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Serum metabolomics approach to monitor the changes in metabolite profiles following renal transplantation.

ABSTRACT: Systemic metabolic changes after renal transplantation reflect the key processes that are related to graft accommodation. In order to describe and better understand these changes, the 1HNMR based metabolomics approach was used. The changes of 47 metabolites in the serum samples of 19 individuals were interpreted over time with respect to their levels prior to transplantation. Considering the specific repeated measures design of the experiments, data analysis was mainly focused on the multiple analyses of variance (ANOVA) methods such as ANOVA simultaneous component analysis and ANOVA-target projection. We also propose here the combined use of ANOVA and classification and regression trees (ANOVA-CART) under the assumption that a small set of metabolites the binary splits on which may better describe the graft accommodation processes over time. This assumption is very important for developing a medical protocol for evaluating a patient's health state. The results showed that besides creatinine, which is routinely used to monitor renal activity, the changes in levels of hippurate, mannitol and alanine may be associated with the changes in renal function during the post-transplantation recovery period. Specifically, the level of hippurate (or histidine) is more sensitive to any short-term changes in renal activity than creatinine.

SUBMITTER: Stanimirova I

PROVIDER: S-EPMC7560840 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Serum metabolomics approach to monitor the changes in metabolite profiles following renal transplantation.

Stanimirova Ivana I Banasik Mirosław M Ząbek Adam A Dawiskiba Tomasz T Kościelska-Kasprzak Katarzyna K Wojtowicz Wojciech W Krajewska Magdalena M Janczak Dariusz D Młynarz Piotr P

Scientific reports 20201014 1

Systemic metabolic changes after renal transplantation reflect the key processes that are related to graft accommodation. In order to describe and better understand these changes, the <sup>1</sup>HNMR based metabolomics approach was used. The changes of 47 metabolites in the serum samples of 19 individuals were interpreted over time with respect to their levels prior to transplantation. Considering the specific repeated measures design of the experiments, data analysis was mainly focused on the ...[more]

PMID: 33057167

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Project description:BackgroundAntineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) is an autoimmune systemic disease, and the majority of AAV patients have renal involvement presenting as rapid progressive glomerulonephritis (GN). Currently, the clinically available AAV markers are limited, and some of the newly reported markers are still in the nascent stage. The particular mechanism of the level changes of various markers and their association with the pathogenesis of AAV are not well defined. With the help of metabolomics analysis, this study aims to explore metabolic changes in AAV patients with renal involvement and lay the foundation for the discovery of novel biomarkers for AAV-related kidney damage.MethodsWe performed liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based on serum samples from patients with AAV (N = 33) and healthy controls (N = 33) in order to characterize the serum metabolic profiling. The principal component analysis (PCA) and orthogonal partial least-squares-discriminant analysis (OPLS-DA) were used to identify the differential metabolites. Least Absolute Shrinkage and Selection Operator (LASSO) and eXtreme Gradient Boosting (XGBoost) analysis were further conducted to identify the potential diagnostic biomarker. A receiver operating characteristic (ROC) curve analysis was conducted to evaluate the diagnostic performance of the identified potential biomarker.ResultsA total of 455 metabolites were detected by LC-MS analysis. PCA and OPLS-DA demonstrated a significant difference between AAV patients with renal involvement and healthy controls, and 135 differentially expressed metabolites were selected, with 121 upregulated and 14 downregulated. Ninety-two metabolic pathways were annotated and enriched based on the KEGG database. N-acetyl-L-leucine, Acetyl-DL-Valine, 5-hydroxyindole-3-acetic acid, and the combination of 1-methylhistidine and Asp-phe could accurately distinguish AAV patients with renal involvement from healthy controls. And 1-methylhistidine was found to be significantly associated with the progression and prognosis of AAV with renal impairment. Amino acid metabolism exhibits significant alternations in AAV with renal involvement.ConclusionThis study identified metabolomic differences between AAV patients with renal involvement and non-AAV individuals. Metabolites that could accurately distinguish patients with AAV renal impairment from healthy controls in this study, and metabolites that were significantly associated with disease progression and prognosis were screened out. Overall, this study provides information on changes in metabolites and metabolic pathways for future studies of AAV-related kidney damage and lays a foundation for the exploration of new biomarkers of AAV-related kidney damage.

Project description:By far, no study has focused on observing the metabolomic profiles in perimenopause-related obesity. This study attempted to identify the metabolic characteristics of subjects with perimenopause obesity (PO). Thirty-nine perimenopausal Chinese women, 21 with PO and 18 without obesity (PN), were recruited in this study. A conventional ultra-high-performance liquid chromatography-quadrupole time-of-flight/mass spectrometry (UHPLC-QTOF/MS) followed by principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA) were used as untargeted metabolomics approaches to explore the serum metabolic profiles. Kyoto Encyclopedia of Genes and Genomes (KEGG) and MetaboAnalyst were used to identify the related metabolic pathways. A total of 46 differential metabolites, along with seven metabolic pathways relevant to PO were identified, which belonged to lipid, amino acids, carbohydrates, and organic acids. As for amino acids, we found a significant increase in l-arginine and d-ornithine in the positive ion (POS) mode and l-leucine, l-valine, l-tyrosine, and N-acetyl-l-tyrosine in the negative ion (NEG) mode and a significant decrease in l-proline in the POS mode of the PO group. We also found phosphatidylcholine (PC) (16:0/16:0), palmitic acid, and myristic acid, which are associated with the significant upregulation of lipid metabolism. Moreover, the serum indole lactic acid and indoleacetic acid were upregulated in the NEG mode. With respect to the metabolic pathways, the d-arginine and d-ornithine metabolisms and the arginine and proline metabolism pathways in POS mode were the most dominant PO-related pathways. The changes of metabolisms of lipid, amino acids, and indoleacetic acid provided a pathophysiological scenario for Chinese women with PO. We believe that the findings of this study are helpful for clinicians to take measures to prevent the women with PO from developing severe incurable obesity-related complications, such as cardiovascular disease and stroke.

Project description:PurposeTo investigate multiparametric functional MRI to characterize acute rejection in a murine allogeneic renal transplant model and evaluate the effect of novel therapeutics.Material and methodsWe performed allogeneic and syngeneic orthotopic transplantations (Balb/c to C57Bl/6 and C57Bl/6 to C57Bl/6). Allogeneic Groups (n = 5) were either treated with the anti-CCL2-Spiegelmer (mNOX-E36) in monotherapy or in combination with low doses of Ciclosporin-A (10mg/kgBW/d) for 10 days. Controls received equivalent doses of a non-functional spiegelmer (revmNOX-E36) or low dose Ciclosporin-A. Diffusion-weighted (DWI) and Dynamic-contrast-enhanced (DCE-) MRI-scans were performed using a clinical 3T-scanner. DWI analysis (b-values from 0-800 s/mm2) was performed mono- and biexponentially, while DCE-MRI was assessed with deconvolution analysis. Therapy effects were assessed ex vivo with histopathology, immunohistochemistry and RT-PCR. Statistical analysis was performed with unpaired t-tests and Spearman´s correlation coefficient.ResultsDWI showed a significant diffusion restriction in allogeneic compared to syngeneic transplants (ADC: 0.63±0.08 vs. 1.29±0.12 mm2/s*103) with decreasing diffusion restriction under therapy. DCE-MRI showed restored organ perfusion under Ciclosporin A alone and combination therapy (Plasma Flow: 43.43±12.49; 38.75±7.53ml/100ml/min) compared to syngeneic controls (51.03±12.49ml/100ml/min). Ex vivo analysis showed reduced monocytic infiltrates, attenuated levels of inflammatory cytokines under mNOX-E36 monotherapy with an additive effect of low dose Ciclosporin A. There was a significant (p<0.05) negative correlation between ADC and interstitial inflammation (r = -0.73) or macrophage infiltration (r = -0.81) and between organ perfusion and intimal arteritis (r = -0.63).ConclusionMultiparametric functional MRI is suited to detect renal allograft rejection in an experimental murine model and allows to characterize effects of immunosuppressive therapy alleviating acute rejection processes in allogeneic transplantation.

Project description:BackgroundHuman plasma and serum are widely used matrices in clinical and biological studies. However, different collecting procedures and the coagulation cascade influence concentrations of both proteins and metabolites in these matrices. The effects on metabolite concentration profiles have not been fully characterized.Methodology/principal findingsWe analyzed the concentrations of 163 metabolites in plasma and serum samples collected simultaneously from 377 fasting individuals. To ensure data quality, 41 metabolites with low measurement stability were excluded from further analysis. In addition, plasma and corresponding serum samples from 83 individuals were re-measured in the same plates and mean correlation coefficients (r) of all metabolites between the duplicates were 0.83 and 0.80 in plasma and serum, respectively, indicating significantly better stability of plasma compared to serum (p?=?0.01). Metabolite profiles from plasma and serum were clearly distinct with 104 metabolites showing significantly higher concentrations in serum. In particular, 9 metabolites showed relative concentration differences larger than 20%. Despite differences in absolute concentration between the two matrices, for most metabolites the overall correlation was high (mean r?=?0.81±0.10), which reflects a proportional change in concentration. Furthermore, when two groups of individuals with different phenotypes were compared with each other using both matrices, more metabolites with significantly different concentrations could be identified in serum than in plasma. For example, when 51 type 2 diabetes (T2D) patients were compared with 326 non-T2D individuals, 15 more significantly different metabolites were found in serum, in addition to the 25 common to both matrices.Conclusions/significanceOur study shows that reproducibility was good in both plasma and serum, and better in plasma. Furthermore, as long as the same blood preparation procedure is used, either matrix should generate similar results in clinical and biological studies. The higher metabolite concentrations in serum, however, make it possible to provide more sensitive results in biomarker detection.

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Serum metabolomics approach to monitor the changes in metabolite profiles following renal transplantation.

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Serum metabolomics approach to monitor the changes in metabolite profiles following renal transplantation.

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