Extracellular matrix hyaluronan modulates fat cell differentiation and primary cilia dynamics

Hyaluronan is an important extracellular matrix component, with poorly documented physiological role in the context of lipid-rich adipose tissue. We have investigated the global impact of hyaluronan removal from adipose tissue environment by in vitro exposure to exogenous hyaluronidase (or heat inactivated enzyme). Gene set expression analysis from RNA sequencing revealed downregulated adipogenesis as a main response to hyaluronan removal from human adipose tissue samples, which was confirmed by hyaluronidase-mediated inhibition of adipocyte differentiation in the 3T3L1 adipose cell line. Hyaluronidase exposure starting from the time of induction with the differentiation cocktail reduced lipid accumulation in mature adipocytes, limited the expression of terminal differentiation marker genes, and impaired the early induction of co-regulated Cebpa and Pparg mRNA. Reduction of Cebpa and Pparg expression by exogenous hyaluronidase was also observed in cultured primary preadipocytes from subcutaneous, visceral or brown adipose tissue of mice. Mechanistically, inhibition of adipogenesis by hyaluronan removal was not caused by changes in osmotic pressure or cell inflammatory status, could not be mimicked by exposure to threose, a metabolite generated by hyaluronan degradation, and was not linked to alteration in endogenous Wnt ligands expression. Rather, we observed that hyaluronan removal associated with disrupted primary cilia dynamics, with elongated cilium and higher proportions of preadipocytes that remained ciliated in hyaluronidase-treated conditions. Thus, our study points to a new link between ciliogenesis and hyaluronan impacting adipose tissue development.

A comparison of different machine-learning techniques for the selection of a panel of metabolites allowing early detection of brain tumors

Metabolomics combined with machine learning methods (MLMs), is a powerful tool for searching novel diagnostic panels. This study was intended to use targeted plasma metabolomics and advanced MLMs to develop strategies for diagnosing brain tumors. Measurement of 188 metabolites was performed on plasma samples collected from 95 patients with gliomas (grade I-IV), 70 with meningioma, and 71 healthy individuals as a control group. Four predictive models to diagnose glioma were prepared using 10 MLMs and a conventional approach. Based on the cross-validation results of the created models, the F1-scores were calculated, then obtained values were compared. Subsequently, the best algorithm was applied to perform five comparisons involving gliomas, meningiomas, and controls. The best results were obtained using the newly developed hybrid evolutionary heterogeneous decision tree (EvoHDTree) algorithm, which was validated using Leave-One-Out Cross-Validation, resulting in an F1-score for all comparisons in the range of 0.476-0.948 and the area under the ROC curves ranging from 0.660 to 0.873. Brain tumor diagnostic panels were constructed with unique metabolites, which reduces the likelihood of misdiagnosis. This study proposes a novel interdisciplinary method for brain tumor diagnosis based on metabolomics and EvoHDTree, exhibiting significant predictive coefficients.

Evaluation of the Oral Bacterial Genome and Metabolites in Patients with Wolfram Syndrome

In Wolfram syndrome (WFS), due to the loss of wolframin function, there is increased ER stress and, as a result, progressive neurodegenerative disorders, accompanied by insulin-dependent diabetes. The aim of the study was to evaluate the oral microbiome and metabolome in WFS patients compared with patients with type 1 diabetes mellitus (T1DM) and controls. The buccal and gingival samples were collected from 12 WFS patients, 29 HbA1c-matched T1DM patients (p = 0.23), and 17 healthy individuals matched by age (p = 0.09) and gender (p = 0.91). The abundance of oral microbiota components was obtained by Illumina sequencing the 16S rRNA gene, and metabolite levels were measured by gas chromatography–mass spectrometry. Streptococcus (22.2%), Veillonella (12.1%), and Haemophilus (10.8%) were the most common bacteria in the WFS patients, while comparisons between groups showed significantly higher abundance of Olsenella, Dialister, Staphylococcus, Campylobacter, and Actinomyces in the WFS group (p < 0.001). An ROC curve (AUC = 0.861) was constructed for the three metabolites that best discriminated WFS from T1DM and controls (acetic acid, benzoic acid, and lactic acid). Selected oral microorganisms and metabolites that distinguish WFS patients from T1DM patients and healthy individuals may suggest their possible role in modulating neurodegeneration and serve as potential biomarkers and indicators of future therapeutic strategies.

Optimization of a GC-MS method for the profiling of microbiota-dependent metabolites in blood samples: An application to type 2 diabetes and prediabetes

Changes in serum or plasma metabolome may reflect gut microbiota dysbiosis, which is also known to occur in patients with prediabetes and type 2 diabetes (T2DM). Thus, developing a robust method for the analysis of microbiota-dependent metabolites (MDMs) is an important issue. Gas chromatography with mass spectrometry (GC–MS) is a powerful approach enabling detection of a wide range of MDMs in biofluid samples with good repeatability and reproducibility, but requires selection of a suitable solvents and conditions. For this reason, we conducted for the first time the study in which, we demonstrated an optimisation of samples preparation steps for the measurement of 75 MDMs in two matrices. Different solvents or mixtures of solvents for MDMs extraction, various concentrations and volumes of derivatizing reagents as well as temperature programs at methoxymation and silylation step, were tested. The stability, repeatability and reproducibility of the 75 MDMs measurement were assessed by determining the relative standard deviation (RSD). Finally, we used the developed method to analyse serum samples from 18 prediabetic (PreDiab group) and 24 T2DM patients (T2DM group) from our 1000PLUS cohort. The study groups were homogeneous and did not differ in age and body mass index. To select statistically significant metabolites, T2DM vs. PreDiab comparison was performed using multivariate statistics. Our experiment revealed changes in 18 MDMs belonging to different classes of compounds, and seven of them, based on the SVM classification model, were selected as a panel of potential biomarkers, able to distinguish between patients with T2DM and prediabetes.

Gas Chromatography-Mass Spectroscopy-Based Metabolomics Analysis Reveals Potential Biochemical Markers for Diagnosis of Gestational Diabetes Mellitus

Due to many adverse effects of gestational diabetes mellitus (GDM) on the mother and fetus, its diagnosis is crucial. The presence of GDM can be confirmed by an abnormal fasting plasma glucose level (aFPG) and/or oral glucose tolerance test (OGTT) performed mostly between 24 and 28 gestational week. Both aFPG and abnormal glucose tolerance (aGT) are used to diagnose GDM. In comparison to measurement of FPG, OGTT is time-consuming, usually inconvenient for the patient, and very often needs to be repeated. Therefore, it is necessary to seek tests that will be helpful and convenient to diagnose GDM. For this reason, we investigated the differences in fasting serum metabolites between GDM women with abnGM and normal FPG (aGT-GDM group), with aFPG and normal glucose metabolism (aFPG-GDM group) as well as pregnant women with normal glucose tolerance (NGT) being a control group. Serum metabolites were measured by an untargeted approach using gas chromatography–mass spectrometry (GC–MS). In the discovery phase, fasting serum samples collected from 79 pregnant women (aFPG-GDM, n = 24; aGT-GDM, n = 26; NGT, n = 29) between 24 and 28 weeks of gestation (gwk) were fingerprinted. A set of metabolites (α–hydroxybutyric acid (α–HB), β–hydroxybutyric acid (β–HB), and several fatty acids) significant in aGT-GDM vs NGT but not significant in aFPG-GDM vs NGT comparison in the discovery phase was selected for validation. These metabolites were quantified by a targeted GC–MS method in a validation cohort consisted of 163 pregnant women (aFPG-GDM, n = 51; aGT-GDM, n = 44; and NGT, n = 68). Targeted analyses were also performed on the serum collected from 92 healthy women in the first trimester (8–14 gwk) who were NGT at this time, but in the second trimester (24–28 gwk) they were diagnosed with GDM. It was found that α–HB, β–HB, and several fatty acids were associated with aGT-GDM. A combination of α–HB, β–HB, and myristic acid was found highly specific and sensitive for the diagnosis of GDM manifested by aGT-GDM (AUC = 0.828) or to select women at a risk of aGT-GDM in the first trimester (AUC = 0.791). Our findings provide new potential markers of GDM and may have implications for its early diagnosis.

Mass spectrometry-based determination of lipids and small molecules composing adipose tissue with a focus on brown adipose tissue

Adipose tissue has been the subject of research for a very long time. Many studies perform a comprehensive analysis of different types of adipose tissue with an emphasis on brown adipose tissue. Mass spectrometry-based approaches are particularly useful in the exploration not only of the metabolic composition of adipose tissue but also its function. In the presented review, a complex and critical overview of publications devoted to the analysis of adipose tissue by means of mass spectrometry was performed. Detailed investigation of analytical aspects related to either untargeted or targeted analysis of adipose tissue was performed, leading to the formation of a collection of hints at the available analytical methods. Moreover, a profound analysis of the metabolic composition of brown adipose tissue was performed. Brown adipose tissue metabolome was characterized on structural and functional levels, providing information about its exact metabolic composition but also connecting these molecules and placing them into biochemical pathways. All our work resulted in a very broad picture of the analysis of adipose tissue, starting from the analytical aspects and finishing on the current knowledge about its composition.

The role of gut microbiota (GM) and GM-related metabolites in diabetes and obesity. A review of analytical methods used to measure GM-related metabolites in fecal samples with a focus on metabolites' derivatization step

Disruption of gut microbiota (GM) composition is increasingly related to the pathogenesis of various metabolic diseases. Additionally, GM is responsible for the production and transformation of metabolites involved in the development of metabolic disorders, such as obesity and type 2 diabetes mellitus (T2DM). The current state of knowledge regarding the composition of GM and GM-related metabolites in relation to the progress and development of obesity and T2DM is presented in this review. To understand the relationships between GM-related metabolites and the development of metabolic disorders, their accurate qualitative and quantitative measurement in biological samples is needed. Feces represent a valuable biological matrix which composition may reflect the health status of the lower gastrointestinal tract and the whole organism. Mass spectrometry (MS), mainly in combination with gas chromatography (GC) or liquid chromatography (LC), is commonly used to measure fecal metabolites. However, profiling metabolites in such a complex matrix as feces is challenging from both analytical chemistry and biochemistry standpoints. Chemical derivatization is one of the most effective methods used to overcome these problems. In this review, we provide a comprehensive summary of the derivatization methods of GM-related metabolites prior to GC-MS or LC-MS analysis, which have been published in the last five years (2015-2020). Additionally, analytical methods used for the analysis of GM-related metabolites without the derivatization step are also presented.

Estimating acute and chronic exposure of children and adults to chlorpyrifos in fruit and vegetables based on the new, lower toxicology data

This paper presents, for the first time, results for chlorpyrifos (CHLP) in Polish fruits and vegetables over the course of a long period of research, 2007-2016, with toxicological aspects. The challenge of this study was to re-evaluate the impact of chlorpyrifos residues in fruit and vegetables on health risk assessed via acute and chronic exposure based on old and new, lower, established values of: Average Daily Intakes (ADIs)/Acute Reference Doses (ARfDs) and Maximum Residue Levels (MRLs). A total of 3 530 samples were collected, and CHLP in the range of 0.005-1.514 mg/kg was present in 10.2% of all samples. The MRL was exceeded in 0.7% of all samples (MRL established in 2009-2015), and recalculation yielded a much greater number of violations for the new MRL (2016), which exceeded 2.9% of all samples. Acute exposure to CHLP calculated according to the old, higher toxicological data (0.10 mg/kg bw/day), does not exceed 14% of its respective ARfDs for adults and both groups of children, but when calculated for incidental cases according to the current value (ARfD 0.005 mg/kg bw) for infants and toddlers, was above 100% of its respective ARfDs in: white cabbage (263.65% and 108.24%), broccoli (216.80% and 194.72%) and apples (153.20% and 167.70%). The chronic exposure calculated for both newly established ADI values (0.001 mg/kg bw/day and 0.100 mg/kg bw/day) appears to be relatively low for adults and children.

The fate of spirotetramat and dissipation metabolites in Apiaceae and Brassicaceae leaf-root and soil system under greenhouse conditions estimated by modified QuEChERS/LC-MS/MS

The aim of this study was to investigate the dissipation of spirotetramat and its four metabolites (B-enol, B-keto, B-mono and B-glu) in different parts of vegetables belong to the minor crops (Appiacea and Brassicaceae) and soil from cultivation. The challenge of this study was to apply an optimized clean up step in QuEChERS to obtain one universal sorbent for different complex matrices like leaves with high levels of pigments, roots containing acids, sugars, polyphenolls and pigments and soil with organic ingredients. Eight commercial (Florisil, neutral alumina, GCB, PSA, C18, diatomaceous earth, VERDE and ChloroFiltr) and one organic (Chitosan) sorbents were tested. A modified clean up step in QuEChERS methodology was used for analysis. The dissipation of spirotetramat and its metabolites was described according to a first-order (FO) kinetics equation with R² between 0.9055 and 0.9838. The results showed that the time after 50% (DT₅₀) of the substance degraded was different for soil, roots and leaves, and amounted to 0.2day, 2.8-2.9days and 2.1-2.4days, respectively. The terminal residues of spiroteramat (expressed as the sum of spirotetramat, B-enol, B-glu, B-keto and B-mono) were much lower than the MRLs.