Päivi Pöhö

The Dynamics of the Human Infant Gut Microbiome in Development and in Progression toward Type 1 Diabetes

Colonization of the fetal and infant gut microbiome results in dynamic changes in diversity, which can impact disease susceptibility. To examine the relationship between human gut microbiome dynamics throughout infancy and type 1 diabetes (T1D), we examined a cohort of 33 infants genetically predisposed to T1D. Modeling trajectories of microbial abundances through infancy revealed a subset of microbial relationships shared across most subjects. Although strain composition of a given species was highly variable between individuals, it was stable within individuals throughout infancy. Metabolic composition and metabolic pathway abundance remained constant across time. A marked drop in alpha-diversity was observed in T1D progressors in the time window between seroconversion and T1D diagnosis, accompanied by spikes in inflammation-favoring organisms, gene functions, and serum and stool metabolites. This work identifies trends in the development of the human infant gut microbiome along with specific alterations that precede T1D onset and distinguish T1D progressors from nonprogressors.

Human gut microbes impact host serum metabolome and insulin sensitivity

Insulin resistance is a forerunner state of ischaemic cardiovascular disease and type 2 diabetes. Here we show how the human gut microbiome impacts the serum metabolome and associates with insulin resistance in 277 non-diabetic Danish individuals. The serum metabolome of insulin-resistant individuals is characterized by increased levels of branched-chain amino acids (BCAAs), which correlate with a gut microbiome that has an enriched biosynthetic potential for BCAAs and is deprived of genes encoding bacterial inward transporters for these amino acids. Prevotella copri and Bacteroides vulgatus are identified as the main species driving the association between biosynthesis of BCAAs and insulin resistance, and in mice we demonstrate that P. copri can induce insulin resistance, aggravate glucose intolerance and augment circulating levels of BCAAs. Our findings suggest that microbial targets may have the potential to diminish insulin resistance and reduce the incidence of common metabolic and cardiovascular disorders.

The influence of sample collection methodology and sample preprocessing on the blood metabolic profile

AIM Blood serum and plasma have intrinsic differences in their composition and the preprocessing, such as clotting temperature in serum, and storage at room temperature may have further effect on metabolite concentrations. METHODS The influence of sampling preprocessing on the metabolic profiles in serum and different types of plasma was investigated using liquid chromatography and comprehensive 2D gas chromatography coupled to a mass spectrometer. RESULTS The profiles of polar metabolites were significantly dependent on the type of the sample, while lipid profiles were similar in serum and different types of plasma. Extended storage of plasma at room temperature resulted in degradation of lipids already after 1 day. Serum clotting at room temperature generally resulted in higher metabolite concentration compared with serum clotting on ice.

Isoenergetic diets differing in their n‐3 fatty acid and polyphenol content reflect different plasma and HDL‐fraction lipidomic profiles in subjects at high cardiovascular risk

SCOPE Dysregulation of lipid homeostasis is related to multiple major healthcare problems. The aim of this study was to investigate the effects of n-3 fatty acid (FA) and polyphenol rich diets on plasma and HDL fraction lipidomic profiles in subjects at high cardiovascular risk. METHODS AND RESULTS Ultra performance LC coupled to quadrupole TOF/MS mass spectrometry global lipidomic profiling was applied to plasma and HDL fraction from an 8 wk randomized intervention with four isoenergetic diets, differing in their natural n-3 FA and polyphenols content, in 78 subjects with a high BMI, abdominal obesity, and at least one other feature of the metabolic syndrome. Dependency network analysis showed a different pattern of associations between lipidomics, dietary, and clinical variables after the dietary interventions. The most remarkable associations between variables were observed after the diet high in n-3 FA and polyphenols, as the inverse association between gallic acid intake and LDL cholesterol levels, which was indirectly associated with a HDL cluster exclusively comprised lysophospholipids. CONCLUSION This is the first human randomized controlled trial showing direct and indirect associations with lipid molecular species and clinical variables of interest in the evaluation of the metabolic syndrome after diets naturally rich in polyphenols.

Lipidome as a predictive tool in progression to type 2 diabetes in Finnish men

BACKGROUND There is a need for early markers to track and predict the development of type 2 diabetes mellitus (T2DM) from the state of normal glucose tolerance through prediabetes. In this study we tested whether the plasma molecular lipidome has biomarker potential to predicting the onset of T2DM. METHODS We applied global lipidomic profiling on plasma samples from well-phenotyped men (107 cases, 216 controls) participating in the longitudinal METSIM study at baseline and at five-year follow-up. To validate the lipid markers, an additional study with a representative sample of adult male population (n=631) was also conducted. A total of 277 plasma lipids were analyzed using the lipidomics platform based on ultra-performance liquid chromatography coupled to time-of-flight mass spectrometry. Lipids with the highest predictive power for the development of T2DM were computationally selected, validated and compared to standard risk models without lipids. RESULTS A persistent lipid signature with higher levels of triacylglycerols and diacyl-phospholipids as well as lower levels of alkylacyl phosphatidylcholines was observed in progressors to T2DM. Lysophosphatidylcholine acyl C18:2 (LysoPC(18:2)), phosphatidylcholines PC(32:1), PC(34:2e) and PC(36:1), and triacylglycerol TG(17:1/18:1/18:2) were selected to the full model that included metabolic risk factors and FINDRISC variables. When further adjusting for BMI and age, these lipids had respective odds ratios of 0.32, 2.4, 0.50, 2.2 and 0.31 (all p<0.05) for progression to T2DM. The independently-validated predictive power improved in all pairwise comparisons between the lipid model and the respective standard risk model without the lipids (integrated discrimination improvement IDI>0; p<0.05). Notably, the lipid models remained predictive of the development of T2DM in the fasting plasma glucose-matched subset of the validation study. CONCLUSION This study indicates that a lipid signature characteristic of T2DM is present years before the diagnosis and improves prediction of progression to T2DM. Molecular lipid biomarkers were shown to have predictive power also in a high-risk group, where standard risk factors are not helpful at distinguishing progressors from non-progressors.

A miniaturised 3D printed polypropylene reactor for online reaction analysis by mass spectrometry

A miniaturised polypropylene reactor was fabricated by 3D printing using fused deposition modeling. A stainless steel nanoelectrospray ionisation capillary and a magnetic stir bar were integrated into the reactor during the printing process. The integrated nanoelectrospray ionisation capillary allows direct sampling of a reaction solution without external pumping. It also allows ionisation of the analytes. Therefore, very rapid online mass spectrometric chemical reaction monitoring is possible. Operation of the miniaturised reactor is shown by the online nanoelectrospray mass spectrometry characterisation of a Diels–Alder reaction and the subsequent retro Diels–Alder reaction.

Serum metabolite profile associates with the development of metabolic co-morbidities in first-episode psychosis

Psychotic patients are at high risk for developing obesity, metabolic syndrome and type 2 diabetes. These metabolic co-morbidities are hypothesized to be related to both treatment side effects as well as to metabolic changes occurring during the psychosis. Earlier metabolomics studies have shown that blood metabolite levels are predictive of insulin resistance and type 2 diabetes in the general population as well as sensitive to the effects of antipsychotics. In this study, we aimed to identify the metabolite profiles predicting future weight gain and other metabolic abnormalities in psychotic patients. We applied comprehensive metabolomics to investigate serum metabolite profiles in a prospective study setting in 36 first-episode psychosis patients during the first year of the antipsychotic treatment and 19 controls. While corroborating several earlier findings when comparing cases and controls and the effects of the antipsychotic medication, we also found that prospective weight gain in psychotic patients was associated with increased levels of triacylglycerols with low carbon number and double-bond count at baseline, that is, lipids known to be associated with increased liver fat. Our study suggests that metabolite profiles may be used to identify the psychotic patients most vulnerable to develop metabolic co-morbidities, and may point to a pharmacological approach to counteract the antipsychotic-induced weight gain.

Lipidomics in biomedical research-practical considerations

Lipids have many central physiological roles including as structural components of cell membranes, energy storage sources and intermediates in signaling pathways. Lipid-related disturbances are known to underlie many diseases and their co-morbidities. The emergence of lipidomics has empowered researchers to study lipid metabolism at the cellular as well as physiological levels at a greater depth than was previously possible. The key challenges ahead in the field of lipidomics in medical research lie in the development of experimental protocols and in silico techniques needed to study lipidomes at the systems level. Clinical questions where lipidomics may have an impact in healthcare settings also need to be identified, both from the health outcomes and health economics perspectives. This article is part of a Special Issue entitled: BBALIP_Lipidomics Opinion Articles edited by Sepp Kohlwein.

Imbalance of plasma amino acids, metabolites and lipids in patients with lysinuric protein intolerance (LPI)

BACKGROUND Lysinuric protein intolerance (LPI [MIM 222700]) is an aminoaciduria with defective transport of cationic amino acids in epithelial cells in the small intestine and proximal kidney tubules due to mutations in the SLC7A7 gene. LPI is characterized by protein malnutrition, failure to thrive and hyperammonemia. Many patients also suffer from combined hyperlipidemia and chronic kidney disease (CKD) with an unknown etiology. METHODS Here, we studied the plasma metabolomes of the Finnish LPI patients (n=26) and healthy control individuals (n=19) using a targeted platform for analysis of amino acids as well as two analytical platforms with comprehensive coverage of molecular lipids and polar metabolites. RESULTS Our results demonstrated that LPI patients have a dichotomy of amino acid profiles, with both decreased essential and increased non-essential amino acids. Altered levels of metabolites participating in pathways such as sugar, energy, amino acid and lipid metabolism were observed. Furthermore, of these metabolites, myo-inositol, threonic acid, 2,5-furandicarboxylic acid, galactaric acid, 4-hydroxyphenylacetic acid, indole-3-acetic acid and beta-aminoisobutyric acid associated significantly (P<0.001) with the CKD status. Lipid analysis showed reduced levels of phosphatidylcholines and elevated levels of triacylglycerols, of which long-chain triacylglycerols associated (P<0.01) with CKD. CONCLUSIONS This study revealed an amino acid imbalance affecting the basic cellular metabolism, disturbances in plasma lipid composition suggesting hepatic steatosis and fibrosis and novel metabolites correlating with CKD in LPI. In addition, the CKD-associated metabolite profile along with increased nitrite plasma levels suggests that LPI may be characterized by increased oxidative stress and apoptosis, altered microbial metabolism in the intestine and uremic toxicity.

Correction: A miniaturised 3D printed polypropylene reactor for online reaction analysis by mass spectrometry

Correction for ‘A miniaturised 3D printed polypropylene reactor for online reaction analysis by mass spectrometry’ by Gianmario Scotti et al., React. Chem. Eng., 2017, 2, 299–303.