Changes in the metabolic profile of human male postmortem frontal cortex and cerebrospinal fluid samples associated with heavy alcohol use.

Heavy alcohol use is one of the top causes of disease and death in the world. The brain is a key organ affected by heavy alcohol use. Here, our aim was to measure changes caused by heavy alcohol use in the human brain metabolic profile. We analyzed human postmortem frontal cortex and cerebrospinal fluid (CSF) samples from males with a history of heavy alcohol use (n = 74) and controls (n = 74) of the Tampere Sudden Death Series cohort. We used a nontargeted liquid chromatography mass spectrometry-based metabolomics method. We observed differences between the study groups in the metabolite levels of both frontal cortex and CSF samples, for example, in amino acids and derivatives, and acylcarnitines. There were more significant alterations in the metabolites of frontal cortex than in CSF. In the frontal cortex, significant alterations were seen in the levels of neurotransmitters (e.g., decreased levels of GABA and acetylcholine), acylcarnitines (e.g., increased levels of acylcarnitine 4:0), and in some metabolites associated with alcohol metabolizing enzymes (e.g., increased levels of 2-piperidone). Some of these changes were also significant in the CSF samples (e.g., elevated 2-piperidone levels). Overall, these results show the metabolites associated with neurotransmitters, energy metabolism and alcohol metabolism, were altered in human postmortem frontal cortex and CSF samples of persons with a history of heavy alcohol use.

Random forest-based imputation outperforms other methods for imputing LC-MS metabolomics data: a comparative study.

BACKGROUND: LC-MS technology makes it possible to measure the relative abundance of numerous molecular features of a sample in single analysis. However, especially non-targeted metabolite profiling approaches generate vast arrays of data that are prone to aberrations such as missing values. No matter the reason for the missing values in the data, coherent and complete data matrix is always a pre-requisite for accurate and reliable statistical analysis. Therefore, there is a need for proper imputation strategies that account for the missingness and reduce the bias in the statistical analysis. RESULTS: Here we present our results after evaluating nine imputation methods in four different percentages of missing values of different origin. The performance of each imputation method was analyzed by Normalized Root Mean Squared Error (NRMSE). We demonstrated that random forest (RF) had the lowest NRMSE in the estimation of missing values for Missing at Random (MAR) and Missing Completely at Random (MCAR). In case of absent values due to Missing Not at Random (MNAR), the left truncated data was best imputed with minimum value imputation. We also tested the different imputation methods for datasets containing missing data of various origin, and RF was the most accurate method in all cases. The results were obtained by repeating the evaluation process 100 times with the use of metabolomics datasets where the missing values were introduced to represent absent data of different origin. CONCLUSION: Type and rate of missingness affects the performance and suitability of imputation methods. RF-based imputation method performs best in most of the tested scenarios, including combinations of different types and rates of missingness. Therefore, we recommend using random forest-based imputation for imputing missing metabolomics data, and especially in situations where the types of missingness are not known in advance.

Tissue-wide metabolomics reveals wide impact of gut microbiota on mice metabolite composition.

The essential role of gut microbiota in health and disease is well recognized, but the biochemical details that underlie the beneficial impact remain largely undefined. To maintain its stability, microbiota participates in an interactive host-microbiota metabolic signaling, impacting metabolic phenotypes of the host. Dysbiosis of microbiota results in alteration of certain microbial and host metabolites. Identifying these markers could enhance early detection of certain diseases. We report LC-MS based non-targeted metabolic profiling that demonstrates a large effect of gut microbiota on mammalian tissue metabolites. It was hypothesized that gut microbiota influences the overall biochemistry of host metabolome and this effect is tissue-specific. Thirteen different tissues from germ-free (GF) and conventionally-raised (MPF) C57BL/6NTac mice were selected and their metabolic differences were analyzed. Our study demonstrated a large effect of microbiota on mammalian biochemistry at different tissues and resulted in statistically-significant modulation of metabolites from multiple metabolic pathways (p ≤ 0.05). Hundreds of molecular features were detected exclusively in one mouse group, with the majority of these being unique to specific tissue. A vast metabolic response of host to metabolites generated by the microbiota was observed, suggesting gut microbiota has a direct impact on host metabolism.

Associations of the serum metabolite profile with a healthy Nordic diet and risk of coronary artery disease.

BACKGROUND & AIM: A healthy Nordic diet (HND) rich in wholegrain cereals, berries, vegetables, and fish, has been associated with a lower risk of cardiovascular disease, but the molecular links remain unclear. Here, we present the application of nontargeted metabolic profiling based on liquid chromatography with tandem mass spectrometry (LC-MS/MS) to identify metabolites that would potentially reflect the adherence to HND and their relationship with the risk of coronary artery disease (CAD). METHODS: From a Finnish population-based prospective cohort (Kuopio Ischaemic Heart Disease Risk Factor Study; KIHD), we collected 364 baseline serum samples in 4 groups: 1) 94 participants with high adherence to HND who developed CAD during the follow-up of 20.4 ± 7.6 years (cases), 2) 88 participants with high adherence who did not develop CAD during follow-up (controls), 3) 93 CAD cases with low adherence, and 4) 89 controls with low adherence. RESULTS: Indolepropionic acid, proline betaine, vitamin E derivatives, and medium-chain acylcarnitines were associated with adherence to HND after adjustments for age, waist-to-hip ratio (WHR), physical activity, and total cholesterol. These metabolites also correlated negatively with blood lipid profiles, BMI, insulin, inflammation marker high-sensitivity C reactive protein (hsCRP), smoking, and alcohol consumption, as well as positively with physical activity. Predictors of CAD risk included several lipid molecules, which also indicated lower adherence to HND. But, only the associations with the plasmalogens PC(O-16:0/18:2) and PC(O-16:1/18:2) remained significant after adjusting for age, smoking, systolic blood pressure, LDL cholesterol, and WHR. These plasmalogens did not correlate with any investigated risk factors of CAD at baseline, which may highlight their potential as novel predictors of CAD risk. Interestingly, the metabolic profile predicting CAD risk differed based on the adherence to HND. Also, HND adherence was more distinct within CAD cases than controls, which may emphasize the interaction between HND adherence and CAD risk. CONCLUSIONS: The association between higher adherence to HND and a lower risk of CAD likely involves a complex interaction of various endogenous, plant-, and microbial-derived metabolites.

"notame": Workflow for Non-Targeted LC-MS Metabolic Profiling.

Metabolomics analysis generates vast arrays of data, necessitating comprehensive workflows involving expertise in analytics, biochemistry and bioinformatics in order to provide coherent and high-quality data that enable discovery of robust and biologically significant metabolic findings. In this protocol article, we introduce notame, an analytical workflow for non-targeted metabolic profiling approaches, utilizing liquid chromatography-mass spectrometry analysis. We provide an overview of lab protocols and statistical methods that we commonly practice for the analysis of nutritional metabolomics data. The paper is divided into three main sections: the first and second sections introducing the background and the study designs available for metabolomics research and the third section describing in detail the steps of the main methods and protocols used to produce, preprocess and statistically analyze metabolomics data and, finally, to identify and interpret the compounds that have emerged as interesting.

Novel data analysis method for multicolour flow cytometry links variability of multiple markers on single cells to a clinical phenotype.

Multicolour Flow Cytometry (MFC) produces multidimensional analytical data on the quantitative expression of multiple markers on single cells. This data contains invaluable biomedical information on (1) the marker expressions per cell, (2) the variation in such expression across cells, (3) the variability of cell marker expression across samples that (4) may vary systematically between cells collected from donors and patients. Current conventional and even advanced data analysis methods for MFC data explore only a subset of these levels. The Discriminant Analysis of MultiAspect CYtometry (DAMACY) we present here provides a comprehensive view on health and disease responses by integrating all four levels. We validate DAMACY by using three distinct datasets: in vivo response of neutrophils evoked by systemic endotoxin challenge, the clonal response of leukocytes in bone marrow of acute myeloid leukaemia (AML) patients, and the complex immune response in blood of asthmatics. DAMACY provided good accuracy 91-100% in the discrimination between health and disease, on par with literature values. Additionally, the method provides figures that give insight into the marker expression and cell variability for more in-depth interpretation, that can benefit both physicians and biomedical researchers to better diagnose and monitor diseases that are reflected by changes in blood leukocytes.