ac-9193dd982ba0","added_by":"auto","created_at":"2025-10-10 09:51:55","extension":"csv","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":8142,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable6.csv","url":"https://assets-eu.researchsquare.com/files/rs-7682951/v1/7aa4b21a623f23a9c27f5a91.csv"},{"id":93215952,"identity":"39bbd280-5af7-4024-8f9f-0d5216adc7b0","added_by":"auto","created_at":"2025-10-10 09:51:54","extension":"csv","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":24040,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable7.csv","url":"https://assets-eu.researchsquare.com/files/rs-7682951/v1/d67483088b2ae6e4d6f7707c.csv"},{"id":93215951,"identity":"de16be12-52af-417e-a5d1-5832d2bef819","added_by":"auto","created_at":"2025-10-10 09:51:54","extension":"csv","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7330,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable8.csv","url":"https://assets-eu.researchsquare.com/files/rs-7682951/v1/1c7b51512cbe8aabde8a5c68.csv"},{"id":93215973,"identity":"b8bbb00c-2697-4c93-82c8-e05254dbdc7a","added_by":"auto","created_at":"2025-10-10 09:51:57","extension":"xlsx","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":24305,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable9.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7682951/v1/caf5f806d50e72fa83775d33.xlsx"},{"id":93215943,"identity":"c5c62eda-a84a-4992-b9c6-e400c1b8855f","added_by":"auto","created_at":"2025-10-10 09:51:51","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":49113,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable2Upgene.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7682951/v1/ef15e75470650a29f5dd7ff9.xlsx"},{"id":93217159,"identity":"56497ab1-519a-4ed6-9ef9-eb0bc7bb013b","added_by":"auto","created_at":"2025-10-10 09:59:57","extension":"xlsx","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":58850,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable3Downgene.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7682951/v1/7461ab79847db5c1c09385ee.xlsx"},{"id":93215961,"identity":"7255eec3-61d7-4d78-9632-a0c7b9992e7c","added_by":"auto","created_at":"2025-10-10 09:51:55","extension":"xml","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":100568,"visible":true,"origin":"","legend":"","description":"","filename":"6bec1aea9ff94f45a06c1608b92e35231enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7682951/v1/fe7a2c9dce77dcaf023e0e69.xml"},{"id":93215956,"identity":"17e1eb96-b206-42b8-ab5f-f516cb72eb6b","added_by":"auto","created_at":"2025-10-10 09:51:55","extension":"jpeg","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":20603058,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7682951/v1/1ba9776f097b5b38bf589fab.jpeg"},{"id":93215979,"identity":"4fdf622c-0cf4-4c73-87f9-1925f1913096","added_by":"auto","created_at":"2025-10-10 09:51:57","extension":"jpeg","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":13991586,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7682951/v1/f9076124df2efc54f3ece283.jpeg"},{"id":93215967,"identity":"a1052187-81fd-4df3-9888-77e5f87039a7","added_by":"auto","created_at":"2025-10-10 09:51:56","extension":"jpeg","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7340932,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7682951/v1/117ac2703d4a5f2d7c005972.jpeg"},{"id":93215977,"identity":"4341e122-c0ca-46d7-b618-54ec25dd8ce8","added_by":"auto","created_at":"2025-10-10 09:51:57","extension":"jpeg","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5833684,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7682951/v1/e5171b1a2229a5caccb056bd.jpeg"},{"id":93215946,"identity":"f1090700-667c-42db-8acd-b757900204fb","added_by":"auto","created_at":"2025-10-10 09:51:53","extension":"jpeg","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":975166,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7682951/v1/76f6f4f4d17344b31e739356.jpeg"},{"id":93215955,"identity":"ccd413ef-c580-406c-b13b-e146de8c0bcc","added_by":"auto","created_at":"2025-10-10 09:51:55","extension":"jpeg","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":17818602,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7682951/v1/05cf802881cf149cd4442ae3.jpeg"},{"id":93215959,"identity":"55ff8adc-070b-4cad-96f0-54bbb16727ac","added_by":"auto","created_at":"2025-10-10 09:51:55","extension":"png","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1256147,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7682951/v1/e9605edc898abcc8eecfe90f.png"},{"id":93215965,"identity":"eccfe6fd-8345-4163-9420-557d42dd964f","added_by":"auto","created_at":"2025-10-10 09:51:56","extension":"png","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1456249,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7682951/v1/080e1da7190e9a49d8f7e973.png"},{"id":93217158,"identity":"5820a030-d07a-4bf9-a36c-3d47b8f42967","added_by":"auto","created_at":"2025-10-10 09:59:57","extension":"png","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1121838,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7682951/v1/4fc739ad9651ef1e2747c109.png"},{"id":93215972,"identity":"f49f8543-8625-4e72-9c54-ce71b5bed24c","added_by":"auto","created_at":"2025-10-10 09:51:57","extension":"png","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1204043,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7682951/v1/a9a1ec4c95377892f60cd805.png"},{"id":93215974,"identity":"e0cc14b3-6c6e-45f1-b22e-4b421f4658dd","added_by":"auto","created_at":"2025-10-10 09:51:57","extension":"png","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":167458,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7682951/v1/389ab7f71fb73269bed7d163.png"},{"id":93215983,"identity":"89cf5535-c03b-4d53-9b58-89342aa1d5ad","added_by":"auto","created_at":"2025-10-10 09:51:58","extension":"png","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1910363,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7682951/v1/c00452aa5aad907dd7704bd4.png"},{"id":93215968,"identity":"3599226b-4ffc-4822-a1f7-68164fb5af9d","added_by":"auto","created_at":"2025-10-10 09:51:56","extension":"xml","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":97000,"visible":true,"origin":"","legend":"","description":"","filename":"6bec1aea9ff94f45a06c1608b92e35231structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7682951/v1/45a7e7b2a0c9357da22bcfec.xml"},{"id":93215976,"identity":"35033751-e9ee-4612-b931-7d9a796c3ce1","added_by":"auto","created_at":"2025-10-10 09:51:57","extension":"html","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":112484,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7682951/v1/6ad94d8ce3866693fff2420a.html"},{"id":93217156,"identity":"466c564c-8c4f-43cb-bf08-f7bc77ee2769","added_by":"auto","created_at":"2025-10-10 09:59:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":10352242,"visible":true,"origin":"","legend":"\u003cp\u003eThe characteristic comparison between PTDM and T2D mouse model.\u003c/p\u003e\n\u003cp\u003eA. \u0026nbsp;The PTDM group showed higher body weight compared with the T2D group (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05);\u003cem\u003e N\u003c/em\u003e = 6 per group.\u003c/p\u003e\n\u003cp\u003eB. The PTDM group showed lower fasting glucose compared with the T2D group (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05);\u003c/p\u003e\n\u003cp\u003eC. The PTDM group had higher alanine aminotransferase compared with the T2D group (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05);\u003c/p\u003e\n\u003cp\u003eD. The PTDM group had higher aspartate aminotransferase compared with the T2D group (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05);\u003c/p\u003e\n\u003cp\u003eE. GSP was not significant between the PTDM group and the T2D group. NS means non-statically significant;\u003c/p\u003e\n\u003cp\u003eF. The PTDM group showed higher plasma insulin levels compared to the T2D group (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05);\u003c/p\u003e\n\u003cp\u003eG. The PTDM group showed lower glucose levels after glucose injection compared with the T2D group (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05);\u003c/p\u003e\n\u003cp\u003eH. The PTDM group showed smaller areas under the GTT curves compared with the T2D group (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05);\u003c/p\u003e\n\u003cp\u003eI. The PTDM group showed lower glucose levels after injection of insulin compared with the T2D group (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05);\u003c/p\u003e\n\u003cp\u003eJ. The PTDM group showed smaller areas under the ITT curves after insulin injection (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05) than the T2D group.\u003c/p\u003e\n\u003cp\u003eK. The representative images of H\u0026amp;E staining of the liver tissue in the PTDM group. Scale bars: 200 μm.\u003c/p\u003e\n\u003cp\u003eL. The representative images of PAS staining of the liver tissue in the PTDM group. Scale bars: 200 μm.\u003c/p\u003e\n\u003cp\u003eM. The representative images of Oil red O staining of the liver tissue in the PTDM group. Scale bars: 200 μm.\u003c/p\u003e\n\u003cp\u003eN. The representative images of Oil red O staining of the adipose tissue in the PTDM group. Scale bars: 200 μm.\u003c/p\u003e\n\u003cp\u003eO. The representative images of H\u0026amp;E staining of the liver tissue in the T2D group. Scale bars: 200 μm.\u003c/p\u003e\n\u003cp\u003eP. The representative images of PAS staining of the liver tissue in the T2D group. Scale bars: 200 μm.\u003c/p\u003e\n\u003cp\u003eQ. The representative images of Oil red O staining of the liver tissue in the T2D group. Scale bars: 200 μm.\u003c/p\u003e\n\u003cp\u003eR. The representative images of Oil red O staining of the adipose tissue in the T2D group. Scale bars: 200 μm.\u003c/p\u003e\n\u003cp\u003eTAC means the PTDM group; STZ means the T2D group.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7682951/v1/f6408cd993a1beb28149e741.png"},{"id":93215938,"identity":"999e1804-600c-4596-9bdf-252ca70bb784","added_by":"auto","created_at":"2025-10-10 09:51:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":11552688,"visible":true,"origin":"","legend":"\u003cp\u003eTranscriptomics analysis among the liver, pancreas and adipose tissue\u003c/p\u003e\n\u003cp\u003eA. Difference analysis showed that 149 genes upregulated (FDR\u0026lt;0.05, log2(FC)\u0026gt;1) and 236 genes downregulated in the liver tissue (FDR\u0026lt;0.05, log2(FC)\u0026lt; -1);\u003cem\u003e N\u003c/em\u003e = 3 per group.\u003c/p\u003e\n\u003cp\u003eB. Difference analysis showed that 94 genes upregulated (FDR\u0026lt;0.05, log2(FC)\u0026gt;1) and 350 genes downregulated in the pancreas tissue (FDR\u0026lt;0.05, log2(FC)\u0026lt; -1);\u003c/p\u003e\n\u003cp\u003eC. Difference analysis showed that 130 genes upregulated (FDR\u0026lt;0.05, log2(FC)\u0026gt;1) and 490 genes downregulated in the adipose tissue (FDR\u0026lt;0.05, log2(FC)\u0026lt; -1);\u003c/p\u003e\n\u003cp\u003eD. The Venn diagram clearly illustrates a shared upregulation in three tissues: liver, pancreas, and adipose tissue (all p\u0026lt;0.05);\u003c/p\u003e\n\u003cp\u003eE. The Venn diagram clearly illustrates a shared downregulation in three tissues: liver, pancreas, and adipose tissue (all p\u0026lt;0.05);\u003c/p\u003e\n\u003cp\u003eF. Figure 2F showed that ighg1 and ngp genes expressed higher in the T2D than PTDM in the pancreas, while slc38a3 expressed higher in the PTDM than T2D in the liver, and serpinh1 expressed higher in the PTDM than T2D in the adipose tissue.\u003c/p\u003e\n\u003cp\u003eG. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses unveiled substantial alterations in the liver;\u003c/p\u003e\n\u003cp\u003eH. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses unveiled substantial alterations in the pancreas;\u003c/p\u003e\n\u003cp\u003eI. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses unveiled substantial alterations in the adipose tissue;\u003c/p\u003e\n\u003cp\u003eJ. FACS shows FOXP3+Treg cells significantly increased in the liver in the PTDM group compared to the T2D group.\u003c/p\u003e\n\u003cp\u003eK. FACS shows FOXP3+Treg cells significantly increased in the pancreas in the PTDM group compared to the T2D group.\u003c/p\u003e\n\u003cp\u003eL. Histogram shows the change of FOXP3+Treg cells in the liver and pancreas in the PTDM group compared to the T2D group.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7682951/v1/a1442d394ea8330b21f036f4.png"},{"id":93215984,"identity":"6aff313f-2c4b-4611-9a2c-f682b056e67f","added_by":"auto","created_at":"2025-10-10 09:51:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":8244266,"visible":true,"origin":"","legend":"\u003cp\u003eWeighted gene co-expression network analysis (WGCNA) showed the module correlated with the glucose levels\u003c/p\u003e\n\u003cp\u003eA. WGCNA was employed to identify the module most relevant to glucose metabolism. Modules with interconnected characteristics were selected based on network analysis, correlation coefficients, and hierarchical clustering. The lightcyan module emerged as highly correlated with blood glucose levels (R=0.47, \u003cem\u003eP\u003c/em\u003e=0.03).\u003c/p\u003e\n\u003cp\u003eB. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of genes in the lightcyan module.\u003c/p\u003e\n\u003cp\u003eC. Gene Ontology (GO) describes molecular function with genes in the lightcyan module;\u003c/p\u003e\n\u003cp\u003eD. GO describes biological processes with genes in the lightcyan module;\u003c/p\u003e\n\u003cp\u003eE. GO describes cellular components with genes in the lightcyan module.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7682951/v1/66d9f0206086f5490b1f1658.png"},{"id":93215954,"identity":"ce734f79-20eb-4424-bd1a-bcdfb78c59e0","added_by":"auto","created_at":"2025-10-10 09:51:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":6020010,"visible":true,"origin":"","legend":"\u003cp\u003eCeRNA network comparison in both liver and pancreas tissue in the PTDM group compared to the T2D group\u003c/p\u003e\n\u003cp\u003eA. The lincRNA-associated ceRNA network difference between PTDM and T2D group in the liver (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 \u0026amp; R\u0026gt;0.2 or \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 \u0026amp; R\u0026lt; -0.2);\u003c/p\u003e\n\u003cp\u003eB. The circRNA-associated ceRNA network difference between PTDM and T2D group in the liver; (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 \u0026amp; R\u0026gt;0.2 or \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 \u0026amp; R\u0026lt; -0.2);\u003c/p\u003e\n\u003cp\u003eC. The lincRNA-associated ceRNA network difference between PTDM and T2D group in the pancreas tissue (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 \u0026amp; R\u0026gt;0.2 or \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 \u0026amp; R\u0026lt; -0.2);\u003c/p\u003e\n\u003cp\u003eD. The circRNA-associated ceRNA network difference between PTDM and T2D group\u003cstrong\u003e \u003c/strong\u003ein the pancreas tissue (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 \u0026amp; R\u0026gt;0.2 or \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 \u0026amp; R\u0026lt; -0.2).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7682951/v1/83098b9913009f12ce7149a1.png"},{"id":93215948,"identity":"0d24b8eb-624c-4b82-99b7-dd2688e3ebc7","added_by":"auto","created_at":"2025-10-10 09:51:53","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":979203,"visible":true,"origin":"","legend":"\u003cp\u003eDifference Analysis of Metabolomics both in the liver and pancreas, respectively\u003c/p\u003e\n\u003cp\u003eA. The discrimination between the PTDM and T2D groups based on metabolite profiles, as determined by orthogonal partial least squares discriminant analysis (OPLA-DA) in the liver in the negative ionization mode.\u003cem\u003e N\u003c/em\u003e = 3 per group.\u003c/p\u003e\n\u003cp\u003eB. The discrimination between the PTDM and T2D groups is based on metabolite profiles, as determined by orthogonal partial least squares discriminant analysis (OPLA-DA) in the pancreas in the negative ionization mode.\u003c/p\u003e\n\u003cp\u003eC. Volcano plot showed the different metabolites in the liver in the negative ionization mode;\u003c/p\u003e\n\u003cp\u003eD. Volcano plot showed the different metabolites in the pancreas in the negative ionization mode;\u003c/p\u003e\n\u003cp\u003eE. Radar plot showed the different metabolites in the liver in the negative ionization mode;\u003c/p\u003e\n\u003cp\u003eF. Radar plot showed the different metabolites in the pancreas in the negative ionization mode;\u003c/p\u003e\n\u003cp\u003eG. Bubble plot showed the different metabolites in the liver in the negative ionization mode;\u003c/p\u003e\n\u003cp\u003eH. Bubble plot showed the different metabolites in the pancreas in the negative ionization mode.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7682951/v1/e2f6c27556abb305deaf5500.png"},{"id":93215941,"identity":"75bcda07-d8d6-4e92-a6df-f0055df2f145","added_by":"auto","created_at":"2025-10-10 09:51:50","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":288492,"visible":true,"origin":"","legend":"\u003cp\u003eCombination analysis of transcriptomics and metabolomics with permutation test\u003c/p\u003e\n\u003cp\u003eA. Comprehensive analysis combining transcriptomics and metabolomics showed that Resveratrol, Aldehydo-D-xylose, 3-Hydroxybutyric acid, 5-Aminoimidazole-4-carboxamide, Leucinic acid and (R)-lipoic acid were regulated by 2510002D24RIK in the liver;\u003c/p\u003e\n\u003cp\u003eB. The expression of 2510002D24RIK was higher in the PTDM group compared to the T2D group in the liver (\u003cem\u003eP\u003c/em\u003e=0.049);\u003c/p\u003e\n\u003cp\u003eC. The correlation of Resveratrol and 2510002D24RIK in the liver (R = 0.94, \u003cem\u003eP\u003c/em\u003e = 0.0047);\u003c/p\u003e\n\u003cp\u003eD. The correlation of Aldehydo-D-xylose and 2510002D24RIK in the liver (R = 0.93, \u003cem\u003eP\u003c/em\u003e= 0.0067);\u003c/p\u003e\n\u003cp\u003eE. The correlation of 3-Hydroxybutyric acid and 2510002D24RIK in the liver (R = 0.93, \u003cem\u003eP\u003c/em\u003e= 0.0064);\u003c/p\u003e\n\u003cp\u003eF. The correlation of 5-Aminoimidazole-4-carboxamide and 2510002D24RIK in the pancreas (R = 0.85, \u003cem\u003eP\u003c/em\u003e = 0.032);\u003c/p\u003e\n\u003cp\u003eG. The correlation of Leucinic acid and 2510002D24RIK in the liver (R = 0.99, \u003cem\u003eP\u003c/em\u003e= 4.3e−05);\u003c/p\u003e\n\u003cp\u003eH. The correlation of (R)-lipoic acid and 2510002D24RIK in the liver (R = 0.84,\u003cem\u003e P\u003c/em\u003e= 0.038) ;\u003c/p\u003e\n\u003cp\u003eI. Summary of the study design.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7682951/v1/20dea615a004066ed59c9df2.png"},{"id":94470383,"identity":"fcac477c-6cd5-43f1-873f-5ebd0a16a965","added_by":"auto","created_at":"2025-10-27 15:31:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":38337078,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7682951/v1/f0583bd7-a30d-4175-ae2d-30f5e607c761.pdf"},{"id":93215939,"identity":"c85162cb-1cab-465b-95fb-50f67dc0a122","added_by":"auto","created_at":"2025-10-10 09:51:50","extension":"csv","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1976,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable4.csv","url":"https://assets-eu.researchsquare.com/files/rs-7682951/v1/b3ba5c6eee5c63b6218bc759.csv"},{"id":93215981,"identity":"b77bdffb-4014-4a93-a370-a16ab7206ba0","added_by":"auto","created_at":"2025-10-10 09:51:57","extension":"csv","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":7875,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable5.csv","url":"https://assets-eu.researchsquare.com/files/rs-7682951/v1/79d04e6c87062e2e3c93a797.csv"},{"id":93215957,"identity":"50f7fdb6-ceff-4b6e-bb16-c76d8889d783","added_by":"auto","created_at":"2025-10-10 09:51:55","extension":"csv","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":8142,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable6.csv","url":"https://assets-eu.researchsquare.com/files/rs-7682951/v1/d48681c64d792684c5b87b23.csv"},{"id":93215945,"identity":"23d2f900-b656-499b-9eb4-b84471ede0d6","added_by":"auto","created_at":"2025-10-10 09:51:52","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":16293,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7682951/v1/2b95b9832c73b7d6d97c1e81.docx"},{"id":93215987,"identity":"09475ea3-74b3-44ae-9c88-83091f27572b","added_by":"auto","created_at":"2025-10-10 09:51:58","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":24305,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable9.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7682951/v1/f46d7490dc3366083e0bcb77.xlsx"},{"id":93215975,"identity":"6f2813c0-638a-4bb7-920b-5a0d6ab060af","added_by":"auto","created_at":"2025-10-10 09:51:57","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":49113,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable2Upgene.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7682951/v1/be0502357848fc6adf3bac57.xlsx"},{"id":93217157,"identity":"00c305f4-5f2b-4286-aac1-cbb2e22f0327","added_by":"auto","created_at":"2025-10-10 09:59:57","extension":"csv","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":7330,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable8.csv","url":"https://assets-eu.researchsquare.com/files/rs-7682951/v1/6c4f9ffea3c1e0309fd878be.csv"},{"id":93215970,"identity":"00f09503-c0e9-46ed-9510-58d672d3c019","added_by":"auto","created_at":"2025-10-10 09:51:57","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":58850,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable3Downgene.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7682951/v1/bcb4401c1f5606c64444d443.xlsx"},{"id":93215950,"identity":"726d5d3e-1251-4cb0-bf9e-a6488222159f","added_by":"auto","created_at":"2025-10-10 09:51:53","extension":"csv","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":24040,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable7.csv","url":"https://assets-eu.researchsquare.com/files/rs-7682951/v1/2b02d8f00df981e4d87e8738.csv"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multi-Omics Profiling Identifies Biomarker Candidates and Pathogenic Divergence Between Post-Transplant Diabetes Mellitus and Type 2 Diabetes","fulltext":[{"header":"Background","content":"\u003cp\u003eSolid organ transplantation, a life-saving medical procedure for individuals with end-stage organ failure, has revolutionized the landscape of modern medicine. Among the challenges faced by transplant recipients, post-transplant diabetes mellitus (PTDM) emerges as a significant metabolic complication that affects their long-term health and quality of life\u003csup\u003e\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. PTDM can occur following various types of organ transplantation, including liver transplantation, and its incidence is estimated to range from 10% to 40%\u003csup\u003e5, 6\u003c/sup\u003e. The cornerstone of successful organ transplantation lies in the use of immunosuppressive regimens that prevent the recipient's immune system from rejecting the transplanted organ. However, within this delicate balance between immune tolerance and graft rejection, lies an unforeseen consequence \u0026ndash; the emergence of PTDM. Among the immunosuppressive agents, tacrolimus, a calcineurin inhibitor, has been particularly implicated as a potential risk factor for PTDM\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Remarkably, whether PTDM should be classified as a subset of type 2 diabetes (T2D) or considered a distinct entity has fueled debates among researchers and clinicians for over three decades \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. PTDM has many characteristics in common with type 2 diabetes mellitus, such as insulin resistance and obesity\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. however, the underlying mechanisms and differences between PTDM and T2D remain poorly understood.\u003c/p\u003e\u003cp\u003eRecent studies have illuminated the existence of intricate crosstalk among insulin-sensitive tissues, including the liver, white adipose tissue, and the pancreas. This cross-tissue communication is pivotal in precisely regulating insulin release and glucose metabolism\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Recognizing the interconnectedness of these tissues and their potential role in the development of PTDM and T2D, our study endeavors to address the lingering questions regarding the differences and connections between these two metabolic disorders. By employing state-of-the-art omics technologies, we aim to delve deep into the molecular and metabolic landscape of PTDM, focusing on understanding its distinctive features compared to T2D, across the liver, pancreas, and adipose tissue. Through this research, we aim to provide a deeper understanding of PTDM, opening new avenues for its prevention and management and, ultimately, improving the long-term outcomes and quality of life for transplant recipients.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003eAnimal treatments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMale C57BL/6 mice, aged 6 weeks, were procured from the Shanghai Laboratory Animal Center affiliated with the Chinese Academy of Sciences. These mice were accommodated in a specific pathogen-free animal facility, maintaining a controlled environment at 20\u0026deg;C with a 12-hour light-dark cycle. They were fed a high-fat diet. Mice were randomly assigned to groups to generate the required numbers based on cage and standardised protocols used to minimise nuisance variables. One group received daily intraperitoneal injections of TAC (2.0 mg/kg/d), sourced from Astellas Ireland Co., Ltd. (Killorglin County), while the other group received streptozotocin (STZ, 45 mg/kg/d) injections from Sigma-Aldrich. Throughout the study, regular records were kept of body weight, as well as food and water intake levels, with measurements taken every 3 days. Blood samples were collected after an overnight fast, and biochemical parameters were assessed using a BioVision system every 2 weeks. Plasma insulin levels were determined using an ELISA Kit (EZRMI-13K, Sigma-Aldrich). The blood trough concentration of TAC was measured using a PRO-trac II TAC ELISA Kit (DiaSorin Inc.) at the end of the fourth week following the initial treatment. To evaluate glucose tolerance (GTT) and insulin sensitivity (ITT), standard protocols were followed\u003csup\u003e13\u003c/sup\u003e. At the end of the study, the animals were euthanized in a fasting state, and both peripheral blood and tissues were collected. All experiments involving animals were conducted according to the ethical policies (Approval no.2024AWS258). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLiver glycogen, hematoxylin and eosin (H\u0026amp;E), periodic acid Schiff (PAS) and Oil Red O\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMice liver tissues (10 mg) were suspended in 100 \u0026mu;L of assay buffer and then homogenized with a pestle on ice. The samples were centrifuged for 2 to 5 minutes at top speed at 4\u0026deg;C in a cold microcentrifuge to remove any insoluble material. The supernatant was collected and transferred to a clean tube. The samples were kept on ice before measurement. The concentration of glucose was measured by using a glucose assay kit (ab65333; Abcam). Liver samples were collected and promptly fixed in 10% neutral-buffered formalin for histopathologic examination. The samples were trimmed, dehydrated and embedded in paraffin wax. Afterward, sections at 4\u0026thinsp;\u0026micro;m for H\u0026amp;E and PAS (both from Sigma-Aldrich) were used for staining. The frozen sections of liver tissue were fixed in 4% paraformaldehyde for 15\u0026thinsp;min after being reheated and dried. Oil red O was added to the slides and stained for 8\u0026ndash;10\u0026thinsp;min, and rinsed with tap water, differentiated with 75% ethanol and re-stained with hematoxylin. Sealed sections were observed and images were recorded by optical microscope (Nikon Eclipse 50i, Tokyo, Japan).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFlow cytometry\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSingle-cell suspensions were prepared and stained with live/dead dye (Invitrogen) and anti-mouse CD16/32 antibody (BioLegend) for 15 min at 4\u0026deg;C to block the nonspecific binding. \u0026nbsp;Immune cell subsets were characterized using antibodies against CD3, CD4, and CD45 (all BioLegend) as published. And then incubated with indicated antibodies at 1:200 dilution in FACS buffer for 30 min at 4\u0026deg;C in the dark. For intranuclear staining, anti-FoxP3 antibodies were purchased from BioLegend. A transcription factor buffer set (BD Pharmingen) was used to fix fixing surface markers and permeabilize cells before FoxP3 staining. Detailed flow cytometry antibodies are listed in \u003cstrong\u003eSupplementary Table 1\u003c/strong\u003e. All samples were acquired on an LSRII cytometer (BD Biosciences) and analyzed using FlowJo software (BD Biosciences).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRNA sequencing (RNA-Seq)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTotal RNAs were extracted from tissues using TRIzol Reagent (Life Technologies). More than three biological replicates were performed for each studied condition. TruSeq Stranded Total Sample Preparation Kit (Illumina, San Diego, CA) was used for sequencing library construction. Libraries were fed onto HiSeq machines according to the activity and expected data volume. The reference genome version is GRCm38.p6 (http://asia.ensembl.org/Mus_musculus/Info/Index) and differentially expressed gene levels were measured as Fragments per kilobase of transcript per Million mapped reads (FPKM) by using DESeq2, except circRNA, which was used DESeq.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMetabolomic profiling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLiver and pancreas samples were used for non-targeted metabolomics (BIOTREE, Shanghai, China). The extracted metabolites were transferred to a fresh glass vial for liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. Ultra-high performance liquid chromatography (UHPLC) separation was carried out using a 1290 Infinity series UHPLC System (Agilent Technologies, Palo Alto, CA), equipped with a UPLC BEH Amide column. The TripleTOF 6600 mass spectrometry (AB Sciex, Foster City, California) was used to acquire tandem mass spectrometry (MS/MS) spectra on an information-dependent basis during LC-MS/MS experiment. The raw data were converted to the mzXML format using ProteoWizard and processed with an in-house program, which was developed using R and based on XCMS, for peak detection, extraction, alignment, and integration. Then an in-house MS2 database (BiotreeDB) was applied to metabolite annotation. The ionization source is electrospray ionization, and there are two ionization modes: positive ion mode (POS) and negative ion mode (NEG).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe statistical significance of differences between the 2 groups was determined with the Student\u0026rsquo;s t-test or Mann-Whitney U test. Correlations between parameters were assessed with the Pearson or Spearman correlation analysis. The filtering thresholds of difference analysis of gene expression were |log2FC| \u0026gt;= 1 and false discovery rate (FDR) \u0026lt; 0.05. The screening criteria for differential metabolites were variable importance for the projection (VIP) greater than 1 and P-value less than 0.05. When combined with transcriptomics, we chose the condition with a P-value less than 0.05 and \u0026beta; more than 0.2 after 1000 permutation. Most analyses were carried out by RStudio and GraphPad software. A value of P \u0026lt; 0.05 was considered statistically significant. * \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; ** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eThe characteristic comparison between PTDM and T2D mouse model\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCompared with the T2D group, the PTDM group showed higher body weight and lower fasting glucose (\u003cstrong\u003eFigure 1A-B\u003c/strong\u003e). Besides, the PTDM group showed higher plasma insulin levels \u003cstrong\u003e(Figure 1F\u003c/strong\u003e). The PTDM group had higher alanine aminotransferase and aspartate aminotransferase, while GSP was not significant \u003cstrong\u003e(Figure 1C-E\u003c/strong\u003e). Interestingly, the PTDM group showed lower glucose levels after insulin injection (\u003cstrong\u003eFigure 1G\u003c/strong\u003e), as well as smaller areas under the ITT curves (\u003cstrong\u003eFigure 1H\u003c/strong\u003e). In GTT, the PTDM group showed lower glucose levels (\u003cstrong\u003eFigure 1I\u003c/strong\u003e) and smaller areas under the GTT curves after injection of insulin (\u003cstrong\u003eFigure 1J\u003c/strong\u003e) compared with the T2D group.\u003c/p\u003e\n\u003cp\u003eTo further validate our findings that the PTDM group had lower glucose than the T2D group, we assessed the glucose and triglyceride content in liver and adipose tissues using PAS and Oil red O staining, respectively (\u003cstrong\u003eFigure 1K-R)\u003c/strong\u003e. Consistently, our results indicated lower liver glycogen content and reduced fat content in the PTDM group, aligning with the outcomes of our biochemical experiments. These findings collectively contribute to understanding the intricate interplay between gene expression and metabolite profiles, especially the liver, which plays a vital role in PTDM development.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTranscriptomics analysis among the liver, pancreas and adipose tissue\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDifference analysis showed that 149 genes upregulated (FDR\u0026lt;0.05, log2(FC)\u0026gt;1) and 236 genes downregulated in liver (FDR\u0026lt;0.05, log2(FC)\u0026lt; -1) (\u003cstrong\u003eFigure 2A)\u003c/strong\u003e. Difference analysis showed that 94 genes upregulated (FDR\u0026lt;0.05, log2(FC)\u0026gt;1) and 350 genes downregulated in the pancreas (FDR\u0026lt;0.05, log2(FC)\u0026lt; -1) (\u003cstrong\u003eFigure 2B)\u003c/strong\u003e. Difference analysis showed that 130 genes upregulated (FDR\u0026lt;0.05, log2(FC)\u0026gt;1) and 490 genes downregulated in Adipose (FDR\u0026lt;0.05, log2(FC)\u0026lt; -1) (\u003cstrong\u003eFigure 2C)\u003c/strong\u003e. The Venn diagram clearly illustrates a shared upregulation of five genes\u0026mdash;serpinh1, slc38a3, prepl, mocs2, and plekha7\u0026mdash;in all three tissues: liver, pancreas, and adipose tissue (\u003cstrong\u003eFigure 2D,\u0026nbsp;\u003c/strong\u003eSupplementary Table 2). Conversely, 31 genes, such as syt13, nfasc, ngp, and ighg1, exhibited a consistent downregulation across these same tissues. (\u003cstrong\u003eFigure 2E,\u0026nbsp;\u003c/strong\u003eSupplementary Table 3). \u003cstrong\u003eFigure 2F\u003c/strong\u003e showed that ighg1 and ngp genes expressed higher in the T2D than PTDM in the pancreas, while slc38a3 expressed higher in the PTDM than T2D in the liver, and serpinh1 expressed higher in the PTDM than T2D in the adipose tissue. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses unveiled substantial alterations in various pathways across different tissues. In the liver (\u003cstrong\u003eFigure 2G\u003c/strong\u003e), noteworthy changes were observed in the MAPK pathway, EGFR pathway, and leukocyte migration. Similarly, in adipose tissue (\u003cstrong\u003eFigure 2I\u003c/strong\u003e), we identified notable modifications in the MAPK signaling pathway, leukocyte migration, and Rap1 pathways. Within the pancreas (\u003cstrong\u003eFigure 2H\u003c/strong\u003e), there were significant shifts in the differentiation of Th17, Th1, and Th2 cells and T cell activation. As the TCR signalling pathway had strong association with PTDM, we sought to measure the content of T cells, and the FACS showed CD4+FOXP3+T significantly increased in the liver and pancreas respectively in the PTDM group compared to the T2D group (\u003cstrong\u003eFigure 2J-L)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWeighted gene co-expression network analysis (WGCNA) showed the module correlated with glucose levels\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWGCNA was employed to identify the module most relevant to glucose metabolism, as depicted in\u003cstrong\u003e\u0026nbsp;Figure 3A\u003c/strong\u003e. Modules with interconnected characteristics were selected based on a combination of network analysis, correlation coefficients, and hierarchical clustering. Notably, the lightcyan module emerged as highly correlated with blood glucose levels (\u003cstrong\u003eFigure 3A\u003c/strong\u003e). This module encompasses genes such as treh, acsm4, cyp2c65, cyp2c66, cyp2d11, and cyp4a12b, with detailed information in Supplementary Table 4. Of particular significance, treh and acsm4 are genes associated with glycolysis.\u003c/p\u003e\n\u003cp\u003eFurthermore, we conducted a comparative analysis between the genes within the lightcyan module and those identified in the differential analysis of liver, pancreas, and adipose tissue. Intriguingly, approximately 7.5%, 0.5%, and 2% of these genes were found to be shared among the liver, pancreas, and adipose tissue, respectively. This observation suggests that the liver may play a predominant role in the development of PTDM.\u003c/p\u003e\n\u003cp\u003eThe results of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses further accentuated the importance of the lightcyan module. These analyses indicated significant alterations in pathways related to steroid hormone biosynthesis, insulin secretion, and oxidoreductase activity (\u003cstrong\u003eFigure 3B-E)\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparison of ceRNA networks between PTDM and T2D in liver and pancreas tissues\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFurthermore, we constructed a network encompassing mRNAs, circRNAs, and lncRNAs, which serve as targets for key miRNAs and exhibit strong correlations within the ceRNA network. Specifically, within the lincRNA-associated ceRNA network, we identified 4 crucial miRNAs (mmu-miR-369-3p, mmu-miR-494-3p, mmu-miR-495-3p, mmu-miR-6240) and 4 lncRNAs (chr11:67687120-67688930, ENSMUST00000236340, XR_871293.4, XR_877383.3) in a subnetwork that play pivotal roles in shaping PTDM in the liver (\u003cstrong\u003eFigure 4A)\u003c/strong\u003e, the details were in Supplementary Table 5. In the circRNA-associated ceRNA network, we identified 6 significant miRNAs (mmu-miR-369-3p, mmu-miR-376b-3p, mmu-miR-409-3p, mmu-miR-411-5p, mmu-miR-494-3p, mmu-miR-495-3p) and 1 circRNA (15_3457930_3551685) within a subnetwork that influencing the development of PTDM in the liver (\u003cstrong\u003eFigure 4B)\u003c/strong\u003e, the details were in Supplementary Table 6.\u003c/p\u003e\n\u003cp\u003eMoving to the pancreas, in the lincRNA-associated ceRNA network, we pinpointed 2 miRNAs (mmu-miR-196b-5p, mmu-miR-410-3p) and 6 lncRNAs (chr2:174710024-174710821, chr8:15030633-15033329, ENSMUST00000185694, ENSMUST00000226736, XR_001780381.3, XR_003951655.1) in a subnetwork that shaping the development of PTDM (\u003cstrong\u003eFigure 4C)\u003c/strong\u003e, the details were in Supplementary Table 7. Similarly, in the circRNA-associated ceRNA network of the pancreas, we identified 2 miRNAs (mmu-miR-196b-5p, mmu-miR-410-3p) and 2 circRNAs (17_51803431_51809287, 19_6341903_6343176) within a subnetwork that serve as crucial regulators influencing the development of PTDM (\u003cstrong\u003eFigure 4D)\u003c/strong\u003e, the details were in Supplementary Table 8.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDifference Analysis of Metabolomics both in the liver and pancreas\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess the alterations in metabolomics within solid organs, we conducted a comparative analysis between the liver and pancreas. \u003cstrong\u003eFigures 5A-B\u003c/strong\u003e visually depict the discrimination between the PTDM and T2D groups based on metabolite profiles, as determined by orthogonal partial least squares discriminant analysis (OPLA-DA).\u003c/p\u003e\n\u003cp\u003eIn the negative ionization mode, several metabolites exhibited significant differences between the PTDM and T2D groups in both the liver and pancreas. In the liver (\u003cstrong\u003eFigure 5C, 5E, 5G\u003c/strong\u003e), metabolites such as 2,2-dimethylsuccinic acid, D-2,3-Dihydroxypropanoic acid, Succinic acid semialdehyde, D-Ribose, Methylsuccinic acid, (R)-lipoic acid, Resveratrol, Pyrrole-2-carboxylic acid, Aldehydo-D-xylose, and Nicotinic acid were notably changed in the PTDM group compared to the T2D group.\u003c/p\u003e\n\u003cp\u003eSimilarly, in the pancreas (\u003cstrong\u003eFigure 5D, 5F, 5H\u003c/strong\u003e), metabolites including 2,2-dimethylsuccinic acid, L-Serine, L-Leucine, N-Acetylserine, and Ketoleucine displayed a significant change in the PTDM group compared to the T2D group.\u003c/p\u003e\n\u003cp\u003eIn the positive ionization mode, distinct metabolites were found to be significantly decreased in the PTDM group as compared to the T2D group. In the liver, metabolites such as Pinostrobin chalcone and PC(18:1(11Z)/P-16:0) exhibited notable increases. Similarly, in the pancreas, metabolites including Pinostrobin chalcone, Caffeine, D-Galactose, and PC(22:4(7Z,10Z,13Z,16Z)/15:0) were significantly decreased in the PTDM group compared to the T2D group (Supplementary Table 9).\u003c/p\u003e\n\u003cp\u003eThese findings underscore the significant metabolic differences between the PTDM and T2D groups within the liver and pancreas, shedding light on the unique metabolic signatures associated with PTDM.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCombination analysis of transcriptomics and metabolomics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThrough a comprehensive analysis combining transcriptomics and metabolomics following differential analysis with permutation test, we observed intriguing correlations involving 2510002D24Rik. In the liver (\u003cstrong\u003eFigure 6A\u003c/strong\u003e), this gene was found to be correlated with changes in Resveratrol, Aldehydo-D-xylose, 3-Hydroxybutyric acid, 5-Aminoimidazole-4-carboxamide, Leucinic acid and (R)-lipoic acid. \u003cstrong\u003eFigure 6B\u0026nbsp;\u003c/strong\u003eshowed that\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e2510002D24Rik was significantly increased in the PTDM group compared to the T2D group. \u003cstrong\u003eFigure 6C-H\u003c/strong\u003e provides a detailed visualization of the correlations between the above metabolites and 2510002D24Rik in the liver, while we did not see a correlation of 2510002D24Rik and metabolites in the pancreas.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eLiver transplantation is a life-saving procedure for patients with end-stage liver disease\u003csup\u003e\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. However, the development of post-transplant diabetes mellitus (PTDM) significantly impacts the long-term survival and quality of life of transplant recipients\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. This study aimed to unravel the intricate mechanisms underlying PTDM, primarily associated with the immunosuppressive agent tacrolimus, and draw meaningful comparisons with type 2 diabetes (T2D) using murine models. Our integrated whole-transcriptomic and metabolomic analyses provided valuable insights into the molecular and metabolic alterations occurring in liver and pancreatic tissues, shedding light on the distinct features of PTDM compared to T2D.\u003c/p\u003e\u003cp\u003eOur findings highlight several vital observations that deepen our understanding of PTDM. PTDM mice displayed a unique metabolic phenotype characterized by higher body weight, lower blood glucose levels, and improved insulin tolerance compared to T2D mice. These differences suggest that the pathophysiology of PTDM may involve distinct molecular mechanisms compared to classical T2D. The MAPK pathway and leukocyte migration emerged as significantly altered processes in the liver of PTDM mice, potentially implicating these pathways in the development of PTDM. These findings align with previous studies that have linked MAPK signaling to glucose homeostasis and insulin resistance\u003csup\u003e\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Interestingly, we also observed changes in the MAPK and leukocyte migration pathways in adipose tissue. In the pancreas, our analyses revealed marked changes in the differentiation of Th17, Th1, and Th2 cells, T cell activation and increased CD4\u0026thinsp;+\u0026thinsp;FOXP3\u0026thinsp;+\u0026thinsp;Treg cells in PTDM. These immune-related alterations are intriguing and may indicate a role for immune dysregulation in developing PTDM. Tacrolimus, a potent immunosuppressive agent commonly used in transplant recipients, is known to decrease the number of circulating Tregs in transplanted patients; the discordance with our results might be because the amounts of Tregs decreased more significantly in T2D\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eFurthermore, our ceRNA network analysis uncovered significant changes in long intergenic non-coding RNA (lincRNA) an circular RNA (circRNA) networks in the liver and pancreas of PTDM mice. These non-coding RNAs have been increasingly recognized as important regulators of gene expression and could potentially play a role in the development of PTDM or other complications\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Future studies should explore the functional significance of these RNA networks in PTDM pathogenesis. Our WGCNA revealed that a small subset of genes from the glucose module were shared among the liver, pancreas, and adipose tissue\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. This cross-tissue gene expression pattern suggests the presence of interconnected regulatory networks that coordinate metabolic processes throughout the body. Intriguingly, approximately 7.5%, 0.5%, and 2% of these genes were found to be shared among the liver, pancreas, and adipose tissue, respectively. This observation suggests that the liver may play a predominant role in developing PTDM\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eImportantly, our metabolomic analysis identified specific metabolites that were significantly altered in both liver and pancreas tissues of PTDM mice compared to T2D. Notably, Resveratrol, Aldehydo-D-xylose, 3-Hydroxybutyric acid, 5-Aminoimidazole-4-carboxamide, Leucinic acid, and (R)-lipoic acid exhibited significant changes, which were regulated by 2510002D24Rik in the liver. These metabolites may serve as potential biomarkers or therapeutic targets for PTDM\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Additionally, it has been demonstrated that 5-Aminoimidazole-4-carboxamide plays a similar role to metformin in inhibiting glucose phosphorylation and glycolysis in rat hepatocytes\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Further investigations into their roles in glucose metabolism and insulin sensitivity are warranted. Histological staining of liver tissue revealed lower glycogen and lipid content in PTDM mice, suggesting altered hepatic metabolism. Using PAS staining and Oil red O staining, our results indicated lower liver glycogen content and reduced fat content in the PTDM group, aligning with the outcomes of our biochemical experiments.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our study provides valuable insights into the pathogenesis of PTDM, offering a comprehensive view of the molecular and metabolic changes that distinguish it from T2D. The unique clinical and pathological characteristics of PTDM, including its distinct metabolic phenotype, immune-related alterations, and non-coding RNA networks, underscore the need for tailored approaches to its prevention and treatment. The shared molecular pathways between PTDM and T2D suggest common therapeutic targets, while the specific metabolites identified in PTDM mice present potential biomarkers and therapeutic candidates. Future research should aim to validate these findings in clinical settings and explore the functional roles of the identified molecular players, ultimately paving the way for improved outcomes and quality of life for liver transplant recipients at risk of PTDM.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll experiments involving animals were conducted according to the ethical policies and procedures approved by the ethics committee of Shanghai General Hospital (Approval no.2024AWS258). \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have reviewed the manuscript and provided their consent for submission.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData is provided within the manuscript or supplementary information files.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by The National Natural Science Foundation of China (No. 82400774).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eY.L. was involved in the study design, conducted the experiments, and drafted the paper: P.Z.W., R.N.G., Z.C.C. and C.Q. performed bioinformatics analysis and performed the experiments; R.W., B.J.S., W.B.A., W.J.S., Y.X., H.Y.W., P.H.W. and T.Z. commented on the study; X.P., J.W.F., and Z.H.P. designed, supervised the study and revised the manuscript.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to the staff of Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1 Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China.\u003c/p\u003e\n\u003cp\u003e2 Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China.\u003c/p\u003e\n\u003cp\u003e3 Department of General Surgery, Changzheng Hospital, Navy Medical University, Shanghai, 200003, China.\u003c/p\u003e\n\u003cp\u003e4 Department of Endocrinology, Fujian Medical University Union Hospital. Fuzhou, 350001, China.\u003c/p\u003e\n\u003cp\u003e5 Organ Transplantation Institute of Xiamen University, Fujian Provincial Key Laboratory of Organ and Tissue Regeneration, School of Medicine, Xiamen University, Xiamen 361000, China.\u003c/p\u003e\n\u003cp\u003e6 Department of General Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450000, Henan, China.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eWang, R., Zhang, Y., Fan, J. Risk Factors for New-Onset Diabetes Mellitus After Heart Transplantation: A Nomogram Approach. Transplant. Proc. 54 (2022) 762-768.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eHalden, T.A.S., Kvitne, K.E., Midtvedt, K. Efficacy and Safety of Empagliflozin in Renal Transplant Recipients With Posttransplant Diabetes Mellitus. Diabetes Care 42 (2019) 1067-1074.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eNewman, J.D., Schlendorf, K.H., Cox, Z.L., Rangaswami, J., Lindenfeld, J., Fong, M.W., et al. Post-transplant diabetes mellitus following heart transplantation. J. Heart Lung Transplant. 41 (2022) 1537-1546.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eEleftheriadis, G., Naik, M.G., Osmanodja, B., Koch, N., Kiencke, P., Oefner, P.J., et al. Continuous glucose monitoring for the prediction of posttransplant diabetes mellitus and impaired glucose tolerance on day 90 after kidney transplantation. Am. J. Transplant. (2024) [Epub ahead of print].\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eJenssen, T., Hartmann, A. Post-transplant diabetes mellitus in patients with solid organ transplants. Nat. Rev. Endocrinol. 15 (2019) 172-188.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eShaked, A., Loza, B.-L., Van Loon, E., Callemeyn, J., Lerut, E., Lamarth\u0026eacute;e, B., et al. Donor and recipient polygenic risk scores influence the risk of post-transplant diabetes. Nat. Med. 28 (2022) 1412\u0026ndash;1422.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSong, J.L., Li, M., Yan, L.N., Wang, Q., Zeng, Y., Wen, T.F., et al. Higher tacrolimus blood concentration is related to increased risk of post-transplantation diabetes mellitus after living donor liver transplantation. Int. J. Surg. 51 (2018) 17-23.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eKo, E.J., Shin, Y.J., Cui, S., Li, Q., Lee, J.Y., Kim, H.J., et al. Effect of dual inhibition of DPP4 and SGLT2 on tacrolimus-induced diabetes mellitus and nephrotoxicity in a rat model. Am. J. Transplant. 22 (2022) 1537-1549.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eRodriguez-Rodriguez, A.E., Porrini, E., Torres, A. Beta-Cell Dysfunction Induced by Tacrolimus: A Way to Explain Type 2 Diabetes? Int. J. Mol. Sci. 22 (2021) 10311.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eKang, E.S., Kim, M.S., Kim, C.H., Han, S.J., Hur, K.Y., Ahn, C.W., et al. Association of common type 2 diabetes risk gene variants and posttransplantation diabetes mellitus in renal allograft recipients in Korea. Transplantation 88 (2009) 693-698.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSharif, A., Cohney, S. Post-transplantation diabetes-state of the art. Lancet Diabetes Endocrinol. 4 (2016) 337-349.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eKleinridders, A., Ferris, H.A., Cai, W., Kahn, C.R. Insulin action in brain regulates systemic metabolism and brain function. Diabetes 63 (2014) 2232-2243.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eQu, Y.L., Deng, C.H., Luo, Q., Hu, Y.R., Yin, W.J., Wang, F.B., et al. Arid1a regulates insulin sensitivity and lipid metabolism. EBioMedicine 42 (2019) 481-493. 481-493.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eShi, B., Liu, Y., Liu, D., Wang, Z., Jiang, W., She, X., et al. Genotype-guided model significantly improves accuracy of tacrolimus initial dosing after liver transplantation. EClinicalMedicine 55 (2023) 101752.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eLiu, Y., Wang, R., Wen, P., Shi, B., Xu, J., Huang, L., et al. Genetic factors underlying tacrolimus intolerance after liver transplantation. Front. Immunol. 13 (2022) 944442.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eHuang, L., Assiri, A.A., Wen, P., Shi, B., Liu, Y., Wang, M., et al. The CYP3A5 genotypes of both liver transplant recipients and donors influence the time-dependent recovery of tacrolimus clearance during the early stage following transplantation. Clin. Transl. Med. 11 (2021) e542.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eBhat, V., Tazari, M., Watt, K.D., Chaiteerakij, R., Torbenson, M.S., Borad, M.J., et al. New-Onset Diabetes and Preexisting Diabetes Are Associated With Comparable Reduction in Long-Term Survival After Liver Transplant: A Machine Learning Approach. Mayo Clin. Proc. 93 (2018) 1794-1802.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eShivaswamy, V., Boerner, B., Larsen, J. Post-Transplant Diabetes Mellitus: Causes, Treatment, and Impact on Outcomes. Endocr. Rev. 37 (2016) 37-61.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eZhu, R., Chen, B., Bai, Y., Miao, T., Rui, L., Zhang, H., et al. Lycopene in protection against obesity and diabetes: A mechanistic review.\u0026nbsp;Pharmacol. Res.\u0026nbsp;159 (2020) 104966.\u003c/li\u003e\n \u003cli\u003eFang, Z., Xu, H., Duan, J., Li, H., Li, Y., Zhu, Z., et al. Short-term tamoxifen administration improves hepatic steatosis and glucose intolerance through JNK/MAPK in mice. Signal Transduct. Target. Ther. 8 (2023) 94.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eKoh, A., Molinaro, A., St\u0026aring;hlman, M., Khan, M.T., Schmidt, C., Manner\u0026aring;s-Holm, L., et al. Microbially Produced Imidazole Propionate Impairs Insulin Signaling through mTORC1. Cell 175 (2018) 947-961.e17.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eCantarovich, D., Kervella, D., Karam, G., Giral-Classe, M., Hourmant, M., Dantal, J., et al. Tacrolimus- versus sirolimus-based immunosuppression after simultaneous pancreas and kidney transplantation: 5-year results of a randomized trial. Am. J. Transplant. 20 (2020) 1679-1690.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eLiu, Y., Zhang, C., Li, L., Wu, Y., Zhao, W., Liu, K., et al. Genome-Wide Association Study of Tacrolimus Pharmacokinetics Identifies Novel Single Nucleotide Polymorphisms in the Convalescence and Stabilization Periods of Post-transplant Liver Function. Front. Genet. 10 (2019) 528.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eWhitehouse, G., Gray, E., Mastoridis, S., Merritt, E., Kodela, E., Yang, J.H.M., et al. IL-2 therapy restores regulatory T-cell dysfunction induced by calcineurin inhibitors. Proc. Natl. Acad. Sci. U. S. A. 114 (2017) 7083-7088.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSu, R., Wei, X., Wei, Q., Li, Y., Wang, M., Li, X., et al. Extrahepatic organs in the development of non-alcoholic fatty liver disease in liver transplant patients. Hepatobiliary Surg. Nutr. 11 (2022) 400-411.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eZhong, H., Liu, S., Cao, F., Zhao, Y., Zhou, J., Tang, F., et al. Dissecting Tumor Antigens and Immune Subtypes of Glioma to Develop mRNA Vaccine.\u0026nbsp;Front. Immunol.\u0026nbsp;12 (2021) 709986.\u003c/li\u003e\n \u003cli\u003eWang, M., Xu, J., Yang, N., Chen, J., Zhou, T., Li, Z., et al. Insight Into the Metabolomic Characteristics of Post-Transplant Diabetes Mellitus by the Integrated LC-MS and GC-MS Approach- Preliminary Study.\u0026nbsp;Front. Endocrinol.\u0026nbsp;12 (2021) 807318.\u003c/li\u003e\n \u003cli\u003eGuigas, B., Bertrand, L., Taleux, N., Foretz, M., Wiernsperger, N., Vertommen, D., et al. 5-Aminoimidazole-4-carboxamide-1-beta-D-ribofuranoside and metformin inhibit hepatic glucose phosphorylation by an AMP-activated protein kinase-independent effect on glucokinase translocation. Diabetes 55 (2006) 865-874.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"post-transplant diabetes mellitus, type 2 diabetes, tacrolimus, streptozotocin, omics","lastPublishedDoi":"10.21203/rs.3.rs-7682951/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7682951/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003ePost-transplant diabetes mellitus (PTDM) is a significant complication following liver transplantation, primarily induced by immunosuppressive agents like tacrolimus. While PTDM shares some clinical features with type 2 diabetes (T2D), its distinct pathogenesis remains poorly understood.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe developed mouse models of PTDM and T2D using tacrolimus, streptozotocin (STZ), and a high-fat diet to simulate disease conditions. Integrated whole-transcriptomics and metabolomics analyses were performed on liver, pancreatic, and adipose tissue to map disease-specific molecular landscapes and nominate candidate biomarkers.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eDespite higher body weight, PTDM mice exhibited lower blood glucose and improved insulin tolerance compared to T2D mice. Multi-omics analyses revealed PTDM-specific activation of the MAPK pathway, marked Treg cell infiltration in in the liver and pancreas, and dysregulation of lincRNA-circRNA networks. Metabolomics identified altered metabolites including 2,2-dimethylsuccinic acid, indicating mitochondrial dysfunction. Most notably, integrative analysis nominated 2510002D24Rik (also known as Pants) \u0026mdash; a previously uncharacterized, liver-restricted gene \u0026mdash; as a hub coordinating immune-metabolic crosstalk, positioning it as a lead candidate biomarker for PTDM.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eOur multi-omics approach uncovers distinct molecular signatures that differentiate PTDM from T2D, providing novel therapeutic interventions for PTDM.\u003c/p\u003e","manuscriptTitle":"Multi-Omics Profiling Identifies Biomarker Candidates and Pathogenic Divergence Between Post-Transplant Diabetes Mellitus and Type 2 Diabetes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-10 09:51:44","doi":"10.21203/rs.3.rs-7682951/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e4e49c1f-d49d-4c28-80dd-6a3ba9809350","owner":[],"postedDate":"October 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-27T13:57:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-10 09:51:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7682951","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7682951","identity":"rs-7682951","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}