Intestinal epithelial dopamine receptor signaling drives sex-specific disease exacerbation in a mouse model of multiple sclerosis.

Although the gut microbiota can influence central nervous system (CNS) autoimmune diseases, the contribution of the intestinal epithelium to CNS autoimmunity is less clear. Here, we showed that intestinal epithelial dopamine D2 receptors (IEC DRD2) promoted sex-specific disease progression in an animal model of multiple sclerosis. Female mice lacking Drd2 selectively in intestinal epithelial cells showed a blunted inflammatory response in the CNS and reduced disease progression. In contrast, overexpression or activation of IEC DRD2 by phenylethylamine administration exacerbated disease severity. This was accompanied by altered lysozyme expression and gut microbiota composition, including reduced abundance of Lactobacillus species. Furthermore, treatment with N2-acetyl-L-lysine, a metabolite derived from Lactobacillus, suppressed microglial activation and neurodegeneration. Taken together, our study indicates that IEC DRD2 hyperactivity impacts gut microbial abundances and increases susceptibility to CNS autoimmune diseases in a female-biased manner, opening up future avenues for sex-specific interventions of CNS autoimmune diseases.

Four-Dimensional Untargeted Profiling of N-Acylethanolamine Lipids in the Mouse Brain Using Ion Mobility-Mass Spectrometry.

N-Acylethanolamines (NAE) are a class of essential signaling lipids that are involved in a variety of physiological processes, such as energy homeostasis, anti-inflammatory responses, and neurological functions. NAE lipids are functionally different yet structurally similar and often have low concentrations in biological systems. Therefore, the comprehensive analysis of NAE lipids in complex biological matrices is very challenging. In this work, we developed an ion mobility-mass spectrometry (IM-MS) based four-dimensional (4D) untargeted technology for comprehensive analysis of NAE lipids. First, we employed the picolinyl derivatization to significantly improve ionization sensitivity of NAE lipids by 2-9-fold. Next, we developed a two-step quantitative structure-retention relationship (QSRR) strategy and used the AllCCS software to curate a 4D library for 170 NAE lipids with information on m/z, retention time, collision cross-section, and MS/MS spectra. Then, we developed a 4D untargeted technology empowered by the 4D library to support unambiguous identifications of NAE lipids. Using this technology, we readily identified a total of 68 NAE lipids across different biological samples. Finally, we used the 4D untargeted technology to comprehensively quantify 47 NAE lipids in 10 functional regions in the mouse brain and revealed a broad spectrum of the age-associated changes in NAE lipids across brain regions. We envision that the comprehensive analysis of NAE lipids will strengthen our understanding of their functions in regulating distinct physiological activities.

Analgesia and stress attenuation of ultrasound-guided modified pectoral nerve block type-II with different volumes of 0.3% ropivacaine in patients undergoing modified radical mastectomy for breast cancer: A prospective parallel randomized double-blind controlled clinical trial.

WHAT IS KNOWN AND OBJECTIVE: A previous randomized clinical trial concluded that an optimal concentration of 0.3% ropivacaine could provide satisfactory analgesia for breast cancer patients undergoing modified radical mastectomy. We wondered if a smaller volume (30 ml vs. 40 ml) of 0.3% ropivacaine could still provide adequate analgesia in an ultrasound-guided PECS II block in modified radical mastectomy. METHODS: We performed a prospective parallel randomized double-blind controlled clinical trial. Eligible patients were assigned to either the P30 or P40 group (30 or 40 ml of 0.3% ropivacaine, respectively). The skin area of hypoesthesia, anaesthetic plane determined with ultrasound, pain visual analogue scale (VAS), anaesthetic dosages, and complications were recorded. Serum levels of interleukin-1ß and interleukin-6 were measured postoperatively. RESULTS AND DISCUSSION: A total of 40 patients completed the trials, with 20 patients in each group. Although the skin area of hypoesthesia and the anaesthetic planes were significantly larger in the P40 group compared with the P30 group (p < 0.05), the VAS, analgesic and opioid doses, serum cytokine levels, anaesthetic toxicity, and complications had no significant differences between the two groups. WHAT IS NEW AND CONCLUSION: Compared with 40 ml, 30 ml of 0.3% ropivacaine could provide adequate analgesia and reduce surgical stress in patients undergoing modified radical mastectomy for breast cancer.

DecoMetDIA: Deconvolution of Multiplexed MS/MS Spectra for Metabolite Identification in SWATH-MS-Based Untargeted Metabolomics.

SWATH-MS-based data-independent acquisition mass spectrometry (DIA-MS) technology has been recently developed for untargeted metabolomics due to its capability to acquire all MS2 spectra with high quantitative accuracy. However, software tools for deconvolving multiplexed MS/MS spectra from SWATH-MS with high efficiency and high quality are still lacking in untargeted metabolomics. Here, we developed a new software tool, namely, DecoMetDIA, to deconvolve multiplexed MS/MS spectra for metabolite identification and support the SWATH-based untargeted metabolomics. In DecoMetDIA, multiple model peaks are selected to model the coeluted and unresolved chromatographic peaks of fragment ions in multiplexed spectra and decompose them into a linear combination of the model peaks. DecoMetDIA enabled us to reconstruct the MS2 spectra of metabolites from a variety of different biological samples with high coverages. We also demonstrated that the deconvolved MS2 spectra from DecoMetDIA were of high accuracy through comparison to the experimental MS2 spectra from data-dependent acquisition (DDA). Finally, about 90% of deconvolved MS2 spectra in various biological samples were successfully annotated using software tools such as MetDNA and Sirius. The results demonstrated that the deconvolved MS2 spectra obtained from DecoMetDIA were accurate and valid for metabolite identification and structural elucidation. The comparison of DecoMetDIA to other deconvolution software such as MS-DIAL demonstrated that it performs very well for small polar metabolites. DecoMetDIA software is freely available at https://github.com/ZhuMSLab/DecoMetDIA .

SWATHtoMRM: Development of High-Coverage Targeted Metabolomics Method Using SWATH Technology for Biomarker Discovery.

The complexity of metabolome presents a great analytical challenge for quantitative metabolite profiling, and restricts the application of metabolomics in biomarker discovery. Targeted metabolomics using multiple-reaction monitoring (MRM) technique has excellent capability for quantitative analysis, but suffers from the limited metabolite coverage. To address this challenge, we developed a new strategy, namely, SWATHtoMRM, which utilizes the broad coverage of SWATH-MS technology to develop high-coverage targeted metabolomics method. Specifically, SWATH-MS technique was first utilized to untargeted profile one pooled biological sample and to acquire the MS2 spectra for all metabolites. Then, SWATHtoMRM was used to extract the large-scale MRM transitions for targeted analysis with coverage as high as 1000-2000 metabolites. Then, we demonstrated the advantages of SWATHtoMRM method in quantitative analysis such as coverage, reproducibility, sensitivity, and dynamic range. Finally, we applied our SWATHtoMRM approach to discover potential metabolite biomarkers for colorectal cancer (CRC) diagnosis. A high-coverage targeted metabolomics method with 1303 metabolites in one injection was developed to profile colorectal cancer tissues from CRC patients. A total of 20 potential metabolite biomarkers were discovered and validated for CRC diagnosis. In plasma samples from CRC patients, 17 out of 20 potential biomarkers were further validated to be associated with tumor resection, which may have a great potential in assessing the prognosis of CRC patients after tumor resection. Together, the SWATHtoMRM strategy provides a new way to develop high-coverage targeted metabolomics method, and facilitates the application of targeted metabolomics in disease biomarker discovery. The SWATHtoMRM program is freely available on the Internet ( http://www.zhulab.cn/software.php ).

MetDIA: Targeted Metabolite Extraction of Multiplexed MS/MS Spectra Generated by Data-Independent Acquisition.

With recent advances in mass spectrometry, there is an increased interest in data-independent acquisition (DIA) techniques for metabolomics. With DIA technique, all metabolite ions are sequentially selected and isolated using a wide window to generate multiplexed MS/MS spectra. Therefore, DIA strategy enables a continuous and unbiased acquisition of all metabolites and increases the data dimensionality, but presents a challenge to data analysis due to the loss of the direct link between precursor ion and fragment ions. However, very few DIA data processing methods are developed for metabolomics application. Here, we developed a new DIA data analysis approach, namely, MetDIA, for targeted extraction of metabolites from multiplexed MS/MS spectra generated using DIA technique. MetDIA approach considers each metabolite in the spectral library as an analysis target. Ion chromatograms for each metabolite (both precursor ion and fragment ions) and MS(2) spectra are readily detected, extracted, and scored for metabolite identification, referred as metabolite-centric identification. A minimum metabolite-centric identification score responsible for 1% false positive rate of identification is determined as 0.8 using fully (13)C labeled biological extracts. Finally, the comparisons of our MetDIA method with data-dependent acquisition (DDA) method demonstrated that MetDIA could significantly detect more metabolites in biological samples, and is more accurate and sensitive for metabolite identifications. The MetDIA program and the metabolite spectral library is freely available on the Internet.

Density Functional and Kinetic Monte Carlo Study of Cu-Catalyzed Cross-Dehydrogenative Coupling Reaction of Thiazoles with THF.

Cu-catalyzed cross-dehydrogenative coupling (CDC) reaction of thiazoles with THF has been studied with the density functional theory method and kinetic Monte Carlo (kMC) simulations. Our results show that the previously proposed concerted metalation-deprotonation mechanism is unfavorable. On the basis of the DFT calculation and kMC simulation results, a new mechanism is proposed. In the favorable mechanism, the Cu(II) catalyst first combines with the thiazoles, forming an organocopper species that then binds to the THF radical. The rate-limiting step, C-C bond formation, is realized through an intramolecular structural rearrangement. The Cu catalyst works as a matchmaker to render the C-C bond formation. Kinetic Monte Carlo simulations demonstrate that one should be careful with the conclusions drawn simply from the calculated barriers.

Purine salvage-associated metabolites as biomarkers for early diagnosis of esophageal squamous cell carcinoma: a diagnostic model-based study.

Esophageal squamous cell carcinoma (ESCC) remains an important health concern in developing countries. Patients with advanced ESCC have a poor prognosis and survival rate, and achieving early diagnosis remains a challenge. Metabolic biomarkers are gradually gaining attention as early diagnostic biomarkers. Hence, this multicenter study comprehensively evaluated metabolism dysregulation in ESCC through an integrated research strategy to identify key metabolite biomarkers of ESCC. First, the metabolic profiles were examined in tissue and serum samples from the discovery cohort (n = 162; ESCC patients, n = 81; healthy volunteers, n = 81), and ESCC tissue-induced metabolite alterations were observed in the serum. Afterward, RNA sequencing of tissue samples (n = 46) was performed, followed by an integrated analysis of metabolomics and transcriptomics. The potential biomarkers for ESCC were further identified by censoring gene-metabolite regulatory networks. The diagnostic value of the identified biomarkers was validated in a validation cohort (n = 220), and the biological function was verified. A total of 457 dysregulated metabolites were identified in the serum, of which 36 were induced by tumor tissues. The integrated analyses revealed significant alterations in the purine salvage pathway, wherein the abundance of hypoxanthine/xanthine exhibited a positive correlation with HPRT1 expression and tumor size. A diagnostic model was developed using two purine salvage-associated metabolites. This model could accurately discriminate patients with ESCC from normal individuals, with an area under the curve (AUC) (95% confidence interval (CI): 0.680-0.843) of 0.765 in the external cohort. Hypoxanthine and HPRT1 exerted a synergistic effect in terms of promoting ESCC progression. These findings are anticipated to provide valuable support in developing novel diagnostic approaches for early ESCC and enhance our comprehension of the metabolic mechanisms underlying this disease.

Metabolic features of adolescent major depressive disorder: A comparative study between treatment-resistant depression and first-episode drug-naive depression.

Major depressive disorder (MDD) is a psychiatric illness that can jeopardize the normal growth and development of adolescents. Approximately 40% of adolescent patients with MDD exhibit resistance to conventional antidepressants, leading to the development of Treatment-Resistant Depression (TRD). TRD is associated with severe impairments in social functioning and learning ability and an elevated risk of suicide, thereby imposing an additional societal burden. In this study, we conducted plasma metabolomic analysis on 53 adolescents diagnosed with first-episode drug-naïve MDD (FEDN-MDD), 53 adolescents with TRD, and 56 healthy controls (HCs) using hydrophilic interaction liquid chromatography-mass spectrometry (HILIC-MS) and reversed-phase liquid chromatography-mass spectrometry (RPLC-MS). We established a diagnostic model by identifying differentially expressed metabolites and applying cluster analysis, metabolic pathway analysis, and multivariate linear support vector machine (SVM) algorithms. Our findings suggest that adolescent TRD shares similarities with FEDN-MDD in five amino acid metabolic pathways and exhibits distinct metabolic characteristics, particularly tyrosine and glycerophospholipid metabolism. Furthermore, through multivariate receiver operating characteristic (ROC) analysis, we optimized the area under the curve (AUC) and achieved the highest predictive accuracy, obtaining an AUC of 0.903 when comparing FEDN-MDD patients with HCs and an AUC of 0.968 when comparing TRD patients with HCs. This study provides new evidence for the identification of adolescent TRD and sheds light on different pathophysiologies by delineating the distinct plasma metabolic profiles of adolescent TRD and FEDN-MDD.

Plasma metabolomics reveals the shared and distinct metabolic disturbances associated with cardiovascular events in coronary artery disease.

Risk prediction for subsequent cardiovascular events remains an unmet clinical issue in patients with coronary artery disease. We aimed to investigate prognostic metabolic biomarkers by considering both shared and distinct metabolic disturbance associated with the composite and individual cardiovascular events. Here, we conducted an untargeted metabolomics analysis for 333 incident cardiovascular events and 333 matched controls. The cardiovascular events were designated as cardiovascular death, myocardial infarction/stroke and heart failure. A total of 23 shared differential metabolites were associated with the composite of cardiovascular events. The majority were middle and long chain acylcarnitines. Distinct metabolic patterns for individual events were revealed, and glycerophospholipids alteration was specific to heart failure. Notably, the addition of metabolites to clinical markers significantly improved heart failure risk prediction. This study highlights the potential significance of plasma metabolites on tailed risk assessment of cardiovascular events, and strengthens the understanding of the heterogenic mechanisms across different events.

Dysregulations of amino acid metabolism and lipid metabolism in urine of children and adolescents with major depressive disorder: a case-control study.

RATIONALE: The mechanisms underlying major depressive disorder (MDD) in children and adolescents are unclear. Metabolomics has been utilized to capture metabolic signatures of various psychiatric disorders; however, urinary metabolic profile of MDD in children and adolescents has not been studied. OBJECTIVES: We analyzed urinary metabolites in children and adolescents with MDD to identify potential biomarkers and metabolic signatures. METHODS: Here, liquid chromatography-mass spectrometry was used to profile metabolites in urine samples from 192 subjects, comprising 80 individuals with antidepressant-naïve MDD (AN-MDD), 37 with antidepressant-treated MDD (AT-MDD) and 75 healthy controls (HC). We performed orthogonal partial least squares discriminant analysis to identify differential metabolites and employed logistic regression and receiver operating characteristic analysis to establish a diagnostic panel. RESULTS: In total, 143 and 71 differential metabolites were identified in AN-MDD and AT-MDD, respectively. These were primarily linked to lipid metabolism, molecular transport, and small molecule biochemistry. AN-MDD additionally exhibited dysregulated amino acid metabolism. Compared to HC, a diagnostic panel of seven metabolites displayed area under the receiver operating characteristic curves of 0.792 for AN-MDD, 0.828 for AT-MDD, and 0.799 for all MDD. Furthermore, the urinary metabolic profiles of children and adolescents with MDD significantly differed from those of adult MDD. CONCLUSIONS: Our research suggests dysregulated amino acid metabolism and lipid metabolism in the urine of children and adolescents with MDD, similar to results in plasma metabolomics studies. This contributes to the comprehension of mechanisms underlying children and adolescents with MDD.

Identifying plasma metabolic characteristics of major depressive disorder, bipolar disorder, and schizophrenia in adolescents.

Major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SCZ) are classified as major mental disorders and together account for the second-highest global disease burden, and half of these patients experience symptom onset in adolescence. Several studies have reported both similar and unique features regarding the risk factors and clinical symptoms of these three disorders. However, it is still unclear whether these disorders have similar or unique metabolic characteristics in adolescents. We conducted a metabolomics analysis of plasma samples from adolescent healthy controls (HCs) and patients with MDD, BD, and SCZ. We identified differentially expressed metabolites between patients and HCs. Based on the differentially expressed metabolites, correlation analysis, metabolic pathway analysis, and potential diagnostic biomarker identification were conducted for disorders and HCs. Our results showed significant changes in plasma metabolism between patients with these mental disorders and HCs; the most distinct changes were observed in SCZ patients. Moreover, the metabolic differences in BD patients shared features with those in both MDD and SCZ, although the BD metabolic profile was closer to that of MDD than to SCZ. Additionally, we identified the metabolites responsible for the similar and unique metabolic characteristics in multiple metabolic pathways. The similar significant differences among the three disorders were found in fatty acid, steroid-hormone, purine, nicotinate, glutamate, tryptophan, arginine, and proline metabolism. Interestingly, we found unique characteristics of significantly altered glycolysis, glycerophospholipid, and sphingolipid metabolism in SCZ; lysine, cysteine, and methionine metabolism in MDD and BD; and phenylalanine, tyrosine, and aspartate metabolism in SCZ and BD. Finally, we identified five panels of potential diagnostic biomarkers for MDD-HC, BD-HC, SCZ-HC, MDD-SCZ, and BD-SCZ comparisons. Our findings suggest that metabolic characteristics in plasma vary across psychiatric disorders and that critical metabolites provide new clues regarding molecular mechanisms in these three psychiatric disorders.

Ginsenoside Rg1/ADSCs supplemented with hyaluronic acid as the matrix improves rabbit temporomandibular joint osteoarthrosis.

OBJECTIVE: To investigate whether and how ginsenoside Rg1/ADSCs supplemented with hyaluronic acid as the matrix can improve rabbit temporomandibular joint osteoarthrosis. METHOD: Isolate and culture adipose stem cells, measure the activity of differentiated chondrocytes by MTT assay and expression of type II collagen in these cells by immunohistochemistry, in order to evaluate the effect of ginsenoside Rg1 on adipose stem cell proliferation and differentiation into chondrocytes.32 New Zealand white rabbits were randomly divided into four groups: blank group, model group, control group and experimental group, 8 in each group. Osteoarthritis model was established by intra-articular injection of papain. Two weeks after successful model building, medication was given for the rabbits in control group and experimental group. 0.6 mL ginsenoside Rg1/ ADSCs suspension was injected into superior joint space for the rabbits in control group, once a week; 0.6 mL ginsenoside Rg1/ ADSCs complex was injected for the rabbits in experimental group, once a week. RESULTS: Ginsenoside Rg1 can promote ADSCs-derived chondrocytes' activity and expression of type II collagen. Scanning electron microscopy histology images showed cartilage lesions of the experimental group was significantly improved in comparison with control group. CONCLUSION: Ginsenoside Rg1 can promote ADSCs differentiate into chondrocytes, and Ginsenoside Rg1/ADSCs supplemented with hyaluronic acid as the matrix can significantly improve rabbit temporomandibular joint osteoarthrosis.

A study on computer vision for facial emotion recognition.

Artificial intelligence has been successfully applied in various fields, one of which is computer vision. In this study, a deep neural network (DNN) was adopted for Facial emotion recognition (FER). One of the objectives in this study is to identify the critical facial features on which the DNN model focuses for FER. In particular, we utilized a convolutional neural network (CNN), the combination of squeeze-and-excitation network and the residual neural network, for the task of FER. We utilized AffectNet and the Real-World Affective Faces Database (RAF-DB) as the facial expression databases that provide learning samples for the CNN. The feature maps were extracted from the residual blocks for further analysis. Our analysis shows that the features around the nose and mouth are critical facial landmarks for the neural networks. Cross-database validations were conducted between the databases. The network model trained on AffectNet achieved 77.37% accuracy when validated on the RAF-DB, while the network model pretrained on AffectNet and then transfer learned on the RAF-DB results in validation accuracy of 83.37%. The outcomes of this study would improve the understanding of neural networks and assist with improving computer vision accuracy.

Discovery of decreased ferroptosis in male colorectal cancer patients with KRAS mutations.

Aberrant tumor metabolism is a hallmark of cancer in which metabolic rewiring can support tumor growth under nutrient deficient conditions. KRAS mutations occur in 35-45% of all colorectal cancer (CRC) cases and are difficult to treat. The relationship between mutant KRAS and aberrant metabolism in CRCs has not been fully explored and could be a target for intervention. We previously acquired non-targeted metabolomics data from 161 tumor tissues and 39 normal colon tissues from stage I-III chemotherapy naïve CRC patients. In this study, we revealed that tumors from male patients with KRAS mutations only, had several altered pathways that suppress ferroptosis, including glutathione biosynthesis, transsulfuration activity, and methionine metabolism. To validate this phenotype, MC38 CRC cells (KRAS G13R ) were treated with a ferroptosis inducer; RAS-selected lethal (RSL3). RSL3 altered metabolic pathways in the opposite direction to that seen in KRAS mutant tumors from male patients confirming a suppressed ferroptosis metabolic phenotype in these patients. We further validated gene expression data from an additional CRC patient cohort (Gene Expression Omnibus (GEO), and similarly observed differences in ferroptosis-related genes by sex and KRAS status. Further examination of the relationship between these genes and overall survival (OS) in the GEO cohort showed that KRAS mutant tumors are associated with poorer 5-year OS compared to KRAS wild type tumors, and only in male patients. Additionally, high compared to low expression of GPX4, FTH1, FTL , which suppressed ferroptosis, were associated with poorer 5-year OS only in KRAS mutant tumors from male CRC patients. Low compared to high expression of ACSL4 was associated with poorer OS for this group. Our results show that KRAS mutant tumors from male CRC patients have suppressed ferroptosis, and gene expression changes that suppress ferroptosis associate with adverse outcomes for these patients, revealing a novel potential avenue for therapeutic approaches.

Discovery of decreased ferroptosis in male colorectal cancer patients with KRAS mutations.

Aberrant tumor metabolism is a hallmark of cancer in which metabolic rewiring can support tumor growth under nutrient deficient conditions. KRAS mutations occur in 35-45% of all colorectal cancer (CRC) cases and are difficult to treat. The relationship between mutant KRAS and aberrant metabolism in CRCs has not been fully explored and could be a target for intervention. We previously acquired non-targeted metabolomics data from 161 tumor tissues and 39 normal colon tissues from stage I-III chemotherapy naïve CRC patients. In this study, we revealed that only in male patients, tumors with KRAS mutations had several altered pathways that suppress ferroptosis, including glutathione biosynthesis, transsulfuration activity, and methionine metabolism. To validate this phenotype, MC38 CRC cells (KRASG13R) were treated with a ferroptosis inducer; RAS-selected lethal (RSL3). RSL3 altered metabolic pathways in the opposite direction to that seen in KRAS mutant tumors from male patients confirming a suppressed ferroptosis metabolic phenotype in these patients. We further validated gene expression data from an additional CRC patient cohort (Gene Expression Omnibus (GEO)), and similarly observed differences in ferroptosis-related genes by sex and KRAS status. Further examination of the relationship between these genes and overall survival (OS) in the GEO cohort showed that KRAS mutant tumors are associated with poorer 5-year OS compared to KRAS wild type tumors, and only in male patients. Additionally, high compared to low expression of GPX4, FTH1, FTL, which suppress ferroptosis, were associated with poorer 5-year OS only in KRAS mutant tumors from male CRC patients. Additionally, low compared to high expression of ACSL4 was associated with poorer OS for this group. Our results show that KRAS mutant tumors from male CRC patients have suppressed ferroptosis, and gene expression changes that suppress ferroptosis associate with adverse outcomes for these patients, revealing a novel potential avenue for therapeutic approaches.

Urinary Paraben Concentrations and Associations with the Periconceptional Urinary Metabolome: Untargeted and Targeted Metabolomics Analyses of Participants from the Early Pregnancy Study.

BACKGROUND: Parabens, found in everyday items from personal care products to foods, are chemicals with endocrine-disrupting activity, which has been shown to influence reproductive function. OBJECTIVES: This study investigated whether urinary concentrations of methylparaben, propylparaben, or butylparaben were associated with the urinary metabolome during the periconceptional period, a critical window for female reproductive function. Changes to the periconceptional urinary metabolome could provide insights into the mechanisms by which parabens could impact fertility. METHODS: Urinary paraben concentrations were measured in paired pre- and postconception urine samples from 42 participants in the Early Pregnancy Study, a prospective cohort of 221 women attempting to conceive. We performed untargeted and targeted metabolomics analyses using ultrahigh-performance liquid chromatography quadrupole time-of-flight mass spectrometry. We used principal component analysis, orthogonal partial least-squares discriminant analysis, and permutation testing, coupled with univariate statistical analyses, to find metabolites associated with paraben concentration at the two time points. Potential confounders were identified with a directed acyclic graph and used to adjust results with multivariable linear regression. Metabolites were identified using fragmentation data. RESULTS: Seven metabolites were associated with paraben concentration (variable importance to projection score >1, false discovery rate-corrected q-value<0.1). We identified four diet-related metabolites to the Metabolomics Standards Initiative (MSI) certainty of identification level 2, including metabolites from smoke flavoring, grapes, and olive oil. One metabolite was identified to the class level only (MSI level 3). Two metabolites were unidentified (MSI level 4). After adjustment, three metabolites remained associated with methylparaben and propylparaben, two of which were diet-related. No metabolomic markers of endocrine disruption were associated with paraben concentrations. DISCUSSION: This study identified novel relationships between urinary paraben concentrations and diet-related metabolites but not with metabolites on endocrine-disrupting pathways, as hypothesized. It demonstrates the feasibility of integrating untargeted metabolomics data with environmental exposure information and epidemiological adjustment for confounders. The findings underscore a potentially important connection between diet and paraben exposure, with applications to nutritional epidemiology and dietary exposure assessment. https://doi.org/10.1289/EHP12125.

Trapped ion mobility spectrometry-mass spectrometry improves the coverage and accuracy of four-dimensional untargeted lipidomics.

Lipids play vital roles in many physiological and pathological processes in living organisms. Due to the high structural diversity and the numerous isomers and isobars of lipids, high-coverage and high-accuracy lipidomic analysis of complex biological samples remain the bottleneck to investigate lipid metabolism. Here, we developed the trapped ion mobility spectrometry-mass spectrometry (TIMS-MS) based four-dimensional untargeted lipidomics to support accurate lipid identification and quantification in biological samples. We first demonstrated that the TIMS based multi-dimensional separation improved the differentiations of isomeric and isobaric lipids, and increased the purity of precursor ion isolation and the quality of MS/MS spectra. Hyphenation of TIMS and PASEF technologies significantly improved the coverages of MS/MS spectra. These technological advantages jointly improved the coverage and accuracy of lipid identification in untargeted lipidomics. We further demonstrated that the CCS values of lipids acquired using TIMS were highly consistent with those from drift tube ion mobility spectrometry (DTIMS). Lipid identification and quantification results of NIST human plasma samples were also verified with inter-laboratory reports. Finally, we applied the TIMS-MS based untargeted lipidomics to characterize the spatial distributions of 1393 distinctive lipids in the mouse brain, and demonstrated that diverse lipid distributions and compositions among brain regions contributed to different functions of brain regions. Altogether, TIMS-MS based four-dimensional untargeted lipidomics significantly improved the coverage and accuracy of untargeted metabolomics, thereby facilitating a system-level understanding of lipid metabolism in biological organisms.

Metabolite annotation from knowns to unknowns through knowledge-guided multi-layer metabolic networking.

Liquid chromatography - mass spectrometry (LC-MS) based untargeted metabolomics allows to measure both known and unknown metabolites in the metabolome. However, unknown metabolite annotation is a major challenge in untargeted metabolomics. Here, we develop an approach, namely, knowledge-guided multi-layer network (KGMN), to enable global metabolite annotation from knowns to unknowns in untargeted metabolomics. The KGMN approach integrates three-layer networks, including knowledge-based metabolic reaction network, knowledge-guided MS/MS similarity network, and global peak correlation network. To demonstrate the principle, we apply KGMN in an in vitro enzymatic reaction system and different biological samples, with ~100-300 putative unknowns annotated in each data set. Among them, >80% unknown metabolites are corroborated with in silico MS/MS tools. Finally, we validate 5 metabolites that are absent in common MS/MS libraries through repository mining and synthesis of chemical standards. Together, the KGMN approach enables efficient unknown annotations, and substantially advances the discovery of recurrent unknown metabolites for common biological samples from model organisms, towards deciphering dark matter in untargeted metabolomics.

Serum metabolic traits reveal therapeutic toxicities and responses of neoadjuvant chemoradiotherapy in patients with rectal cancer.

Neoadjuvant chemoradiotherapy (nCRT) has become the standard treatment for patients with locally advanced rectal cancer (LARC). Therapeutic efficacy of nCRT is significantly affected by treatment-induced diarrhea and hematologic toxicities. Metabolic alternations in cancer therapy are key determinants to therapeutic toxicities and responses, but exploration in large-scale clinical studies remains limited. Here, we analyze 743 serum samples from 165 LARC patients recruited in a phase III clinical study using untargeted metabolomics and identify responsive metabolic traits over the course of nCRT. Pre-therapeutic serum metabolites successfully predict the chances of diarrhea and hematologic toxicities during nCRT. Particularly, levels of acyl carnitines are linked to sex disparity in nCRT-induced diarrhea. Finally, we show that differences in phenylalanine metabolism and essential amino acid metabolism may underlie distinct therapeutic responses of nCRT. This study illustrates the metabolic dynamics over the course of nCRT and provides potential to guide personalized nCRT treatment using responsive metabolic traits.