Search Results (18)

Early diagnosis of pancreatic cancer, particularly in intraductal papillary mucinous neoplasm (IPMN), remains challenging despite advances in imaging and biomarkers. Pancreatic adenocarcinoma (PDAC) has a high mortality rate; therefore, its early detection and adequate interventions are necessary to improve the disease outcome. Most IPMNs are asymptomatic and discovered incidentally. Magnetic resonance imaging (MRI) is a preferred tool for diagnosing malignant IPMNs, with a sensitivity of 90.7–94.1% and a specificity of 84.7–87.2% in detecting mural nodules > 5 mm, a strong predictor of high-risk lesions. Radiomics further enhances diagnostic accuracy (sensitivity 91–96%, specificity 78–81%), especially when combined with CA 19-9, which has lower sensitivity (73–90%) but higher specificity (79–95%). Computed tomography (CT), though less effective for small mural nodules, remains widely used; its accuracy improves with radiomics and clinical variables (sensitivity 90.4%, specificity 74%). Conventional endoscopic ultrasonography (EUS) shows lower performance (sensitivity 60%, specificity 80%), but its advanced variations have improved outcomes. Contrast-enhanced EUS (CE-EUS) visualizes mural nodules with more than 90% sensitivity and involvement of the main pancreatic duct, with a sensitivity of 83.5% and a specificity of 87%. EUS–fine-needle aspiration (EUS-FNA) allows cyst fluid analysis; however, CEA, glucose, and KRAS/GNAS mutations show poor value for malignancy risk. Cytology has low sensitivity (28.7–64.8%) but high specificity (84–94%) in diagnostic malignant changes and strongly affects further management. EUS–through-the-needle biopsy (EUS-TTNB) yields high diagnostic accuracy (sensitivity 90%, specificity 95%) but carries a range of 2–23% adverse events, which limits its wide use. EUS–confocal laser endomicroscopy (EUS-nCLE) provides real-time microscopic evaluation, detecting malignant IPMN with a sensitivity of 90% and a specificity of 73%, though its availability is limited. New emerging biomarkers available in cyst fluid or blood include mucins, miRNA panels (sensitivity 66.7–89%, specificity 89.7–100%), lipidomics, and cancer metabolite profiling, with diagnostic accuracy approaching 89–91%. Pancreatoscopy (POP) enables direct main pancreatic duct (MPD) visualization and biopsy with a sensitivity of 64–100% and a specificity of 75–100%, though adverse events occur in around 12% cases. Combining advanced imaging, EUS-based tissue acquisition, and novel biomarkers holds promise for earlier and more accurate detection of malignant IPMN, potentially improving PDAC outcomes. Full article

The main objective of this research consists in finding a rapid method for cheese lipidomics based on NMR data. This study plays an important role in differentiation and characterization of cheese samples in accordance with fat composition, especially in the case of fat substitution with exogenous animal or vegetal fat. Our findings play an important role in relation to religious requirements regarding non-allowed foods (pork fat, for example, in some cultures) and in the correct characterization of foods according to their lipidic profile. The approach consists in establishing a fingerprint region (0.86–0.93 ppm from ¹H-NMR spectra) and then creating a database of the results obtained. The evaluation of the long-chain saturated fatty acids and the saturated short-chain fatty acids (C4 to C8) was established with a newly developed set of equations that make the computation possible even when mixtures of fats from different sources are present. This was accomplished by developing a new method for quantification of the fatty acid composition of different types of cheese, based on ¹H-NMR spectroscopy. Principal component analysis (PCA) was applied to 40 cheese samples with varying degrees (0%, 5%, 12%, or 15%) of milk fat substitution (pork fat, vegetable fat, hydrogenated oils) and different clotting agents (calcium chloride or citric acid). The best sample discrimination was achieved using fatty acid profiles estimated from ¹H-NMR data (using a total of six variables), explaining 89.7% of the total variance. Clear separation was observed between samples containing only milk fat and those with added fats. These results demonstrate that the integration of ¹H-NMR spectroscopy with principal component analysis (PCA) provides a reliable approach for discriminating cheese samples according to their fat composition. Full article

With innovations and advancements in analytical instruments and computer technology, omics studies based on statistical analysis, such as phytochemical omics, oilomics/lipidomics, proteomics, metabolomics, and glycomics, are increasingly popular in the areas of food chemistry and nutrition science. However, a remaining hurdle is the labor-intensive data process because learning coding skills and software operations are usually time-consuming for researchers without coding backgrounds. A MATLAB^® coding basis and three-in-one integrated method, ‘Ana’, was created for data visualizations and statistical analysis in this work. The program loaded and analyzed an omics dataset from an Excel^® file with 7 samples * 22 compounds as an example, and output six figures for three types of data visualization, including a 3D heatmap, heatmap hierarchical clustering analysis, and principal component analysis (PCA), in 18 s on a personal computer (PC) with a Windows 10 system and in 20 s on a Mac with a MacOS Monterey system. The code is rapid and efficient to print out high-quality figures up to 150 or 300 dpi. The output figures provide enough contrast to differentiate the omics dataset by both color code and bar size adjustments per their higher or lower values, allowing the figures to be qualified for publication and presentation purposes. It provides a rapid analysis method that would liberate researchers from labor-intensive and time-consuming manual or coding basis data analysis. A coding example with proper code annotations and completed user guidance is provided for undergraduate and postgraduate students to learn coding basis statistical data analysis and to help them utilize such techniques for their future research. Full article

Metabolic dysfunction-associated fatty liver disease (MAFLD) is a complex disorder that is implicated in dysregulations in multiple biological pathways, orchestrated by interactions between genetic predisposition, metabolic syndromes and environmental factors. The limited knowledge of its pathogenesis is one of the bottlenecks in the development of prognostic and therapeutic options for MAFLD. Moreover, the extent to which metabolic pathways are altered due to ongoing hepatic steatosis, inflammation and fibrosis and subsequent liver damage remains unclear. To uncover potential MAFLD pathogenesis in humans, we employed an untargeted nuclear magnetic resonance (NMR) spectroscopy- and high-resolution mass spectrometry (HRMS)-based multiplatform approach combined with a computational multiblock omics framework to characterize the plasma metabolomes and lipidomes of obese patients without (n = 19) or with liver biopsy confirmed MAFLD (n = 63). Metabolite features associated with MAFLD were identified using a metabolome-wide association study pipeline that tested for the relationships between feature responses and MAFLD. A metabolic pathway enrichment analysis revealed 16 pathways associated with MAFLD and highlighted pathway changes, including amino acid metabolism, bile acid metabolism, carnitine shuttle, fatty acid metabolism, glycerophospholipid metabolism, arachidonic acid metabolism and steroid metabolism. These results suggested that there were alterations in energy metabolism, specifically amino acid and lipid metabolism, and pointed to the pathways being implicated in alerted liver function, mitochondrial dysfunctions and immune system disorders, which have previously been linked to MAFLD in human and animal studies. Together, this study revealed specific metabolic alterations associated with MAFLD and supported the idea that MAFLD is fundamentally a metabolism-related disorder, thereby providing new perspectives for diagnostic and therapeutic strategies. Full article

Background: Type 1 diabetes (T1D) is a devastating disease with serious health complications. Early T1D biomarkers that could enable timely detection and prevention before the onset of clinical symptoms are paramount but currently unavailable. Despite their promise, omics approaches have so far failed to deliver such biomarkers, likely due to the fragmented nature of information obtained through the single omics approach. We recently demonstrated the utility of parallel multi-omics for the identification of T1D biomarker signatures. Our studies also identified challenges. Methods: Here, we evaluated a novel computational approach of data imputation and amplification as one way to overcome challenges associated with the relatively small number of subjects in these studies. Results: Using proprietary algorithms, we amplified our quadra-omics (proteomics, metabolomics, lipidomics, and transcriptomics) dataset from nine subjects a thousand-fold and analyzed the data using Ingenuity Pathway Analysis (IPA) software to assess the change in its analytical capabilities and biomarker prediction power in the amplified datasets compared to the original. These studies showed the ability to identify an increased number of T1D-relevant pathways and biomarkers in such computationally amplified datasets, especially, at imputation ratios close to the “golden ratio” of 38.2%:61.8%. Specifically, the Canonical Pathway and Diseases and Functions modules identified higher numbers of inflammatory pathways and functions relevant to autoimmune T1D, including novel ones not identified in the original data. The Biomarker Prediction module also predicted in the amplified data several unique biomarker candidates with direct links to T1D pathogenesis. Conclusions: These preliminary findings indicate that such large-scale data imputation and amplification approaches are useful in facilitating the discovery of candidate integrated biomarker signatures of T1D or other diseases by increasing the predictive range of existing data mining tools, especially when the size of the input data is inherently limited. Full article

The third-generation anaplastic lymphoma tyrosine kinase inhibitor (ALK-TKI) lorlatinib has a unique side effect profile that includes hypercholesteremia and hypertriglyceridemia in >80% of lung cancer patients. Here, we tested the hypothesis that lorlatinib might directly promote the accumulation of cholesterol and/or triglycerides in human hepatic cells. We investigated the capacity of the hepatoprotectant silibinin to modify the lipid-modifying activity of lorlatinib. To predict clinically relevant drug–drug interactions if silibinin were used to clinically manage lorlatinib-induced hyperlipidemic effects in hepatic cells, we also explored the capacity of silibinin to interact with and block CYP3A4 activity using in silico computational descriptions and in vitro biochemical assays. A semi-targeted ultrahigh pressure liquid chromatography accurate mass quadrupole time-of-flight mass spectrometry with electrospray ionization (UHPLC-ESI-QTOF-MS/MS)-based lipidomic approach revealed that short-term treatment of hepatic cells with lorlatinib promotes the accumulation of numerous molecular species of cholesteryl esters and triglycerides. Silibinin treatment significantly protected the steady-state lipidome of hepatocytes against the hyperlipidemic actions of lorlatinib. Lipid staining confirmed the ability of lorlatinib to promote neutral lipid overload in hepatocytes upon long-term exposure, which was prevented by co-treatment with silibinin. Computational analyses and cell-free biochemical assays predicted a weak to moderate inhibitory activity of clinically relevant concentrations of silibinin against CYP3A4 when compared with recommended (rosuvastatin) and non-recommended (simvastatin) statins for lorlatinib-associated dyslipidemia. The elevated plasma cholesterol and triglyceride levels in lorlatinib-treated lung cancer patients might involve primary alterations in the hepatic accumulation of lipid intermediates. Silibinin could be clinically explored to reduce the undesirable hyperlipidemic activity of lorlatinib in lung cancer patients. Full article

Sudden infant death syndrome (SIDS) represents the leading cause of death in under one year of age in developing countries. Even in our century, its etiology is not clear, and there is no biomarker that is discriminative enough to predict the risk of suffering from it. Therefore, in this work, taking a public dataset on the lipidomic profile of babies who died from this syndrome compared to a control group, a univariate analysis was performed using the Mann–Whitney U test, with the aim of identifying the characteristics that enable discriminating between both groups. Those characteristics with a p-value less than or equal to 0.05 were taken; once these characteristics were obtained, classification models were implemented (random forests (RF), logistic regression (LR), support vector machine (SVM) and naive Bayes (NB)). We used seventy percent of the data for model training, subjecting it to a cross-validation (k = 5) and later submitting to validation in a blind test with 30% of the remaining data, which allows simulating the scenario in real life—that is, with an unknown population for the model. The model with the best performance was RF, since in the blind test, it obtained an AUC of 0.9, specificity of 1, and sensitivity of 0.8. The proposed model provides the basis for the construction of a SIDS risk prediction computer tool, which will contribute to prevention, and proposes lines of research to deal with this pathology. Full article

In childhood tuberculosis (TB), with an estimated 69% of missed cases in children under 5 years of age, the case detection gap is larger than in other age groups, mainly due to its paucibacillary nature and children’s difficulties in delivering sputum specimens. Accurate and accessible point-of-care tests (POCTs) are needed to detect TB disease in children and, in turn, reduce TB-related morbidity and mortality in this vulnerable population. In recent years, several POCTs for TB have been developed. These include new tools to improve the detection of TB in respiratory and gastric samples, such as molecular detection of Mycobacterium tuberculosis using loop-mediated isothermal amplification (LAMP) and portable polymerase chain reaction (PCR)-based GeneXpert. In addition, the urine-based detection of lipoarabinomannan (LAM), as well as imaging modalities through point-of-care ultrasonography (POCUS), are currently the POCTs in use. Further to this, artificial intelligence-based interpretation of ultrasound imaging and radiography is now integrated into computer-aided detection products. In the future, portable radiography may become more widely available, and robotics-supported ultrasound imaging is currently being trialed. Finally, novel blood-based tests evaluating the immune response using “omic-“techniques are underway. This approach, including transcriptomics, metabolomic, proteomics, lipidomics and genomics, is still distant from being translated into POCT formats, but the digital development may rapidly enhance innovation in this field. Despite these significant advances, TB-POCT development and implementation remains challenged by the lack of standard ways to access non-sputum-based samples, the need to differentiate TB infection from disease and to gain acceptance for novel testing strategies specific to the conditions and settings of use. Full article

Non-alcoholic steatohepatitis (NASH) is a chronic liver disease affecting up to 6.5% of the general population. There is no simple definition of NASH, and the molecular mechanism underlying disease pathogenesis remains elusive. Studies applying single omics technologies have enabled a better understanding of the molecular profiles associated with steatosis and hepatic inflammation—the commonly accepted histologic features for diagnosing NASH, as well as the discovery of novel candidate biomarkers. Multi-omics analysis holds great potential to uncover new insights into disease mechanism through integrating multiple layers of molecular information. Despite the technical and computational challenges associated with such efforts, a few pioneering studies have successfully applied multi-omics technologies to investigate NASH. Here, we review the most recent technological developments in mass spectrometry (MS)-based proteomics, metabolomics, and lipidomics. We summarize multi-omics studies and emerging omics biomarkers in NASH and highlight the biological insights gained through these integrated analyses. Full article

Coronary microvascular dysfunction (MVD) is a syndrome of abnormal regulation of vascular tone, particularly during increased metabolic demand. While there are several risk factors for MVD, some of which are similar to those for coronary artery disease (CAD), the cause of MVD is not understood. We hypothesized that MVD in symptomatic non-elderly subjects would be characterized by specific lipidomic profiles. Subjects (n = 20) aged 35–60 years and referred for computed tomography coronary angiography (CTA) for chest pain but who lacked obstructive CAD (>50% stenosis), underwent quantitative regadenoson stress-rest myocardial contrast echocardiography (MCE) perfusion imaging for MVD assessment. The presence of MVD defined by kinetic analysis of MCE data was correlated with lipidomic profiles in plasma measured by liquid chromatography and high-resolution mass spectrometry. Nine of twenty subjects had evidence of MVD, defined by reduced hyperemic perfusion versus other subjects (beta-value 1.62 ± 0.44 vs. 2.63 ± 0.99 s⁻¹, p = 0.009). Neither the presence of high-risk but non-obstructive CAD on CTA, nor CAD risk factors were different for those with versus without MVD. Lipidomic analysis revealed that patients with MVD had lower concentrations of long-carbon chain triacylglycerols and diacylglycerols, and higher concentrations of short-chain triacylglycerols. The diacylglycerol containing stearic and linoleic acid classified all participants correctly. We conclude that specific lipidomic plasma profiles occur in MVD involving saturated long-chain fatty acid-containing acylglycerols that are distinctly different from those in non-obstructive CAD. These patterns could be used to better characterize the pathobiology and potential treatments for this condition. Full article

Lipids play an important role in biological systems and have the potential to serve as biomarkers in medical applications. Advances in lipidomics allow identification of hundreds of lipid species from biological samples. However, a systems biological analysis of the lipidome, by incorporating pathway information remains challenging, leaving lipidomics behind compared to other omics disciplines. An especially uncharted territory is the integration of statistical and network-based approaches for studying global lipidome changes. Here we developed the Lipid Network Explorer (LINEX), a web-tool addressing this gap by providing a way to visualize and analyze functional lipid metabolic networks. It utilizes metabolic rules to match biochemically connected lipids on a species level and combine it with a statistical correlation and testing analysis. Researchers can customize the biochemical rules considered, to their tissue or organism specific analysis and easily share them. We demonstrate the benefits of combining network-based analyses with statistics using publicly available lipidomics data sets. LINEX facilitates a biochemical knowledge-based data analysis for lipidomics. It is availableas a web-application and as a publicly available docker container. Full article

Background: coronary computed tomography angiography (CCTA) is a first line non-invasive imaging modality for detection of coronary atherosclerosis. Computational modeling with lipidomics analysis can be used for prediction of coronary atherosclerotic plaque progression. Methods: 187 patients (480 vessels) with stable coronary artery disease (CAD) undergoing CCTA scan at baseline and after 6.2 ± 1.4 years were selected from the SMARTool clinical study cohort (Clinicaltrial.gov Identifiers NCT04448691) according to a computed tomography (CT) scan image quality suitable for three-dimensional (3D) reconstruction of coronary arteries and the absence of implanted coronary stents. Clinical and biohumoral data were collected, and plasma lipidomics analysis was performed. Blood flow and low-density lipoprotein (LDL) transport were modeled using patient-specific data to estimate endothelial shear stress (ESS) and LDL accumulation based on a previously developed methodology. Additionally, non-invasive Fractional Flow Reserve (FFR) was calculated (SmartFFR). Plaque progression was defined as significant change of at least two of the morphological metrics: lumen area, plaque area, plaque burden. Results: a multi-parametric predictive model, including traditional risk factors, plasma lipids, 3D imaging parameters, and computational data demonstrated 88% accuracy to predict site-specific plaque progression, outperforming current computational models. Conclusions: Low ESS and LDL accumulation, estimated by computational modeling of CCTA imaging, can be used to predict site-specific progression of coronary atherosclerotic plaques. Full article

Brown adipose tissue (BAT) activation is a possible therapeutic strategy to increase energy expenditure and improve metabolic homeostasis in obesity. Recent studies have revealed novel interactions between BAT and circulating lipid species—in particular, the non-esterified fatty acid (NEFA) and oxylipin lipid classes. This study aimed to identify individual lipid species that may be associated with cold-stimulated BAT activity in humans. A panel of 44 NEFA and 41 oxylipin species were measured using mass-spectrometry-based lipidomics in the plasma of fourteen healthy male participants before and after 90 min of mild cold exposure. Lipid measures were correlated with BAT activity measured via ¹⁸F-fluorodeoxyglucose ([¹⁸F]FDG) positron emission tomography/computed tomography (PET/CT), along with norepinephrine (NE) concentration (a surrogate marker of sympathetic activity). The study identified a significant increase in total NEFA concentration following cold exposure that was positively associated with NE concentration change. Individually, 33 NEFA and 11 oxylipin species increased significantly in response to cold exposure. The concentration of the omega-3 NEFA, docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA) at baseline was significantly associated with BAT activity, and the cold-induced change in 18 NEFA species was significantly associated with BAT activity. No significant associations were identified between BAT activity and oxylipins. Full article

Long-term exposure of liquid preserved boar spermatozoa to seminal plasma (SP) can cause dramatic sperm injury. This study examined whether boar specificity exists in the sensitivity of spermatozoa to SP and whether correspondent biomarkers can be identified. Consecutive ejaculates (n = 4–5) collected from 19 boars were centrifuged, diluted with a pH-stablising extender with 10% (v/v) autologous SP and evaluated by computer-assisted semen analysis and flow cytometry. Up until 144 h storage, four boars showed consistently high sperm motility, viability and mitochondria activity, and one boar showed consistently low values. Intra-boar variability was high in the other boars. Screening of SP (n = 12 samples) for protein markers using mass spectrometry identified three protein candidates of which the granulin precursor, legumain and AWN were 0.5 to 0.9 log2-fold less abundant (p < 0.05) in SP-resistant compared to SP-sensitive samples. Lipidome analysis by mass spectrometry revealed 568 lipids showing no difference between the SP-groups. The most abundant lipids were cholesterol (42,442 pmol), followed by phosphatidylserine (20,956 pmol) and ether-linked phosphatidylethanolamine (13,039 pmol). In conclusion, three candidate proteins were identified which might be indicative of SP-tolerance of sperm during long-term storage. Noteworthy, a first lipidomic profile of boar SP is presented. Full article

Priming is a natural phenomenon that pre-conditions plants for enhanced defence against a wide range of pathogens. It represents a complementary strategy, or sustainable alternative that can provide protection against disease. However, a comprehensive functional and mechanistic understanding of the various layers of priming events is still limited. A non-targeted metabolomics approach was used to investigate metabolic changes in plant growth-promoting rhizobacteria (PGPR)-primed Sorghum bicolor seedlings infected with the anthracnose-causing fungal pathogen, Colletotrichum sublineolum, with a focus on the post-challenge primed state phase. At the 4-leaf growth stage, the plants were treated with a strain of Paenibacillus alvei at 10⁸ cfu mL⁻¹. Following a 24 h PGPR application, the plants were inoculated with a C. sublineolum spore suspension (10⁶ spores mL⁻¹), and the infection monitored over time: 1, 3, 5, 7 and 9 days post-inoculation. Non-infected plants served as negative controls. Intracellular metabolites from both inoculated and non-inoculated plants were extracted with 80% methanol-water. The extracts were chromatographically and spectrometrically analysed on an ultra-high performance liquid chromatography (UHPLC) system coupled to high-definition mass spectrometry. The acquired multidimensional data were processed to create data matrices for chemometric modelling. The computed models indicated time-related metabolic perturbations that reflect primed responses to the fungal infection. Evaluation of orthogonal projection to latent structure-discriminant analysis (OPLS-DA) loading shared and unique structures (SUS)-plots uncovered the differential stronger defence responses against the fungal infection observed in primed plants. These involved enhanced levels of amino acids (tyrosine, tryptophan), phytohormones (jasmonic acid and salicylic acid conjugates, and zeatin), and defence-related components of the lipidome. Furthermore, other defence responses in both naïve and primed plants were characterised by a complex mobilisation of phenolic compounds and de novo biosynthesis of the flavones, apigenin and luteolin and the 3-deoxyanthocyanidin phytoalexins, apigeninidin and luteolinidin, as well as some related conjugates. Full article