A novel culture medium with reduced nutrient concentrations supports the development and viability of mouse embryos

Further refinement of culture media is needed to improve the quality of embryos generated in vitro. Previous results from our laboratory demonstrated that uptake of nutrients by the embryo is significantly less than what is supplied in traditional culture media. Our objective was to determine the impact of reduced nutrient concentrations in culture medium on mouse embryo development, metabolism, and quality as a possible platform for next generation medium formulation. Concentrations of carbohydrates, amino acids, and vitamins could be reduced by 50% with no detrimental effects, but blastocyst development was impaired at 25% of standard nutrient provision (reduced nutrient medium; RN). Addition of pyruvate and L-lactate (+PL) to RN at 50% of standard concentrations restored blastocyst development, hatching, and cell number. In addition, blastocysts produced in RN\u2009+PL contained more ICM cells and ATP than blastocysts cultured in our control (100% nutrient) medium; however, metabolic activity was altered. Similarly, embryos produced in the RN medium with elevated (50% control) concentrations of pyruvate and lactate in the first step medium and EAA and Glu in the second step medium were competent to implant and develop into fetuses at a similar rate as embryos produced in the control medium. This novel approach to culture medium formulation could help define the optimal nutrient requirements of embryos in culture and provide a means of shifting metabolic activity towards the utilization of specific metabolic pathways that may be beneficial for embryo viability

Current practices in lc-ms untargeted metabolomics: a scoping review on the use of pooled quality control samples

Untargeted metabolomics is an analytical approach with numerous applications serving as an effective metabolic phenotyping platform to characterize small molecules within a biological system. Data quality can be challenging to evaluate and demonstrate in metabolomics experiments. This has driven the use of pooled quality control (QC) samples for monitoring and, if necessary, correcting for analytical variance introduced during sample preparation and data acquisition stages. Described herein is a scoping literature review detailing the use of pooled QC samples in published untargeted liquid chromatography-mass spectrometry (LC-MS) based metabolomics studies. A literature query was performed, the list of papers was filtered, and suitable articles were randomly sampled. In total, 109 papers were each reviewed by at least five reviewers, answering predefined questions surrounding the use of pooled quality control samples. The results of the review indicate that use of pooled QC samples has been relatively widely adopted by the metabolomics community and that it is used at a similar frequency across biological taxa and sample types in both small- and large-scale studies. However, while many studies generated and analyzed pooled QC samples, relatively few reported the use of pooled QC samples to improve data quality. This demonstrates a clear opportunity for the field to more frequently utilize pooled QC samples for quality reporting, feature filtering, analytical drift correction, and metabolite annotation. Additionally, our survey approach enabled us to assess the ambiguity in the reporting of the methods used to describe the generation and use of pooled QC samples. This analysis indicates that many details of the QC framework are missing or unclear, limiting the reader's ability to determine which QC steps have been taken. Collectively, these results capture the current state of pooled QC sample usage and highlight existing strengths and deficiencies as they are applied in untargeted LC-MS metabolomics

Non-targeted metabolomics combined with genetic analyses identifies bile acid synthesis and phospholipid metabolism as being associated with incident type 2 diabetes.

AIMS/HYPOTHESIS: Identification of novel biomarkers for type 2 diabetes and their genetic determinants could lead to improved understanding of causal pathways and improve risk prediction. METHODS: In this study, we used data from non-targeted metabolomics performed using liquid chromatography coupled with tandem mass spectrometry in three Swedish cohorts (Uppsala Longitudinal Study of Adult Men [ULSAM], n = 1138; Prospective Investigation of the Vasculature in Uppsala Seniors [PIVUS], n = 970; TwinGene, n = 1630). Metabolites associated with impaired fasting glucose (IFG) and/or prevalent type 2 diabetes were assessed for associations with incident type 2 diabetes in the three cohorts followed by replication attempts in the Cooperative Health Research in the Region of Augsburg (KORA) S4 cohort (n = 855). Assessment of the association of metabolite-regulating genetic variants with type 2 diabetes was done using data from a meta-analysis of genome-wide association studies. RESULTS: Out of 5961 investigated metabolic features, 1120 were associated with prevalent type 2 diabetes and IFG and 70 were annotated to metabolites and replicated in the three cohorts. Fifteen metabolites were associated with incident type 2 diabetes in the four cohorts combined (358 events) following adjustment for age, sex, BMI, waist circumference and fasting glucose. Novel findings included associations of higher values of the bile acid deoxycholic acid and monoacylglyceride 18:2 and lower concentrations of cortisol with type 2 diabetes risk. However, adding metabolites to an existing risk score improved model fit only marginally. A genetic variant within the CYP7A1 locus, encoding the rate-limiting enzyme in bile acid synthesis, was found to be associated with lower concentrations of deoxycholic acid, higher concentrations of LDL-cholesterol and lower type 2 diabetes risk. Variants in or near SGPP1, GCKR and FADS1/2 were associated with diabetes-associated phospholipids and type 2 diabetes. CONCLUSIONS/INTERPRETATION: We found evidence that the metabolism of bile acids and phospholipids shares some common genetic origin with type 2 diabetes. ACCESS TO RESEARCH MATERIALS: Metabolomics data have been deposited in the Metabolights database, with accession numbers MTBLS93 (TwinGene), MTBLS124 (ULSAM) and MTBLS90 (PIVUS)