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.

Machine-learning facilitates selection of a novel diagnostic panel of metabolites for the detection of heart failure.

The metabolic derangement is common in heart failure with reduced ejection fraction (HFrEF). The aim of the study was to check feasibility of the combined approach of untargeted metabolomics and machine learning to create a simple and potentially clinically useful diagnostic panel for HFrEF. The study included 67 chronic HFrEF patients (left ventricular ejection fraction-LVEF 24.3 ± 5.9%) and 39 controls without the disease. Fasting serum samples were fingerprinted by liquid chromatography-mass spectrometry. Feature selection based on random-forest models fitted to resampled data and followed by linear modelling, resulted in selection of eight metabolites (uric acid, two isomers of LPC 18:2, LPC 20:1, deoxycholic acid, docosahexaenoic acid and one unknown metabolite), demonstrating their predictive value in HFrEF. The accuracy of a model based on metabolites panel was comparable to BNP (0.85 vs 0.82), as verified on the test set. Selected metabolites correlated with clinical, echocardiographic and functional parameters. The combination of two innovative tools (metabolomics and machine-learning methods), both unrestrained by the gaps in the current knowledge, enables identification of a novel diagnostic panel. Its diagnostic value seems to be comparable to BNP. Large scale, multi-center studies using validated targeted methods are crucial to confirm clinical utility of proposed markers.

LC-MS-based serum fingerprinting reveals significant dysregulation of phospholipids in chronic heart failure.

Cardiac and extracardiac lipid metabolism is known to be significantly altered in the course of the heart failure with reduced ejection fraction (HF-REF), however the precise mechanisms are not fully elucidated. The aim of the study was to use of untargeted metabolomics to identify and validate changes in the blood metabolites profile, occurring as a result of HF-REF development. The analyses were performed first in the derivation set (36 chronic HF-REF patients and 19 controls without the disease) and repeated in validation cohort (31 chronic HF-REF patients and 20 controls). Independent analyses of both sets revealed statistically significant decline in intensities of phosphatidylcholine (PC): 34:4 and 36:5, lysophosphatidylcholine (lyso-PC): 14:0, 15:0, 18:0, 18:2, 20:3, lysophosphatidylethanolamine (lyso-PE): 18:1 and 18:2 in chronic HF-REF patients. More symptomatic patients and those with ischaemic etiology of HF-REF presented greater deficit in phospholipids (PLs) intensities. The decrease of identified PLs intensities (as compared to controls) correlated with decreased serum cholesterol level, impaired renal function, reduced exercise capacity, enhanced ventilatory response and metabolic parameters associated with altered fatty acids oxidation. In multiple regression analysis PLs deficit was significantly associated with age, carnitines serum intensity, renal function, uric acid, cholesterol level. In conclusion, HF-REF is associated with significant disturbances in phospholipids metabolism. Greater reduction in serum intensities of particular identified PLs is associated with older age, worse clinical condition, impaired oxidative muscle metabolism and enhanced catabolic status.

Metabolomics - A wide-open door to personalized treatment in chronic heart failure?

Heart failure (HF) is a complex syndrome representing a final stage of various cardiovascular diseases. Despite significant improvement in the diagnosis and treatment (e.g. ACE-inhibitors, ß-blockers, aldosterone antagonists, cardiac resynchronization therapy) of the disease, prognosis of optimally treated patients remains very serious and HF mortality is still unacceptably high. Therefore there is a strong need for further exploration of novel analytical methods, predictive and prognostic biomarkers and more personalized treatment. The metabolism of the failing heart being significantly impaired from its baseline state may be a future target not only for biomarker discovery but also for the pharmacologic intervention. However, an assessment of a particular, isolated metabolite or protein cannot be fully informative and makes a correct interpretation difficult. On the other hand, metabolites profile analysis may greatly assist investigator in an interpretation of the altered pathway dynamics, especially when combined with other lines of evidence (e.g. metabolites from the same pathway, transcriptomics, proteomics). Despite many prior studies on metabolism, the knowledge of peripheral and cardiac pathophysiological mechanisms responsible for the metabolic imbalance and progression of the disease is still insufficient. Metabolomics enabling comprehensive characterization of low molecular weight metabolites (e.g. lipids, sugars, organic acids, amino acids) that reflects the complete metabolic phenotype seems to be the key for further potential improvement in HF treatment (diet-based or biochemical-based). Will this -omics technique one day open a door to easy patients identification before they have a heart failure onset or its decompensation?

FI-chemiluminometric study of thiazides by on-line photochemical reaction.

The present manuscript deals with a simple and sensitive flow-injection method for the chemiluminescent determination of thiazides. The method is based on the on-line photodegradation and chemiluminescent determination of the resulting photo-fragments. The on-line photodegradation is performed in basic medium by using a photoreactor consisting of a 550cm long x 0.8mm ID piece of PTFE tubing helically coiled around an 8W low-pressure mercury lamp. The determination of the photo-irradiated thiazides is performed by a chemiluminescent oxidative reaction with Ce(IV) in sulphuric acid medium. A heterogeneous group of thiazides (indapamide, metolazone, hydroflumethiazide, chlorthalidone and bendroflumethiazide) has been studied. Hydrochlorothiazide was selected as a test substance. The "on-line" photochemical reaction approach allows the sensitive chemiluminescent determination of thiazides which do not present native chemiluminescence in the absence of sensitizers such as Rhodamine 6G. Linear calibration graphs were typically over the range 0.5-12 microgml(-1) (indapamide, metolazone, hydroflumethiazide and chlorthalidone); and over the range 0.5-5 microgml(-1) (hydrochlorothiazide). Limits of detection ranged between 0.005 microgml(-1) (hydrochlorothiazide) and 0.06 microgml(-1) (bendroflumethiazide). The relative standard deviation for the test substance was 2.0% for 2 microgl(-1) of the drug (n = 11) and the throughput was 65 h(-1) in all cases. The assessment of the photodegradation step on the molecular structure of thiazides was established by recording UV and fluorimetric spectra. The viability of the on-line photoinduced fluorescent determination of hydroflumethiazide and bendroflumethiazide was confirmed. The method was also applied to the determination of hydrochlorothiazide in commercially available formulation.