Biology subjects

Dragsted, L. O.

Publications and source records attributed to Dragsted, L. O..

6 recordsLinked to original sources

bioRxiv2024

QC4Metabolomics: Real-time and Retrospective Quality Control of Metabolomics Data

MotivationThe ability to answer complex biological questions in metabolomics relies on the acquisition of high-quality data. However, due to the complex nature of liquid chromatography-mass spectrometry acquisition, data quality checks are often not done comprehensively and only at the post-processing step. This can be too late to mitigate analytical problems such as loss of m/z calibration, retention time drift and severe ion suppression. It is often not practically or economically feasible to reanalyze samples, and interpretation of the acquired compromised data, if at all possible, is limited, despite the considerable expenses incurred to obtain them. ResultsWe therefore introduce QC4Metabolomics, a real-time quality control monitoring software for untargeted metabolomics data. QC4Metabolomics monitors files as they are acquired or retrospectively by tracking any user-defined compound(s) and extracting diagnostic information such as observed m/z, retention time, intensity and peak shape, and presents the results on a web dashboard. QC4Metabolomics also monitors the levels of common or user-defined contaminants. We report herein real-world examples where QC4Metabolomics easily reveals analytical problems retrospectively that could have been immediately addressed with real-time monitoring, so that the samples would have been analyzed without any quality control issues. Availability and ImplementationQC4Metabolomics is available as code at https://github.com/stanstrup/QC4Metabolomics. A Docker image, a docker-compose setup file and demo data are also provided for easy deployment.

bioinformatics↗

bioRxiv2024

Gut environmental factors explain variations in the gut microbiome composition and metabolism within and between healthy adults

The human gut microbiome is highly personal. However, the contribution of the gut environment to variations in the gut microbiome remains elusive. Here, we profiled the gut microbiome composition and metabolism over 9 consecutive days in 61 healthy adults and assessed gut environmental factors including segmental transit time and pH using a wireless motility capsule. Day-to-day fluctuations in gut environmental factors as well as segmental transit time and pH varied substantially between individuals. The gut environment explained more variations in gut microbiome and urine metabolome than dietary macronutrients or personal characteristics. Finally, we identified coffee-derived metabolites to be negatively correlated with small intestinal transit time and several microbial metabolites to be associated with colonic transit time including urinary proteolytic markers, faecal short-chain fatty acids, and breath methane. Our work suggests that the gut environment is key for understanding the individuality of the human gut microbiome composition and metabolism.

microbiology↗

bioRxiv2023

Characterizing human postprandial metabolic response using multiway data analysis

Analysis of time-resolved postprandial metabolomics data can enhance our knowledge about human metabolism by providing a better understanding of similarities and differences in postprandial responses of individuals, with the potential to advance precision nutrition and medicine. Traditional data analysis methods focus on clustering methods relying on summaries of data across individuals or use univariate methods analyzing one metabolite at a time. However, they fail to provide a compact summary revealing the underlying patterns, i.e., groups of subjects, clusters of metabolites, and their temporal profiles. In this study, we analyze NMR (Nuclear Magnetic Resonance) spectroscopy measurements of plasma samples collected at multiple time points during a meal challenge test from 299 individuals from the COPSAC2000 cohort. We arrange the data as a three-way array: subjects by metabolites by time, and use the CAN-DECOMP/PARAFAC (CP) tensor factorization model to capture the underlying patterns. We analyze the fasting state data to reveal static patterns of subject group differences, and the fasting state-corrected postprandial data to reveal dynamic markers of group differences. Our analysis demonstrates that the CP model reveals replicable and biologically meaningful patterns capturing certain metabolite groups and their temporal profiles, and showing differences among males according to their body mass index (BMI). Furthermore, we observe that certain lipoproteins relate to the group difference differently in the fasting vs. dynamic state in males. While similar dynamic patterns are observed in response to the challenge test in males and females, the BMI-related group difference is only observed in males in the dynamic state.

systems biology↗

bioRxiv2020

Breastmilk-promoted bifidobacteria produce aromatic lactic acids in the infant gut

Breastfeeding profoundly shapes the infant gut microbiota, which is critical for early life immune development. However, few breastmilk-dependent microbial metabolites mediating host-microbiota interactions are currently known. We here demonstrate that breastmilk-promoted Bifidobacterium species convert aromatic amino acids (tryptophan, phenylalanine and tyrosine) into their respective aromatic lactic acids (indolelactate, phenyllactate and 4-hydroxyphenyllactate) via a previously unrecognised aromatic lactate dehydrogenase. By longitudinal profiling of the gut microbiota composition and metabolome of stool samples of infants obtained from birth until 6 months of age, we show that stool concentrations of aromatic lactic acids are determined by the abundance of human milk oligosaccharide degrading Bifidobacterium species containing the aromatic lactate dehydrogenase. We demonstrate that stool concentrations of Bifidobacterium-derived indolelactate, the most abundant aromatic lactic acid in vivo, are associated with the capacity of infant stool samples to activate the aryl hydrocarbon receptor (AhR), a receptor important for controlling intestinal homeostasis and immune responses. Finally, we show that indolelactate modulates ex vivo immune responses of human CD4+ T-cells and monocytes in a dose-dependent manner by acting as an agonist of both, the AhR and hydroxycarboxylic acid receptor 3 (HCAR3). Our findings reveal that breastmilk-promoted Bifidobacterium produce aromatic lactic acids in the gut of infants and suggest that these microbial metabolites may impact immune function in early life.

microbiology↗

bioRxiv2020

Lipidomics of human adipose tissue reveals diversity between body areas

Background and aimsAdipose tissue plays a pivotal role in storing excess fat and its composition reflects the history of persons lifestyle and metabolic health. Broad profiling of lipids with mass spectrometry has potential for uncovering new knowledge on the pathology of obesity, metabolic syndrome, diabetes and other related conditions. Here, we developed a lipidomic method for analyzing human subcutaneous adipose biopsies. We applied the method to four body areas to understand the differences in lipid composition between these areas. Materials and methodsAdipose tissue biopsies from 10 participants were analyzed using ultra-high-performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry. The method development included the optimization of the lipid extraction, the sample amount and the sample dilution factor to detect lipids in an appropriate concentration range. Lipidomic analyses were performed for adipose tissue collected from the abdomen, breast, thigh and lower back. Differences in lipid levels between tissues were visualized with heatmaps. ResultsLipidomic analysis on human adipose biopsies lead to the identification of 187 lipids in 2 mg of sample. Technical variation of the lipid-class specific internal standards were below 5 %, thus indicating acceptable repeatability. Triacylglycerols were highly represented in the adipose tissue samples, and lipids from 13 lipid classes were identified. Long polyunsaturated triacylglycerols in higher levels in thigh (q<0.05), when compared with the abdomen, breast and lower back, indicating that the lipidome was area-specific. ConclusionThe method presented here is suitable for the analysis of lipid profiles in 2 mg of adipose tissue. The amount of fat across the body is important for health but we argue that also the distribution and the particular profile of the lipidome may be relevant for metabolic outcomes. We suggest that the method presented in this paper could be useful for detecting such aberrations.

biophysics↗

bioRxiv2019

Describing the fecal metabolome in cryogenically collected samples from healthy participants.

IntroductionThe chemical composition of feces plays an important role in human metabolism. Metabolomics and lipidomics are valuable tools for screening the metabolite composition in feces. Here we set out to describe fecal metabolite composition in healthy participants in frozen stools.\n\nMethodsFrozen stool samples were collected from 10 healthy volunteers and cryogenically drilled in four areas along the specimen. Polar metabolites were analyzed using derivatization followed by two-dimensional gas chromatography and time of flight mass spectrometry. Lipids were detected using ultra high-performance liquid chromatography coupled with quadruple time-of-flight mass spectrometry. The technical variation threshold was set to 30% in pooled quality control samples and metabolite variation was then assessed in four areas per specimen. A data-generated network using metabolites found in all areas was computed for healthy participants.\n\nResults2326 metabolic features were detected. Out of a total of 298 metabolites that were annotated we report here 185 that showed a technical variation of x< 30%. These metabolites included amino acids, fatty acid derivatives, carboxylic acids and phenolic compounds. Lipids predominantly belonged to the groups of diacylglycerols, triacylglycerols and ceramides. Metabolites varied between sampling areas (14%-80%). A network using metabolites present in all areas showed two main clusters, DAG lipids and phenyllactic acid.\n\nConclusionsIn feces from healthy participants, the main groups detected were phenolic compounds, ceramides, diacylglycerols and triacylglycerols. Metabolite levels differed considerably depending on the sampling area.\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=52 SRC=\"FIGDIR/small/708685v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (16K):\norg.highwire.dtl.DTLVardef@17db665org.highwire.dtl.DTLVardef@51e140org.highwire.dtl.DTLVardef@19b55d7org.highwire.dtl.DTLVardef@120d399_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗