Plasma metabolomic and lipidomic profiles accurately classify mothers of children with congenital heart disease: an observational study.

INTRODUCTION: Congenital heart disease (CHD) is the most common congenital anomaly, representing a significant global disease burden. Limitations exist in our understanding of aetiology, diagnostic methodology and screening, with metabolomics offering promise in addressing these. OBJECTIVE: To evaluate maternal metabolomics and lipidomics in prediction and risk factor identification for childhood CHD. METHODS: We performed an observational study in mothers of children with CHD following pregnancy, using untargeted plasma metabolomics and lipidomics by ultrahigh performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS). 190 cases (157 mothers of children with structural CHD (sCHD); 33 mothers of children with genetic CHD (gCHD)) from the children OMACp cohort and 162 controls from the ALSPAC cohort were analysed. CHD diagnoses were stratified by severity and clinical classifications. Univariate, exploratory and supervised chemometric methods were used to identify metabolites and lipids distinguishing cases and controls, alongside predictive modelling. RESULTS: 499 metabolites and lipids were annotated and used to build PLS-DA and SO-CovSel-LDA predictive models to accurately distinguish sCHD and control groups. The best performing model had an sCHD test set mean accuracy of 94.74% (sCHD test group sensitivity 93.33%; specificity 96.00%) utilising only 11 analytes. Similar test performances were seen for gCHD. Across best performing models, 37 analytes contributed to performance including amino acids, lipids, and nucleotides. CONCLUSIONS: Here, maternal metabolomic and lipidomic analysis has facilitated the development of sensitive risk prediction models classifying mothers of children with CHD. Metabolites and lipids identified offer promise for maternal risk factor profiling, and understanding of CHD pathogenesis in the future.

Sub-5-min RP-UHPLC-TIMS for high-throughput untargeted lipidomics and its application to multiple matrices.

Untargeted lipidomics, with its ability to take a snapshot of the lipidome landscape, is an important tool to highlight lipid changes in pathology or drug treatment models. One of the shortcomings of most untargeted lipidomics based on UHPLC-HRMS is the low throughput, which is not compatible with large-scale screening. In this contribution, we evaluate the application of a sub-5-min high-throughput four-dimensional trapped ion mobility mass spectrometry (HT-4D-TIMS) platform for the fast profiling of multiple complex biological matrices. Human AC-16 cells and mouse brain, liver, sclera, and feces were used as samples. By using a fast 4-min RP gradient, the implementation of TIMS allows us to differentiate coeluting isomeric and isobaric lipids, with correct precursor ion isolation, avoiding co-fragmentation and chimeric MS/MS spectra. Globally, the HT-4D-TIMS allowed us to annotate 1910 different lipid species, 1308 at the molecular level and 602 at the sum composition level, covering 58 lipid subclasses, together with quantitation capability covering more than three orders of magnitude. Notably, TIMS values were highly comparable with respect to longer LC gradients (CV% = 0.39%). These results highlight how HT-4D-TIMS-based untargeted lipidomics possess high coverage and accuracy, halving the analysis time with respect to conventional UHPLC methods, and can be used for fast and accurate untargeted analysis of complex matrices to rapidly evaluate changes of lipid metabolism in disease models or drug discovery campaigns.

Multi-omics analysis reveals attenuation of cellular stress by empagliflozin in high glucose-treated human cardiomyocytes.

BACKGROUND: Sodium-glucose cotransporter 2 (SGLT2) inhibitors constitute the gold standard treatment for type 2 diabetes mellitus (T2DM). Among them, empagliflozin (EMPA) has shown beneficial effects against heart failure. Because cardiovascular diseases (mainly diabetic cardiomyopathy) are the leading cause of death in diabetic patients, the use of EMPA could be, simultaneously, cardioprotective and antidiabetic, reducing the risk of death from cardiovascular causes and decreasing the risk of hospitalization for heart failure in T2DM patients. Interestingly, recent studies have shown that EMPA has positive benefits for people with and without diabetes. This finding broadens the scope of EMPA function beyond glucose regulation alone to include a more intricate metabolic process that is, in part, still unknown. Similarly, this significantly increases the number of people with heart diseases who may be eligible for EMPA treatment. METHODS: This study aimed to clarify the metabolic effect of EMPA on the human myocardial cell model by using orthogonal metabolomics, lipidomics, and proteomics approaches. The untargeted and multivariate analysis mimicked the fasting blood sugar level of T2DM patients (hyperglycemia: HG) and in the average blood sugar range (normal glucose: NG), with and without the addition of EMPA. RESULTS: Results highlighted that EMPA was able to modulate and partially restore the levels of multiple metabolites associated with cellular stress, which were dysregulated in the HG conditions, such as nicotinamide mononucleotide, glucose-6-phosphate, lactic acid, FA 22:6 as well as nucleotide sugars and purine/pyrimidines. Additionally, EMPA regulated the levels of several lipid sub-classes, in particular dihydroceramide and triacylglycerols, which tend to accumulate in HG conditions resulting in lipotoxicity. Finally, EMPA counteracted the dysregulation of endoplasmic reticulum-derived proteins involved in cellular stress management. CONCLUSIONS: These results could suggest an effect of EMPA on different metabolic routes, tending to rescue cardiomyocyte metabolic status towards a healthy phenotype.

Integrated plasma metabolomics and lipidomics profiling highlights distinctive signature of hepatocellular carcinoma in HCV patients.

BACKGROUND: Early diagnosis of hepatocellular carcinoma (HCC) is essential towards the improvement of prognosis and patient survival. Circulating markers such as α-fetoprotein (AFP) and micro-RNAs represent useful tools but still have limitations. Identifying new markers can be fundamental to improve both diagnosis and prognosis. In this approach, we harness the potential of metabolomics and lipidomics to uncover potential signatures of HCC. METHODS: A combined untargeted metabolomics and lipidomics plasma profiling of 102 HCV-positive patients was performed by HILIC and RP-UHPLC coupled to Mass Spectrometry. Biochemical parameters of liver function (AST, ALT, GGT) and liver cancer biomarkers (AFP, CA19.9 e CEA) were evaluated by standard assays. RESULTS: HCC was characterized by an elevation of short and long-chain acylcarnitines, asymmetric dimethylarginine, methylguanine, isoleucylproline and a global reduction of lysophosphatidylcholines. A supervised PLS-DA model showed that the predictive accuracy for HCC class of metabolomics and lipidomics was superior to AFP for the test set (100.00% and 94.40% vs 55.00%). Additionally, the model was applied to HCC patients with AFP values < 20 ng/mL, and, by using only the top 20 variables selected by VIP scores achieved an Area Under Curve (AUC) performance of 0.94. CONCLUSION: These exploratory findings highlight how metabo-lipidomics enables the distinction of HCC from chronic HCV conditions. The identified biomarkers have high diagnostic potential and could represent a viable tool to support and assist in HCC diagnosis, including AFP-negative patients.

Characterisation and Classification of Foodborne Bacteria Using Reflectance FTIR Microscopic Imaging.

This work investigates the application of reflectance Fourier transform infrared (FTIR) microscopic imaging for rapid, and non-invasive detection and classification between Bacillus subtilis and Escherichia coli cell suspensions dried onto metallic substrates (stainless steel (STS) and aluminium (Al) slides) in the optical density (OD) concentration range of 0.001 to 10. Results showed that reflectance FTIR of samples with OD lower than 0.1 did not present an acceptable spectral signal to enable classification. Two modelling strategies were devised to evaluate model performance, transferability and consistency among concentration levels. Modelling strategy 1 involves training the model with half of the sample set, consisting of all concentrations, and applying it to the remaining half. Using this approach, for the STS substrate, the best model was achieved using support vector machine (SVM) classification, providing an accuracy of 96% and Matthews correlation coefficient (MCC) of 0.93 for the independent test set. For the Al substrate, the best SVM model produced an accuracy and MCC of 91% and 0.82, respectively. Furthermore, the aforementioned best model built from one substrate was transferred to predict the bacterial samples deposited on the other substrate. Results revealed an acceptable predictive ability when transferring the STS model to samples on Al (accuracy = 82%). However, the Al model could not be adapted to bacterial samples deposited on STS (accuracy = 57%). For modelling strategy 2, models were developed using one concentration level and tested on the other concentrations for each substrate. Results proved that models built from samples with moderate (1 OD) concentration can be adapted to other concentrations with good model generalization. Prediction maps revealed the heterogeneous distribution of biomolecules due to the coffee ring effect. This work demonstrated the feasibility of applying FTIR to characterise spectroscopic fingerprints of dry bacterial cells on substrates of relevance for food processing.

Evaluating microplastic emission from takeaway containers: A Micro-Raman approach across diverse exposure scenarios.

Based on inconsistencies observed in literature regarding microplastic levels released by takeaway plastic containers, this study investigates the release from takeaway containers composed of polypropylene (PP), polystyrene (PS), and polyethylene terephthalate (PET). To simulate real-world conditions, experiments were conducted using Milli-Q water at room temperature, 100 °C, and at pH 4.5. Containers were subjected to 20-min exposure with agitation, and microplastics were quantified via optical microscopy, with micro-Raman spectroscopy to confirm the particle polymeric nature. The results indicate that PET and PS containers released microplastic in varying quantities: 9 and 1 at room temperature, 7 and 3 in acidified water, and 17 and 30 at 100 °C, respectively. The particle sizes ranged between 13 and 32 µm. Notably, no microplastics were detected from PP containers under any tested conditions. This study underscores the significant release of microplastics from PET and PS containers, particularly at elevated temperatures, suggesting that PP may represent a safer alternative.

Experimental Design Step by Step: A Practical Guide for Beginners.

Inside the world of chemometrics, a fundamental role is played by the experimental design. Although introduced almost a century ago (1935), it is still not widely employed by chemists and its usefulness continues to be underestimated. When asking why, the answers are often the following: it is too difficult to apply and too much experimental effort is required. Actually, a deeper knowledge on the topic could offer a different point of view. The aim of the present tutorial is to introduce the experimental design to beginners, by providing the theoretical basic principles as well as a practical guide to use the most common designs, from the experimental plan to the final optimization. Response surface methodology will be discussed, and the main terms related to model computation and statistical evaluations usually performed by software will be explained, in order to give suitable tools to properly use them.

Exploring the identification of multiple bacteria on stainless steel using multi-scale spectral imaging from microscopic to macroscopic.

This work investigates non-contact reflectance spectral imaging techniques, i.e. microscopic Fourier transform infrared (FTIR) imaging, macroscopic visible-near infrared (VNIR), and shortwave infrared (SWIR) spectral imaging, for the identification of bacteria on stainless steel. Spectral images of two Gram-positive (GP) bacteria (Bacillus subtilis (BS) and Lactobacillus plantarum (LP)), and three Gram-negative (GN) bacteria (Escherichia coli (EC), Cronobacter sakazakii (CS), and Pseudomonas fluorescens (PF)), were collected from dried suspensions of bacterial cells dropped onto stainless steel surfaces. Through the use of multiple independent biological replicates for model validation and testing, FTIR reflectance spectral imaging was found to provide excellent GP/GN classification accuracy (> 96%), while the fused VNIR-SWIR data yielded classification accuracy exceeding 80% when applied to the independent test sets. However, classification within gram type was far less reliable, with lower accuracies for classification within the GP (< 75%) and GN (≤ 51%) species when calibration models were applied to the independent test sets, underlining the importance of independent model validation when dealing with samples of high biological variability.

MALDI Mass Spectrometry Imaging Highlights Specific Metabolome and Lipidome Profiles in Salivary Gland Tumor Tissues.

Salivary gland tumors are relatively uncommon neoplasms that represent less than 5% of head and neck tumors, and about 90% are in the parotid gland. The wide variety of histologies and tumor characteristics makes diagnosis and treatment challenging. In the present study, Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) was used to discriminate the pathological regions of patient-derived biopsies of parotid neoplasms by metabolomic and lipidomic profiles. Fresh frozen parotid tissues were analyzed by MALDI time-of-flight (TOF) MSI, both in positive and negative ionization modes, and additional MALDI-Fourier-transform ion cyclotron resonance (FT-ICR) MSI was carried out for metabolite annotation. MALDI-TOF-MSI spatial segmentation maps with different molecular signatures were compared with the histologic annotation. To maximize the information related to specific alterations between the pathological and healthy tissues, unsupervised (principal component analysis, PCA) and supervised (partial least squares-discriminant analysis, PLS-DA) multivariate analyses were performed presenting a 95.00% accuracy in cross-validation. Glycerophospholipids significantly increased in tumor tissues, while sphingomyelins and triacylglycerols, key players in the signaling pathway and energy production, were sensibly reduced. In addition, a significant increase of amino acids and nucleotide intermediates, consistent with the bioenergetics request of tumor cells, was observed. These results underline the potential of MALDI-MSI as a complementary diagnostic tool to improve the specificity of diagnosis and monitoring of pharmacological therapies.

Untargeted lipidomics reveals specific lipid profiles in COVID-19 patients with different severity from Campania region (Italy).

COVID-19 infection evokes various systemic alterations that push patients not only towards severe acute respiratory syndrome but causes an important metabolic dysregulation with following multi-organ alteration and potentially poor outcome. To discover novel potential biomarkers able to predict disease's severity and patient's outcome, in this study we applied untargeted lipidomics, by a reversed phase ultra-high performance liquid chromatography-trapped ion mobility mass spectrometry platform (RP-UHPLC-TIMS-MS), on blood samples collected at hospital admission in an Italian cohort of COVID-19 patients (45 mild, 54 severe, 21 controls). In a subset of patients, we also collected a second blood sample in correspondence of clinical phenotype modification (longitudinal population). Plasma lipid profiles revealed several lipids significantly modified in COVID-19 patients with respect to controls and able to discern between mild and severe clinical phenotype. Severe patients were characterized by a progressive decrease in the levels of LPCs, LPC-Os, PC-Os, and, on the contrary, an increase in overall TGs, PEs, and Ceramides. A machine learning model was built by using both the entire dataset and with a restricted lipid panel dataset, delivering comparable results in predicting severity (AUC= 0.777, CI: 0.639-0.904) and outcome (AUC= 0.789, CI: 0.658-0.910). Finally, re-building the model with 25 longitudinal (t1) samples, this resulted in 21 patients correctly classified. In conclusion, this study highlights specific lipid profiles that could be used monitor the possible trajectory of COVID-19 patients at hospital admission, which could be used in targeted approaches.

Geographical characterization by MAE-HPLC and NIR methodologies and carbonic anhydrase inhibition of Saffron components.

A microwave-assisted extraction method was optimised for the recovery of bioactive compounds from Crocus sativus L. stigmas with the use of water/ethanol mixture. HPLC-DAD was employed to evaluate the extraction parameters, in particular, solvent type and volume, and the duration of the procedure. Microwave-assisted extraction enhanced the recovery of the active principles, limiting extraction time and solvent waste. Moreover, NIR experiments were performed in order to compare spectra in pseudo-absorbance of Saffron samples with different geographical origins through the application of the chemometric techniques. Moreover, the biological evaluation of crocin 1, safranal and its semisynthetic derivatives as selective inhibitors of five isoforms of human carbonic anhydrase was also explored.