Effects of Lupron and surgical castration on fecal androgen metabolite concentrations and intermale aggression in capybaras ( Hydrochoerus hydrochaeris )

This study was conducted in order to understand the taste and odour problems that occurred in the Seine and the Marne rivers during the severe drought of 1976. Samples were taken every 15 days from several locations in the rivers themselves and from storage reservoirs upstream from Paris. Algae and actinomycetes were identified and counted. Metabolite concentrations were measured. These data were correlated with threshold odor numbers and bacteriological water quality parameters.

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Urinary phthalate metabolite concentrations and hot flashes in women from an urban convenience sample of midlife women

Environmental Research ◽

10.1016/j.envres.2021.110891 ◽

2021 ◽

Vol 197 ◽

pp. 110891

Author(s):

Genoa R. Warner ◽

Diana C. Pacyga ◽

Rita S. Strakovsky ◽

Rebecca Smith ◽

Tamarra James-Todd ◽

...

Keyword(s):

Hot Flashes ◽

Midlife Women ◽

Convenience Sample ◽

Metabolite Concentrations ◽

Phthalate Metabolite

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Using Out-of-Batch Reference Populations to Improve Untargeted Metabolomics for Screening Inborn Errors of Metabolism

Metabolites ◽

10.3390/metabo11010008 ◽

2020 ◽

Vol 11 (1) ◽

pp. 8

Author(s):

Michiel Bongaerts ◽

Ramon Bonte ◽

Serwet Demirdas ◽

Edwin H. Jacobs ◽

Esmee Oussoren ◽

...

Keyword(s):

Regression Model ◽

Inborn Errors Of Metabolism ◽

Untargeted Metabolomics ◽

Detection Accuracy ◽

Inborn Errors ◽

Mixed Effect ◽

Metabolomics Data ◽

Normalization Methods ◽

Metabolite Concentrations ◽

Reference Samples

Untargeted metabolomics is an emerging technology in the laboratory diagnosis of inborn errors of metabolism (IEM). Analysis of a large number of reference samples is crucial for correcting variations in metabolite concentrations that result from factors, such as diet, age, and gender in order to judge whether metabolite levels are abnormal. However, a large number of reference samples requires the use of out-of-batch samples, which is hampered by the semi-quantitative nature of untargeted metabolomics data, i.e., technical variations between batches. Methods to merge and accurately normalize data from multiple batches are urgently needed. Based on six metrics, we compared the existing normalization methods on their ability to reduce the batch effects from nine independently processed batches. Many of those showed marginal performances, which motivated us to develop Metchalizer, a normalization method that uses 10 stable isotope-labeled internal standards and a mixed effect model. In addition, we propose a regression model with age and sex as covariates fitted on reference samples that were obtained from all nine batches. Metchalizer applied on log-transformed data showed the most promising performance on batch effect removal, as well as in the detection of 195 known biomarkers across 49 IEM patient samples and performed at least similar to an approach utilizing 15 within-batch reference samples. Furthermore, our regression model indicates that 6.5–37% of the considered features showed significant age-dependent variations. Our comprehensive comparison of normalization methods showed that our Log-Metchalizer approach enables the use out-of-batch reference samples to establish clinically-relevant reference values for metabolite concentrations. These findings open the possibilities to use large scale out-of-batch reference samples in a clinical setting, increasing the throughput and detection accuracy.

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Metabolic phenotyping reveals a reduction in the bioavailability of serotonin and kynurenine pathway metabolites in both the urine and serum of individuals living with Alzheimer’s disease

Alzheimer s Research & Therapy ◽

10.1186/s13195-020-00741-z ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Luke Whiley ◽

◽

Katie E. Chappell ◽

Ellie D’Hondt ◽

Matthew R. Lewis ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Systemic Inflammation ◽

Metabolic Phenotyping ◽

Acid Urine ◽

Metabolite Concentrations ◽

Ssri Medication ◽

The Impact ◽

Urine And Serum ◽

Urine Serum

Abstract Background Both serotonergic signalling disruption and systemic inflammation have been associated with the pathogenesis of Alzheimer’s disease (AD). The common denominator linking the two is the catabolism of the essential amino acid, tryptophan. Metabolism via tryptophan hydroxylase results in serotonin synthesis, whilst metabolism via indoleamine 2,3-dioxygenase (IDO) results in kynurenine and its downstream derivatives. IDO is reported to be activated in times of host systemic inflammation and therefore is thought to influence both pathways. To investigate metabolic alterations in AD, a large-scale metabolic phenotyping study was conducted on both urine and serum samples collected from a multi-centre clinical cohort, consisting of individuals clinically diagnosed with AD, mild cognitive impairment (MCI) and age-matched controls. Methods Metabolic phenotyping was applied to both urine (n = 560) and serum (n = 354) from the European-wide AddNeuroMed/Dementia Case Register (DCR) biobank repositories. Metabolite data were subsequently interrogated for inter-group differences; influence of gender and age; comparisons between two subgroups of MCI - versus those who remained cognitively stable at follow-up visits (sMCI); and those who underwent further cognitive decline (cMCI); and the impact of selective serotonin reuptake inhibitor (SSRI) medication on metabolite concentrations. Results Results revealed significantly lower metabolite concentrations of tryptophan pathway metabolites in the AD group: serotonin (urine, serum), 5-hydroxyindoleacetic acid (urine), kynurenine (serum), kynurenic acid (urine), tryptophan (urine, serum), xanthurenic acid (urine, serum), and kynurenine/tryptophan ratio (urine). For each listed metabolite, a decreasing trend in concentrations was observed in-line with clinical diagnosis: control > MCI > AD. There were no significant differences in the two MCI subgroups whilst SSRI medication status influenced observations in serum, but not urine. Conclusions Urine and serum serotonin concentrations were found to be significantly lower in AD compared with controls, suggesting the bioavailability of the neurotransmitter may be altered in the disease. A significant increase in the kynurenine/tryptophan ratio suggests that this may be a result of a shift to the kynurenine metabolic route due to increased IDO activity, potentially as a result of systemic inflammation. Modulation of the pathways could help improve serotonin bioavailability and signalling in AD patients.

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