Integrative metabolomics-genomics analysis identifies key networks in a stem cell-based model of schizophrenia.

Schizophrenia (SCZ) is a neuropsychiatric disorder, caused by a combination of genetic and environmental factors. The etiology behind the disorder remains elusive although it is hypothesized to be associated with the aberrant response to neurotransmitters, such as dopamine and glutamate. Therefore, investigating the link between dysregulated metabolites and distorted neurodevelopment holds promise to offer valuable insights into the underlying mechanism of this complex disorder. In this study, we aimed to explore a presumed correlation between the transcriptome and the metabolome in a SCZ model based on patient-derived induced pluripotent stem cells (iPSCs). For this, iPSCs were differentiated towards cortical neurons and samples were collected longitudinally at various developmental stages, reflecting neuroepithelial-like cells, radial glia, young and mature neurons. The samples were analyzed by both RNA-sequencing and targeted metabolomics and the two modalities were used to construct integrative networks in silico. This multi-omics analysis revealed significant perturbations in the polyamine and gamma-aminobutyric acid (GABA) biosynthetic pathways during rosette maturation in SCZ lines. We particularly observed the downregulation of the glutamate decarboxylase encoding genes GAD1 and GAD2, as well as their protein product GAD65/67 and their biochemical product GABA in SCZ samples. Inhibition of ornithine decarboxylase resulted in further decrease of GABA levels suggesting a compensatory activation of the ornithine/putrescine pathway as an alternative route for GABA production. These findings indicate an imbalance of cortical excitatory/inhibitory dynamics occurring during early neurodevelopmental stages in SCZ. Our study supports the hypothesis of disruption of inhibitory circuits to be causative for SCZ and establishes a novel in silico approach that enables for integrative correlation of metabolic and transcriptomic data of psychiatric disease models.

Intrinsic and extrinsic factors interact during development to influence telomere length in a long-lived reptile.

The mechanisms connecting environmental conditions to plasticity in biological aging trajectories are fundamental to understanding individual variation in functional traits and life history. Recent findings suggest that telomere biology is especially dynamic during early life stages and has long-term consequences for subsequent reproduction and survival. However, our current understanding is mostly derived from studies investigating ecological and anthropogenic factors separately, leaving the effects of complex environmental interactions unresolved. American alligators (Alligator mississippiensis) are long-lived apex predators that rely on incubation temperature during a discrete period during development and endocrine cues to determine sex, making them especially vulnerable to current climatic variability and exposure to anthropogenic contaminants interfering with hormone function. Here, we combine field studies with a factorial design to understand how the developmental environment, along with intrinsic biological variation contribute to persistent telomere variation. We found that exposure to a common endocrine disrupting contaminant, DDE, affects telomere length, but that the directionality is highly dependent upon incubation temperature. Variation in hatchling growth, underlies a strong clutch effect. We also assess concentrations of a panel of glucocorticoid hormones and find that contaminant exposure elicits an increase in circulating glucocorticoids. Consistent with emerging evidence linking stress and aging trajectories, GC levels also appear to trend with shorter telomere length. Thus, we add support for a mechanistic link between contaminants and glucocorticoid signalling, which interacts with ecological aspects of the developmental environment to alter telomere dynamics.

A plasma metabolite panel as biomarkers for early primary breast cancer detection.

In recent years, metabolites have attracted substantial attention as promising novel biomarkers of various diseases. However, breast cancer plasma metabolite studies are still in their infancy. Here, we investigated the potential of metabolites to serve as minimally invasive, early detection markers of primary breast cancer. We profiled metabolites extracted from the plasma of primary breast cancer patients and healthy controls using tandem mass spectrometry (UHPLC-MS/MS and FIA-MS/MS). Two metabolites were found to be upregulated, while 16 metabolites were downregulated in primary breast cancer patients compared to healthy controls in both the training and validation cohorts. A panel of seven metabolites was selected by LASSO regression analysis. This panel could differentiate primary breast cancer patients from healthy controls, with an AUC of 0.87 (95% CI: 0.81 ~ 0.92) in the training cohort and an AUC of 0.80 (95% CI: 0.71 ~ 0.87) in the validation cohort. These significantly differentiated metabolites are mainly involved in the amino acid metabolism and breast cancer cell growth pathways. In conclusion, using a metabolomics approach, we identified metabolites that have potential value for development of a multimarker blood-based test to complement and improve early breast cancer detection. The panel identified herein might be part of a prescreening tool, especially for younger women or for closely observing women with certain risks, to facilitate decision making regarding which individuals should undergo further diagnostic tests. In the future, the combination of metabolites and other blood-based molecular marker sets, such as DNA methylation, microRNA, and cell-free DNA mutation markers, will be an attractive option.

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.

Altered bile acid profile associates with cognitive impairment in Alzheimer's disease-An emerging role for gut microbiome.

INTRODUCTION: Increasing evidence suggests a role for the gut microbiome in central nervous system disorders and a specific role for the gut-brain axis in neurodegeneration. Bile acids (BAs), products of cholesterol metabolism and clearance, are produced in the liver and are further metabolized by gut bacteria. They have major regulatory and signaling functions and seem dysregulated in Alzheimer's disease (AD). METHODS: Serum levels of 15 primary and secondary BAs and their conjugated forms were measured in 1464 subjects including 370 cognitively normal older adults, 284 with early mild cognitive impairment, 505 with late mild cognitive impairment, and 305 AD cases enrolled in the AD Neuroimaging Initiative. We assessed associations of BA profiles including selected ratios with diagnosis, cognition, and AD-related genetic variants, adjusting for confounders and multiple testing. RESULTS: In AD compared to cognitively normal older adults, we observed significantly lower serum concentrations of a primary BA (cholic acid [CA]) and increased levels of the bacterially produced, secondary BA, deoxycholic acid, and its glycine and taurine conjugated forms. An increased ratio of deoxycholic acid:CA, which reflects 7α-dehydroxylation of CA by gut bacteria, strongly associated with cognitive decline, a finding replicated in serum and brain samples in the Rush Religious Orders and Memory and Aging Project. Several genetic variants in immune response-related genes implicated in AD showed associations with BA profiles. DISCUSSION: We report for the first time an association between altered BA profile, genetic variants implicated in AD, and cognitive changes in disease using a large multicenter study. These findings warrant further investigation of gut dysbiosis and possible role of gut-liver-brain axis in the pathogenesis of AD.

A validated liquid chromatography-high resolution-tandem mass spectrometry method for the simultaneous quantitation of tryptophan, kynurenine, kynurenic acid, and quinolinic acid in human plasma.

Tryptophan (TRP) catabolism via the kynurenine pathway is considered to represent a major link between inflammation and various diseases, including neurodegenerative disorders, depression, schizophrenia, multiple sclerosis, cardiovascular disease, and cancer. The kynurenine pathway and levels of TRP and its metabolites kynurenine (KYN), kynurenic acid (KYNA) and quinolinic acid (QUIN) are well regulated under physiological conditions but may be altered as part of the activated immune response. A simple, sensitive, and specific liquid chromatography-time of flight mass spectrometry method was developed for determining levels of the four compounds in human plasma samples. The workflow involves protein precipitation with acetonitrile, chromatographic separation on a Phenomenex Luna NH2 column by applying a linear 6 min gradient of 50-5% acetonitrile in aqueous ammonium acetate solution (5 mM, pH 9.5), and mass spectrometric detection with high-resolution tandem mass spectrometry. Charcoal-treated plasma served as surrogate matrix for external standard calibration. Stable-isotope-labeled analogues were used as internal standards. The calibration ranges were 0.5-50 µg/ml for TRP, 20-1000 ng/mL for KYN und QUIN, and 1-50 ng/mL for KYNA. Validation proved fitness of the developed workflow for the intended purpose. The established method was applied to the quantification of the four targets in 100 authentic plasma samples.

Assessment of Plasma Amino Acid Dynamics in Response to ACTH Stimulation by Liquid Chromatography Tandem-Mass Spectrometry.

BACKGROUND: The adrenocorticotropic hormone (ACTH) stimulation test is a widely used diagnostic tool to assess the adrenal gland function. Beyond that the ACTH test can be used in stress research to induce a biochemical stress response under standardized conditions. To study the impact of the stress response on protein metabolism, time-course plasma amino acid profiling in healthy individuals was performed with high performance liquid chromatography tandem-mass spectrometry (HPLC-MS/MS). METHODS: A set of 39 samples (pre/post 30´ and 60´ IV-ACTH) from 13 healthy individuals (age range 26 - 58, 3 female and 10 male) was investigated. Plasma amino acids were quantified by LC-MS/MS using the AbsoluteIDQ® p180 Kit (Biocrates Life Science, Innsbruck, Austria) including 19 biogenic amino acids, ornithine, and citrulline. RESULTS: Statistically significant decreases were observed for 11 proteinogenic amino acids (alanine, asparagine, isoleucine, leucine, tyrosine, phenylalanine, tryptophan, valine, methionine, aspartate, and threonine). The amino acids alanine, asparagine, and isoleucine showed markedly pronounced relative changes with short-term reduction of median inter-individual plasma concentrations of up to 25%. CONCLUSIONS: Amino acid profiling with LC-MS/MS revealed highly dynamic plasma alterations upon application of exogenous corticotropin as a stress model. Our findings provide novel insights into the biochemical stress response and improve our understanding of short-term metabolic consequences. Further studies should elucidate the impact of corticotropin mediated stress responses on amino acid catabolism.

Harmonizing lipidomics: NIST interlaboratory comparison exercise for lipidomics using SRM 1950-Metabolites in Frozen Human Plasma.

As the lipidomics field continues to advance, self-evaluation within the community is critical. Here, we performed an interlaboratory comparison exercise for lipidomics using Standard Reference Material (SRM) 1950-Metabolites in Frozen Human Plasma, a commercially available reference material. The interlaboratory study comprised 31 diverse laboratories, with each laboratory using a different lipidomics workflow. A total of 1,527 unique lipids were measured across all laboratories and consensus location estimates and associated uncertainties were determined for 339 of these lipids measured at the sum composition level by five or more participating laboratories. These evaluated lipids detected in SRM 1950 serve as community-wide benchmarks for intra- and interlaboratory quality control and method validation. These analyses were performed using nonstandardized laboratory-independent workflows. The consensus locations were also compared with a previous examination of SRM 1950 by the LIPID MAPS consortium. While the central theme of the interlaboratory study was to provide values to help harmonize lipids, lipid mediators, and precursor measurements across the community, it was also initiated to stimulate a discussion regarding areas in need of improvement.

Increased urine acylcarnitines in diabetic ApoE-/- mice: Hydroxytetradecadienoylcarnitine (C14:2-OH) reflects diabetic nephropathy in a context of hyperlipidemia.

Hyperlipidemia is a risk factor for initiation and progression of diabetic nephropathy but the metabolic pathways altered in the diabetic kidney in a context of hyperlipidemia remain incompletely described. Assuming that changes in urine composition reflect the alteration of renal metabolism and function, we analyzed the urine metabolite composition of diabetic (streptozotocin-treatment) and control (non diabetic) ApoE-/- mice fed a high cholesterol diet using targeted quantitative metabolomics. Urine metabolome was also compared to the plasma metabolome of the same animals. As previously shown, urine albuminuria/urine creatinine ratio (uACR) and glomerular area and plasma lipids (cholesterol, triglycerides) were more elevated in diabetic mice compared to control. After adjustment to urine creatinine, the abundance of 52 urine metabolites was significantly different in diabetic mice compared to control. Among them was a unique metabolite, C14:2-OH (3-hydroxytetradecadienoylcarnitine) that, in diabetic mice, was positively and significantly correlated with uACR, glomerular hypertrophy, blood glucose and plasma lipids. That metabolite was not detected in plasma. C14:2-OH is a long-chain acylcarnitine reminiscent of altered fatty acid beta oxidation. Other acylcarnitines, particularly the short chains C3-OH, C3-DC, C4:1, C5-DC, C5-M-DC, C5-OH that are reminiscent of altered oxidation of branched and aromatic amino acids were also exclusively detected in urine but were only correlated with plasma lipids. Finally, the renal gene expression of several enzymes involved in fatty acid and/or amino acid oxidation was significantly reduced in diabetic mice compared to control. This included the bifunctional enoyl-CoA hydratase/3-hydroxyacyl-CoA (Ehhadh) that might play a central role in C14:2-OH production. This study indicate that the development of diabetes in a context of hyperlipidemia is associated with a reduced capacity of kidney to oxidize fatty acids and amino acids with the consequence of an elevation of urinary acetylcarnitines including C14:2-OH that specifically reflects diabetic nephropathy.

Metabolic network failures in Alzheimer's disease: A biochemical road map.

INTRODUCTION: The Alzheimer's Disease Research Summits of 2012 and 2015 incorporated experts from academia, industry, and nonprofit organizations to develop new research directions to transform our understanding of Alzheimer's disease (AD) and propel the development of critically needed therapies. In response to their recommendations, big data at multiple levels are being generated and integrated to study network failures in disease. We used metabolomics as a global biochemical approach to identify peripheral metabolic changes in AD patients and correlate them to cerebrospinal fluid pathology markers, imaging features, and cognitive performance. METHODS: Fasting serum samples from the Alzheimer's Disease Neuroimaging Initiative (199 control, 356 mild cognitive impairment, and 175 AD participants) were analyzed using the AbsoluteIDQ-p180 kit. Performance was validated in blinded replicates, and values were medication adjusted. RESULTS: Multivariable-adjusted analyses showed that sphingomyelins and ether-containing phosphatidylcholines were altered in preclinical biomarker-defined AD stages, whereas acylcarnitines and several amines, including the branched-chain amino acid valine and α-aminoadipic acid, changed in symptomatic stages. Several of the analytes showed consistent associations in the Rotterdam, Erasmus Rucphen Family, and Indiana Memory and Aging Studies. Partial correlation networks constructed for Aß1-42, tau, imaging, and cognitive changes provided initial biochemical insights for disease-related processes. Coexpression networks interconnected key metabolic effectors of disease. DISCUSSION: Metabolomics identified key disease-related metabolic changes and disease-progression-related changes. Defining metabolic changes during AD disease trajectory and its relationship to clinical phenotypes provides a powerful roadmap for drug and biomarker discovery.

Transcriptional networks underlying a primary ovarian insufficiency disorder in alligators naturally exposed to EDCs.

Interactions between the endocrine system and environmental contaminants are responsible for impairing reproductive development and function. Despite the taxonomic diversity of affected species and attendant complexity inherent to natural systems, the underlying signaling pathways and cellular consequences are mostly studied in lab models. To resolve the genetic and endocrine pathways that mediate affected ovarian function in organisms exposed to endocrine disrupting contaminants in their natural environments, we assessed broad-scale transcriptional and steroidogenic responses to exogenous gonadotropin stimulation in juvenile alligators (Alligator missippiensis) originating from a lake with well-documented pollution (Lake Apopka, FL) and a nearby reference site (Lake Woodruff, FL). We found that individuals from Lake Apopka are characterized by hyperandrogenism and display hyper-sensitive transcriptional responses to gonadotropin stimulation when compared to individuals from Lake Woodruff. Site-specific transcriptomic divergence appears to be driven by wholly distinct subsets of transcriptional regulators, indicating alterations to fundamental genetic pathways governing ovarian function. Consistent with broad-scale transcriptional differences, ovaries of Lake Apopka alligators displayed impediments to folliculogenesis, with larger germinal beds and decreased numbers of late-stage follicles. After resolving the ovarian transcriptome into clusters of co-expressed genes, most site-associated modules were correlated to ovarian follicule phenotypes across individuals. However, expression of two site-specific clusters were independent of ovarian cellular architecture and are hypothesized to represent alterations to cell-autonomous transcriptional programs. Collectively, our findings provide high resolution mapping of transcriptional patterns to specific reproductive function and advance our mechanistic understanding regarding impaired reproductive health in an established model of environmental endocrine disruption.

Complexity and pitfalls of mass spectrometry-based targeted metabolomics in brain research.

Current quantitative metabolomic research in brain tissue is challenged by several analytical issues. To compare data of metabolite pattern, ratios of individual metabolite concentrations and composed classifiers characterizing a distinct state, standardized workup conditions, and extraction medium are crucial. Differences in physicochemical properties of individual compounds and compound classes such as polarity determine extraction yields and, thus, ratios of compounds with varying properties. Also, variations in suppressive effects related to coextracted matrix components affect standards or references and their concentration-dependent responses.The selection of a common tissue extraction protocol is an ill-posed problem because it can be regarded as a multiple objective decision depending on factors such as sample handling practicability, measurement precision, control of matrix effects, and relevance of the chemical assay. This study systematically evaluates the impact of extraction solvents and the impact of the complex brain tissue on measured metabolite levels, taking into account ionization efficiency as well as challenges encountered in the trace-level quantification of the analytes in brain matrices. In comparison with previous studies that relied on nontargeted platforms, consequently emphasizing the global behavior of the metabolomic fingerprint, here we focus on several series of metabolites spanning over extensive polarity, concentration, and molecular mass ranges.

Targeted metabolomics for biomarker discovery.

Metabolomics is a truly interdisciplinary field of science, which combines analytical chemistry, platform technology, mass spectrometry, and NMR spectroscopy with sophisticated data analysis. Applied to biomarker discovery, it includes aspects of pathobiochemistry, systems biology/medicine, and molecular diagnostics and requires bioinformatics and multivariate statistics. While successfully established in the screening of inborn errors in neonates, metabolomics is now widely used in the characterization and diagnostic research of an ever increasing number of diseases. In this Review we highlight important technical prerequisites as well as recent developments in metabolomics and metabolomics data analysis with special emphasis on their utility in biomarker identification and qualification, as well as targeted metabolomics by employing high-throughput mass spectrometry.

Bile acids targeted metabolomics and medication classification data in the ADNI1 and ADNIGO/2 cohorts.

Alzheimer's disease (AD) is the most common cause of dementia. The mechanism of disease development and progression is not well understood, but increasing evidence suggests multifactorial etiology, with a number of genetic, environmental, and aging-related factors. There is a growing body of evidence that metabolic defects may contribute to this complex disease. To interrogate the relationship between system level metabolites and disease susceptibility and progression, the AD Metabolomics Consortium (ADMC) in partnership with AD Neuroimaging Initiative (ADNI) is creating a comprehensive biochemical database for patients in the ADNI1 cohort. We used the Biocrates Bile Acids platform to evaluate the association of metabolic levels with disease risk and progression. We detail the quantitative metabolomics data generated on the baseline samples from ADNI1 and ADNIGO/2 (370 cognitively normal, 887 mild cognitive impairment, and 305 AD). Similar to our previous reports on ADNI1, we present the tools for data quality control and initial analysis. This data descriptor represents the third in a series of comprehensive metabolomics datasets from the ADMC on the ADNI.

Bile acid quantification of 20 plasma metabolites identifies lithocholic acid as a putative biomarker in Alzheimer's disease.

INTRODUCTION: There is still a clear need for a widely available, inexpensive and reliable method to diagnose Alzheimer's disease (AD) and monitor disease progression. Liquid chromatography-mass spectrometry (LC-MS) is a powerful analytic technique with a very high sensitivity and specificity. OBJECTIVES: The aim of the present study is to measure concentrations of 20 bile acids using the novel Kit from Biocrates Life Sciences based on LC-MS technique. METHODS: Twenty bile acid metabolites were quantitatively measured in plasma of 30 cognitively healthy subjects, 20 patients with mild cognitive impairment (MCI) and 30 patients suffering from AD. RESULTS: Levels of lithocholic acid were significantly enhanced in plasma of AD patients (50 ± 6 nM, p = 0.004) compared to healthy controls (32 ± 3 nM). Lithocholic acid plasma levels of MCI patients (41 ± 4 nM) were not significantly different from healthy subjects or AD patients. Levels of glycochenodeoxycholic acid, glycodeoxycholic acid and glycolithocholic acid were significantly higher in AD patients compared to MCI patients (p < 0.05). All other cholic acid metabolites were not significantly different between healthy subjects, MCI patients and AD patients. ROC analysis shows an overall accuracy of about 66%. Discriminant analysis was used to classify patients and we found that 15/23 were correctly diagnosed. We further showed that LCA levels increased by about 3.2 fold when healthy subjects converted to AD patients within a 8-9 year follow up period. Pathway analysis linked these changes to a putative toxic cholesterol pathway. CONCLUSION: In conclusion, 4 bile acids may be useful to diagnose AD in plasma samples despite limitations in diagnostic accuracy.

Metabolomic profiling of ascending thoracic aortic aneurysms and dissections - Implications for pathophysiology and biomarker discovery.

OBJECTIVE: Our basic understanding of ascending thoracic aortic aneurysm (ATAA) pathogenesis is still very limited, hampering early diagnosis, risk prediction, and development of treatment options. "Omics"-technologies, ideal to reveal tissue alterations from the normal physiological state due to disease have hardly been applied in the field. Using a metabolomic approach, with this study the authors seek to define tissue differences between controls and various forms of ATAAs. METHODS: Using a targeted FIA-MS/MS metabolomics approach, we analysed and compared the metabolic profiles of ascending thoracic aortic wall tissue of age-matched controls (n = 8), bicuspid aortic valve-associated aneurysms (BAV-A; n = 9), tricuspid aortic valve-associated aneurysms (TAV-A; n = 14), and tricuspid aortic valve-associated aortic dissections (TAV-Diss; n = 6). RESULTS: With sphingomyelin (SM) (OH) C22:2, SM C18:1, SM C22:1, and SM C24:1 only 4 out of 92 detectable metabolites differed significantly between controls and BAV-A samples. Between controls and TAV-Diss samples only phosphatidylcholine (PC) ae C32:1 differed. Importantly, our analyses revealed a general increase in the amount of total sphingomyelin levels in BAV-A and TAV-Diss samples compared to controls. CONCLUSIONS: Significantly increased levels of sphingomyelins in BAV-A and TAV-Diss samples compared to controls may argue for a repression of sphingomyelinase activity and the sphingomyelinase-ceramide pathway, which may result in an inhibition of tissue regeneration; a potential basis for disease initiation and progression.

Targeted metabolomics and medication classification data from participants in the ADNI1 cohort.

Alzheimer's disease (AD) is the most common neurodegenerative disease presenting major health and economic challenges that continue to grow. Mechanisms of disease are poorly understood but significant data point to metabolic defects that might contribute to disease pathogenesis. The Alzheimer Disease Metabolomics Consortium (ADMC) in partnership with Alzheimer Disease Neuroimaging Initiative (ADNI) is creating a comprehensive biochemical database for AD. Using targeted and non- targeted metabolomics and lipidomics platforms we are mapping metabolic pathway and network failures across the trajectory of disease. In this report we present quantitative metabolomics data generated on serum from 199 control, 356 mild cognitive impairment and 175 AD subjects enrolled in ADNI1 using AbsoluteIDQ-p180 platform, along with the pipeline for data preprocessing and medication classification for confound correction. The dataset presented here is the first of eight metabolomics datasets being generated for broad biochemical investigation of the AD metabolome. We expect that these collective metabolomics datasets will provide valuable resources for researchers to identify novel molecular mechanisms contributing to AD pathogenesis and disease phenotypes.

Quantification of the carbapenem antibiotic ertapenem in human plasma by a validated liquid chromatography-mass spectrometry method.

BACKGROUND: Ertapenem (Invanz) is a newly developed carbapenem beta-lactam antibiotic. LC-MS is the method of choice for therapeutic drug monitoring (TDM) of a variety of drugs including antibiotics. No validated LC-MS method for ertapenem quantification is described in the literature so far. METHODS: A rapid and robust LC-MS quantification method for ertapenem was developed and validated for clinical routine application in plasma samples. After immediate stabilisation with MES buffer (pH 6.5), samples were prepared for LC-MS analysis using simple protein precipitation. LC-MS coupling was realised by the use of a Phenomenex Synergi 4micro Polar-RP A80 Mercury LC column (10 x 2.0 mm) in combination with a Single-MS (Agilent 1100 LC-MSD SL) operating in negative selected ion monitoring (SIM) detection mode with ceftazidime as internal standard for adequate selective and sensitive analysis. RESULTS: LC-MS method validation by means of determination of limit of detection (LOD 0.1 microg mL-1), lower limit of quantification (LLOQ 1 microg mL-1), linearity (0.1-50 microg mL-1), recovery (> 90%), intra- and inter-day precision (RSD < 10%), accuracy (> 90%), inter-subject variability (< 10% at LLOQ), drug stability in plasma (> 3 months) and in post-extracted samples (> 99% for 24 h), and matrix effects (process efficiency > 90%) showed excellent performance parameters considering Guidance for Industry - Bioanalytical Method Validation. CONCLUSION: This method is perfectly appropriate for routine quantification of ertapenem and possibly other polar carbapenem beta-lactam antibiotics.

Progress in Metabolomics Standardisation and its Significance in Future Clinical Laboratory Medicine.

Today, the technology of 'targeted' based metabolomics is pivotal in the clinical analysis workflow as it provides information of metabolic phenotyping (metabotypes) by enhancing our understanding of metabolism of complex diseases, biomarker discovery for disease development, progression, treatment, and drug function and assessment. This review is focused on surveying and providing a gap analysis on metabolic phenotyping with a focus on targeted based metabolomics from an instrumental, technical point-of-view discussing the state-of-the-art instrumentation, pre- to post- analytical aspects as well as an overall future necessity for biomarker discovery and future (pre-) clinical routine application.

Achievements and Future Directions of the APFCB Mass Spectrometry Harmonisation Project on Serum Testosterone.

As an outcome of the 2010 Asian Pacific Conference for Chromatography and Mass Spectrometry in Hong Kong, a collaborative working group was formed to promote the harmonisation of mass spectrometry methods. The Mass Spectrometry Harmonisation Working Group resides under the combined auspices of the Asia-Pacific Federation for Clinical Biochemistry and Laboratory Medicine (APFCB) and the Australasian Association of Clinical Biochemists (AACB). A decision was made to initially focus attention on serum steroids due to the common interest of members in this area; with the first steroid to assess being testosterone. In principle, full standardisation with traceability should be achievable for all steroids as they are small compounds with defined molecular weight and structure. In order to achieve this we need certified reference materials, reference methods, reference laboratories, reference intervals and external quality assurance programs; each being an important pillar in the process. When all the pillars are present, such as for serum testosterone, it is feasible to fully standardise the liquid chromatography - tandem mass spectrometry (LC-MS/MS) methods. In a collaborative process with interested stakeholders, we commenced on a pathway to provide ongoing assessment and seek opportunities for improvement in the LC-MS/MS methods for serum steroids. Here we discuss the outcomes to date and major challenges related to the accurate measurement of serum steroids with a focus on serum testosterone.