Metabolomic screening of radioiodine refractory thyroid cancer patients and the underlying chemical mechanism of iodine resistance.

Radioiodine refractory (RAIR) patients do not benefit from iodine-131 therapy. Thus, timely identification of RAIR patients is critical for avoiding ineffective radioactive iodine therapy. In addition, determining the causes of iodine resistance will facilitate the development of novel treatment strategies. This study was comprised of 20 RAIR and 14 non-radioiodine refractory (non-RAIR) thyroid cancer patients. Liquid chromatography-mass spectrometry was used to identify differences in the serum metabolites of RAIR and non-RAIR patients. In addition, chemical assays were performed to determine the effects of the differential metabolites on iodine uptake. Metabolic pathway enrichment analysis of the differential metabolites revealed significant differences in the phenylalanine and tyrosine metabolic pathways. Notably, quinate and shikimic acid, metabolites of the tyrosine pathway, were significantly increased in the RAIR group. In contrast, the phenylalanine pathway metabolites, hippuric acid and 2-phenylacetamide, were markedly decreased in the RAIR group. Thyroid peroxidase plays an important role in catalyzing the iodination of tyrosine residues, while the ionic state of iodine promotes the iodination reaction. Quinate, shikimic acid, hippuric acid, and 2-phenylacetamide were found to be involved in the iodination of tyrosine, which is a key step in thyroid hormone synthesis. Specifically, quinate and shikimic acid were found to inhibit iodination, while hippuric acid and 2-phenylacetamide promoted iodination. Abnormalities in phenylalanine and tyrosine metabolic pathways are closely associated with iodine resistance. Tyrosine is required for thyroid hormone synthesis and could be a potential cause of iodine resistance.

Exploring disease-specific metabolite signatures in hereditary angioedema patients.

Introduction: Hereditary angioedema (HAE) is a rare, life-threatening autosomal dominant genetic disorder caused by a deficient and/or dysfunctional C1 esterase inhibitor (C1-INH) (type 1 and type 2) leading to recurrent episodes of edema. This study aims to explore HAE patients' metabolomic profiles and identify novel potential diagnostic biomarkers for HAE. The study also examined distinguishing HAE from idiopathic angioedema (AE). Methods: Blood plasma samples from 10 HAE (types 1/2) patients, 15 patients with idiopathic AE, and 20 healthy controls were collected in Latvia and analyzed using LC-MS based targeted metabolomics workflow. T-test and fold change calculation were used to identify metabolites with significant differences between diseases and control groups. ROC analysis was performed to evaluate metabolite based classification model. Results: A total of 33 metabolites were detected and quantified. The results showed that isovalerylcarnitine, cystine, and hydroxyproline were the most significantly altered metabolites between the disease and control groups. Aspartic acid was identified as a significant metabolite that could differentiate between HAE and idiopathic AE. The mathematical combination of metabolites (hydroxyproline * cystine)/(creatinine * isovalerylcarnitine) was identified as the diagnosis signature for HAE. Furthermore, glycine/asparagine ratio could differentiate between HAE and idiopathic AE. Conclusion: Our study identified isovalerylcarnitine, cystine, and hydroxyproline as potential biomarkers for HAE diagnosis. Identifying new biomarkers may offer enhanced prospects for accurate, timely, and economical diagnosis of HAE, as well as tailored treatment selection for optimal patient care.

Widely-targeted metabolomics and transcriptomics identify metabolites associated with flowering regulation of Choy Sum.

Choy Sum, a stalk vegetable highly valued in East and Southeast Asia, is characterized by its rich flavor and nutritional profile. Metabolite accumulation is a key factor in Choy Sum stalk development; however, no research has focused on metabolic changes during the development of Choy Sum, especially in shoot tip metabolites, and their effects on growth and flowering. Therefore, in the present study, we used a widely targeted metabolomic approach to analyze metabolites in Choy Sum stalks at the seedling (S1), bolting (S3), and flowering (S5) stages. In total, we identified 493 metabolites in 31 chemical categories across all three developmental stages. We found that the levels of most carbohydrates and amino acids increased during stalk development and peaked at S5. Moreover, the accumulation of amino acids and their metabolites was closely related to G6P, whereas the expression of flowering genes was closely related to the content of T6P, which may promote flowering by upregulating the expressions of BcSOC1, BcAP1, and BcSPL5. The results of this study contribute to our understanding of the relationship between the accumulation of stem tip substances during development and flowering and of the regulatory mechanisms of stalk development in Choy Sum and other related species.

A combined observational and Mendelian randomization investigation reveals NMR-measured analytes to be risk factors of major cardiovascular diseases.

Dyslipidaemias is the leading risk factor of several major cardiovascular diseases (CVDs), but there is still a lack of sufficient evidence supporting a causal role of lipoprotein subspecies in CVDs. In this study, we comprehensively investigated several lipoproteins and their subspecies, as well as other metabolites, in relation to coronary heart disease (CHD), heart failure (HF) and ischemic stroke (IS) longitudinally and by Mendelian randomization (MR) leveraging NMR-measured metabolomic data from 118,012 UK Biobank participants. We found that 123, 110 and 36 analytes were longitudinally associated with myocardial infarction, HF and IS (FDR < 0.05), respectively, and 25 of those were associated with all three outcomes. MR analysis suggested that genetically predicted levels of 70, 58 and 7 analytes were associated with CHD, HF and IS (FDR < 0.05), respectively. Two analytes, ApoB/ApoA1 and M-HDL-C were associated with all three CVD outcomes in the MR analyses, and the results for M-HDL-C were concordant in both observational and MR analyses. Our results implied that the apoB/apoA1 ratio and cholesterol in medium size HDL were particularly of importance to understand the shared pathophysiology of CHD, HF and IS and thus should be further investigated for the prevention of all three CVDs.

Organs-specific metabolomics and anticholinesterase activity suggests a trade-off between metabolites for therapeutic advantages of Trillium govanianum Wall. ex D. Don.

Trillium govanianum is traditionally used to treat innumerable alignments like sexual disorders, cancer, inflammation etc. Mainly rhizomes of T. govanianum have been explored for phytochemical profiling but comprehensive metabolomics of other parts has not been yet deeply investigated. Thus, current study was aimed for organs-specific (roots, rhizomes, rhizomatous buds, stems, leaves, and fruits) phytochemical profiling of T. govanianum via metabolomics approach. Targeted (steroidal saponins and free sugars) and non-targeted metabolomics were performed by UPLC-PDA/ELSD & UHPLC-Q-TOF-IMS. Among steroidal compounds, 20-hydroxyecdysone, pennogenin-3-O-ß-chacotrioside, dioscin were found predominantly in all samples while diosgenin was identified only in rhizomes. Further, four free sugars viz. 2-deoxyribose (116.24 ± 1.26 mg/g: leaves), fructose (454.76 ± 12.14 mg/g: rhizomes), glucose (243.21 ± 7.53 mg/g: fruits), and galactose (69.06 ± 2.14 mg/g: fruits) were found significant in respective parts of T. govanianum. Elemental analysis of targeted samples was determined by atomic absorption spectrophotometer. Heavy metals (Cd, Hg, Pd, As) were absent while micro- (Mn, Na, Zn, Cu) and macro- (Ca, Fe, Mg, K) elements were found in all samples. Furthermore, UHPLC-Q-TOF-IMS had identified 103 metabolites based on their mass fragmentation patterns and 839 were tentatively predicted using METLIN database. The multivariate statistical analysis showed organs specific clustering and variance of metabolites. Apart from this, extracts were evaluated for in vitro anticholinesterase activity, and found potentials inhibitors with IC50 values 2.02 ± 0.15 to 27.65 ± 0.89 mg/mL and 3.58 ± 0.12 to 16.81 ± 2.48 mg/mL of acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) enzyme, respectively. Thus, comprehensive metabolomics and anti-cholinesterase activity of different parts of T. govanianum would lay the foundation for improving medicinal importance and health benefits of T. govanianum.

Adrenal pheochromocytoma impacts three main pathways: cysteine-methionine, pyrimidine, and tyrosine metabolism. / è‚¾ä¸Šè ºå—œé“¬ç»†èƒžç˜¤å½±å“ä¸‰æ¡ä¸»è¦ä»£è°¢é€šè·¯ï¼šåŠèƒ±æ°¨é ¸-è›‹æ°¨é ¸ä»£è°¢ã€å˜§å•¶ä»£è°¢å’Œé ªæ°¨é ¸ä»£è°¢é€šè·¯.

Pheochromocytomas and paragangliomas (PPGLs) cause symptoms by altering the circulation levels of catecholamines and peptide hormones. Currently, the diagnosis of PPGLs relies on diagnostic imaging and the detection of catecholamines. In this study, we used ultra-performance liquid chromatography (UPLC)/quadrupole time-of-flight mass spectrometry (Q-TOF MS) analysis to identify and measure the perioperative differential metabolites in the plasma of adrenal pheochromocytoma patients. We identified differentially expressed genes by comparing the transcriptomic data of pheochromocytoma with the normal adrenal medulla. Through conducting two steps of metabolomics analysis, we identified 111 differential metabolites between the healthy group and the patient group, among which 53 metabolites were validated. By integrating the information of differential metabolites and differentially expressed genes, we inferred that the cysteine-methionine, pyrimidine, and tyrosine metabolism pathways were the three main metabolic pathways altered by the neoplasm. The analysis of transcription levels revealed that the tyrosine and cysteine-methionine metabolism pathways were downregulated in pheochromocytoma, whereas the pyrimidine pathway showed no significant difference. Finally, we developed an optimized diagnostic model of two metabolites, L-dihydroorotic acid and vanylglycol. Our results for these metabolites suggest that they may serve as potential clinical biomarkers and can be used to supplement and improve the diagnosis of pheochromocytoma.

Integrated multi-omic approach reveals the effect of a Graminaceae-derived biostimulant and its lighter fraction on salt-stressed lettuce plants.

Plant biostimulants are widely applied in agriculture for their ability to improve plant fitness. In the present work, the impact of Graminaceae-derived protein hydrolysate (P) and its lighter molecular fraction F3 (< 1 kDa) on lettuce plants, subjected to either no salt or high salt conditions, was investigated through the combination of metabolomics and transcriptomics. The results showed that both treatments significantly modulated the transcriptome and metabolome of plants under salinity stress, highlighting an induction of the hormonal response. Nevertheless, P and F3 also displayed several peculiarities. F3 specifically modulated the response to ethylene and MAPK signaling pathway, whereas P treatment induced a down-accumulation of secondary metabolites, albeit genes controlling the biosynthesis of osmoprotectants and antioxidants were up-regulated. Moreover, according with the auxin response modulation, P promoted cell wall biogenesis and plasticity in salt-stressed plants. Notably, our data also outlined an epigenetic control of gene expression induced by P treatment. Contrarily, experimental data are just partially in agreement when not stressed plants, treated with P or F3, were considered. Indeed, the reduced accumulation of secondary metabolites and the analyses of hormone pathways modulation would suggest a preferential allocation of resources towards growth, that is not coherent with the down-regulation of the photosynthetic machinery, the CO2 assimilation rate and leaves biomass. In conclusion, our data demonstrate that, although they might activate different mechanisms, both the P and F3 can result in similar benefits, as far as the accumulation of protective osmolytes and the enhanced tolerance to oxidative stress are concerned. Notably, the F3 fraction exhibits slightly greater growth promotion effects under high salt conditions. Most importantly, this research further corroborates that biostimulants' mode of action is dependent on plants' physiological status and their composition, underscoring the importance of investigating the bioactivity of the different molecular components to design tailored applications for the agricultural practice.

Deciphering the Impact of Defecation Frequency on Gut Microbiome Composition and Diversity.

This study explores the impact of defecation frequency on the gut microbiome structure by analyzing fecal samples from individuals categorized by defecation frequency: infrequent (1-3 times/week, n = 4), mid-frequent (4-6 times/week, n = 7), and frequent (daily, n = 9). Utilizing 16S rRNA gene-based sequencing and LC-MS/MS metabolome profiling, significant differences in microbial diversity and community structures among the groups were observed. The infrequent group showed higher microbial diversity, with community structures significantly varying with defecation frequency, a pattern consistent across all sampling time points. The Ruminococcus genus was predominant in the infrequent group, but decreased with more frequent defecation, while the Bacteroides genus was more common in the frequent group, decreasing as defecation frequency lessened. The infrequent group demonstrated enriched biosynthesis genes for aromatic amino acids and branched-chain amino acids (BCAAs), in contrast to the frequent group, which had a higher prevalence of genes for BCAA catabolism. Metabolome analysis revealed higher levels of metabolites derived from aromatic amino acids and BCAA metabolism in the infrequent group, and lower levels of BCAA-derived metabolites in the frequent group, consistent with their predicted metagenomic functions. These findings underscore the importance of considering stool consistency/frequency in understanding the factors influencing the gut microbiome.

Plasma Metabolite Profiling in the Search for Early-Stage Biomarkers for Lung Cancer: Some Important Breakthroughs.

Lung cancer is the leading cause of cancer-related mortality worldwide. In order to improve its overall survival, early diagnosis is required. Since current screening methods still face some pitfalls, such as high false positive rates for low-dose computed tomography, researchers are still looking for early biomarkers to complement existing screening techniques in order to provide a safe, faster, and more accurate diagnosis. Biomarkers are biological molecules found in body fluids, such as plasma, that can be used to diagnose a condition or disease. Metabolomics has already been shown to be a powerful tool in the search for cancer biomarkers since cancer cells are characterized by impaired metabolism, resulting in an adapted plasma metabolite profile. The metabolite profile can be determined using nuclear magnetic resonance, or NMR. Although metabolomics and NMR metabolite profiling of blood plasma are still under investigation, there is already evidence for its potential for early-stage lung cancer diagnosis, therapy response, and follow-up monitoring. This review highlights some key breakthroughs in this research field, where the most significant biomarkers will be discussed in relation to their metabolic pathways and in light of the altered cancer metabolism.

Anatomical and Metabolome Features of Haloxylon aphyllum and Haloxylon persicum Elucidate the Resilience against Gall-Forming Insects.

Globally, gall-forming insects significantly contribute to the degradation of desert ecosystems. Recent studies have demonstrated that Haloxylon persicum suffers less damage from gall-formers compared to Haloxylon aphyllum. However, the mechanisms driving the long-term metabolic responses of these species to gall-forming biotic stress in their natural environment remain unclear. The current study comparatively analyzes the anatomical features and metabolomic changes in H. aphyllum and H. persicum damaged by gall-forming insects. This research aimed to uncover potential metabolic tolerance mechanisms through GC-MS analysis. The study findings indicate that gall-forming insects cause a reduction in nearly all the anatomical structures of Haloxylon shoots, with the effects being less severe in H. persicum than in H. aphyllum. Thus, the metabolic pathways responsible for the biosynthesis of biologically active substances that enhance resistance to gall inducers were different, specifically in H. aphyllum-the biosynthesis of fatty acids (+their derivatives) and γ-tocopherol (vitamin E) and H. persicum-the biosynthesis of fatty acids (+their derivatives), dialkyl ethers, carbohydrates (+their derivatives), aromatic acid derivatives, phytosterols, γ-tocopherol (vitamin E), phenols, and terpenoids. The results suggest that the modulation of metabolic pathways under biotic stress plays a crucial role in the enhanced survival and growth of H. persicum.

Physiological, Transcriptome, and Metabolome Analyses Reveal the Tolerance to Cu Toxicity in Red Macroalgae Gracilariopsis lemaneiformis.

Heavy metal copper (Cu) will inevitably impact the marine macroalgae Gracilariopsis lemaneiformis (G. lemaneiformis), which is a culture of economic importance along China's coastline. In this study, the detoxification mechanism of Cu stress on G. lemaneiformis was revealed by assessing physiological indicators in conjunction with transcriptome and metabolome analyses at 1 d after Cu stress. Our findings revealed that 25 µM Cu stimulated ROS synthesis and led to the enzymatic oxidation of arachidonic acid residues. This process subsequently impeded G. lemaneiformis growth by suppressing photosynthesis, nitrogen metabolism, protein synthesis, etc. The entry of Cu ions into the algae was facilitated by ZIPs and IRT transporters, presenting as Cu2+. Furthermore, there was an up-regulation of Cu efflux transporters HMA5 and ABC family transporters to achieve compartmentation to mitigate the toxicity. The results revealed that G. lemaneiformis elevated the antioxidant enzyme superoxide dismutase and ascorbate-glutathione cycle to maintain ROS homeostasis. Additionally, metabolites such as flavonoids, 3-O-methylgallic acid, 3-hydroxy-4-keto-gama-carotene, and eicosapentaenoic acid were up-regulated compared with the control, indicating that they might play roles in response to Cu stress. In summary, this study offers a comprehensive insight into the detoxification mechanisms driving the responses of G. lemaneiformis to Cu exposure.

Examination of the Complex Molecular Landscape in Obesity and Type 2 Diabetes.

The escalating prevalence of metabolic disorders, notably type 2 diabetes (T2D) and obesity, presents a critical global health challenge, necessitating deeper insights into their molecular underpinnings. Our study integrates proteomics and metabolomics analyses to delineate the complex molecular landscapes associated with T2D and obesity. Leveraging data from 130 subjects, including individuals with T2D and obesity as well as healthy controls, we elucidate distinct molecular signatures and identify novel biomarkers indicative of disease progression. Our comprehensive characterization of cardiometabolic proteins and serum metabolites unveils intricate networks of biomolecular interactions and highlights differential protein expression patterns between T2D and obesity cohorts. Pathway enrichment analyses reveal unique mechanisms underlying disease development and progression, while correlation analyses elucidate the interplay between proteomics, metabolomics, and clinical parameters. Furthermore, network analyses underscore the interconnectedness of cardiometabolic proteins and provide insights into their roles in disease pathogenesis. Our findings may help to refine diagnostic strategies and inform the development of personalized interventions, heralding a new era in precision medicine and healthcare innovation. Through the integration of multi-omics approaches and advanced analytics, our study offers a crucial framework for deciphering the intricate molecular underpinnings of metabolic disorders and paving the way for transformative therapeutic strategies.

GC/MS-based untargeted metabolomics reveals the differential metabolites for discriminating vintage of Chenxiang-type baijiu.

The "outstanding and unique aged aroma" of Chinese Chenxiang-type baijiu (CXB)-Daoguang 25 (DG25) mainly originates from a "extraordinary storage technology" of Mujiuhai (a wooden container), so it is mysterious and interesting. In this study, an untargeted GC/MS-based metabolomics was used to reveals the volatile differential metabolites for discriminating six different vintages of DG25 combing with chemometrics. A total of 100 volatile metabolites (including unknowns) were extracted and identified, including esters (41%), alcohols (10%) and acids (7%) so on. Finally, 33 differential metabolites were identified as aging-markers. Among them, 25 aging-markers showed a downtrend, including 17 esters such as ethyl acetate, ethyl hexanoate and ethyl palmitate so on. Moreover, it was interesting and to further study that furans showed a significant downtrend. Statistically speaking, ethyl benzoate played an important role in discriminating vintage of 1Y and 3Y, and the other 24 differential metabolites with downtrend discriminating the unstored (0Y-aged) DG25. Eight differential metabolites, such as ethyl octanoate, benzaldehyde, 3-methylbutanol and 1,1-diethoxyaccetal so on increased during aging of DG25, and they played a statistical role in discriminating the 5Y-, 10Y- and 20Y-aged DG25. This study provides a theoretical basis way for the formation mechanism of aging aroma for CXB.

Metabolic network analysis of pre-ASD newborns and 5-year-old children with autism spectrum disorder.

Classical metabolomic and new metabolic network methods were used to study the developmental features of autism spectrum disorder (ASD) in newborns (n = 205) and 5-year-old children (n = 53). Eighty percent of the metabolic impact in ASD was caused by 14 shared biochemical pathways that led to decreased anti-inflammatory and antioxidant defenses, and to increased physiologic stress molecules like lactate, glycerol, cholesterol, and ceramides. CIRCOS plots and a new metabolic network parameter, V ° net, revealed differences in both the kind and degree of network connectivity. Of 50 biochemical pathways and 450 polar and lipid metabolites examined, the developmental regulation of the purine network was most changed. Purine network hub analysis revealed a 17-fold reversal in typically developing children. This purine network reversal did not occur in ASD. These results revealed previously unknown metabolic phenotypes, identified new developmental states of the metabolic correlation network, and underscored the role of mitochondrial functional changes, purine metabolism, and purinergic signaling in autism spectrum disorder.

Identification of metabolic components of carotid plaque in high-risk patients utilizing liquid chromatography-tandem mass spectrometry.

OBJECTIVE: Carotid atherosclerosis is a chronic progressive vascular disease that can be complicated by stroke in severe cases. Prompt diagnosis and treatment of high-risk patients are quite difficult due to the lack of reliable clinical biomarkers. This study aimed to explore potential plaque metabolic markers of stroke-prone risk and relevant targets for pharmacological intervention. METHOD: Carotid intima and plaque sample tissues were obtained from 20 patients with cerebrovascular symptoms of carotid origin. An untargeted metabolomics approach based on liquid chromatography-tandem mass spectrometry was utilized to characterize the metabolic profiles of the tissues. Multivariate and univariate analysis tools were used. RESULTS: A total of 154 metabolites were significantly altered in carotid plaque when compared with thickened intima. Of these, 62 metabolites were upregulated, whereas 92 metabolites were downregulated. Support vector machines identified the 15 most important metabolites, such as N-(cyclopropylmethyl)-N'-phenylurea, 9(S)-HOTrE, ACar 12:2, quinoxaline-2,3-dithiol, and l-thyroxine, as biomarkers for high-risk plaques. Metabolic pathway analysis showed that abnormal purine and nucleotide metabolism, amino acid metabolism, glutathione metabolism, and vitamin metabolism may contribute to the occurrence and progression of carotid atherosclerotic plaque. CONCLUSIONS: Our study identifies the biomarkers and related metabolic mechanisms of carotid plaque, which is stroke-prone, and provides insights and ideas for the precise prevention and targeted intervention of the disease.

Potential application of body fluids autofluorescence in the non-invasive diagnosis of endometrial cancer.

BACKGROUND: Endometrial carcinoma (EC) is the most common cancer of the female reproductive tract in developed countries. The prognosis and 5-year survival rates are closely tied to the stage diagnosis. Current routine diagnostic methods of EC are either lacking specificity or are uncomfortable, invasive and painful for the patient. As of now, the gold diagnostic standard is endometrial biopsy. Early and non-invasive diagnosis of EC requires the identification of new biomarkers of disease and a screening test applicable to routine laboratory diagnostics. The application of untargeted metabolomics combined with artificial intelligence and biostatistics tools has the potential to qualitatively and quantitatively represent the metabolome, but its introduction into routine diagnostics is currently unrealistic due to the financial, time and interpretation challenges. Fluorescence spectral analysis of body fluids utilizes autofluorescence of certain metabolites to define the composition of the metabolome under physiological conditions. PURPOSE: This review highlights the potential of fluorescence spectroscopy in the early detection of EC. Data obtained by three-dimensional fluorescence spectroscopy define the quantitative and qualitative composition of the complex fluorescent metabolome and are useful for identifying biochemical metabolic changes associated with endometrial carcinogenesis. Autofluorescence of biological fluids has the prospect of providing new molecular markers of EC. By integrating machine learning and artificial intelligence algorithms in the data analysis of the fluorescent metabolome, this technique has great potential to be implemented in routine laboratory diagnostics.

Characterizing human postprandial metabolic response using multiway data analysis.

INTRODUCTION: Analysis of time-resolved postprandial metabolomics data can improve our understanding of the human metabolism by revealing similarities and differences in postprandial responses of individuals. Traditional data analysis methods often rely on data summaries or univariate approaches focusing on one metabolite at a time. OBJECTIVES: Our goal is to provide a comprehensive picture in terms of the changes in the human metabolism in response to a meal challenge test, by revealing static and dynamic markers of phenotypes, i.e., subject stratifications, related clusters of metabolites, and their temporal profiles. METHODS: We analyze Nuclear Magnetic Resonance (NMR) spectroscopy measurements of plasma samples collected during a meal challenge test from 299 individuals from the COPSAC2000 cohort using a Nightingale NMR panel at the fasting and postprandial states (15, 30, 60, 90, 120, 150, 240 min). We investigate the postprandial dynamics of the metabolism as reflected in the dynamic behaviour of the measured metabolites. The data is arranged as a three-way array: subjects by metabolites by time. We analyze the fasting state data to reveal static patterns of subject group differences using principal component analysis (PCA), and fasting state-corrected postprandial data using the CANDECOMP/PARAFAC (CP) tensor factorization to reveal dynamic markers of group differences. RESULTS: Our analysis reveals dynamic markers consisting of certain metabolite groups and their temporal profiles showing differences among males according to their body mass index (BMI) in response to the meal challenge. We also show that certain lipoproteins relate to the group difference differently in the fasting vs. dynamic state. Furthermore, while similar dynamic patterns are observed in males and females, the BMI-related group difference is observed only in males in the dynamic state. CONCLUSION: The CP model is an effective approach to analyze time-resolved postprandial metabolomics data, and provides a compact but a comprehensive summary of the postprandial data revealing replicable and interpretable dynamic markers crucial to advance our understanding of changes in the metabolism in response to a meal challenge.

Postmortem metabolomics: influence of time since death on the level of endogenous compounds in human femoral blood. Necessary to be considered in metabolome study planning?

INTRODUCTION: The (un)targeted analysis of endogenous compounds has gained interest in the field of forensic postmortem investigations. The blood metabolome is influenced by many factors, and postmortem specimens are considered particularly challenging due to unpredictable decomposition processes. OBJECTIVES: This study aimed to systematically investigate the influence of the time since death on endogenous compounds and its relevance in designing postmortem metabolome studies. METHODS: Femoral blood samples of 427 authentic postmortem cases, were collected at two time points after death (854 samples in total; t1: admission to the institute, 1.3-290 h; t2: autopsy, 11-478 h; median ∆t = 71 h). All samples were analyzed using an untargeted metabolome approach, and peak areas were determined for 38 compounds (acylcarnitines, amino acids, phospholipids, and others). Differences between t2 and t1 were assessed by Wilcoxon signed-ranked test (p < 0.05). Moreover, all samples (n = 854) were binned into time groups (6 h, 12 h, or 24 h intervals) and compared by Kruskal-Wallis/Dunn's multiple comparison tests (p < 0.05 each) to investigate the effect of the estimated time since death. RESULTS: Except for serine, threonine, and PC 34:1, all tested analytes revealed statistically significant changes between t1 and t2 (highest median increase 166%). Unpaired analysis of all 854 blood samples in-between groups indicated similar results. Significant differences were typically observed between blood samples collected within the first and later than 48 h after death, respectively. CONCLUSIONS: To improve the consistency of comprehensive data evaluation in postmortem metabolome studies, it seems advisable to only include specimens collected within the first 2 days after death.

Insights into modifiers effects in differential mobility spectrometry: A data science approach for metabolomics and peptidomics.

Utilizing a data-driven approach, this study investigates modifier effects on compensation voltage in differential mobility spectrometry-mass spectrometry (DMS-MS) for metabolites and peptides. Our analysis uncovers specific factors causing signal suppression in small molecules and pinpoints both signal suppression mechanisms and the analytes involved. In peptides, machine learning models discern a relationship between molecular weight, topological polar surface area, peptide charge, and proton transfer-induced signal suppression. The models exhibit robust performance, offering valuable insights for the application of DMS to metabolites and tryptic peptides analysis by DMS-MS.

Untargeted lipidomics analysis in women with morbid obesity and type 2 diabetes mellitus: A comprehensive study.

There is a phenotype of obese individuals termed metabolically healthy obese that present a reduced cardiometabolic risk. This phenotype offers a valuable model for investigating the mechanisms connecting obesity and metabolic alterations such as Type 2 Diabetes Mellitus (T2DM). Previously, in an untargeted metabolomics analysis in a cohort of morbidly obese women, we observed a different lipid metabolite pattern between metabolically healthy morbid obese individuals and those with associated T2DM. To validate these findings, we have performed a complementary study of lipidomics. In this study, we assessed a liquid chromatography coupled to a mass spectrometer untargeted lipidomic analysis on serum samples from 209 women, 73 normal-weight women (control group) and 136 morbid obese women. From those, 65 metabolically healthy morbid obese and 71 with associated T2DM. In this work, we find elevated levels of ceramides, sphingomyelins, diacyl and triacylglycerols, fatty acids, and phosphoethanolamines in morbid obese vs normal weight. Conversely, decreased levels of acylcarnitines, bile acids, lyso-phosphatidylcholines, phosphatidylcholines (PC), phosphatidylinositols, and phosphoethanolamine PE (O-38:4) were noted. Furthermore, comparing morbid obese women with T2DM vs metabolically healthy MO, a distinct lipid profile emerged, featuring increased levels of metabolites: deoxycholic acid, diacylglycerol DG (36:2), triacylglycerols, phosphatidylcholines, phosphoethanolamines, phosphatidylinositols, and lyso-phosphatidylinositol LPI (16:0). To conclude, analysing both comparatives, we observed decreased levels of deoxycholic acid, PC (34:3), and PE (O-38:4) in morbid obese women vs normal-weight. Conversely, we found elevated levels of these lipids in morbid obese women with T2DM vs metabolically healthy MO. These profiles of metabolites could be explored for the research as potential markers of metabolic risk of T2DM in morbid obese women.