Metabonomic Analysis of Serum Metabolites in Kidney Transplant Recipients With Cyclosporine A- or Tacrolimus-Based Immunosuppression

Gut microbiota and metabolomic profile changes play critical roles in tacrolimus-induced diabetes in rats

Aims

Hyperglycemia is one of the adverse effects of tacrolimus (TAC), but the underlying mechanism is not fully identified. We used multi-omics analysis to evaluate the changes in the gut microbiota and metabolic profile of rats with TAC-induced diabetes.

Methods

To establish a diabetic animal model, Sprague Dawley rats were divided randomly into two groups. Those in the TAC group received intraperitoneal injections of TAC (3 mg/kg) for 8 weeks, and those in the CON group served as the control. 16S rRNA sequencing was used to analyze fecal microbiota. The metabolites of the two groups were detected and analyzed by nontargeted and targeted metabolomics, including amino acids (AAs), bile acids (BAs), and short-chain fatty acids (SCFAs).

Results

The rats treated with TAC exhibited hyperglycemia as well as changes in the gut microbiota and metabolites. Specifically, their gut microbiota had significantly higher abundances of Escherichia-Shigella, Enterococcus, and Allobaculum, and significantly lower abundances of Ruminococcus, Akkermansia, and Roseburia. In addition, they had significantly reduced serum levels of AAs including asparagine, aspartic acid, glutamic acid, and methionine. With respect to BAs, they had significantly higher serum levels of taurocholic acid (TCA), and glycochenodeoxycholic acid (GCDCA), but significantly lower levels of taurodeoxycholic acid (TDCA) and tauroursodeoxycholic acid (TUDCA). There were no differences in the levels of SCFAs between the two groups. Correlations existed among glucose metabolism indexes (fasting blood glucose and fasting insulin), gut microbiota (Ruminococcus and Akkermansia), and metabolites (glutamic acid, hydroxyproline, GCDCA, TDCA, and TUDCA).

Conclusions

Both AAs and BAs may play crucial roles as signaling molecules in the regulation of TAC-induced diabetes.

Circulating metabolic markers after surgery identify patients at risk for severe postoperative complications: a prospective cohort study in colorectal cancer

BACKGROUND

Early detection of postoperative complications after colorectal cancer (CRC) surgery is associated with improved outcomes. The aim was to investigate early metabolomics signatures capable to detect patients at risk for severe postoperative complications after CRC surgery.

MATERIALS AND METHODS

Prospective cohort study of patients undergoing CRC surgery from 2015 to 2018. Plasma samples were collected before and after surgery, and analyzed by mass spectrometry obtaining 188 metabolites and 21 ratios. Postoperative complications were registered with Clavien-Dindo Classification and Comprehensive Complication Index.

RESULTS

One hundred forty-six patients were included. Surgery substantially modified metabolome and metabolic changes after surgery were quantitatively associated with the severity of postoperative complications. The strongest positive relationship with both Clavien-Dindo and Comprehensive Complication Index (β=4.09 and 63.05, P <0.001) corresponded to kynurenine/tryptophan, against an inverse relationship with lysophosphatidylcholines (LPCs) and phosphatidylcholines (PCs). Patients with LPC18:2/PCa36:2 below the cut-off 0.084 µM/µM resulted in a sevenfold higher risk of major complications (OR=7.38, 95% CI: 2.82-21.25, P <0.001), while kynurenine/tryptophan above 0.067 µM/µM a ninefold (OR=9.35, 95% CI: 3.03-32.66, P <0.001). Hexadecanoylcarnitine below 0.093 µM displayed a 12-fold higher risk of anastomotic leakage-related complications (OR=11.99, 95% CI: 2.62-80.79, P =0.004).

CONCLUSION

Surgery-induced phospholipids and amino acid dysregulation is associated with the severity of postoperative complications after CRC surgery, including anastomotic leakage-related outcomes. The authors provide quantitative insight on metabolic markers, measuring vulnerability to postoperative morbidity that might help guide early decision-making and improve surgical outcomes.

Multi-omics Approach in Kidney Transplant: Lessons Learned from COVID-19 Pandemic

Purpose of Review

Multi-omics approach has advanced our knowledge on transplantation-associated clinical outcomes, such as acute rejection and infection, and emerging omics data are becoming available in kidney transplant and COVID-19. Herein, we discuss updated findings of multi-omics data on kidney transplant outcomes, as well as COVID-19 and kidney transplant.

Recent Findings

Transcriptomics, proteomics, and metabolomics revealed various inflammation pathways associated with kidney transplantation-related outcomes and COVID-19. Although multi-omics data on kidney transplant and COVID-19 is limited, activation of innate immune pathways and suppression of adaptive immune pathways were observed in the active phase of COVID-19 in kidney transplant recipients.

Summary

Multi-omics analysis has led us to a deeper exploration and a more comprehensive understanding of key biological pathways in complex clinical settings, such as kidney transplantation and COVID-19. Future multi-omics analysis leveraging multi-center biobank collaborative will further advance our knowledge on the precise immunological responses to allograft and emerging pathogens.

Precision Medicine in Diabetic Kidney Disease: A Narrative Review Framed by Lived Experience

Purpose of review

Diabetic kidney disease (DKD) is a leading cause of chronic kidney disease (CKD) for which many treatments exist that have been shown to prevent CKD progression and kidney failure. However, DKD is a complex and heterogeneous etiology of CKD with a spectrum of phenotypes and disease trajectories. In this narrative review, we discuss precision medicine approaches to DKD, including genomics, metabolomics, proteomics, and their potential role in the management of diabetes mellitus and DKD. A patient and caregivers of patients with lived experience with CKD were involved in this review.

Sources of information

Original research articles were identified from MEDLINE and Google Scholar using the search terms "diabetes," "diabetic kidney disease," "diabetic nephropathy," "chronic kidney disease," "kidney failure," "dialysis," "nephrology," "genomics," "metabolomics," and "proteomics."

Methods

A focused review and critical appraisal of existing literature regarding the precision medicine approaches to the diagnosis, prognosis, and treatment of diabetes and DKD framed by a patient partner's/caregiver's lived experience.

Key findings

Distinguishing diabetic nephropathy from CKD due to other types of DKD and non-DKD is challenging and typically requires a kidney biopsy for a diagnosis. Biomarkers have been identified to assist with the prediction of the onset and progression of DKD, but they have yet to be incorporated and evaluated relative to clinical standard of care CKD and kidney failure risk prediction tools. Genomics has identified multiple causal genetic variants for neonatal diabetes mellitus and monogenic diabetes of the young that can be used for diagnostic purposes and to specify antiglycemic therapy. Genome-wide-associated studies have identified genes implicated in DKD pathophysiology in the setting of type 1 and 2 diabetes but their translational benefits are lagging beyond polygenetic risk scores. Metabolomics and proteomics have been shown to improve diagnostic accuracy in DKD, have been used to identify novel pathways involved in DKD pathogenesis, and can be used to improve the prediction of CKD progression and kidney failure as well as predict response to DKD therapy.

Limitations

There are a limited number of large, high-quality prospective observational studies and no randomized controlled trials that support the use of precision medicine based approaches to improve clinical outcomes in adults with or at risk of diabetes and DKD. It is unclear which patients may benefit from the clinical use of genomics, metabolomics and proteomics along the spectrum of DKD trajectory.

Implications

Additional research is needed to evaluate the role of the use of precision medicine for DKD management, including diagnosis, differentiation of diabetic nephropathy from other etiologies of DKD and CKD, short-term and long-term risk prognostication kidney outcomes, and the prediction of response to and safety of disease-modifying therapies.

The Use of Machine Learning Algorithms and the Mass Spectrometry Lipidomic Profile of Serum for the Evaluation of Tacrolimus Exposure and Toxicity in Kidney Transplant Recipients

Tacrolimus has a narrow therapeutic window; a whole-blood trough target concentration of between 5 and 8 ng/mL is considered a safe level for stable kidney transplant recipients. Tacrolimus serum levels must be closely monitored to obtain a balance between maximizing efficacy and minimizing dose-related toxic effects. Currently, there is no specific tacrolimus toxicity biomarker except a graft biopsy. Our study aimed to identify specific serum metabolites correlated with tacrolinemia levels using serum high-precision liquid chromatography–mass spectrometry and standard laboratory evaluation. Three machine learning algorithms were used (Naïve Bayes, logistic regression, and Random Forest) in 19 patients with high tacrolinemia (8 ng/mL) and 23 patients with low tacrolinemia (5 ng/mL). Using a selected panel of five lipid metabolites (phosphatidylserine, phosphatidylglycerol, phosphatidylethanolamine, arachidyl palmitoleate, and ceramide), Mg2+, and uric acid, all three machine learning algorithms yielded excellent classification accuracies between the two groups. The highest classification accuracy was obtained by Naïve Bayes, with an area under the curve of 0.799 and a classification accuracy of 0.756. Our results show that using our identified five lipid metabolites combined with Mg2+ and uric acid serum levels may provide a novel tool for diagnosing tacrolimus toxicity in kidney transplant recipients. Further validation with targeted MS and biopsy-proven TAC toxicity is needed.

Urinary Concentration Defect and Renal Glycosuria in Cyclosporine-treated Rats

Background

Methods

Results

Conclusion

Metabolomics Study for Identification of Potential Biomarkers of Long-term Survival in Kidney Transplantation Recipients

BACKGROUND

The recent progress and appropriate use of immunosuppressive drugs have considerably improved the short-term survival in kidney transplantation recipients (KTRs). The development of new strategies to improve long-term survival outcome after kidney transplantation is also becoming important. Although current diagnosis of allograft dysfunction relies on serum creatinine concentration and biopsy, they are nonspecific indicators of allograft function. Therefore, noninvasive, sensitive, and specific biomarkers for the prediction of long-term survival are needed. The aim of this study was to discover potential biomarkers for long-term survival in KTRs through the use of liquid chromatography-tandem mass spectrometry.

METHODS

We used the metabolic approach to explore the change of metabolites in the serum of KTRs. Twenty-four KTRs with long-term good survival (LGS) and 10 KTRs with chronic antibody-mediated rejection (CAMR) were included in this study. After quantile normalization with chromatographic data, multivariate statistical analysis was performed. We attempted to analyze metabolic profiling with LGS and CAMR groups.

RESULTS

The orthogonal partial least-squares discriminant analysis score plot showed a separation between 2 groups in the principal component. In the corresponding loading plot, 344 metabolites responsible for the separation observed in the score plot were identified (variable influence on projection ≥1.0). We then selected 54 metabolites to compare mass with charge by searching a web database, and 11 compounds were identified.

CONCLUSIONS

We found metabolites in serum that differ in LGS and CAMR groups. Further studies are needed to figure out potential metabolomic biomarkers to predict long-term survival in KTRs.

Patients with Treatment-Requiring Chronic Graft versus Host Disease after Allogeneic Stem Cell Transplantation Have Altered Metabolic Profiles due to the Disease and Immunosuppressive Therapy: Potential Implication for Biomarkers

Chronic graft versus host disease (cGVHD) is a common long-term complication after allogeneic hematopoietic stem cell transplantation. The objective of our study was to compare the metabolic profiles for allotransplant recipients and thereby identify metabolic characteristics of patients with treatment-requiring cGVHD. The study included 51 consecutive patients (29 men and 22 women; median age: 44 years, range: 15–66 years) transplanted with peripheral blood stem cells derived from human leukocyte antigen-matched family donors. All serum samples investigated by global metabolomic profiling were collected approximately 1 year posttransplant (median 358 days). Thirty-one of the 51 patients (61%) had cGVHD 1 year posttransplant. The affected organs were (number of patients) liver/bile duct (23), eyes (15), gastrointestinal tract (14), skin (13), mouth (10), lungs (3), and urogenital tract (1). We compared the metabolic profile for patients with and without cGVHD, and a Random Forrest Classification Analysis then resulted in 75% accuracy in differentiating the two groups. The 30 top-ranked metabolites from this comparison included increased levels of bile acids, several metabolites from the cytokine-responsive kynurenine pathway for tryptophan degradation, pro-inflammatory lipid metabolites, phenylalanine and tyrosine metabolites derived from the gut microbial flora, and metabolites reflecting increased oxidative stress. However, nine of these 30 top-ranked metabolites were probably altered due to cyclosporine or steroid treatment, and we therefore did a hierarchical clustering analysis including all 51 patients but only based on the other 21 cGVHD-specific metabolites. This analysis identified three patient subsets: one cluster included mainly patients without cGVHD and had generally low metabolite levels; another cluster included mainly patients with cGVHD (most patients with at least three affected organs) and high metabolite levels, and the last intermediate group including cGVHD patients with limited organ involvement. We conclude that allotransplant recipients with cGVHD have an altered metabolic profile caused both by the disease and its immunosuppressive treatment.

Ischemia/reperfusion-associated tubular cells injury in renal transplantation: Can metabolomics inform about mechanisms and help identify new therapeutic targets?

Tubular cells are central targets of ischemia-reperfusion (I/R) injury in kidney transplantation. Inflammation and metabolic disturbances occurring within these cells are deleterious by themselves but also favor secondary events, such as activation of immune response. It is critical to have an in depth understanding of the mechanisms governing tubular cells response to I/R if one wants to define pertinent biomarkers or to elaborate targeted therapeutic interventions. As oxidative damage was shown to be central in the patho-physiological mechanisms, the impact of I/R on proximal tubular cells metabolism has been widely studied, contrary to its effects on expression and activity of membrane transporters of the proximal tubular cells. Yet, temporal modulation of transporters over ischemia and reperfusion periods appears to play a central role, not only in the induction of cells injury but also in graft function recovery. Metabolomics in cell models or diverse biofluids has the potential to provide large pictures of biochemical consequences of I/R. Metabolomic studies conducted in experimental models of I/R or in transplanted patients indeed retrieved metabolites belonging to the pathways known to be particularly affected. Interestingly, they also revealed that metabolic disturbances and transporters activities are in very close mutual interplay. As well as helping to select diagnostic biomarkers, such analyses could also contribute to identify new pharmacological targets and to set up innovative nephroprotective strategies for the future. Even if various therapeutic approaches have been evaluated for a long time to prevent or treat I/R injuries, metabolomics has helped identifying new ones, those related to membrane transporters seeming to be of particular interest. However, considering the very complex and multifactorial effects of I/R in the context of kidney transplantation, all tracks must be followed if one wants to prevent or limit its deleterious consequences.

The Personalization of Clopidogrel Antiplatelet Therapy: The Role of Integrative Pharmacogenetics and Pharmacometabolomics

Dual antiplatelet therapy of aspirin and clopidogrel is pivotal for patients undergoing percutaneous coronary intervention. However, the variable platelets reactivity response to clopidogrel may lead to outcome failure and recurrence of cardiovascular events. Although many genetic and nongenetic factors are known, great portion of clopidogrel variable platelets reactivity remain unexplained which challenges the personalization of clopidogrel therapy. Current methods for clopidogrel personalization include CYP2C19 genotyping, pharmacokinetics, and platelets function testing. However, these methods lack precise prediction of clopidogrel outcome, often leading to insufficient prediction. Pharmacometabolomics which is an approach to identify novel biomarkers of drug response or toxicity in biofluids has been investigated to predict drug response. The advantage of pharmacometabolomics is that it does not only predict the response but also provide extensive information on the metabolic pathways implicated with the response. Integrating pharmacogenetics with pharmacometabolomics can give insight on unknown genetic and nongenetic factors associated with the response. This review aimed to review the literature on factors associated with the variable platelets reactivity response to clopidogrel, as well as appraising current methods for the personalization of clopidogrel therapy. We also aimed to review the literature on using pharmacometabolomics approach to predict drug response, as well as discussing the plausibility of using it to predict clopidogrel outcome.

Metabolomic Profiling in Individuals with a Failing Kidney Allograft

BACKGROUND

Alteration of certain metabolites may play a role in the pathophysiology of renal allograft disease.

METHODS

To explore metabolomic abnormalities in individuals with a failing kidney allograft, we analyzed by liquid chromatography-mass spectrometry (LC-MS/MS; for ex vivo profiling of serum and urine) and two dimensional correlated spectroscopy (2D COSY; for in vivo study of the kidney graft) 40 subjects with varying degrees of chronic allograft dysfunction stratified by tertiles of glomerular filtration rate (GFR; T1, T2, T3). Ten healthy non-allograft individuals were chosen as controls.

RESULTS

LC-MS/MS analysis revealed a dose-response association between GFR and serum concentration of tryptophan, glutamine, dimethylarginine isomers (asymmetric [A]DMA and symmetric [S]DMA) and short-chain acylcarnitines (C4 and C12), (test for trend: T1-T3 = p<0.05; p = 0.01; p<0.001; p = 0.01; p = 0.01; p<0.05, respectively). The same association was found between GFR and urinary levels of histidine, DOPA, dopamine, carnosine, SDMA and ADMA (test for trend: T1-T3 = p<0.05; p<0.01; p = 0.001; p<0.05; p = 0.001; p<0.001; p<0.01, respectively). In vivo 2D COSY of the kidney allograft revealed significant reduction in the parenchymal content of choline, creatine, taurine and threonine (all: p<0.05) in individuals with lower GFR levels.

CONCLUSIONS

We report an association between renal function and altered metabolomic profile in renal transplant individuals with different degrees of kidney graft function.

Pharmacokinetics, Pharmacodynamics, and Pharmacogenomics of Immunosuppressants in Allogeneic Hematopoietic Cell Transplantation: Part II

Part I of this article included a pertinent review of allogeneic hematopoietic cell transplantation (alloHCT), the role of postgraft immunosuppression in alloHCT, and the pharmacokinetics, pharmacodynamics, and pharmacogenomics of the calcineurin inhibitors and methotrexate. In this article (Part II), we review the pharmacokinetics, pharmacodynamics, and pharmacogenomics of mycophenolic acid (MPA), sirolimus, and the antithymocyte globulins (ATG). We then discuss target concentration intervention (TCI) of these postgraft immunosuppressants in alloHCT patients, with a focus on current evidence for TCI and on how TCI may improve clinical management in these patients. Currently, TCI using trough concentrations is conducted for sirolimus in alloHCT patients. Several studies demonstrate that MPA plasma exposure is associated with clinical outcomes, with an increasing number of alloHCT patients needing TCI of MPA. Compared with MPA, there are fewer pharmacokinetic/dynamic studies of rabbit ATG and horse ATG in alloHCT patients. Future pharmacokinetic/dynamic research of postgraft immunosuppressants should include '-omics'-based tools: pharmacogenomics may be used to gain an improved understanding of the covariates influencing pharmacokinetics as well as proteomics and metabolomics as novel methods to elucidate pharmacodynamic responses.

Mass spectrometric imaging of metabolites in kidney tissues from rats treated with furosemide

In the kidney, metabolic processes are different among the cortex (COR), outer medulla (OM), and inner medulla (IM). Using matrix-assisted laser desorption/ionization (MALDI) and imaging mass spectrometry (IMS), we examined the change of metabolites in the COR, OM, and IM of the rat kidney after furosemide treatment compared with vehicle-treated controls. Osmotic minipumps were implanted in male Sprague-Dawley rats to deliver 12 mg·day(-1)·rat(-1) of furosemide. Vehicle-treated (n = 14) and furosemide-treated (furosemide rats, n = 15) rats in metabolic cages received a fixed amount of rat chow (15 g·220 g body wt(-1)·day(-1) for each rat) with free access to water intake for 6 days. At day 6, higher urine output (32 ± 4 vs. 9 ± 1 ml/day) and lower urine osmolality (546 ± 44 vs. 1,677 ± 104 mosmol/kgH2O) were observed in furosemide rats. Extracts of COR, OM, and IM were analyzed by ultraperformance liquid chromatography coupled with quadrupole time-of-flight (TOF) mass spectrometry, where multivariate analysis revealed significant differences between the two groups. Several metabolites, including acetylcarnitine, betaine, carnitine, choline, and glycerophosphorylcholine (GPC), were significantly changed. The changes of metabolites were further identified by MALDI-TOF/TOF and IMS. Their spatial distribution and relative quantitation in the kidneys were analyzed by IMS. Carnitine compounds were increased in COR and IM, whereas carnitine and acetylcarnitine were decreased in OM. Choline compounds were increased in COR and OM but decreased in IM from furosemide rats. Betaine and GPC were decreased in OM and IM. Taken together, MALDI-TOF/TOF and IMS successfully provide the spatial distribution and relative quantitation of metabolites in the kidney.

Blood, urine and faecal metabolite profiles in the study of adult renal disease

Chronic kidney disease (CKD) is a major public health burden and to date traditional biomarkers of renal function (such as serum creatinine and cystatin C) are unable to identify at-risk individuals before the disease process is well under way. To help preventive strategies and maximize the potential for effective interventions, it is important to characterise the molecular changes that take place in the development of renal damage. Metabolomics is a promising tool to identify markers of renal disease since the kidneys are involved in the handling of major biochemical classes of metabolites. These metabolite levels capture a snap-shot of the metabolic profile of the individual, allowing for the potential identification of early biomarkers, and the monitoring of real-time kidney function. In this review, we describe the current status of the identification of blood/urine/faecal metabolic biomarkers in different entities of kidney diseases including: acute kidney injury, chronic kidney disease, renal transplant, diabetic nephropathy and other disorders.

Elucidating time-dependent changes in the urinary metabolome of renal transplant patients by a combined (1)H NMR and GC-MS approach

Urine metabolomic profiling can identify biochemical alterations resulting from various injuries affecting the graft outcome after renal transplantation. Here, we aimed to describe in depth the metabolite content of urines of renal transplant patients and to link it with the major injury factors acting at critical stages following transplantation. Morning urine samples were prospectively collected from 38 kidney transplant patients at 7 days (D7), 3 months (M3) and 12 months (M12) after transplantation. Twenty-five patients were treated with tacrolimus (Tac) and thirteen patients with cyclosporine (CsA). (1)H-NMR (proton nuclear magnetic resonance) and gas chromatography-mass spectrometry (GC-MS) were used to examine the overall metabolomic signature of each sample. Multivariate analysis was performed to study the changes in the metabolic profile over time and their dependency on the type of calcineurin inhibitor (CNI) administered to patients. Biological pathways affected by transplantation were identified using a metabolomics pathway analysis (MetPA) web-tool. The metabolic profile of urine samples clearly varied with time. Markers of medullary injury, tubule cell oxidative metabolism and impaired tubular reabsorption or secretion were present at D7. Differences in metabolic profiles became less marked as time passed on, urine content being quite similar at M3 and M12. The metabolite profile tended to differ between patients receiving Tac and those receiving CsA but no clear discriminating profiles can be found. The combination of (1)H-NMR and GC-MS for the analysis of urine metabolomic profiles is a very useful method to study patho-physiological alterations in kidney transplant patients over time.

Metabolomics in nephrotoxicity

Nephrotoxicity or renal toxicity can be a result of hemodynamic changes, direct injury to cells and tissue, inflammatory tissue injury, and/or obstruction of renal excretion. Nephrotoxicity is frequently induced by a wide spectrum of therapeutic drugs and environ mental pollutants. Knowledge of the complex molecular and pathophysiologic mechanisms leading to nephrotoxicity remains limited, in part, by research that historically focused on single or relatively few risk markers. As such, current kidney injury biomarkers are inadequate in terms of sensitivity and specificity. In contrast, metabolomics enables screening of a vast array of metabolites simultaneously using NMR and MS to assess their role in nephrotoxicity development and progression. A more comprehensive understanding of these biochemical pathways would also provide valuable insight to disease mechanisms critical for drug development and treatment.

1H NMR-based metabolite profiling of plasma in a rat model of chronic kidney disease

Chronic kidney disease (CKD) is characterized by the gradual loss of the kidney function to excrete wastes and fluids from the blood. ¹H NMR-based metabolomics was exploited to investigate the altered metabolic pattern in rats with CKD induced by surgical reduction of the renal mass (i.e., 5/6 nephrectomy (5/6 Nx)), particularly for identifying specific metabolic biomarkers associated with early of CKD. Plasma metabolite profiling was performed in CKD rats (at 4- or 8-weeks after 5/6 Nx) compared to sham-operated rats. Principle components analysis (PCA), partial least squares-discriminant analysis (PLS-DA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) score plots showed a significant separation between the groups. The resulting metabolic profiles demonstrated significantly increased plasma levels of organic anions, including citrate, β-hydroxybutyrate, lactate, acetate, acetoacetate, and formate in CKD. Moreover, levels of alanine, glutamine, and glutamate were significantly higher. These changes were likely to be associated with complicated metabolic acidosis in CKD for counteracting systemic metabolic acidosis or increased protein catabolism from muscle. In contrast, levels of VLDL/LDL (CH₂)_n and N-acetylglycoproteins were decreased. Taken together, the observed changes of plasma metabolite profiles in CKD rats provide insights into the disturbed metabolism in early phase of CKD, in particular for the altered metabolism of acid-base and/or amino acids.

Metabolic phenotyping in clinical and surgical environments

Metabolic phenotyping involves the comprehensive analysis of biological fluids or tissue samples. This analysis allows biochemical classification of a person's physiological or pathological states that relate to disease diagnosis or prognosis at the individual level and to disease risk factors at the population level. These approaches are currently being implemented in hospital environments and in regional phenotyping centres worldwide. The ultimate aim of such work is to generate information on patient biology using techniques such as patient stratification to better inform clinicians on factors that will enhance diagnosis or the choice of therapy. There have been many reports of direct applications of metabolic phenotyping in a clinical setting.

A pilot metabolic profiling study in serum of patients with chronic kidney disease based on (1) H-NMR-spectroscopy

BACKGROUND

Chronic kidney disease (CKD) is the end point of a number of renal and systemic diseases. The metabolomics with a highly multiplexed and efficient manner is a challenging goal in nephrology.

METHODS

A (1) H-NMR based metabolomics approach was applied to establish a human CKD serum metabolic profile. Serum samples were obtained from CKD patients with four stages (N= 80) and healthy controls (N= 28). The data acquired by CMPG spectrum were further processed by pattern recognition (PR) analysis. Principal components analysis (PCA) and partial least-squares-discriminant analysis (PLS-DA) was capable of clustering the disease groups and establishing disease-specific metabolites profile.

RESULTS

The classification models could grade CKD patients with considerably high value of Q(2) and R(2) . The significant endogenous metabolites that contributed to distinguish CKD in different stages included the products of glycolysis (glucose, lactate), amino acids (valine, alanine, glutamate, glycine), organic osmolytes (betaine, myo-inositol, taurine, glycerophosphcholine), and so on. Based on these metabolites, the model for diagnosing patients with CKD achieved the sensitivity and specificity of 100%.

CONCLUSION

The study illustrated that serum metabolic profile was altered in response to renal dysfunction and the progression of CKD. The identified metabolic biomarkers may provide useful information for the diagnosis of CKD, especially in early stages.

Patterns of gene and metabolite define the effects of extracellular osmolality on kidney collecting duct

To investigate the effects of changes in extracellular osmolality on the function of kidney collecting duct cells, particularly on water and sodium reabsorption in the conditions of diuresis and antidiuresis, we generated transcriptome and metabolome profiles of primary cultured inner medullary collecting duct (IMCD) cells. They were grown in hyperosmolar culture medium (640 mOsm) for 4 days and then exposed to either reduced (300 mOsm) or same osmolality for 1 or 2 days more. Integrated analysis of the transcriptome and metabolome revealed that decreased extracellular osmolality was associated with decreased levels of organic osmolytes, glucose, intermediates of citric acid cycle, and branched-chain amino acids (BCAA) in IMCD cells, along with significantly decreased gene expression and protein abundance of P-type transporters (ATP1B1), ABC transporters (ABCC5 and ABCG1), and insulin signaling pathways (IRS2). Quantitative real-time RT-PCR and semiquantitative immunoblotting confirmed the changes of transcript levels of differentially expressed genes and protein levels. Taken together, integrated analysis of omics data demonstrated that water and sodium reabsorption could be reduced by decreased extracellular osmolality per se, through decreased levels of ABC transporters and IRS2, which play a potential role in the transport of organic osmolytes, BCAA, glucose, and trafficking of epithelial sodium channel.

Serum metabolite concentrations and decreased GFR in the general population

BACKGROUND

Metabolites such as creatinine and urea are established kidney function markers. High-throughput metabolomic studies have not been reported in large general population samples spanning normal kidney function and chronic kidney disease (CKD).

STUDY DESIGN

Cross-sectional observational studies of the general population.

SETTING AND PARTICIPANTS

2 independent samples: KORA F4 (discovery sample, n = 3,011) and Twins UK (validation sample, n = 984). EXPOSURE FACTORS: 151 serum metabolites, quantified by targeted mass spectrometry.

OUTCOMES AND MEASUREMENTS

Metabolites and their 22,650 ratios were analyzed by multivariable-adjusted linear regression for their association with glomerular filtration rate (eGFR), estimated separately from creatinine and cystatin C levels by CKD-EPI (CKD Epidemiology Collaboration) equations. After correction for multiple testing, significant metabolites (P < 3.3 × 10(-4) for single metabolites; P < 2.2 × 10(-6) for ratios) were meta-analyzed with independent data from the TwinsUK Study.

RESULTS

Replicated associations with eGFR were observed for 22 metabolites and 516 metabolite ratios. Pooled P values ranged from 7.1 × 10(-7) to 1.8 × 10(-69) for the replicated single metabolites. Acylcarnitines such as glutarylcarnitine were associated inversely with eGFR (-3.73 mL/min/1.73 m(2) per standard deviation [SD] increase, pooled P = 1.8 × 10(-69)). The replicated ratio with the strongest association was the ratio of serine to glutarylcarnitine (P = 3.6 × 10(-81)). Almost all replicated phenotypes associated with decreased eGFR (<60 mL/min/1.73 m(2); n = 172 cases) in KORA F4: per 1-SD increment, ORs ranged from 0.29-2.06. Across categories of a metabolic score consisting of 3 uncorrelated metabolites, the prevalence of decreased eGFR increased from 3% to 53%.

LIMITATIONS

Cross-sectional study design, GFR was estimated, limited number of metabolites.

CONCLUSIONS

Distinct metabolic phenotypes were reproducibly associated with eGFR in 2 separate population studies. They may provide novel insights into renal metabolite handling, improve understanding of pathophysiology, or aid in the diagnosis of kidney disease. Longitudinal studies are needed to clarify whether changes in metabolic phenotypes precede or result from kidney function impairment.

Urinary metabolomics in newborns and infants

Metabolomics is a new approach based on the systematic study of the full complement of metabolites in a biological sample. This technology consists of two sequential steps: (1) an experimental technique, based on nuclear magnetic resonance (NMR) spectroscopy or mass spectrometry, designed to profile low-molecular-weight compounds, and (2) multivariate data analysis. The metabolomic analysis of biofluids or tissues has been successfully used in the fields of physiology, diagnostics, functional genomics, pharmacology, toxicology, and nutrition. Recent studies have evaluated how physiological variables or pathological conditions can affect metabolomic profiles of different biofluids in pediatric populations. The overall metabolic status of the neonate is little known. If more information on perinatal/neonatal maturational processes and their metabolic background were available, the management of sick or preterm newborns might be improved. Currently, the use of metabolomics in neonatology is still in the pioneering phase. Meaningful diagnostic information and simple, noninvasive collection techniques make urine a particularly suitable biofluid for metabolomic approach in neonatal medicine, although blood has also been investigated. Different fields of neonatology such as postnatal maturation, asphyxia/hypoxia, inborn errors of metabolism, nutrition, nephrouropathies, nephrotoxicity, cardiovascular diseases, and other conditions have been investigated using a metabolomic approach. Together with genomics and proteomics, metabolomics appears to be a promising tool in neonatology for the monitoring of postnatal metabolic maturation, the identification of biomarkers as early predictors of outcome, the diagnosis and monitoring of various diseases, and the "tailored" management of neonatal disorders.