Gastric Cancer and Intestinal Metaplasia: Differential Metabolic Landscapes and New Pathways to Diagnosis.

Gastric cancer (GC) is the fifth most common cause of cancer-related death worldwide. Early detection is crucial for improving survival rates and treatment outcomes. However, accurate GC-specific biomarkers remain unknown. This study aimed to identify the metabolic differences between intestinal metaplasia (IM) and GC to determine the pathways involved in GC. A metabolic analysis of IM and tissue samples from 37 patients with GC was conducted using ultra-performance liquid chromatography with tandem mass spectrometry. Overall, 665 and 278 significant features were identified in the aqueous and 278 organic phases, respectively, using false discovery rate analysis, which controls the expected proportion of false positives among the significant results. sPLS-DA revealed a clear separation between IM and GC samples. Steroid hormone biosynthesis, tryptophan metabolism, purine metabolism, and arginine and proline metabolism were the most significantly altered pathways. The intensity of 11 metabolites, including N1, N2-diacetylspermine, creatine riboside, and N-formylkynurenine, showed significant elevation in more advanced GC. Based on pathway enrichment analysis and cancer stage-specific alterations, we identified six potential candidates as diagnostic biomarkers: aldosterone, N-formylkynurenine, guanosine triphosphate, arginine, S-adenosylmethioninamine, and creatine riboside. These metabolic differences between IM and GC provide valuable insights into gastric carcinogenesis. Further validation is needed to develop noninvasive diagnostic tools and targeted therapies to improve the outcomes of patients with GC.

Comparison of protein characterization using In solution and S-Trap digestion methods for proteomics.

Protein extraction and digestion are important analytical steps in the study of proteomics. The use of sodium dodecyl sulfate (SDS) buffer makes it possible to effectively analyze various proteins. Its use was evaluated using the S-Trap digestion method and compared to the traditional In solution digestion method. Differences in protein composition were examined for each protein preparation method. S-Trap digestion followed by SDS buffer extraction clearly increased the number of identified proteins, including more mitochondrial and membrane-related proteins. The S-Trap digestion method with 5% SDS buffer was applied to the pellet remaining from the removal of RIPA buffer-soluble proteins, which identified more extracellular space proteins than the conventional S-Trap digestion method. S-Trap digestion of the pellet was particularly advantageous for identifying proteins located inside multilayer membranes.

Transcriptome-metabolome-wide association study (TMWAS) in rats revealed a potential carcinogenic effect of DEHP in thyroid associated with eicosanoids.

The incidence of thyroid cancer (TC) has increased considerably in the last few decades. Environmental factors, including plasticizers, are recognized as potential risks leading to thyroid cancer in humans. In this study, we used a transcriptome-metabolome-wide association study to find the unidentified carcinogenic mechanism of di-2-ethylhexyl phthalate (DEHP) in thyroid and biomarkers for non-invasive diagnosis. Rats were treated with different doses of DEHP (0, 0.3, 3, 30, 150 mg DEHP/kg bw/day) for 13 weeks. Then, the thyroids were processed for Ki67 staining and RNA-seq. Also, 17-h urine samples were collected for high-resolution metabolomics analysis. After a high dose of DEHP exposure, the terminal body weights and the thyroid and parathyroid glands weights were not altered. However, the liver weights and numbers of Ki67-positive cells were increased. Further, multivariate statistical analysis revealed that metabolic shifts were considerably altered above 30 mg DEHP/kg bw/day. In RNA-seq analysis, some cancer-related genes were altered, including 18 upregulated and 9 downregulated transcripts. These cancer transcripts and whole metabolome data were integrated to uncover thyroid cancer-related metabolic pathways, which revealed that cancer-related transcripts had a network structure linked to eicosanoids such as leukotriene D4 and prostaglandin. In brief, our study demonstrated that DEHP can induce thyroid hyperplasia through the eicosanoid-associated pathway, providing further insight into the mechanism of DEHP-associated thyroid cancer.

An in vitro study on the differentiated metabolic mechanism of chloroquine-resistant Plasmodium falciparum using high-resolution metabolomics.

Chloroquine (CQ) is an important drug used therapeutically for treatment of malaria. However, due to limited number of studies on metabolic targets of chloroquine (CQ), it is difficult to attribute mechanisms underlying resistance associated with usage of this drug. The present study aimed to investigate the metabolic signatures of CQ-resistant Plasmodium falciparum (PfDd2) compared to CQ-sensitive Plasmodium falciparum (Pf3D7). Both Pf3D7 and PfDd2 were treated with CQ at 200 nM for 48 hr; thereafter, the harvested red blood cells (RBCs) and media were subjected to microscopy and high-resolution metabolomics (HRM). Glutathione, γ-L-glutamyl-L-cysteine, spermidine, inosine monophosphate, alanine, and fructose-1,6-bisphosphate were markedly altered in PfDd2 of RBC. In the media, cysteine, cysteic acid, spermidine, phenylacetaldehyde, and phenylacetic acid were significantly altered in PfDd2. These differential metabolic signatures related signaling pathways of PfDd2, such as oxidative stress pathway and glycolysis may provide evidence for understanding the resistance mechanism and pathogenesis of the CQ-resistant parasite.

Global metabolomics analysis of serum from humans at risk of thrombotic stroke.

We aimed to determine the serum concentrations of altered compounds to understand the changes in metabolism and pathophysiology that occur prior to thrombotic stroke. In this prospective cohort study, high-resolution metabolomics (HRM) was employed to analyze serum samples obtained from patients at risk of stroke (n = 99) and non-risk controls (n = 301). Partial least-squares discriminant analysis (PLS-DA), along with univariate analysis using a false discovery rate (FDR) of q = 0.05 were employed to identify the discriminant metabolic profiles and to determine significantly different metabolites between healthy control and stroke risk groups. PLS-DA satisfactorily separated the stroke risk sera from control sera. Additionally, these discriminant metabolic profiles were not related to hypertension, smoking, diabetes mellitus, or insulin sensitivity. A group of 35 metabolites, most of them amino acids, that were capable of discriminating stroke risk sera from controls were identified using untargeted metabolomics. Further, the targeted metabolomics approach confirmed that the quantified concentrations of l-tryptophan, 3-methoxytyramine, methionine, homocysteinesulfinic acid, cysteine, isoleucine, carnitine, arginine, linoleic acid, and sphingosine were specifically elevated in the sera of patients who were later diagnosed with stroke. Our untargeted and targeted metabolomics approaches support investigating these compounds as novel biomarkers for early and non-invasive detection of thrombotic stroke.

Noninvasive Serum Metabolomic Profiling Reveals Elevated Kynurenine Pathway's Metabolites in Humans with Prostate Cancer.

This study aimed to apply high-resolution metabolomics to detect compounds that may contribute significantly to prostate cancer (PCa) development. The test population's sera for evaluating the metabolic differences consisted of healthy control ( n = 96) and PCa ( n = 50) groups. PCa patients were further divided into two groups based on whether their PSA level was >4 ( n = 25) or <4 ( n = 25). Univariate analysis was performed with the false discovery rate (FDR) at q = 0.05 to determine significantly different metabolites. Principal component analysis (PCA) and hierarchical clustering analysis (HCA) clearly distinguished healthy subjects from PCa groups, while no significant difference was observed in PCa patients with PSA level < 4 or > 4. Mummichog, in combination with the KEGG and MetaboAnalyst, showed that tryptophan metabolism along the kynurenine pathway was most significantly enriched, with -log ( p) < 0.05. l-Tryptophan, kynurenine, anthranilate, isophenoxazine, glutaryl-CoA, ( S)-3-hydroxybutanoyl-CoA, acetoacetyl-CoA, and acetyl-CoA were upregulated in correlation with the PSA level of PCa patients; in contrast, indoxyl, indolelactate, and indole-3-ethanol, involved in the alternative pathway, were downregulated in the PCa patients. Validation and quantification of potential metabolites by MS/MS further confirmed the disruption of tryptophan, kynurenine, and anthranilate, suggesting that the metabolites of this pathway are potential biomarkers in patients with PCa.

Discovery of metabolic alterations in the serum of patients infected with Plasmodium spp. by high-resolution metabolomics.

INTRODUCTION: Despite the advances in diagnosis and treatment, malaria has still not been eradicated. Metabolic interactions between the host and Plasmodium may present novel targets for malaria control, but such interactions are yet to be deciphered. An exploration of metabolic interactions between humans and two Plasmodium species by high-resolution metabolomics may provide fundamental insights that can aid the development of a new strategy for the control of malaria. OBJECTIVES: This study aimed at exploring the metabolic changes in the sera of patients infected with Plasmodium falciparum and Plasmodium vivax. METHODS: Uni- and multivariate metabolomic analyses were performed on the sera of four groups of patients, namely normal control (N, n = 100), P. falciparum-infected patients (PF, n = 21), P. vivax-infected patients (PV, n = 74), and non-malarial pyretic patients (Pyr, n = 25). RESULTS: Univariate and multivariate analyses of N, PF, and PV groups showed differential metabolic phenotypes and subsequent comparisons in pairs revealed significant features. Pathway enrichment test with significant features showed the affected pathways, namely glycolysis/gluconeogenesis for PF and retinol metabolism for PV. The metabolites belonging to the affected pathways included significantly low 2,3-diphosphoglycerate and glyceraldehyde-3-phosphate in the sera of PF. The sera of PV had significantly low levels of retinol but high levels of retinoic acid. CONCLUSION: Our study reveals metabolic alterations induced by Plasmodium spp. in human serum and would serve as a milestone in the development of novel anti-malarial strategies.

High-resolution metabolomics to identify urine biomarkers in corticosteroid-resistant asthmatic children.

BACKGROUND: Corticosteroid (CS) treatment has been established as the first anti-inflammatory treatment for adults and children with asthma. However, a subset of patients fails to respond to combined systemic and inhaled CS treatment. OBJECTIVE: This study was aimed at further understanding CS resistance among children with severe asthma. METHODS: High-resolution metabolomics was performed on urine samples from CS-respondent (n = 15) and CS-nonrespondent (n = 15) children to determine possible urine biomarkers related to CS resistance. The metabolic phenotypes of CS responders and CS nonresponders were analyzed using bioinformatics including Manhattan plot with false- discovery rate, hierarchical cluster analysis, Kyoto Encyclopedia Genes and Genomes, and Mummichog pathway analysis. RESULTS: The 2-way hierarchical cluster analysis study determined 30 metabolites showing significantly different levels between CS responders and CS nonresponders. The important metabolites annotated were 3,6-dihydronicotinic acid (126.05 m/z, RT: 106, [M+H]+), 3-methoxy-4-hydroxyphenyl(ethylene)glycol (185.05 m/z, RT: 155, [M+H]+), 3,4-dihydroxy-phenylalanine (198.07 m/z, RT: 446, [M+H]+), γ-glutamylcysteine (236.06 m/z, RT: 528, [M+S(34)+H]+), Cys-Gly, (253.06 m/z, RT: 528, [M-NH3+H]+), and reduced Flavin mononucleotide (517.0794 m/z, RT: 533, [M+NaCl]+). Tyrosine metabolism, degradation of aromatic compounds, and glutathione metabolism are suggested to be significant pathways relating to CS resistance. CONCLUSIONS: High-resolution metabolomics is a promising approach in asthma research. Five candidate markers were identified to be related to CS-resistant children with severe asthma. These compounds, upon validation, may contribute further in the understanding of CS resistance among children with severe asthma through the use of urine.

High resolution metabolomics to discriminate compounds in serum of male lung cancer patients in South Korea.

BACKGROUND: The cancer death rate escalated during 20th century. In South Korea, lung cancer is expected to contribute 12,736 deaths in men, the highest amount among all cancers. Several risk factors may increase the chance to acquiring lung cancer, with mostly related to exogenous compounds found in cigarette smoke and synthetic manufacturing materials. As the mortality rate of lung cancer increases, deeper understanding is necessary to explore risk factors that may lead to this malignancy. In this regard, this study aims to apply high resolution metabolomics (HRM) using LC-MS to detect significant compounds that might contribute in inducing lung cancer and find the correlation of these compounds to the subjects' smoking habit. METHODS: The comparison was made between healthy control and lung cancer groups for metabolic differences. Further analyses to determine if these differences are related to tobacco-induced lung cancer (past-smoker control vs. past-smoker lung cancer patients (LCPs) and non-smoker control vs. current-smoker LCPs) were selected. The univariate analysis was performed, including a false discovery rate (FDR) of q = 0.05, to determine the significant metabolites between the analyses. Hierarchical clustering analysis (HCA) was done to discriminate metabolites between the control and case subjects. Selected compounds based on significant m/z features of human serum then experienced MS/MS examination, showing that for many m/z, the patterns of ion dissociation matched with standards. Then, the significant metabolites were identified using Metlin database and features were mapped on the human metabolic pathway mapping tool of the Kyoto Encyclopedia of Genes and Genomes (KEGG). RESULTS: Using metabolomics-wide association studies, metabolic changes were observed among control group and lung cancer patients. Bisphenol A (211.11, [M + H-H2O](+)), retinol (287.23, [M + H](+)) and L-proline (116.07, [M + H](+)) were among the significant compounds found to have contributed in the discrimination between these groups, suggesting that these compounds might be related in the development of lung cancer. Retinol has been seen to have a correlation with smoking while both bisphenol A and L-proline were found to be unrelated. CONCLUSIONS: Two potential biomarkers, retinol and L-proline, were identified and these findings may create opportunities for the development of new lung cancer diagnostic tools.

Significance of metabolite extraction method for evaluating sulfamethazine toxicity in adult zebrafish using metabolomics.

Recently, environmental metabolomics has been introduced as a next generation environmental toxicity method which helps in evaluating toxicity of bioactive compounds to non-target organisms. In general, efficient metabolite extraction from target cells is one of the keys to success to better understand the effects of toxic substances to organisms. In this regard, the aim of this study is (1) to compare two sample extraction methods in terms of abundance and quality of metabolites and (2) investigate how this could lead to difference in data interpretation using pathway analysis. For this purpose, the antibiotic sulfamethazine and zebrafish (Danio rerio) were selected as model toxic substance and target organism, respectively. The zebrafish was exposed to four different sulfamethazine concentrations (0, 10, 30, and 50mg/L) for 72h. Metabolites were extracted using two different methods (Bligh and Dyer and solid-phase extraction). A total of 13,538 and 12,469 features were detected using quadrupole time-of-flight liquid chromatography mass spectrometry (QTOF LC-MS). Of these metabolites, 4278 (Bligh and Dyer) and 332 (solid phase extraction) were found to be significant after false discovery rate adjustment at a significance threshold of 0.01. Metlin and KEGG pathway analysis showed comprehensive information from fish samples extracted using Bligh and Dyer compared to solid phase extraction. This study shows that proper selection of sample extraction method is critically important for interpreting and analyzing the toxicity data of organisms when metabolomics is applied.

High-resolution metabolomics to discover potential parasite-specific biomarkers in a Plasmodium falciparum erythrocytic stage culture system.

BACKGROUND: Current available malaria diagnostic methods each have some limitations to meet the need for real-time and large-scale screening of asymptomatic and low density malaria infection at community level. It was proposed that malaria parasite-specific low molecular-weight metabolites could be used as biomarkers for the development of a malaria diagnostic tool aimed to address this diagnostic challenge. In this study, high resolution metabolomics (HRM) was employed to identify malaria parasite-specific metabolites in Plasmodium falciparum in vitro culture samples. METHODS: Supernatants were collected at 12 hours interval from 3% haematocrit in vitro 48-hour time-course asynchronized culture system of P. falciparum. Liquid chromatography coupled with high resolution mass spectrometry was applied to discover potential parasite-specific metabolites in the cell culture supernatant. A metabolome-wide association study was performed to extract metabolites using Manhattan plot with false discovery rate (FDR) and hierarchical cluster analysis. The significant metabolites based on FDR cutoff were annotated using Metlin database. Standard curves were created using corresponding chemical compounds to accurately quantify potential Plasmodium-specific metabolites in culture supernatants. RESULTS: The number of significant metabolite features was 1025 in the supernatant of the Plasmodium infected culture based on Manhattan plot with FDR q=0.05. A two way hierarchical cluster analysis showed a clear segregation of the metabolic profile of parasite infected supernatant from non-infected supernatant at four time points during the 48 hour culture. Among the 1025 annotated metabolites, the intensities of four molecules were significantly increased with culture time suggesting a positive association between the quantity of these molecules and level of parasitaemia: i) 3-methylindole, a mosquito attractant, ii) succinylacetone, a haem biosynthesis inhibitor, iii) S-methyl-L-thiocitrulline, a nitric oxide synthase inhibitor, and iv) O-arachidonoyl glycidol, a fatty acid amide hydrolase inhibitor, The highest concentrations of 3-methylindole and succinylacetone were 178 ± 18.7 pmoles at 36 hours and 157±30.5 pmoles at 48 hours respectively in parasite infected supernatant. CONCLUSION: HRM with bioinformatics identified four potential parasite-specific metabolite biomarkers using in vitro culture supernatants. Further study in malaria infected human is needed to determine presence of the molecules and its relationship with parasite densities.

Predicting network activity from high throughput metabolomics.

The functional interpretation of high throughput metabolomics by mass spectrometry is hindered by the identification of metabolites, a tedious and challenging task. We present a set of computational algorithms which, by leveraging the collective power of metabolic pathways and networks, predict functional activity directly from spectral feature tables without a priori identification of metabolites. The algorithms were experimentally validated on the activation of innate immune cells.

Global perspectives on bipolar disorder treatment: in-depth comparative analysis of international guidelines for medication selection.

BACKGROUND: Bipolar disorder, a chronic mental health condition characterised by fluctuations in mood, energy and functionality, affects millions of individuals worldwide. Its management requires a comprehensive approach, and, as such, treatment guidelines have a pivotal role in guiding clinicians to alleviate symptoms, prevent relapse and enhance overall patient well-being. However, the treatment landscape is far from homogenous, with significant variations existing across different countries. AIMS: This study aimed to explore and compare treatment guidelines for bipolar disorder in various regions, shedding light on the factors that influence therapeutic approaches and thus offering insights that could contribute to the ongoing refinement of evidence-based practices in management. METHOD: The study explores various international treatment guidelines for bipolar disorder that have been updated after 2014. Guidelines from the UK, Canada, Australia/New Zealand, South Korea and the International College of Neuropsychopharmacology are scrutinised to identify factors contributing to the observed differences among them. RESULTS: The variations in recommended drugs across guidelines arise from the approaches employed in guideline development - whether relying on expert consensus or meta-analysis results. Timing disparities in conducting these analyses and the selection of studies also exert influence. Moreover, differences in metabolic enzymes among diverse races and the health policies implemented by individual nations play a significant part in shaping these differences. CONCLUSION: The primary hindrance to consistent treatment conclusions lies in the scarcity of high-quality research results, leading to variations in guidelines. Enhancing evidence-based recommendations necessitates the undertaking of large-scale studies dedicated to assessing treatments for bipolar disorder.

Korean Red Ginseng and Rb1 restore altered social interaction, gene expressions in the medial prefrontal cortex, and gut metabolites under post-weaning social isolation in mice.

Background: Post-weaning social isolation (SI) reduces sociability, gene expressions including myelin genes in the medial prefrontal cortex (mPFC), and alters microbiome compositions in rodent models. Korean Red Ginseng (KRG) and its major ginsenoside Rb1 have been reported to affect myelin formation and gut metabolites. However, their effects under post-weaning SI have not been investigated. This study investigated the effects of KRG and Rb1 on sociability, gene expressions in the mPFC, and gut metabolites under post-weaning SI. Methods: C57BL/6J mice were administered with water or KRG (150, 400 mg/kg) or Rb1 (0.1 mg/kg) under SI or regular environment (RE) for 2 weeks during the post-weaning period (P21-P35). After this period, mice underwent a sociability test, and then brains and ceca were collected for qPCR/immunohistochemistry and non-targeted metabolomics, respectively. Results: SI reduced sociability compared to RE; however, KRG (400 mg/kg) and Rb1 significantly restored sociability under SI. In the mPFC, expressions of genes related to myelin, neurotransmitter, and oxidative stress were significantly reduced in mice under SI compared to RE conditions. Under SI, KRG and Rb1 recovered the altered expressions of several genes in the mPFC. In gut metabolomics, 313 metabolites were identified as significant among 3027 detected metabolites. Among the significantly changed metabolites in SI, some were recovered by KRG or Rb1, including metabolites related to stress axis, inflammation, and DNA damage. Conclusion: Altered sociability, gene expression levels in the mPFC, and gut metabolites induced by two weeks of post-weaning SI were at least partially recovered by KRG and Rb1.

Hybrid feature detection and information accumulation using high-resolution LC-MS metabolomics data.

Feature detection is a critical step in the preprocessing of liquid chromatography-mass spectrometry (LC-MS) metabolomics data. Currently, the predominant approach is to detect features using noise filters and peak shape models based on the data at hand alone. Databases of known metabolites and historical data contain information that could help boost the sensitivity of feature detection, especially for low-concentration metabolites. However, utilizing such information in targeted feature detection may cause large number of false positives because of the high levels of noise in LC-MS data. With high-resolution mass spectrometry such as liquid chromatograph-Fourier transform mass spectrometry (LC-FTMS), high-confidence matching of peaks to known features is feasible. Here we describe a computational approach that serves two purposes. First it boosts feature detection sensitivity by using a hybrid procedure of both untargeted and targeted peak detection. New algorithms are designed to reduce the chance of false-positives by nonparametric local peak detection and filtering. Second, it can accumulate information on the concentration variation of metabolites over large number of samples, which can help find rare features and/or features with uncommon concentration in future studies. Information can be accumulated on features that are consistently found in real data even before their identities are found. We demonstrate the value of the approach in a proof-of-concept study. The method is implemented as part of the R package apLCMS at http://www.sph.emory.edu/apLCMS/ .

Nutritional metabolomics: progress in addressing complexity in diet and health.

Nutritional metabolomics is rapidly maturing to use small-molecule chemical profiling to support integration of diet and nutrition in complex biosystems research. These developments are critical to facilitate transition of nutritional sciences from population-based to individual-based criteria for nutritional research, assessment, and management. This review addresses progress in making these approaches manageable for nutrition research. Important concept developments concerning the exposome, predictive health, and complex pathobiology serve to emphasize the central role of diet and nutrition in integrated biosystems models of health and disease. Improved analytic tools and databases for targeted and nontargeted metabolic profiling, along with bioinformatics, pathway mapping, and computational modeling, are now used for nutrition research on diet, metabolism, microbiome, and health associations. These new developments enable metabolome-wide association studies (MWAS) and provide a foundation for nutritional metabolomics, along with genomics, epigenomics, and health phenotyping, to support the integrated models required for personalized diet and nutrition forecasting.

Microbiota dysbiosis associated with type 2 diabetes-like effects caused by chronic exposure to a mixture of chlorinated persistent organic pollutants in zebrafish.

Mixtures of chlorinated persistent organic pollutants (C-POPs-Mix) are chemically related risk factors for type 2 diabetes mellitus (T2DM); however, the effects of chronic exposure to C-POPs-Mix on microbial dysbiosis remain poorly understood. Herein, male and female zebrafish were exposed to C-POPs-Mix at a 1:1 ratio of five organochlorine pesticides and Aroclor 1254 at concentrations of 0.02, 0.1, and 0.5 µg/L for 12 weeks. We measured T2DM indicators in blood and profiled microbial abundance and richness in the gut as well as transcriptomic and metabolomic alterations in the liver. Exposure to C-POPs-Mix significantly increased blood glucose levels while decreasing the abundance and alpha diversity of microbial communities only in females at concentrations of 0.02 and 0.1 µg/L. The majorly identified microbial contributors to microbial dysbiosis were Bosea minatitlanensis, Rhizobium tibeticum, Bifidobacterium catenulatum, Bifidobacterium adolescentis, and Collinsella aerofaciens. PICRUSt results suggested that altered pathways were associated with glucose and lipid production and inflammation, which are linked to changes in the transcriptome and metabolome of the zebrafish liver. Metagenomics outcomes revealed close relationships between intestinal and liver disruptions to T2DM-related molecular pathways. Thus, microbial dysbiosis in T2DM-triggered zebrafish occurred as a result of chronic exposure to C-POPs-Mix, indicating strong host-microbiome interactions.

Identification of Novel Biomarkers for Early Diagnosis of Atherosclerosis Using High-Resolution Metabolomics.

Atherosclerosis (AS) is a metabolic disorder and the pre-stage of several cardiovascular diseases, including myocardial infarction, stroke, and angina pectoris. Early detection of AS can provide the opportunity for effective management and better clinical results, along with the prevention of further progression of the disease. In the current study, an untargeted and targeted metabolomic approach was used to identify possible metabolic signatures that have altered levels in AS patients. A total of 200 serum samples from individuals with AS and normal were analyzed via liquid chromatography-high-resolution mass spectrometry. Univariate and multivariate analysis approaches were used to identify differential metabolites. A group of metabolites associated with bile acids, amino acids, steroid hormones, and purine metabolism were identified that are capable of distinguishing AS-risk sera from normal. Further, the targeted metabolomics approach confirmed that six metabolites, namely taurocholic acid, cholic acid, cortisol, hypoxanthine, trimethylamine N-oxide (TMAO), and isoleucine, were found to be significantly upregulated, while the concentrations of glycoursodeoxycholic acid, glycocholic acid, testosterone, leucine, methionine, phenylalanine, tyrosine, and valine were found to be significantly downregulated in the AS-risk sera. The receiver operating characteristic curves of three metabolites, including cortisol, hypoxanthine, and isoleucine, showed high sensitivity and specificity. Taken together, these findings suggest cortisol, hypoxanthine, and isoleucine as novel biomarkers for the early and non-invasive detection of AS. Thus, this study provides new insights for further investigations into the prevention and management of AS.

The effects of dietary self-monitoring intervention on anthropometric and metabolic changes via a mobile application or paper-based diary: a randomized trial.

BACKGROUND/OBJECTIVES: Weight loss via a mobile application (App) or a paper-based diary (Paper) may confer favorable metabolic and anthropometric changes. SUBJECTS/METHODS: A randomized parallel trial was conducted among 57 adults whose body mass indices (BMIs) were 25 kg/m2 or greater. Participants randomly assigned to either the App group (n = 30) or the Paper group (n = 27) were advised to record their foods and supplements through App or Paper during the 12-week intervention period. Relative changes of anthropometries and biomarker levels were compared between the 2 intervention groups. Untargeted metabolic profiling was identified to discriminate metabolic profiles. RESULTS: Out of the 57 participants, 54 participants completed the trial. Changes in body weight and BMI were not significantly different between the 2 groups (P = 0.11). However, body fat and low-density lipoprotein (LDL)-cholesterol levels increased in the App group but decreased in the Paper group, and the difference was statistically significant (P = 0.03 for body fat and 0.02 for LDL-cholesterol). In the metabolomics analysis, decreases in methylglyoxal and (S)-malate in pyruvate metabolism and phosphatidylcholine (lecithin) in linoleic acid metabolism from pre- to post-intervention were observed in the Paper group. CONCLUSIONS: In the 12-week randomized parallel trial of weight loss through a App or a Paper, we found no significant difference in change in BMI or weight between the App and Paper groups, but improvement in body fatness and LDL-cholesterol levels only in the Paper group under the circumstances with minimal contact by dietitians or health care providers. Trial Registration: Clinical Research Information Service Identifier: KCT0004226.

Advanced assessment through intact glycopeptide analysis of Infliximab's biologics and biosimilar.

Characterization of therapeutic monoclonal antibodies (mAbs) represents a major challenge for analytical sciences due to their heterogeneity associated with post-translational modifications (PTMs). The protein glycosylation requires comprehensive identification, which could influence on the mAbs' structure and their function. Here, we demonstrated high-resolution tandem mass spectrometry with an ultra-high-performance liquid chromatography for characterization and comparison between biologics and biosimilar of infliximab at an advanced level. Comparing the N- and O-glycopeptides profiles, a total of 49 and 54 glycopeptides was identified for each product of the biologics and biosimilar, respectively. We also discovered one novel N-glycosylation site at the light chain from both biopharmaceuticals and one novel O-glycopeptide at the heavy chain from only biosimilar. Site-specific glycopeptide analysis process will be a robust and useful technique for evaluating therapeutic mAbs and complex glycoprotein products.