Amygdalobacter indicium gen. nov., sp. nov., and Amygdalobacter nucleatus sp. nov., gen. nov.: novel bacteria from the family Oscillospiraceae isolated from the female genital tract.

Four obligately anaerobic Gram-positive bacteria representing one novel genus and two novel species were isolated from the female genital tract. Both novel species, designated UPII 610-JT and KA00274T, and an additional isolate of each species were characterized utilizing biochemical, genotypic and phylogenetic analyses. All strains were non-motile and non-spore forming, asaccharolytic, non-cellulolytic and indole-negative coccobacilli. Fatty acid methyl ester analysis for UPII 610-JT and KA00274T and additional isolates revealed C16 : 0, C18 : 0, C18:1ω9c and C18:2ω6,9c to be the major fatty acids for both species. UPII 610-JT had a 16S rRNA gene sequence similarity of 99.4 % to an uncultured clone sequence (AY724740) designated as Bacterial Vaginosis Associated Bacterium 2 (BVAB2). KA00274T had a 16S rRNA gene sequence similarity of 96.5 % to UPII 610-JT. Whole genomic DNA mol% G+C content was 42.2 and 39.3 % for UPII 610-JT and KA00274T, respectively. Phylogenetic analyses indicate these isolates represent a novel genus and two novel species within the Oscillospiraceae family. We propose the names Amygdalobacter indicium gen. nov., sp. nov., for UPII 610-JT representing the type strain of this species (=DSM 112989T, =ATCC TSD-274T) and Amygdalobacter nucleatus gen. nov., sp. nov., for KA00274T representing the type strain of this species (=DSM 112988T, =ATCC TSD-275T).

Use of sensitivity-enhanced nuclear magnetic resonance spectroscopy equipped with a 1.7-mm cryogenically cooled micro-coil probe in identifying human sperm intracellular metabolites.

CONTEXT: The clinical value of human sperm metabolites has not been established due to the technical complexity in detecting these metabolites when sperm numbers are low. AIMS: To detect endogenous intracellular metabolites in fresh and post-thaw human spermatozoa using 800MHz nuclear magnetic resonance (NMR) spectroscopy equipped with a 1.7-mm cryo-probe. METHODS: Processed spermatozoa from 25 normozoospermic ejaculates were subjected to extraction of intracellular metabolites and then profiled by sensitivity-enhanced NMR spectroscopy equipped with a 1.7-mm cryogenically cooled micro-coil probe. In parallel, some of the processed sperm fractions were subjected to freeze-thawing and were then analysed for intracellular metabolites. KEY RESULTS: Twenty-three metabolites were profiled from only 1.25million sperm cells. Comparison of the metabolomic signature of pre-freeze and post-thaw sperm cells did not show significant changes in the levels of metabolites. CONCLUSIONS: Sensitivity-enhanced NMR spectroscopy equipped with a 1.7-mm cryogenically cooled micro-coil probe is a potential tool for identifying intracellular metabolites when sperm number is low. IMPLICATIONS: Use of sensitivity-enhanced NMR spectroscopy opens up the opportunity to test for endogenous metabolites in samples with a limited number of spermatozoa, to understand the patho-physiology of infertility.

Four-Day Food Record Macronutrient Intake, With and Without Biomarker Calibration, and Chronic Disease Risk in Postmenopausal Women.

We recently evaluated associations of biomarker-calibrated protein intake, protein density, carbohydrate intake, and carbohydrate density with the incidence of cardiovascular disease, cancer, and diabetes among postmenopausal women in the Women's Health Initiative (1993-present, 40 US clinical centers). The biomarkers relied on serum and urine metabolomics profiles, and biomarker calibration used regression of biomarkers on food frequency questionnaires. Here we develop corresponding calibration equations using food records and dietary recalls. In addition, we use calibrated intakes based on food records in disease association estimation in a cohort subset (n = 29,294) having food records. In this analysis, more biomarker variation was explained by food records than by FFQs for absolute macronutrient intake, with 24-hour recalls being intermediate. However, the percentage of biomarker variation explained was similar for each assessment approach for macronutrient densities. Invasive breast cancer risk was related inversely to carbohydrate and protein densities using food records, in analyses that included (calibrated) total energy intake and body mass index. Corresponding analyses for absolute intakes did not differ from the null, nor did absolute or relative intakes associate significantly with colorectal cancer or coronary heart disease. These analyses do not suggest major advantages for food records or dietary recalls in comparison with less costly and logistically simpler food frequency questionnaires for these nutritional variables.

Biomarkers for Components of Dietary Protein and Carbohydrate with Application to Chronic Disease Risk in Postmenopausal Women.

BACKGROUND: We recently developed protein and carbohydrate intake biomarkers using metabolomics profiles in serum and urine, and used them to correct self-reported dietary data for measurement error. Biomarker-calibrated carbohydrate density was inversely associated with chronic disease risk, whereas protein density associations were mixed. OBJECTIVES: To elucidate and extend this earlier work through biomarker development for protein and carbohydrate components, including animal protein and fiber. METHODS: Prospective disease association analyses were undertaken in Women's Health Initiative (WHI) cohorts of postmenopausal US women, aged 50-79 y when enrolled at 40 US clinical centers. Biomarkers were developed using an embedded human feeding study (n = 153). Calibration equations for protein and carbohydrate components were developed using a WHI nutritional biomarker study (n = 436). Calibrated intakes were associated with chronic disease incidence in WHI cohorts (n = 81,954) over a 20-y (median) follow-up period, using HR regression methods. RESULTS: Previously reported elevations in cardiovascular disease (CVD) with higher-protein diets tended to be explained by animal protein density. For example, for coronary heart disease a 20% increment in animal protein density had an HR of 1.20 (95% CI: 1.02, 1.42) relative to the HR for total protein density. In comparison, cancer and diabetes risk showed little association with animal protein density beyond that attributable to total protein density. Inverse carbohydrate density associations with total CVD were mostly attributable to fiber density, with a 20% increment HR factor of 0.89 (95% CI: 0.83, 0.94). Cancer risk showed little association with fiber density, whereas diabetes risk had a 20% increment HR of 0.93 (95% CI: 0.88, 0.98) relative to the HRs for total carbohydrate density. CONCLUSIONS: In a population of postmenopausal US women, CVD risk was associated with high-animal-protein and low-fiber diets, cancer risk was associated with low-carbohydrate diets, and diabetes risk was associated with low-fiber/low-carbohydrate diets.

Biomarker-Calibrated Red and Combined Red and Processed Meat Intakes with Chronic Disease Risk in a Cohort of Postmenopausal Women.

BACKGROUND: The associations of red and processed meat with chronic disease risk remain to be clarified, in part because of measurement error in self-reported diet. OBJECTIVES: We sought to develop metabolomics-based biomarkers for red and processed meat, and to evaluate associations of biomarker-calibrated meat intake with chronic disease risk among postmenopausal women. METHODS: Study participants were women who were members of the Women's Health Initiative (WHI) study cohorts. These participants were postmenopausal women aged 50-79 y when enrolled during 1993-1998 at 40 US clinical centers with embedded human feeding and nutrition biomarker studies. Literature reports of metabolomics correlates of meat consumption were used to develop meat intake biomarkers from serum and 24-h urine metabolites in a 153-participant feeding study (2010-2014). Resulting biomarkers were used in a 450-participant biomarker study (2007-2009) to develop linear regression calibration equations that adjust FFQ intakes for random and systematic measurement error. Biomarker-calibrated meat intakes were associated with cardiovascular disease, cancer, and diabetes incidence among 81,954 WHI participants (1993-2020). RESULTS: Biomarkers and calibration equations meeting prespecified criteria were developed for consumption of red meat and red plus processed meat combined, but not for processed meat consumption. Following control for nondietary confounding factors, hazard ratios were calculated for a 40% increment above the red meat median intake for coronary artery disease (HR: 1.10; 95% CI: 1.07, 1.14), heart failure (HR: 1.26; 95% CI: 1.20, 1.33), breast cancer (HR: 1.10; 95% CI: 1.07, 1.13) for, total invasive cancer (HR: 1.07; 95% CI: 1.05, 1.09), and diabetes (HR: 1.37; 95% CI: 1.34, 1.39). HRs for red plus processed meat intake were similar. HRs were close to the null, and mostly nonsignificant following additional control for dietary potential confounding factors, including calibrated total energy consumption. CONCLUSIONS: A relatively high-meat dietary pattern is associated with somewhat higher chronic disease risks. These elevations appear to be largely attributable to the dietary pattern, rather than to consumption of red or processed meat per se.

Biomarker-Calibrated Macronutrient Intake and Chronic Disease Risk among Postmenopausal Women.

BACKGROUND: Knowledge about macronutrient intake and chronic disease risk has been limited by the absence of objective macronutrient measures. Recently, we proposed novel biomarkers for protein, protein density, carbohydrate, and carbohydrate density, using established biomarkers and serum and urine metabolomics profiles in a human feeding study. OBJECTIVES: We aimed to use these biomarkers to develop calibration equations for macronutrient variables using dietary self-reports and personal characteristics and to study the association between biomarker-calibrated intake estimates and cardiovascular disease, cancer, and diabetes risk in Women's Health Initiative (WHI) cohorts. METHODS: Prospective disease association analyses are based on WHI cohorts of postmenopausal US women aged 50-79 y when enrolled at 40 US clinical centers (n = 81,954). We used biomarker intake values in a WHI nutritional biomarker study (n = 436) to develop calibration equations for each macronutrient variable, leading to calibrated macronutrient intake estimates throughout WHI cohorts. We then examined the association of these intakes with chronic disease incidence over a 20-y (median) follow-up period using HR regression methods. RESULTS: In analyses that included doubly labeled water-calibrated total energy, HRs for cardiovascular diseases and cancers were mostly unrelated to calibrated protein density. However, many were inversely related to carbohydrate density, with HRs (95% CIs) for a 20% increment in carbohydrate density of 0.81 (0.69, 0.95) and 0.83 (0.74, 0.93), respectively, for primary outcomes of coronary heart disease and breast cancer, as well as 0.74 (0.60, 0.91) and 0.87 (0.81, 0.93) for secondary outcomes of heart failure and total invasive cancer. Corresponding HRs (95% CIs) for type 2 diabetes incidence in relation to protein density and carbohydrate density were 1.17 (1.09, 1.75) and 0.73 (0.66, 0.80), respectively. CONCLUSIONS: At specific energy intake, a diet high in carbohydrate density is associated with substantially reduced risk of major chronic diseases in a population of US postmenopausal women. This trial was registered at clinicaltrials.gov as NCT00000611.

Evaluation of potential metabolomic-based biomarkers of protein, carbohydrate and fat intakes using a controlled feeding study.

PURPOSE: Objective biomarkers of dietary exposure are needed to establish reliable diet-disease associations. Unfortunately, robust biomarkers of macronutrient intakes are scarce. We aimed to assess the utility of serum, 24-h urine and spot urine high-dimensional metabolites for the development of biomarkers of daily intake of total energy, protein, carbohydrate and fat, and the percent of energy from these macronutrients (%E). METHODS: A 2-week controlled feeding study mimicking the participants' habitual diets was conducted among 153 postmenopausal women from the Women's Health Initiative (WHI). Fasting serum metabolomic profiles were analyzed using a targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) assay for aqueous metabolites and a direct-injection-based quantitative lipidomics platform. Urinary metabolites were analyzed using 1H nuclear magnetic resonance (NMR) spectroscopy at 800 MHz and by untargeted gas chromatography-mass spectrometry (GC-MS). Variable selection was performed to build prediction models for each dietary variable. RESULTS: The highest cross-validated multiple correlation coefficients (CV-R2) for protein intake (%E) and carbohydrate intake (%E) using metabolites only were 36.3 and 37.1%, respectively. With the addition of established dietary biomarkers (doubly labeled water for energy and urinary nitrogen for protein), the CV-R2 reached 55.5% for energy (kcal/d), 52.0 and 45.0% for protein (g/d, %E), 55.9 and 37.0% for carbohydrate (g/d, %E). CONCLUSION: Selected panels of serum and urine metabolites, without the inclusion of doubly labeled water and urinary nitrogen biomarkers, give a reliable and robust prediction of daily intake of energy from protein and carbohydrate.

Megasphaera lornae sp. nov., Megasphaera hutchinsoni sp. nov., and Megasphaera vaginalis sp. nov.: novel bacteria isolated from the female genital tract.

Six strictly anaerobic Gram-negative bacteria representing three novel species were isolated from the female reproductive tract. The proposed type strains for each species were designated UPII 199-6T, KA00182T and BV3C16-1T. Phylogenetic analyses based on 16S rRNA gene sequencing indicated that the bacterial isolates were members of the genus Megasphaera. UPII 199-6T and KA00182T had 16S rRNA gene sequence identities of 99.9 % with 16S rRNA clone sequences previously amplified from the human vagina designated as Megasphaera type 1 and Megasphaera type 2, members of the human vaginal microbiota associated with bacterial vaginosis, preterm birth and HIV acquisition. UPII 199-6T exhibited sequence identities ranging from 92.9 to 93.6 % with validly named Megasphaera isolates and KA00182T had 16S rRNA gene sequence identities ranging from 92.6-94.2 %. BV3C16-1T was most closely related to Megasphaera cerevisiae with a 16S rRNA gene sequence identity of 95.4 %. Cells were coccoid or diplococcoid, non-motile and did not form spores. Genital tract isolates metabolized organic acids but were asaccharolytic. The isolates also metabolized amino acids. The DNA G+C content for the genome sequences of UPII 199-6T, KA00182T and BV3C16-1T were 46.4, 38.9 and 49.8 mol%, respectively. Digital DNA-DNA hybridization and average nucleotide identity between the genital tract isolates and other validly named Megasphaera species suggest that each isolate type represents a new species. The major fatty acid methyl esters include the following: C12 : 0, C16 : 0, C16 : 0 dimethyl acetal (DMA) and summed feature 5 (C15 : 0 DMA and/or C14 : 0 3-OH) in UPII 199-6T; C16 : 0 and C16 : 1 cis 9 in KA00182T; C12 : 0; C14 : 0 3-OH; and summed feature 5 in BV3C16-1T. The isolates produced butyrate, isobutyrate, and isovalerate but there were specific differences including production of formate and propionate. Together, these data indicate that UPII 199-6T, KA00182T and BV3C16-1T represent novel species within the genus Megasphaera. We propose the following names: Megasphaera lornae sp. nov. for UPII 199-6T representing the type strain of this species (=DSM 111201T=ATCC TSD-205T), Megasphaera hutchinsoni sp. nov. for KA00182T representing the type strain of this species (=DSM 111202T=ATCC TSD-206T) and Megasphaera vaginalis sp. nov. for BV3C16-1T representing the type strain of this species (=DSM 111203T=ATCC TSD-207T).

Altered metabolite levels and correlations in patients with colorectal cancer and polyps detected using seemingly unrelated regression analysis.

Introduction: Metabolomics technologies enable the identification of putative biomarkers for numerous diseases; however, the influence of confounding factors on metabolite levels poses a major challenge in moving forward with such metabolites for pre-clinical or clinical applications. Objectives: To address this challenge, we analyzed metabolomics data from a colorectal cancer (CRC) study, and used seemingly unrelated regression (SUR) to account for the effects of confounding factors including gender, BMI, age, alcohol use, and smoking. Methods: A SUR model based on 113 serum metabolites quantified using targeted mass spectrometry, identified 20 metabolites that differentiated CRC patients (n = 36), patients with polyp (n = 39), and healthy subjects (n = 83). Models built using different groups of biologically related metabolites achieved improved differentiation and were significant for 26 out of 29 groups. Furthermore, the networks of correlated metabolites constructed for all groups of metabolites using the ParCorA algorithm, before or after application of the SUR model, showed significant alterations for CRC and polyp patients relative to healthy controls. Results: The results showed that demographic covariates, such as gender, BMI, BMI2, and smoking status, exhibit significant confounding effects on metabolite levels, which can be modeled effectively. Conclusion: These results not only provide new insights into addressing the major issue of confounding effects in metabolomics analysis, but also shed light on issues related to establishing reliable biomarkers and the biological connections between them in a complex disease.

Recommendations and Standardization of Biomarker Quantification Using NMR-Based Metabolomics with Particular Focus on Urinary Analysis.

NMR-based metabolomics has shown considerable promise in disease diagnosis and biomarker discovery because it allows one to nondestructively identify and quantify large numbers of novel metabolite biomarkers in both biofluids and tissues. Precise metabolite quantification is a prerequisite to move any chemical biomarker or biomarker panel from the lab to the clinic. Among the biofluids commonly used for disease diagnosis and prognosis, urine has several advantages. It is abundant, sterile, and easily obtained, needs little sample preparation, and does not require invasive medical procedures for collection. Furthermore, urine captures and concentrates many "unwanted" or "undesirable" compounds throughout the body, providing a rich source of potentially useful disease biomarkers; however, incredible variation in urine chemical concentrations makes analysis of urine and identification of useful urinary biomarkers by NMR challenging. We discuss a number of the most significant issues regarding NMR-based urinary metabolomics with specific emphasis on metabolite quantification for disease biomarker applications and propose data collection and instrumental recommendations regarding NMR pulse sequences, acceptable acquisition parameter ranges, relaxation effects on quantitation, proper handling of instrumental differences, sample preparation, and biomarker assessment.

Altered glucose metabolism in Harvey-ras transformed MCF10A cells.

Metabolic reprogramming that alters the utilization of glucose including the "Warburg effect" is critical in the development of a tumorigenic phenotype. However, the effects of the Harvey-ras (H-ras) oncogene on cellular energy metabolism during mammary carcinogenesis are not known. The purpose of this study was to determine the effect of H-ras transformation on glucose metabolism using the untransformed MCF10A and H-ras oncogene transfected (MCF10A-ras) human breast epithelial cells, a model for early breast cancer progression. We measured the metabolite fluxes at the cell membrane by a selective micro-biosensor, [(13)C6 ]glucose flux by (13)C-mass isotopomer distribution analysis of media metabolites, intracellular metabolite levels by NMR, and gene expression of glucose metabolism enzymes by quantitative PCR. Results from these studies indicated that MCF10A-ras cells exhibited enhanced glycolytic activity and lactate production, decreased glucose flux through the tricarboxylic acid (TCA) cycle, as well as an increase in the utilization of glucose in the pentose phosphate pathway (PPP). These results provide evidence for a role of H-ras oncogene in the metabolic reprogramming of MCF10A cells during early mammary carcinogenesis.

Brain-targeted proanthocyanidin metabolites for Alzheimer's disease treatment.

While polyphenolic compounds have many health benefits, the potential development of polyphenols for the prevention/treatment of neurological disorders is largely hindered by their complexity as well as by limited knowledge regarding their bioavailability, metabolism, and bioactivity, especially in the brain. We recently demonstrated that dietary supplementation with a specific grape-derived polyphenolic preparation (GP) significantly improves cognitive function in a mouse model of Alzheimer's disease (AD). GP is comprised of the proanthocyanidin (PAC) catechin and epicatechin in monomeric (Mo), oligomeric, and polymeric forms. In this study, we report that following oral administration of the independent GP forms, only Mo is able to improve cognitive function and only Mo metabolites can selectively reach and accumulate in the brain at a concentration of ∼400 nM. Most importantly, we report for the first time that a biosynthetic epicatechin metabolite, 3'-O-methyl-epicatechin-5-O-ß-glucuronide (3'-O-Me-EC-Gluc), one of the PAC metabolites identified in the brain following Mo treatment, promotes basal synaptic transmission and long-term potentiation at physiologically relevant concentrations in hippocampus slices through mechanisms associated with cAMP response element binding protein (CREB) signaling. Our studies suggest that select brain-targeted PAC metabolites benefit cognition by improving synaptic plasticity in the brain, and provide impetus to develop 3'-O-Me-EC-Gluc and other brain-targeted PAC metabolites to promote learning and memory in AD and other forms of dementia.

15N-cholamine--a smart isotope tag for combining NMR- and MS-based metabolite profiling.

Recently, the enhanced resolution and sensitivity offered by chemoselective isotope tags have enabled new and enhanced methods for detecting hundreds of quantifiable metabolites in biofluids using nuclear magnetic resonance (NMR) spectroscopy or mass spectrometry. However, the inability to effectively detect the same metabolites using both complementary analytical techniques has hindered the correlation of data derived from the two powerful platforms and thereby the maximization of their combined strengths for applications such as biomarker discovery and the identification of unknown metabolites. With the goal of alleviating this bottleneck, we describe a smart isotope tag, (15)N-cholamine, which possesses two important properties: an NMR sensitive isotope and a permanent charge for MS sensitivity. Using this tag, we demonstrate the detection of carboxyl group containing metabolites in both human serum and urine. By combining the individual strengths of the (15)N label and permanent charge, the smart isotope tag facilitates effective detection of the carboxyl-containing metabolome by both analytical methods. This study demonstrates a unique approach to exploit the combined strength of MS and NMR in the field of metabolomics.

RAMSY: ratio analysis of mass spectrometry to improve compound identification.

The complexity of biological samples poses a major challenge for reliable compound identification in mass spectrometry (MS). The presence of interfering compounds that cause additional peaks in the spectrum can make interpretation and assignment difficult. To overcome this issue, new approaches are needed to reduce complexity and simplify spectral interpretation. Recently, focused on unknown metabolite identification, we presented a new approach, RANSY (ratio analysis of nuclear magnetic resonance spectroscopy; Anal. Chem. 2011, 83, 7616-7623), which extracts the (1)H signals related to the same metabolite based on peak intensity ratios. On the basis of this concept, we present the ratio analysis of mass spectrometry (RAMSY) method, which facilitates improved compound identification in complex MS spectra. RAMSY works on the principle that, under a given set of experimental conditions, the abundance/intensity ratios between the mass fragments from the same metabolite are relatively constant. Therefore, the quotients of average peak ratios and their standard deviations, generated using a small set of MS spectra from the same ion chromatogram, efficiently allow the statistical recovery of the metabolite peaks and facilitate reliable identification. RAMSY was applied to both gas chromatography/MS and liquid chromatography tandem MS (LC-MS/MS) data to demonstrate its utility. The performance of RAMSY is typically better than the results from correlation methods. RAMSY promises to improve unknown metabolite identification for MS users in metabolomics or other fields.

Targeted metabolic profiling of hepatocellular carcinoma and hepatitis C using LC-MS/MS.

Hepatitis C virus (HCV) infection of the liver is a global health problem and a major risk factor for the development of hepatocellular carcinoma (HCC). Sensitive methods are needed for the improved and earlier detection of HCC, which would provide better therapy options. Metabolic profiling of the high-risk population (HCV patients) and those with HCC provides insights into the process of liver carcinogenesis and possible biomarkers for earlier cancer detection. Seventy-three blood metabolites were quantitatively profiled in HCC (n = 30) and cirrhotic HCV (n = 22) patients using a targeted approach based on LC-MS/MS. Sixteen of 73 targeted metabolites differed significantly (p < 0.05) and their levels varied up to a factor of 3.3 between HCC and HCV. Four of these 16 metabolites (methionine, 5-hydroxymethyl-2'-deoxyuridine, N2,N2-dimethylguanosine, and uric acid) that showed the lowest p values were used to develop and internally validate a classification model using partial least squares discriminant analysis. The model exhibited high classification accuracy for distinguishing the two groups with sensitivity, specificity, and area under the receiver operating characteristic curve of 97%, 95%, and 0.98, respectively. A number of perturbed metabolic pathways, including amino acid, purine, and nucleotide metabolism, were identified based on the 16 biomarker candidates. These results provide a promising methodology to distinguish cirrhotic HCV patients, who are at high risk to develop HCC, from those who have already progressed to HCC. The results also provide insights into the altered metabolism between HCC and HCV.

Biomarkers for Duchenne muscular dystrophy progression: impact of age in the mdx tongue spared muscle.

Background: Duchenne muscular dystrophy (DMD) is a severe form of muscular dystrophy without an effective treatment, caused by mutations in the DMD gene, leading to the absence of dystrophin. DMD results in muscle weakness, loss of ambulation and death at an early age. Metabolomics studies in mdx mice, the most used model for DMD, reveal changes in metabolites associated with muscle degeneration and aging. In DMD, the tongue muscles exhibit unique behavior, initially showing partial protection against inflammation but later experiencing fibrosis and loss of muscle fibers. Certain metabolites and proteins, like TNF-α and TGF-ß, are potential biomarkers for dystrophic muscle characterization. Methods: To investigate disease progression and aging, we utilized young (1-month old) and old (21-25 months old) mdx and wild-type mice. Metabolite changes were analyzed using 1-H Nuclear Magnetic Resonance, while TNF-α and TGF-ß were assessed using Western blotting to examine inflammation, and fibrosis. Morphometric analysis was conducted to assess the extent of myofiber damage between groups. Results: The histological analysis of the tongue showed no differences between groups. No differences were found between the concentrations of metabolites from wild type or mdx animals of the same age. The metabolites alanine, methionine, 3-methylhistidine were higher, and taurine and glycerol were lower in young animals in both wild type and mdx (p < 0.001). The metabolites glycine (p < 0.001) and glutamic acid (p = 0.0018) were different only in the mdx groups, being higher in young mdx mice. Acetic acid, phosphocreatine, isoleucine, succinic acid, creatine and the proteins TNF-α and TGF-ß had no difference in the analysis between groups (p > 0.05). Conclusions: Surprisingly, histological and protein analysis reveals that the tongue of young and old mdx animals is protected from severe myonecrosis observed in other muscles. The metabolites alanine, methionine, 3-methylhistidine, taurine, and glycerol may be effective for specific assessments, although their use for disease progression monitoring should be cautious due to age-related changes. Acetic acid, phosphocreatine, isoleucine, succinate, creatine, TNF-α, and TGF-ß do not vary with aging and remain constant in spared muscles, suggesting their potential as specific biomarkers for DMD progression independent of aging.

"Add to subtract": a simple method to remove complex background signals from the 1H nuclear magnetic resonance spectra of mixtures.

Because of its highly reproducible and quantitative nature and minimal requirements for sample preparation or separation, (1)H nuclear magnetic resonance (NMR) spectroscopy is widely used for profiling small-molecule metabolites in biofluids. However (1)H NMR spectra contain many overlapped peaks. In particular, blood serum/plasma and diabetic urine samples contain high concentrations of glucose, which produce strong peaks between 3.2 ppm and 4.0 ppm. Signals from most metabolites in this region are overwhelmed by the glucose background signals and become invisible. We propose a simple "Add to Subtract" background subtraction method and show that it can reduce the glucose signals by 98% to allow retrieval of the hidden information. This procedure includes adding a small drop of concentrated glucose solution to the sample in the NMR tube, mixing, waiting for an equilibration time, and acquisition of a second spectrum. The glucose-free spectra are then generated by spectral subtraction using Bruker Topspin software. Subsequent multivariate statistical analysis can then be used to identify biomarker candidate signals for distinguishing different types of biological samples. The principle of this approach is generally applicable for all quantitative spectral data and should find utility in a variety of NMR-based mixture analyses as well as in metabolite profiling.

Isotope enhanced approaches in metabolomics.

The rapidly growing area of "metabolomics," in which a large number of metabolites from body fluids, cells or tissue are detected quantitatively, in a single step, promises immense potential for a number of disciplines including early disease diagnosis, therapy monitoring, systems biology, drug discovery and nutritional science. Because of its ability to detect a large number of metabolites in intact biological samples reproducibly and quantitatively, nuclear magnetic resonance (NMR) spectroscopy has emerged as one of the most powerful analytical techniques in metabolomics. NMR spectroscopy of biological samples with isotope labeling of metabolites using nuclei such as (2)H, (13)C, (15)N and (31)P, either in vivo or ex vivo, has dramatically improved our ability to identify low concentrated metabolites and trace important metabolic pathways. Considering the somewhat limited sensitivity and high complexity of NMR spectra of biological samples, efforts have been made to increase sensitivity and selectivity through isotope labeling methods, which pave novel avenues to unravel biological complexity and understand cellular functions in health and various disease conditions. This chapter describes current developments in isotope labeling of metabolites in vivo as well as ex vivo, and their potential metabolomics applications.

Duration of dry and humidified incubation of single-step embryo culture medium and oxygen tension during sham culture do not alter medium composition.

Background: The extended embryo culture using single-step medium gained popularity in clinical in vitro fertilisation (IVF). However, there are concerns about the degradation of unstable medium components and their negative effects on the developing embryos. Further, dry-incubation can increase osmolality, which can in-turn enhance the concentration of constituents of the media and their stability. Hence, this study was conducted to understand the immediate changes in the culture media metabolites in relation to clinically comparable situations such as single-step extended embryo culture and use of dry and humidified-incubation in two-different gaseous conditions. Methods: Commercially available single-step medium was sham-cultured in droplets under oil in two different conditions viz. dry (37°C; 6%CO 2; 5%O 2) and humidified (37°C; 6% CO 2; atmospheric O 2) for 0h, 72h, and 120h intervals. Droplets were subjected to the sensitivity-enhanced nuclear magnetic resonance (NMR)-based profiling using 800 MHz NMR equipped with a cryogenically cooled micro-coil (1.7mm) probe. Metabolomic signatures between the two groups were comprehensively assessed. Results: A total of ten amino acids and four energy substrates were identified from the culture medium. Metabolite levels showed a non-significant increase in the dry-incubation group at 72h and then declined at 120h. Humidified incubation had no effects on the level of the metabolite until 120h. No significant differences in the levels of metabolites were observed between the dry and humidified-groups at various time-points tested. Conclusions: A non-significant variation in the levels of metabolites observed in the dry-incubation of single-step medium most unlikely to influence a clinical outcome. However, the impact of these subtle changes on the (epi)genetic integrity of the embryos in a clinical set-up to be addressed.