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.

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.

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.

Shaping immune landscape of colorectal cancer by cholesterol metabolites.

Cancer immunotherapies have achieved unprecedented success in clinic, but they remain largely ineffective in some major types of cancer, such as colorectal cancer with microsatellite stability (MSS CRC). It is therefore important to study tumor microenvironment of resistant cancers for developing new intervention strategies. In this study, we identify a metabolic cue that determines the unique immune landscape of MSS CRC. Through secretion of distal cholesterol precursors, which directly activate RORγt, MSS CRC cells can polarize T cells toward Th17 cells that have well-characterized pro-tumor functions in colorectal cancer. Analysis of large human cancer cohorts revealed an asynchronous pattern of the cholesterol biosynthesis in MSS CRC, which is responsible for the abnormal accumulation of distal cholesterol precursors. Inhibiting the cholesterol biosynthesis enzyme Cyp51, by pharmacological or genetic interventions, reduced the levels of intratumoral distal cholesterol precursors and suppressed tumor progression through a Th17-modulation mechanism in preclinical MSS CRC models. Our study therefore reveals a novel mechanism of cancer-immune interaction and an intervention strategy for the difficult-to-treat MSS CRC.

Mediator subunit MED8 interacts with heat shock transcription factor HSF3 to promote fucoxanthin synthesis in the diatom Phaeodactylum tricornutum.

Fucoxanthin, a natural carotenoid that has substantial pharmaceutical value due to its anticancer, antioxidant, antiobesity, and antidiabetic properties, is biosynthesized from glyceraldehyde-3-phosphate (G3P) via a series of enzymatic reactions. However, our understanding of the transcriptional mechanisms involved in fucoxanthin biosynthesis remains limited. Using reverse genetics, the med8 mutant was identified based on its phenotype of reduced fucoxanthin content, and the biological functions of MED8 in fucoxanthin synthesis were characterized using approaches such as gene expression, protein subcellular localization, protein-protein interaction and chromatin immunoprecipitation assay. Gene-editing mutants of MED8 exhibited decreased fucoxanthin content as well as reduced expression levels of six key genes involved in fucoxanthin synthesis, namely DXS, PSY1, ZDS-like, CRTISO5, ZEP1, and ZEP3, when compared to the wild-type (WT) strain. Furthermore, we showed that MED8 interacts with HSF3, and genetic analysis revealed their shared involvement in the genetic pathway governing fucoxanthin synthesis. Additionally, HSF3 was required for MED8 association with the promoters of the six fucoxanthin synthesis genes. In conclusion, MED8 and HSF3 are involved in fucoxanthin synthesis by modulating the expression of the fucoxanthin synthesis genes. Our results increase the understanding of the molecular regulation mechanisms underlying fucoxanthin synthesis in the diatom P. tricornutum.

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.

Allosterically inhibited PFKL via prostaglandin E2 withholds glucose metabolism and ovarian cancer invasiveness.

Metastasis is the leading cause of high ovarian-cancer-related mortality worldwide. Three major processes constitute the whole metastatic cascade: invasion, intravasation, and extravasation. Tumor cells often reprogram their metabolism to gain advantages in proliferation and survival. However, whether and how those metabolic alterations contribute to the invasiveness of tumor cells has yet to be fully understood. Here we performed a genome-wide CRISPR-Cas9 screening to identify genes participating in tumor cell dissemination and revealed that PTGES3 acts as an invasion suppressor in ovarian cancer. Mechanistically, PTGES3 binds to phosphofructokinase, liver type (PFKL) and generates a local source of prostaglandin E2 (PGE2) to allosterically inhibit the enzymatic activity of PFKL. Repressed PFKL leads to downgraded glycolysis and the subsequent TCA cycle for glucose metabolism. However, ovarian cancer suppresses the expression of PTGES3 and disrupts the PTGES3-PGE2-PFKL inhibitory axis, leading to hyperactivation of glucose oxidation, eventually facilitating ovarian cancer cell motility and invasiveness.

Neutral lysophosphatidylcholine mediates α-synuclein-induced synaptic vesicle clustering.

α-synuclein (α-Syn) is a presynaptic protein that is involved in Parkinson's and other neurodegenerative diseases and binds to negatively charged phospholipids. Previously, we reported that α-Syn clusters synthetic proteoliposomes that mimic synaptic vesicles. This vesicle-clustering activity depends on a specific interaction of α-Syn with anionic phospholipids. Here, we report that α-Syn surprisingly also interacts with the neutral phospholipid lysophosphatidylcholine (lysoPC). Even in the absence of anionic lipids, lysoPC facilitates α-Syn-induced vesicle clustering but has no effect on Ca2+-triggered fusion in a single vesicle-vesicle fusion assay. The A30P mutant of α-Syn that causes familial Parkinson disease has a reduced affinity to lysoPC and does not induce vesicle clustering. Taken together, the α-Syn-lysoPC interaction may play a role in α-Syn function.

AllCCS2: Curation of Ion Mobility Collision Cross-Section Atlas for Small Molecules Using Comprehensive Molecular Representations.

The development of ion mobility-mass spectrometry (IM-MS) has revolutionized the analysis of small molecules, such as metabolomics, lipidomics, and exposome studies. The curation of comprehensive reference collision cross-section (CCS) databases plays a pivotal role in the successful application of IM-MS for small-molecule analysis. In this study, we presented AllCCS2, an enhanced version of AllCCS, designed for the universal prediction of the ion mobility CCS values of small molecules. AllCCS2 incorporated newly available experimental CCS data, including 10,384 records and 7713 unified values, as training data. By leveraging a neural network trained on diverse molecular representations encompassing mass spectrometry features, molecular descriptors, and graph features extracted using a graph convolutional network, AllCCS2 achieved exceptional prediction accuracy. AllCCS2 achieved median relative error (MedRE) values of 0.31, 0.72, and 1.64% in the training, validation, and testing sets, respectively, surpassing existing CCS prediction tools in terms of accuracy and coverage. Furthermore, AllCCS2 exhibited excellent compatibility with different instrument platforms (DTIMS, TWIMS, and TIMS). The prediction uncertainties in AllCCS2 from the training data and the prediction model were comprehensively investigated by using representative structure similarity and model prediction variation. Notably, small molecules with high structural similarities to the training set and lower model prediction variation exhibited improved accuracy and lower relative errors. In summary, AllCCS2 serves as a valuable resource to support applications of IM-MS technologies. The AllCCS2 database and tools are freely accessible at http://allccs.zhulab.cn/.

The Oxysterol Receptor EBI2 Links Innate and Adaptive Immunity to Limit IFN Response and Systemic Lupus Erythematosus.

Systemic lupus erythematosus (SLE) is a complex autoimmune disease with abnormal activation of the immune system. Recent attention is increasing about how aberrant lipid and cholesterol metabolism is linked together with type I interferon (IFN-I) signaling in the regulation of the pathogenesis of SLE. Here, a metabonomic analysis is performed and increased plasma concentrations of oxysterols, especially 7α, 25-dihydroxycholesterol (7α, 25-OHC), are identified in SLE patients. The authors find that 7α, 25-OHC binding to its receptor Epstein-Barr virus-induced gene 2 (EBI2) in macrophages can suppress STAT activation and the production of IFN-ß, chemokines, and cytokines. Importantly, monocytes/macrophages from SLE patients and mice show significantly reduced EBI2 expression, which can be triggered by IFN-γ produced in activated T cells. Previous findings suggest that EBI2 enhances immune cell migration. Opposite to this effect, the authors demonstrate that EBI2-deficient macrophages produce higher levels of chemokines and cytokines, which recruits and activates myeloid cells,T and B lymphocytes to exacerbate tetramethylpentadecane-induced SLE. Together, via sensing the oxysterol 7α, 25-OHC, EBI2 in macrophages can modulate innate and adaptive immune responses, which may be used as a potential diagnostic marker and therapeutic target for SLE.

Exhaustion-associated cholesterol deficiency dampens the cytotoxic arm of antitumor immunity.

The concept of targeting cholesterol metabolism to treat cancer has been widely tested in clinics, but the benefits are modest, calling for a complete understanding of cholesterol metabolism in intratumoral cells. We analyze the cholesterol atlas in the tumor microenvironment and find that intratumoral T cells have cholesterol deficiency, while immunosuppressive myeloid cells and tumor cells display cholesterol abundance. Low cholesterol levels inhibit T cell proliferation and cause autophagy-mediated apoptosis, particularly for cytotoxic T cells. In the tumor microenvironment, oxysterols mediate reciprocal alterations in the LXR and SREBP2 pathways to cause cholesterol deficiency of T cells, subsequently leading to aberrant metabolic and signaling pathways that drive T cell exhaustion/dysfunction. LXRß depletion in chimeric antigen receptor T (CAR-T) cells leads to improved antitumor function against solid tumors. Since T cell cholesterol metabolism and oxysterols are generally linked to other diseases, the new mechanism and cholesterol-normalization strategy might have potential applications elsewhere.

Encoding LC-MS-Based Untargeted Metabolomics Data into Images toward AI-Based Clinical Diagnosis.

Liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics provides comprehensive and quantitative profiling of metabolites in clinical investigations. The use of whole metabolome profiles is a promising strategy for disease diagnosis but technically challenging. Here, we developed an approach, namely MetImage, to encode LC-MS-based untargeted metabolomics data into multi-channel digital images. Then, the images that represent the comprehensive metabolome profiles can be employed for developing deep learning-based AI models toward clinical diagnosis. In this work, we demonstrated the application of MetImage for clinical screening of esophageal squamous cell carcinoma (ESCC) in a clinical cohort with 1104 participants. A convolutional neuronal network-based AI model was trained to distinguish ESCC screening positive and negative subjects using their serum metabolomics data. Superior performances such as sensitivity (85%), specificity (92%), and area under curve (0.95) were validated in an independent testing cohort (N = 442). Importantly, we demonstrated that our AI-based ESCC screening model is not a "black box". The encoded images reserved the characteristics of mass spectra from the raw LC-MS data; therefore, metabolite identifications in key image features were readily achieved. Altogether, MetImage is a unique approach that encodes raw LC-MS-based untargeted metabolomics data into images and facilitates the utilization of whole metabolome profiles for AI-based clinical applications with improved interpretability.

A mass spectrum-oriented computational method for ion mobility-resolved untargeted metabolomics.

Ion mobility (IM) adds a new dimension to liquid chromatography-mass spectrometry-based untargeted metabolomics which significantly enhances coverage, sensitivity, and resolving power for analyzing the metabolome, particularly metabolite isomers. However, the high dimensionality of IM-resolved metabolomics data presents a great challenge to data processing, restricting its widespread applications. Here, we develop a mass spectrum-oriented bottom-up assembly algorithm for IM-resolved metabolomics that utilizes mass spectra to assemble four-dimensional peaks in a reverse order of multidimensional separation. We further develop the end-to-end computational framework Met4DX for peak detection, quantification and identification of metabolites in IM-resolved metabolomics. Benchmarking and validation of Met4DX demonstrates superior performance compared to existing tools with regard to coverage, sensitivity, peak fidelity and quantification precision. Importantly, Met4DX successfully detects and differentiates co-eluted metabolite isomers with small differences in the chromatographic and IM dimensions. Together, Met4DX advances metabolite discovery in biological organisms by deciphering the complex 4D metabolomics data.

Selective Covalent Targeting of Pyruvate Kinase M2 Using Arsenous Warheads.

There is growing interest in covalent targeted inhibitors in drug discovery against previously "undruggable" sites and targets. These molecules typically feature an electrophilic warhead that reacts with nucleophilic groups of protein residues, most notably the thiol group of cysteines. One main challenge in the field is to develop versatile utilizable warheads. Here, we characterize the unique features of novel arsenous warheads for reaction with thiol species in a reversible manner and further demonstrate that organoarsenic probes can be chemically tuned toward specific molecular targets by developing selective and potent inhibitors of pyruvate kinase M2 (PKM2). We show that compound 24 is a covalent and allosteric inhibitor of PKM2 and its orally bioavailable prodrug 25 exerts efficacious inhibition of PKM2-dependent tumor growth in vitro and in vivo. Our results introduce 25 and its derivatives as useful pharmacological tools and provide a general road map for targeting the protein cysteinome using arsenous warheads.

ONE-seq: epitranscriptome and gene-specific profiling of NAD-capped RNA.

The hub metabolite, nicotinamide adenine dinucleotide (NAD), can be used as an initiating nucleotide in RNA synthesis to result in NAD-capped RNAs (NAD-RNA). Since NAD has been heightened as one of the most essential modulators in aging and various age-related diseases, its attachment to RNA might indicate a yet-to-be discovered mechanism that impacts adult life-course. However, the unknown identity of NAD-linked RNAs in adult and aging tissues has hindered functional studies. Here, we introduce ONE-seq method to identify the RNA transcripts that contain NAD cap. ONE-seq has been optimized to use only one-step chemo-enzymatic biotinylation, followed by streptavidin capture and the nudix phosphohydrolase NudC-catalyzed elution, to specifically recover NAD-capped RNAs for epitranscriptome and gene-specific analyses. Using ONE-seq, we discover more than a thousand of previously unknown NAD-RNAs in the mouse liver and reveal epitranscriptome-wide dynamics of NAD-RNAs with age. ONE-seq empowers the identification of NAD-capped RNAs that are responsive to distinct physiological states, facilitating functional investigation into this modification.

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.

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.

Quantitative Acetylomics Reveals Dynamics of Protein Lysine Acetylation in Mouse Livers During Aging and Upon the Treatment of Nicotinamide Mononucleotide.

Lysine acetylation is a reversible and dynamic post-translational modification that plays vital roles in regulating multiple cellular processes including aging. However, acetylome-wide analysis in the aging process remains poorly studied in mammalian tissues. Nicotinamide adenine dinucleotide (NAD+), a hub metabolite, benefits health span at least in part due to the activation of Sirtuins, a family of NAD+-consuming deacetylases, indicating changes in acetylome. Here, we combine two antibodies for the enrichment of acetylated peptides and perform label-free quantitative acetylomic analysis of mouse livers during natural aging and upon the treatment of beta-nicotinamide mononucleotide (NMN), a NAD+ booster. Our study describes previously unknown acetylation sites and reveals the acetylome-wide dynamics with age as well as upon the treatment of NMN. We discover protein acetylation events as potential aging biomarkers. We demonstrate that the life-beneficial effect of NMN could be partially reflected by the changes in age-related protein acetylation. Our quantitative assessment indicates that NMN has mild effects on acetylation sites previously reported as substrates of Sirtuins. Collectively, our data analyze protein acetylation with age, laying critical foundation for the functional study of protein post-translational modification essential for healthy aging and perhaps disease conditions.

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.