First mitochondrial genome-wide association study with metabolomics.

In the era of personalized medicine with more and more patient-specific targeted therapies being used, we need reliable, dynamic, faster and sensitive biomarkers both to track the causes of disease and to develop and evolve therapies during the course of treatment. Metabolomics recently has shown substantial evidence to support its emerging role in disease diagnosis and prognosis. Aside from biomarkers and development of therapies, it is also an important goal to understand the involvement of mitochondrial DNA (mtDNA) in metabolic regulation, aging and disease development. Somatic mutations of the mitochondrial genome are also heavily implicated in age-related disease and aging. The general hypothesis is that an alteration in the concentration of metabolite profiles (possibly conveyed by lifestyle and environmental factors) influences the increase of mutation rate in the mtDNA and thereby contributes to a range of pathophysiological alterations observed in complex diseases. We performed an inverted mitochondrial genome-wide association analysis between mitochondrial nucleotide variants (mtSNVs) and concentration of metabolites. We used 151 metabolites and the whole sequenced mitochondrial genome from 2718 individuals to identify the genetic variants associated with metabolite profiles. Because of the high coverage, next-generation sequencing-based analysis of the mitochondrial genome allows for an accurate detection of mitochondrial heteroplasmy and for the identification of variants associated with the metabolome. The strongest association was found for mt715G > A located in the MT-12SrRNA with the metabolite ratio of C2/C10:1 (P-value = 6.82*10-09, ß = 0.909). The second most significant mtSNV was found for mt3714A > G located in the MT-ND1 with the metabolite ratio of phosphatidylcholine (PC) ae C42:5/PC ae C44:5 (P-value = 1.02*10-08, ß = 3.631). A large number of significant metabolite ratios were observed involving PC aa C36:6 and the variant mt10689G > A, located in the MT-ND4L gene. These results show an important interconnection between mitochondria and metabolite concentrations. Considering that some of the significant metabolites found in this study have been previously related to complex diseases, such as neurological disorders and metabolic conditions, these associations found here might play a crucial role for further investigations of such complex diseases. Understanding the mechanisms that control human health and disease, in particular, the role of genetic predispositions and their interaction with environmental factors is a prerequisite for the development of safe and efficient therapies for complex disorders.

TIGER: technical variation elimination for metabolomics data using ensemble learning architecture.

Large metabolomics datasets inevitably contain unwanted technical variations which can obscure meaningful biological signals and affect how this information is applied to personalized healthcare. Many methods have been developed to handle unwanted variations. However, the underlying assumptions of many existing methods only hold for a few specific scenarios. Some tools remove technical variations with models trained on quality control (QC) samples which may not generalize well on subject samples. Additionally, almost none of the existing methods supports datasets with multiple types of QC samples, which greatly limits their performance and flexibility. To address these issues, a non-parametric method TIGER (Technical variation elImination with ensemble learninG architEctuRe) is developed in this study and released as an R package (https://CRAN.R-project.org/package=TIGERr). TIGER integrates the random forest algorithm into an adaptable ensemble learning architecture. Evaluation results show that TIGER outperforms four popular methods with respect to robustness and reliability on three human cohort datasets constructed with targeted or untargeted metabolomics data. Additionally, a case study aiming to identify age-associated metabolites is performed to illustrate how TIGER can be used for cross-kit adjustment in a longitudinal analysis with experimental data of three time-points generated by different analytical kits. A dynamic website is developed to help evaluate the performance of TIGER and examine the patterns revealed in our longitudinal analysis (https://han-siyu.github.io/TIGER_web/). Overall, TIGER is expected to be a powerful tool for metabolomics data analysis.

Bile acid profiles and mRNA abundance of bile acid-related genes in adipose tissue of dairy cows with high versus normal body condition.

Besides their lipid-digestive role, bile acids (BA) influence overall energy homeostasis, such as glucose and lipid metabolism. We hypothesized that BA along with their receptors, regulatory enzymes, and transporters are present in subcutaneous adipose tissue (scAT). In addition, we hypothesized that their mRNA abundance varies with the body condition of dairy cows around calving. Therefore, we analyzed BA in serum and scAT as well as the mRNA abundance of BA -related enzymes, transporters, and receptors in scAT during the transition period in cows with different body conditions around calving. In a previously established animal model, 38 German Holstein cows were divided into either a high (HBCS; n = 19) or normal BCS (NBCS; n = 19) group based on their body condition score (BCS) and back fat thickness (BFT). Cows were fed different diets to achieve the targeted differences in BCS and BFT (NBCS: BCS <3.5, BFT <1.2 cm; HBCS: BCS >3.75, BFT >1.4 cm) until dry-off at 7 wk ante partum. During the dry period and subsequent lactation, both groups were fed the same diets regarding their demands. Using a targeted metabolomics approach via LC-ESI-MS /MS, BA were analyzed in serum and scAT at wk -7, 1, 3, and 12 relative to parturition. In serum, 15 BA (cholic acid (CA), chenodeoxycholic acid (CDCA), glycocholic acid (GCA), taurocholic acid (TCA), glycochenodeoxycholic acid (GCDCA), taurochenodeoxycholic acid (TCDCA), deoxycholic acid (DCA), lithocholic acid (LCA), glycodeoxycholic acid (GDCA), glycolithocholic acid (GLCA), taurodeoxycholic acid (TDCA), taurolithocholic acid (TLCA), ß-muricholic acid (MCA(b)), tauromuricholic acid (sum of α and ß) (TMCA (a+b)), glycoursodeoxycholic acid (GUDCA)) were observed, whereas in scAT 7 BA (CA, GCA, TCA, GCDCA, TCDCA, GDCA, TDCA) were detected. In serum and scAT samples, the primary BA CA and its conjugate GCA were predominantly detected. Increasing serum concentrations of CA, CDCA, TCA, GCA, GCDCA, DCA, and MCA(b) with the onset of lactation might be related to the increasing DMI after parturition. Furthermore, serum concentrations of CA, CDCA, GCA, DCA, GCDCA, TCA, LCA, and GDCA were lower in HBCS cows compared with NBCS cows, concomitant with increased lipolysis in HBCS cows. The correlation between CA in serum and scAT may point to the transport of CA across cell membranes. Overall, the findings of the present study suggest a potential role of BA in lipid metabolism depending on the body condition of periparturient dairy cows.

Bile acid profiles and mRNA expression of bile acid-related genes in the liver of dairy cows with high versus normal body condition.

Bile acids (BA) play a crucial role not only in lipid digestion but also in the regulation of overall energy homeostasis, including glucose and lipid metabolism. The aim of this study was to investigate BA profiles and mRNA expression of BA-related genes in the liver of high versus normal body condition in dairy cows. We hypothesized that body condition and the transition from gestation to lactation affect hepatic BA concentrations as well as the mRNA abundance of BA-related receptors, regulatory enzymes, and transporters. Therefore, we analyzed BA in the liver as well as the mRNA abundance of BA-related synthesizing enzymes, transporters, and receptors in the liver during the transition period in cows with different body conditions around calving. In a previously established animal model, 38 German Holstein cows were divided into groups with high body condition score (BCS) (HBCS; n = 19) or normal BCS (NBCS; n = 19) based on BCS and backfat thickness (BFT). Cows were fed diets aimed at achieving the targeted differences in BCS and BFT (NBCS: BCS <3.5, BFT <1.2 cm; HBCS: BCS >3.75, BFT >1.4 cm) until they were dried off at wk 7 before parturition. Both groups were fed identical diets during the dry period and subsequent lactation. Liver biopsies were taken at wk -7, 1, 3, and 12 relative to parturition. For BA measurement, a targeted metabolomics approach with LC-ESI-MS/MS was used to analyze BA in the liver. The mRNA abundance of targeted genes related to BA-synthesizing enzymes, transporters, and receptors in the liver was analyzed using microfluidic quantitative PCR. In total, we could detect 14 BA in the liver: 6 primary and 8 secondary BA, with glycocholic acid (GCA) being the most abundant one. The increase of glycine-conjugated BA after parturition, in parallel to increasing serum glycine concentrations may originate from an enhanced mobilization of muscle protein to meet the high nutritional requirements in early lactating cows. Higher DMI in NBCS cows compared with HBCS cows was associated with higher liver BA concentrations such as GCA, deoxycholic acid (DCA), and cholic acid (CA). The mRNA abundance of BA-related enzymes measured herein suggests the dominance of the alternative signaling pathway in the liver of HBCS cows. Overall, BA profiles and BA metabolism in the liver depend on both, the body condition and lactation-induced effects in periparturient dairy cows.

Circulating amino acid levels and colorectal cancer risk in the European Prospective Investigation into Cancer and Nutrition and UK Biobank cohorts.

BACKGROUND: Amino acid metabolism is dysregulated in colorectal cancer patients; however, it is not clear whether pre-diagnostic levels of amino acids are associated with subsequent risk of colorectal cancer. We investigated circulating levels of amino acids in relation to colorectal cancer risk in the European Prospective Investigation into Cancer and Nutrition (EPIC) and UK Biobank cohorts. METHODS: Concentrations of 13-21 amino acids were determined in baseline fasting plasma or serum samples in 654 incident colorectal cancer cases and 654 matched controls in EPIC. Amino acids associated with colorectal cancer risk following adjustment for the false discovery rate (FDR) were then tested for associations in the UK Biobank, for which measurements of 9 amino acids were available in 111,323 participants, of which 1221 were incident colorectal cancer cases. RESULTS: Histidine levels were inversely associated with colorectal cancer risk in EPIC (odds ratio [OR] 0.80 per standard deviation [SD], 95% confidence interval [CI] 0.69-0.92, FDR P-value=0.03) and in UK Biobank (HR 0.93 per SD, 95% CI 0.87-0.99, P-value=0.03). Glutamine levels were borderline inversely associated with colorectal cancer risk in EPIC (OR 0.85 per SD, 95% CI 0.75-0.97, FDR P-value=0.08) and similarly in UK Biobank (HR 0.95, 95% CI 0.89-1.01, P=0.09) In both cohorts, associations changed only minimally when cases diagnosed within 2 or 5 years of follow-up were excluded. CONCLUSIONS: Higher circulating levels of histidine were associated with a lower risk of colorectal cancer in two large prospective cohorts. Further research to ascertain the role of histidine metabolism and potentially that of glutamine in colorectal cancer development is warranted.

Identification of candidate metabolite biomarkers for metabolic syndrome and its five components in population-based human cohorts.

BACKGROUND: Metabolic Syndrome (MetS) is characterized by risk factors such as abdominal obesity, hypertriglyceridemia, low high-density lipoprotein cholesterol (HDL-C), hypertension, and hyperglycemia, which contribute to the development of cardiovascular disease and type 2 diabetes. Here, we aim to identify candidate metabolite biomarkers of MetS and its associated risk factors to better understand the complex interplay of underlying signaling pathways. METHODS: We quantified serum samples of the KORA F4 study participants (N = 2815) and analyzed 121 metabolites. Multiple regression models adjusted for clinical and lifestyle covariates were used to identify metabolites that were Bonferroni significantly associated with MetS. These findings were replicated in the SHIP-TREND-0 study (N = 988) and further analyzed for the association of replicated metabolites with the five components of MetS. Database-driven networks of the identified metabolites and their interacting enzymes were also constructed. RESULTS: We identified and replicated 56 MetS-specific metabolites: 13 were positively associated (e.g., Val, Leu/Ile, Phe, and Tyr), and 43 were negatively associated (e.g., Gly, Ser, and 40 lipids). Moreover, the majority (89%) and minority (23%) of MetS-specific metabolites were associated with low HDL-C and hypertension, respectively. One lipid, lysoPC a C18:2, was negatively associated with MetS and all of its five components, indicating that individuals with MetS and each of the risk factors had lower concentrations of lysoPC a C18:2 compared to corresponding controls. Our metabolic networks elucidated these observations by revealing impaired catabolism of branched-chain and aromatic amino acids, as well as accelerated Gly catabolism. CONCLUSION: Our identified candidate metabolite biomarkers are associated with the pathophysiology of MetS and its risk factors. They could facilitate the development of therapeutic strategies to prevent type 2 diabetes and cardiovascular disease. For instance, elevated levels of lysoPC a C18:2 may protect MetS and its five risk components. More in-depth studies are necessary to determine the mechanism of key metabolites in the MetS pathophysiology.

Metabolic Signatures of Healthy Lifestyle Patterns and Colorectal Cancer Risk in a European Cohort.

BACKGROUND & AIMS: Colorectal cancer risk can be lowered by adherence to the World Cancer Research Fund/American Institute for Cancer Research (WCRF/AICR) guidelines. We derived metabolic signatures of adherence to these guidelines and tested their associations with colorectal cancer risk in the European Prospective Investigation into Cancer and Nutrition cohort. METHODS: Scores reflecting adherence to the WCRF/AICR recommendations (scale, 1-5) were calculated from participant data on weight maintenance, physical activity, diet, and alcohol among a discovery set of 5738 cancer-free European Prospective Investigation into Cancer and Nutrition participants with metabolomics data. Partial least-squares regression was used to derive fatty acid and endogenous metabolite signatures of the WCRF/AICR score in this group. In an independent set of 1608 colorectal cancer cases and matched controls, odds ratios (ORs) and 95% CIs were calculated for colorectal cancer risk per unit increase in WCRF/AICR score and per the corresponding change in metabolic signatures using multivariable conditional logistic regression. RESULTS: Higher WCRF/AICR scores were characterized by metabolic signatures of increased odd-chain fatty acids, serine, glycine, and specific phosphatidylcholines. Signatures were inversely associated more strongly with colorectal cancer risk (fatty acids: OR, 0.51 per unit increase; 95% CI, 0.29-0.90; endogenous metabolites: OR, 0.62 per unit change; 95% CI, 0.50-0.78) than the WCRF/AICR score (OR, 0.93 per unit change; 95% CI, 0.86-1.00) overall. Signature associations were stronger in male compared with female participants. CONCLUSIONS: Metabolite profiles reflecting adherence to WCRF/AICR guidelines and additional lifestyle or biological risk factors were associated with colorectal cancer. Measuring a specific panel of metabolites representative of a healthy or unhealthy lifestyle may identify strata of the population at higher risk of colorectal cancer.

Inflammatory macrophage memory in nonsteroidal anti-inflammatory drug-exacerbated respiratory disease.

BACKGROUND: Nonsteroidal anti-inflammatory drug-exacerbated respiratory disease (N-ERD) is a chronic inflammatory condition, which is driven by an aberrant arachidonic acid metabolism. Macrophages are major producers of arachidonic acid metabolites and subject to metabolic reprogramming, but they have been neglected in N-ERD. OBJECTIVE: This study sought to elucidate a potential metabolic and epigenetic macrophage reprogramming in N-ERD. METHODS: Transcriptional, metabolic, and lipid mediator profiles in macrophages from patients with N-ERD and healthy controls were assessed by RNA sequencing, Seahorse assays, and LC-MS/MS. Metabolites in nasal lining fluid, sputum, and plasma from patients with N-ERD (n = 15) and healthy individuals (n = 10) were quantified by targeted metabolomics analyses. Genome-wide methylomics were deployed to define epigenetic mechanisms of macrophage reprogramming in N-ERD. RESULTS: This study shows that N-ERD monocytes/macrophages exhibit an overall reduction in DNA methylation, aberrant metabolic profiles, and an increased expression of chemokines, indicative of a persistent proinflammatory activation. Differentially methylated regions in N-ERD macrophages included genes involved in chemokine signaling and acylcarnitine metabolism. Acylcarnitines were increased in macrophages, sputum, nasal lining fluid, and plasma of patients with N-ERD. On inflammatory challenge, N-ERD macrophages produced increased levels of acylcarnitines, proinflammatory arachidonic acid metabolites, cytokines, and chemokines as compared to healthy macrophages. CONCLUSIONS: Together, these findings decipher a proinflammatory metabolic and epigenetic reprogramming of macrophages in N-ERD.

The liver-alpha cell axis associates with liver fat and insulin resistance: a validation study in women with non-steatotic liver fat levels.

AIMS/HYPOTHESIS: Many individuals who develop type 2 diabetes also display increased glucagon levels (hyperglucagonaemia), which we have previously found to be associated with the metabolic syndrome. The concept of a liver-alpha cell axis provides a possible link between hyperglucagonaemia and elevated liver fat content, a typical finding in the metabolic syndrome. However, this association has only been studied in individuals with non-alcoholic fatty liver disease. Hence, we searched for a link between the liver and the alpha cells in individuals with non-steatotic levels of liver fat content. We hypothesised that the glucagon-alanine index, an indicator of the functional integrity of the liver-alpha cell axis, would associate with liver fat and insulin resistance in our cohort of women with low levels of liver fat. METHODS: We analysed data from 79 individuals participating in the Prediction, Prevention and Subclassification of Type 2 Diabetes (PPSDiab) study, a prospective observational study of young women at low to high risk for the development of type 2 diabetes. Liver fat content was determined by MRI. Insulin resistance was calculated as HOMA-IR. We conducted Spearman correlation analyses of liver fat content and HOMA-IR with the glucagon-alanine index (the product of fasting plasma levels of glucagon and alanine). The prediction of the glucagon-alanine index by liver fat or HOMA-IR was tested in multivariate linear regression analyses in the whole cohort as well as after stratification for liver fat content ≤0.5% (n = 39) or >0.5% (n = 40). RESULTS: The glucagon-alanine index significantly correlated with liver fat and HOMA-IR in the entire cohort (ρ = 0.484, p < 0.001 and ρ = 0.417, p < 0.001, respectively). These associations resulted from significant correlations in participants with a liver fat content >0.5% (liver fat, ρ = 0.550, p < 0.001; HOMA-IR, ρ = 0.429, p = 0.006). In linear regression analyses, the association of the glucagon-alanine index with liver fat remained significant after adjustment for age and HOMA-IR in all participants and in those with liver fat >0.5% (ß = 0.246, p = 0.0.23 and ß = 0.430, p = 0.007, respectively) but not in participants with liver fat ≤0.5% (ß = -0.184, p = 0.286). CONCLUSIONS/INTERPRETATION: We reproduced the previously reported association of liver fat content and HOMA-IR with the glucagon-alanine index in an independent study cohort of young women with low to high risk for type 2 diabetes. Furthermore, our data indicates an insulin-resistance-independent association of liver fat content with the glucagon-alanine index. In summary, our study supports the concept that even lower levels of liver fat (from 0.5%) are connected to relative hyperglucagonaemia, reflecting an imminent impairment of the liver-alpha cell axis.

The blood metabolome of incident kidney cancer: A case-control study nested within the MetKid consortium.

BACKGROUND: Excess bodyweight and related metabolic perturbations have been implicated in kidney cancer aetiology, but the specific molecular mechanisms underlying these relationships are poorly understood. In this study, we sought to identify circulating metabolites that predispose kidney cancer and to evaluate the extent to which they are influenced by body mass index (BMI). METHODS AND FINDINGS: We assessed the association between circulating levels of 1,416 metabolites and incident kidney cancer using pre-diagnostic blood samples from up to 1,305 kidney cancer case-control pairs from 5 prospective cohort studies. Cases were diagnosed on average 8 years after blood collection. We found 25 metabolites robustly associated with kidney cancer risk. In particular, 14 glycerophospholipids (GPLs) were inversely associated with risk, including 8 phosphatidylcholines (PCs) and 2 plasmalogens. The PC with the strongest association was PC ae C34:3 with an odds ratio (OR) for 1 standard deviation (SD) increment of 0.75 (95% confidence interval [CI]: 0.68 to 0.83, p = 2.6 × 10-8). In contrast, 4 amino acids, including glutamate (OR for 1 SD = 1.39, 95% CI: 1.20 to 1.60, p = 1.6 × 10-5), were positively associated with risk. Adjusting for BMI partly attenuated the risk association for some-but not all-metabolites, whereas other known risk factors of kidney cancer, such as smoking and alcohol consumption, had minimal impact on the observed associations. A mendelian randomisation (MR) analysis of the influence of BMI on the blood metabolome highlighted that some metabolites associated with kidney cancer risk are influenced by BMI. Specifically, elevated BMI appeared to decrease levels of several GPLs that were also found inversely associated with kidney cancer risk (e.g., -0.17 SD change [ßBMI] in 1-(1-enyl-palmitoyl)-2-linoleoyl-GPC (P-16:0/18:2) levels per SD change in BMI, p = 3.4 × 10-5). BMI was also associated with increased levels of glutamate (ßBMI: 0.12, p = 1.5 × 10-3). While our results were robust across the participating studies, they were limited to study participants of European descent, and it will, therefore, be important to evaluate if our findings can be generalised to populations with different genetic backgrounds. CONCLUSIONS: This study suggests a potentially important role of the blood metabolome in kidney cancer aetiology by highlighting a wide range of metabolites associated with the risk of developing kidney cancer and the extent to which changes in levels of these metabolites are driven by BMI-the principal modifiable risk factor of kidney cancer.

Epigenetic alterations in longevity regulators, reduced life span, and exacerbated aging-related pathology in old father offspring mice.

Advanced age is not only a major risk factor for a range of disorders within an aging individual but may also enhance susceptibility for disease in the next generation. In humans, advanced paternal age has been associated with increased risk for a number of diseases. Experiments in rodent models have provided initial evidence that paternal age can influence behavioral traits in offspring animals, but the overall scope and extent of paternal age effects on health and disease across the life span remain underexplored. Here, we report that old father offspring mice showed a reduced life span and an exacerbated development of aging traits compared with young father offspring mice. Genome-wide epigenetic analyses of sperm from aging males and old father offspring tissue identified differentially methylated promoters, enriched for genes involved in the regulation of evolutionarily conserved longevity pathways. Gene expression analyses, biochemical experiments, and functional studies revealed evidence for an overactive mTORC1 signaling pathway in old father offspring mice. Pharmacological mTOR inhibition during the course of normal aging ameliorated many of the aging traits that were exacerbated in old father offspring mice. These findings raise the possibility that inherited alterations in longevity pathways contribute to intergenerational effects of aging in old father offspring mice.

Alterations of the acylcarnitine profiles in blood serum and in muscle from periparturient cows with normal or elevated body condition.

The objective of the current study was to characterize muscle and blood serum acylcarnitine (AcylCN) profiles and to determine the mRNA abundance of muscle carnitine acyltransferases in periparturient dairy cows with high (HBCS) and normal body condition (NBCS). Fifteen weeks antepartum, 38 pregnant multiparous Holstein cows were assigned to 2 groups that were fed differently to reach the targeted BCS and backfat thickness (BFT) until dry-off at -49 d before calving (HBCS: BCS >3.75 and BFT >1.4 cm; NBCS: <3.5 and <1.2 cm). Thereafter, both groups were fed identical diets. Blood samples and biopsies from the semitendinosus muscle were collected on d -49, 3, 21, and 84 relative to calving. Actual BCS at d -49 were 3.02 ± 0.24 and 3.82 ± 0.33 (mean ± SD) for NBCS and HBCS, respectively. In both groups, serum profiles showed marked changes during the periparturient period, with decreasing concentrations of free carnitine and increasing concentrations of long-chain AcylCN. Compared with NBCS, HBCS had greater serum long-chain AcylCN in early lactation, which may point to an insufficient adaptation of their metabolism in response to the metabolic load of fatty acids around parturition. The muscle concentrations of C5-, C9-, C18:1-, and C18:2-AcylCN were lower and those of C14:2-AcylCN were greater in HBCS than in NBCS cows. The mRNA abundance of carnitine palmitoyltransferase (CPT)1, muscle isoform (CPT1b) and CPT2 increased from d -49 to early lactation (d 3, d 21), followed by a decline to nearly antepartum values by d 84; this change was not affected by group. In conclusion, over-conditioning around calving seems to be associated with mitochondrial overload, which can result in incomplete fatty acid oxidation in dairy cows.

Intergenerational Metabolomic Analysis of Mothers with a History of Gestational Diabetes Mellitus and Their Offspring.

Shared metabolomic patterns at delivery have been suggested to underlie the mother-to-child transmission of adverse metabolic health. This study aimed to investigate whether mothers with gestational diabetes mellitus (GDM) and their offspring show similar metabolomic patterns several years postpartum. Targeted metabolomics (including 137 metabolites) was performed in plasma samples obtained during an oral glucose tolerance test from 48 mothers with GDM and their offspring at a cross-sectional study visit 8 years after delivery. Partial Pearson's correlations between the area under the curve (AUC) of maternal and offspring metabolites were calculated, yielding so-called Gaussian graphical models. Spearman's correlations were applied to investigate correlations of body mass index (BMI), Matsuda insulin sensitivity index (ISI-M), dietary intake, and physical activity between generations, and correlations of metabolite AUCs with lifestyle variables. This study revealed that BMI, ISI-M, and the AUC of six metabolites (carnitine, taurine, proline, SM(-OH) C14:1, creatinine, and PC ae C34:3) were significantly correlated between mothers and offspring several years postpartum. Intergenerational metabolite correlations were independent of shared BMI, ISI-M, age, sex, and all other metabolites. Furthermore, creatinine was correlated with physical activity in mothers. This study suggests that there is long-term metabolic programming in the offspring of mothers with GDM and informs us about targets that could be addressed by future intervention studies.

International Ring Trial of a High Resolution Targeted Metabolomics and Lipidomics Platform for Serum and Plasma Analysis.

A challenge facing metabolomics in the analysis of large human cohorts is the cross-laboratory comparability of quantitative metabolomics measurements. In this study, 14 laboratories analyzed various blood specimens using a common experimental protocol provided with the Biocrates AbsoluteIDQ p400HR kit, to quantify up to 408 metabolites. The specimens included human plasma and serum from male and female donors, mouse and rat plasma, as well as NIST SRM 1950 reference plasma. The metabolite classes covered range from polar (e.g., amino acids and biogenic amines) to nonpolar (e.g., diacyl- and triacyl-glycerols), and they span 11 common metabolite classes. The manuscript describes a strict system suitability testing (SST) criteria used to evaluate each laboratory's readiness to perform the assay, and provides the SST Skyline documents for public dissemination. The study found approximately 250 metabolites were routinely quantified in the sample types tested, using Orbitrap instruments. Interlaboratory variance for the NIST SRM-1950 has a median of 10% for amino acids, 24% for biogenic amines, 38% for acylcarnitines, 25% for glycerolipids, 23% for glycerophospholipids, 16% for cholesteryl esters, 15% for sphingolipids, and 9% for hexoses. Comparing to consensus values for NIST SRM-1950, nearly 80% of comparable analytes demonstrated bias of <50% from the reference value. The findings of this study result in recommendations of best practices for system suitability, quality control, and calibration. We demonstrate that with appropriate controls, high-resolution metabolomics can provide accurate results with good precision across laboratories, and the p400HR therefore is a reliable approach for generating consistent and comparable metabolomics data.

Neutral endopeptidase inhibitors blunt kidney fibrosis by reducing myofibroblast formation.

Kidney fibrosis is the common pathophysiological mechanism in end-stage renal disease characterized by excessive accumulation of myofibroblast-derived extracellular matrix. Natriuretic peptides have been demonstrated to have cyclic guanosine monophosphate (cGMP)-dependent anti-fibrotic properties likely due to interference with pro-fibrotic tissue growth factor ß (TGF-ß) signaling. However, in vivo, natriuretic peptides are rapidly degraded by neutral endopeptidases (NEP). In a unilateral ureteral obstruction (UUO) mouse model for kidney fibrosis we assessed the anti-fibrotic effects of SOL1, an orally active compound that inhibits NEP and endothelin-converting enzyme (ECE). Mice (n=10 per group) subjected to UUO were treated for 1 week with either solvent, NEP-/ECE-inhibitor SOL1 (two doses), reference NEP-inhibitor candoxatril or the angiotensin II receptor type 1 (AT1)-antagonist losartan. While NEP-inhibitors had no significant effect on blood pressure, they did increase urinary cGMP levels as well as endothelin-1 (ET-1) levels. Immunohistochemical staining revealed a marked decrease in renal collagen (∼55% reduction, P<0.05) and α-smooth muscle actin (α-SMA; ∼40% reduction, P<0.05). Moreover, the number of α-SMA positive cells in the kidneys of SOL1-treated groups inversely correlated with cGMP levels consistent with a NEP-dependent anti-fibrotic effect. To dissect the molecular mechanisms associated with the anti-fibrotic effects of NEP inhibition, we performed a 'deep serial analysis of gene expression (Deep SAGE)' transcriptome and targeted metabolomics analysis of total kidneys of all treatment groups. Pathway analyses linked increased cGMP and ET-1 levels with decreased nuclear receptor signaling (peroxisome proliferator-activated receptor [PPAR] and liver X receptor/retinoid X receptor [LXR/RXR] signaling) and actin cytoskeleton organization. Taken together, although our transcriptome and metabolome data indicate metabolic dysregulation, our data support the therapeutic potential of NEP inhibition in the treatment of kidney fibrosis via cGMP elevation and reduced myofibroblast formation.

Metabolomics meets machine learning: Longitudinal metabolite profiling in serum of normal versus overconditioned cows and pathway analysis.

This study aimed to investigate the differences in the metabolic profiles in serum of dairy cows that were normal or overconditioned when dried off for elucidating the pathophysiological reasons for the increased health disturbances commonly associated with overconditioning. Fifteen weeks antepartum, 38 multiparous Holstein cows were allocated to either a high body condition (HBCS; n = 19) group or a normal body condition (NBCS; n = 19) group and were fed different diets until dry-off to amplify the difference. The groups were also stratified for comparable milk yields (NBCS: 10,361 ± 302 kg; HBCS: 10,315 ± 437 kg; mean ± standard deviation). At dry-off, the cows in the NBCS group (parity: 2.42 ± 1.84; body weight: 665 ± 64 kg) had a body condition score (BCS) <3.5 and backfat thickness (BFT) <1.2 cm, whereas the HBCS cows (parity: 3.37 ± 1.67; body weight: 720 ± 57 kg) had BCS >3.75 and BFT >1.4 cm. During the dry period and the subsequent lactation, both groups were fed identical diets but maintained the BCS and BFT differences. A targeted metabolomics (AbsoluteIDQ p180 kit, Biocrates Life Sciences AG, Innsbruck, Austria) approach was performed in serum samples collected on d -49, +3, +21, and +84 relative to calving for identifying and quantifying up to 188 metabolites from 6 different compound classes (acylcarnitines, AA, biogenic amines, glycerophospholipids, sphingolipids, and hexoses). The concentrations of 170 metabolites were above the limit of detection and could thus be used in this study. We used various machine learning (ML) algorithms (e.g., sequential minimal optimization, random forest, alternating decision tree, and naïve Bayes-updatable) to analyze the metabolome data sets. The performance of each algorithm was evaluated by a leave-one-out cross-validation method. The accuracy of classification by the ML algorithms was lowest on d 3 compared with the other time points. Various ML methods (partial least squares discriminant analysis, random forest, information gain ranking) were then performed to identify those metabolites that were contributing most significantly to discriminating the groups. On d 21 after parturition, 12 metabolites (acetylcarnitine, hexadecanoyl-carnitine, hydroxyhexadecenoyl-carnitine, octadecanoyl-carnitine, octadecenoyl-carnitine, hydroxybutyryl-carnitine, glycine, leucine, phosphatidylcholine-diacyl-C40:3, trans-4-hydroxyproline, carnosine, and creatinine) were identified in this way. Pathway enrichment analysis showed that branched-chain AA degradation (before calving) and mitochondrial ß-oxidation of long-chain fatty acids along with fatty acid metabolism, purine metabolism, and alanine metabolism (after calving) were significantly enriched in HBCS compared with NBCS cows. Our results deepen the insights into the phenotype related to overconditioning from the preceding lactation and the pathophysiological sequelae such as increased lipolysis and ketogenesis and decreased feed intake.

Altered metabolism distinguishes high-risk from stable carotid atherosclerotic plaques.

Aims: Identification and treatment of the rupture prone atherosclerotic plaque remains a challenge for reducing the burden of cardiovascular disease. The interconnection of metabolic and inflammatory processes in rupture prone plaques is poorly understood. Herein, we investigate associations between metabolite profiles, inflammatory mediators and vulnerability in carotid atherosclerotic plaques. Methods and results: We collected 159 carotid plaques from patients undergoing endarterectomy and measured 165 different metabolites in a targeted metabolomics approach. We identified a metabolite profile in carotid plaques that associated with histologically evaluated vulnerability and inflammatory mediators, as well as presence of symptoms in patients. The distinct metabolite profiles identified in high-risk and stable plaques were in line with different transcription levels of metabolic enzymes in the two groups, suggesting an altered metabolism in high-risk plaques. The altered metabolic signature in high-risk plaques was consistent with a change to increased glycolysis, elevated amino acid utilization and decreased fatty acid oxidation, similar to what is found in activated leucocytes and cancer cells. Conclusion: These results highlight a possible key role of cellular metabolism to support inflammation and a high-risk phenotype of atherosclerotic plaques. Targeting the metabolism of atherosclerotic plaques with novel metabolic radiotracers or inhibitors might therefore be valid future approaches to identify and treat the high-risk atherosclerotic plaque.

Long-Term Stability of Human Plasma Metabolites during Storage at -80 °C.

Prolonged storage of biospecimen can lead to artificially altered metabolite concentrations and thus bias data analysis in metabolomics experiments. To elucidate the potential impact of long-term storage on the metabolite profile, a pooled human plasma sample was aliquoted and stored at -80 °C. During a time period of five years, 1012 of the aliquots were measured with the Biocrates AbsoluteIDQ p180 targeted-metabolomics assay at 193 time points. Modeling the concentration courses over time revealed that 55 out of 111 metabolites remained stable. The statistically significantly changed metabolites showed on average an increase or decrease of +13.7% or -14.5%, respectively. In detail, increased concentration levels were observed for amino acids (mean: + 15.4%), the sum of hexoses (+7.9%), butyrylcarnitine (+9.4%), and some phospholipids mostly with chain lengths exceeding 40 carbon atoms (mean: +18.0%). Lipids tended to exhibit decreased concentration levels with the following mean concentration changes: acylcarnitines, -12.1%; lysophosphatidylcholines, -15.1%; diacyl-phosphatidylcholines, -17.0%; acyl-alkyl-phosphatidylcholines, -13.3%; sphingomyelins, -14.8%. We conclude that storage of plasma samples at -80 °C for up to five years can lead to altered concentration levels of amino acids, acylcarnitines, glycerophospholipids, sphingomyelins, and the sum of hexoses. These alterations must be considered when analyzing metabolomics data from long-term epidemiological studies.

Metabolite ratios as potential biomarkers for type 2 diabetes: a DIRECT study.

AIMS/HYPOTHESIS: Circulating metabolites have been shown to reflect metabolic changes during the development of type 2 diabetes. In this study we examined the association of metabolite levels and pairwise metabolite ratios with insulin responses after glucose, glucagon-like peptide-1 (GLP-1) and arginine stimulation. We then investigated if the identified metabolite ratios were associated with measures of OGTT-derived beta cell function and with prevalent and incident type 2 diabetes. METHODS: We measured the levels of 188 metabolites in plasma samples from 130 healthy members of twin families (from the Netherlands Twin Register) at five time points during a modified 3 h hyperglycaemic clamp with glucose, GLP-1 and arginine stimulation. We validated our results in cohorts with OGTT data (n = 340) and epidemiological case-control studies of prevalent (n = 4925) and incident (n = 4277) diabetes. The data were analysed using regression models with adjustment for potential confounders. RESULTS: There were dynamic changes in metabolite levels in response to the different secretagogues. Furthermore, several fasting pairwise metabolite ratios were associated with one or multiple clamp-derived measures of insulin secretion (all p < 9.2 × 10-7). These associations were significantly stronger compared with the individual metabolite components. One of the ratios, valine to phosphatidylcholine acyl-alkyl C32:2 (PC ae C32:2), in addition showed a directionally consistent positive association with OGTT-derived measures of insulin secretion and resistance (p ≤ 5.4 × 10-3) and prevalent type 2 diabetes (ORVal_PC ae C32:2 2.64 [ß 0.97 ± 0.09], p = 1.0 × 10-27). Furthermore, Val_PC ae C32:2 predicted incident diabetes independent of established risk factors in two epidemiological cohort studies (HRVal_PC ae C32:2 1.57 [ß 0.45 ± 0.06]; p = 1.3 × 10-15), leading to modest improvements in the receiver operating characteristics when added to a model containing a set of established risk factors in both cohorts (increases from 0.780 to 0.801 and from 0.862 to 0.865 respectively, when added to the model containing traditional risk factors + glucose). CONCLUSIONS/INTERPRETATION: In this study we have shown that the Val_PC ae C32:2 metabolite ratio is associated with an increased risk of type 2 diabetes and measures of insulin secretion and resistance. The observed effects were stronger than that of the individual metabolites and independent of known risk factors.

Disruption of glucagon receptor signaling causes hyperaminoacidemia exposing a possible liver-alpha-cell axis.

Glucagon secreted from the pancreatic alpha-cells is essential for regulation of blood glucose levels. However, glucagon may play an equally important role in the regulation of amino acid metabolism by promoting ureagenesis. We hypothesized that disruption of glucagon receptor signaling would lead to an increased plasma concentration of amino acids, which in a feedback manner stimulates the secretion of glucagon, eventually associated with compensatory proliferation of the pancreatic alpha-cells. To address this, we performed plasma profiling of glucagon receptor knockout ( Gcgr-/-) mice and wild-type (WT) littermates using liquid chromatography-mass spectrometry (LC-MS)-based metabolomics, and tissue biopsies from the pancreas were analyzed for islet hormones and by histology. A principal component analysis of the plasma metabolome from Gcgr-/- and WT littermates indicated amino acids as the primary metabolic component distinguishing the two groups of mice. Apart from their hyperaminoacidemia, Gcgr-/- mice display hyperglucagonemia, increased pancreatic content of glucagon and somatostatin (but not insulin), and alpha-cell hyperplasia and hypertrophy compared with WT littermates. Incubating cultured α-TC1.9 cells with a mixture of amino acids (Vamin 1%) for 30 min and for up to 48 h led to increased glucagon concentrations (~6-fold) in the media and cell proliferation (~2-fold), respectively. In anesthetized mice, a glucagon receptor-specific antagonist (Novo Nordisk 25-2648, 100 mg/kg) reduced amino acid clearance. Our data support the notion that glucagon secretion and hepatic amino acid metabolism are linked in a close feedback loop, which operates independently of normal variations in glucose metabolism.