Omics reveal the difference between PTDM and T2D among the liver, pancreas and adipose tissue

doi:10.21203/rs.3.rs-3560838/v1

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Omics reveal the difference between PTDM and T2D among the liver, pancreas and adipose tissue

https://doi.org/10.21203/rs.3.rs-3560838/v1

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Backgroud: This research delved into the underlying mechanisms responsible for post-transplant diabetes mellitus (PTDM) and comparisons with type 2 diabetes (T2D).

Methods: Comprehensive analyses, encompassing both transcriptomics and metabolomics, were conducted on liver and pancreatic tissues from the PTDM and T2D groups. Furthermore, distinctions in competing endogenous RNA (ceRNA) networks were explored. Weighted gene co-expression network analysis (WGCNA)was implemented to identify clusters of genes exhibiting strong correlations among the liver, pancreas, and adipose tissue.

Results: Compared to their T2D counterparts, PTDM mice exhibited notable differences in higher body weight (P <0.05), lower blood glucose levels (P <0.05), and enhanced insulin tolerance (P <0.05). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses indicated significant alterations in the MAPK pathway and leukocyte migration within the liver, as well as variations in the differentiation of Th17, Th1, and Th2 cells, along with T cell activation, within the pancreas. In addition, the MAPK signaling pathway and leukocyte migration pathways were significantly modified in adipose tissue. The ceRNA network analysis highlighted substantial changes, revealing 164 long intergenic non-coding RNA (lincRNA) and 154 circular RNA (circRNA) networks significantly changed in the liver, and 445 lincRNA and 135 circRNA networks in the pancreas that were significantly altered in the PTDM group compared to T2D group. Notably, the metabolites Resveratrol, Aldehydo-D-xylose, 3-Hydroxybutyric acid, 5-Aminoimidazole-4-carboxamide, Leucinic acid and (R)-lipoic acid displayed significant changes in the liver in PTDM mice, with their regulation attributed to 2510002D24RIK.

Conclusion:In summary, PTDM mice exhibited distinctive multi-omics and pathological characteristics compared to their T2D counterparts.

Post-transplant diabetes mellitus

type 2 diabetes

tacrolimus

streptozotocin

omics

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(1–3). PTDM can occur following various types of organ transplantation, including liver transplantation, and its incidence is estimated to range from 10–40%(4, 5). 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(6, 7). Remarkably, the question of 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 (5, 8, 9). PTDM has many characteristics in common with type 2 diabetes mellitus, such as insulin resistance and obesity(10). 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 orchestrating the precise regulation of insulin release and glucose metabolism(10, 11). Recognizing the interconnectedness of these tissues and the potential role they play in the development of both 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, with a focus 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.

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. A total of 6 mice were randomly divided into two groups. 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 test ( GTT ) and insulin tolerance test ( ITT ), standard protocols were followed(12). The experiments involving the animals received approval from the Institutional Animal Care and Use Committee and the Ethics Committee of Shanghai General Hospital. At the end of the study, the animals were euthanized in a fasting state, and both peripheral blood and tissues were collected (see Fig. 1)

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).

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.

The characteristic comparison between PTDM and T2D mouse model

Mice remained in good condition throughout the experimental period (Fig. 1A). Compared with the T2D group, the PTDM group showed higher body weight and lower fasting glucose (Fig. 1B-C, both P < 0.05). Besides, the PTDM group showed higher plasma insulin levels (Fig. 1G, P < 0.05). The PTDM group had higher alanine aminotransferase (P < 0.05) and aspartate aminotransferase (P < 0.05), while GSP was not significant (Fig. 1D-F). Interestingly, the PTDM group showed lower glucose levels after insulin injection (Fig. 1H, P < 0.05), as well as smaller areas under the ITT curves (Fig. 1I, P < 0.05). In GTT, the PTDM group showed lower glucose levels (Fig. 1J, P < 0.05) and smaller areas under the GTT curves after injection of insulin (Fig. 1K, P < 0.05) compared with the T2D group.(Fig. 1)

Transcriptomics analysis among the liver, pancreas and adipose tissue

Difference analysis showed that 149 genes upregulated (FDR < 0.05, log2(FC) > 1) and 236 genes downregulated in liver (FDR < 0.05, log2(FC)< -1) (Fig. 2A). Difference analysis showed that 94 genes upregulated (FDR < 0.05, log2(FC) > 1) and 350 genes downregulated in the pancreas (FDR < 0.05, log2(FC)< -1) (Fig. 2B). Difference analysis showed that 130 genes upregulated (FDR < 0.05, log2(FC) > 1) and 490 genes downregulated in Adipose (FDR < 0.05, log2(FC)< -1) (Fig. 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 (Fig. 2D, Supplementary Table 1, all p < 0.05). Conversely, 31 genes, such as syt13, nfasc, ngp, and ighg1, exhibited a consistent downregulation across these same tissues. (Fig. 2E, Supplementary Table 2, all p < 0.05). 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 (Fig. 2G), noteworthy changes were observed in the MAPK pathway, EGFR pathway, and leukocyte migration. Within the pancreas (Fig. 2H), there were significant shifts in the differentiation of Th17, Th1, and Th2 cells, as well as T cell activation. Similarly, in adipose tissue (Fig. 2I), we identified notable modifications in the MAPK signaling pathway, leukocyte migration, and Rap1 pathways. These results underscore the substantial impact of the immune system, particularly in terms of leukocyte migration, on the development of PTDM.(Fig. 2)

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 Fig. 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 (R = 0.47, P = 0.03, Fig. 3B). This module encompasses genes such as treh, acsm4, cyp2c65, cyp2c66, cyp2d11, and cyp4a12b, with detailed information available in Supplementary Table 3. 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 (Fig. 3C-F). (Fig. 3)

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 (Fig. 4A), the details were in Supplementary Table 4. 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 (Fig. 4B), the details were in Supplementary Table 5.

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 (Fig. 4C), the details were in Supplementary Table 6. 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 (Fig. 4D), the details were in Supplementary Table 7.

This comprehensive approach provides a deeper understanding and introduces a novel perspective on which ceRNA networks are different between PTDM and T2D, particularly in the liver and pancreas, respectively. (Fig. 4)

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 (Fig. 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 (Fig. 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 8).

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.(Fig. 5)

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 (Fig. 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 T2D group (P = 0.049). Figure 6C-H provide a detailed visualization of the correlations between above metabolites and 2510002D24RIK in the liver, while we did not see a correlation of 2510002D24RIK and metabolites in the pancreas.

To further validate our findings that PTDM group had a lower glucose than T2D group, we assessed the glucose and triglyceride content in liver tissues and adipose tissues using PAS staining and Oil red O staining respectively (Fig. 6I-P). 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 our understanding of the intricate interplay between gene expression and metabolite profiles, especially that liver played an important role in the PTDM development.(Fig. 6)

Liver transplantation is a life-saving procedure for patients with end-stage liver disease(13–15). However, the development of post-transplant diabetes mellitus (PTDM) significantly impacts the long-term survival and quality of life of transplant recipients(1, 16, 17). In this study, we 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 key observations that deepen our understanding of PTDM. Notably, 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(18–20). Interestingly, we also observed the change of the MAPK pathway and leukocyte migration pathways in adipose tissue. In the pancreas, our analyses revealed marked changes in the differentiation of Th17, Th1, and Th2 cells, as well as T cell activation in PTDM mice. These immune-related alterations are intriguing and may indicate a role for immune dysregulation in the development of PTDM. Tacrolimus, a potent immunosuppressive agent commonly used in transplant recipients, is known to modulate T cell function, and its effects on pancreatic immune cells may contribute to PTDM(14, 21, 22).

Furthermore, our ceRNA network analysis uncovered significant changes in long intergenic non-coding RNA (lincRNA) and 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(23). 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(24). 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 the development of PTDM(4).

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(25). 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(26). 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.

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.

PTDM: post-transplant diabetes mellitus;

T2D: type 2 diabetes;

ceRNA: competing endogenous RNA;

WGCNA: Weighted gene co-expression network analysis;

GO: Gene Ontology;

KEGG: Kyoto Encyclopedia of Genes and Genomes;

lincRNA: long intergenic non-coding RNA;

circRNA: circular RNA;

GTT: glucose tolerance test;

ITT: insulin tolerance test;

H&E: hematoxylin and eosin;

PAS: periodic acid Schiff;

RNA-Seq: RNA sequencing;

UHPLC: ultra-high performance liquid chromatography;

POS: positive ion mode;

NEG: negative ion mode;

FDR: false discovery rate;

VIP: variable importance for the projection;

OPLA-DA: orthogonal partial least squares discriminant analysis.

Ethics approval and consent to participate

The experiments involving the animals received approval from the Institutional Animal Care and Use Committee and the Ethics Committee of Shanghai General Hospital.

Consent for publication

Not applicable

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors declare that they have no competing interests.

Funding

the Key Program of the National Natural Science Foundation of China (No. 81530044); National Natural Science Foundation of China (No. 82070677 and 81903417); Characteristic research project of Shanghai First People's Hospital (CCTR-2022B10); National Science and Technology Major Project of the Ministry of Science and Technology of China (2016YFC0901704).

Authors' contributions

Y.L. was involved in the study design, conducted the experiments, and drafted the paper; P.Z.W., Y.Y. and Q.N.H. performed bioinformatics analysis and performed the experiments; W.J.S., Y.X., H.Y.W., Y.Y.T., R.W. and T.Z. commented on the study; J.W.F.,and Z.H.P. designed, supervised the study, and revised the manuscript.

Acknowledgements:

Not applicable.

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No competing interests reported.

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Omics reveal the difference between PTDM and T2D among the liver, pancreas and adipose tissue

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Abstract

Figures

Introduction

Methods

Animal treatments

Liver glycogen, hematoxylin and eosin (H&E), periodic acid Schiff (PAS) and Oil Red O

RNA sequencing (RNA-Seq)

Metabolomic profiling

Statistical analysis

Results

The characteristic comparison between PTDM and T2D mouse model

Transcriptomics analysis among the liver, pancreas and adipose tissue

Weighted gene co-expression network analysis (WGCNA) showed the module correlated with glucose levels

Comparison of ceRNA networks between PTDM and T2D in liver and pancreas tissues

Difference Analysis of Metabolomics both in the liver and pancreas

Combination analysis of transcriptomics and metabolomics

Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

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