A Pathogen-Responsive Gene Cluster for Highly Modified Fatty Acids in Tomato.

In response to biotic stress, plants produce suites of highly modified fatty acids that bear unusual chemical functionalities. Despite their chemical complexity and proposed roles in pathogen defense, little is known about the biosynthesis of decorated fatty acids in plants. Falcarindiol is a prototypical acetylenic lipid present in carrot, tomato, and celery that inhibits growth of fungi and human cancer cell lines. Using a combination of untargeted metabolomics and RNA sequencing, we discovered a biosynthetic gene cluster in tomato (Solanum lycopersicum) required for falcarindiol production. By reconstituting initial biosynthetic steps in a heterologous host and generating transgenic pathway mutants in tomato, we demonstrate a direct role of the cluster in falcarindiol biosynthesis and resistance to fungal and bacterial pathogens in tomato leaves. This work reveals a mechanism by which plants sculpt their lipid pool in response to pathogens and provides critical insight into the complex biochemistry of alkynyl lipid production.

Single-Cell Proteomics.

The inability to make broad, minimally biased measurements of a cell's proteome stands as a major bottleneck for understanding how gene expression translates into cellular phenotype. Unlike sequencing for nucleic acids, there is no dominant method for making single-cell proteomic measurements. Instead, methods typically focus on either absolute quantification of a small number of proteins or highly multiplexed protein measurements. Advances in microfluidics and output encoding have led to major improvements in both aspects. Here, we review the most recent progress that has enabled hundreds of protein measurements and ultrahigh-sensitivity quantification. We also highlight emerging technologies such as single-cell mass spectrometry that may enable unbiased measurement of cellular proteomes.

An integrated deep learning framework for the interpretation of untargeted metabolomics data.

Untargeted metabolomics is gaining widespread applications. The key aspects of the data analysis include modeling complex activities of the metabolic network, selecting metabolites associated with clinical outcome and finding critical metabolic pathways to reveal biological mechanisms. One of the key roadblocks in data analysis is not well-addressed, which is the problem of matching uncertainty between data features and known metabolites. Given the limitations of the experimental technology, the identities of data features cannot be directly revealed in the data. The predominant approach for mapping features to metabolites is to match the mass-to-charge ratio (m/z) of data features to those derived from theoretical values of known metabolites. The relationship between features and metabolites is not one-to-one since some metabolites share molecular composition, and various adduct ions can be derived from the same metabolite. This matching uncertainty causes unreliable metabolite selection and functional analysis results. Here we introduce an integrated deep learning framework for metabolomics data that take matching uncertainty into consideration. The model is devised with a gradual sparsification neural network based on the known metabolic network and the annotation relationship between features and metabolites. This architecture characterizes metabolomics data and reflects the modular structure of biological system. Three goals can be achieved simultaneously without requiring much complex inference and additional assumptions: (1) evaluate metabolite importance, (2) infer feature-metabolite matching likelihood and (3) select disease sub-networks. When applied to a COVID metabolomics dataset and an aging mouse brain dataset, our method found metabolic sub-networks that were easily interpretable.

Clinical Proteomics for Solid Organ Tissues.

The evaluation of biopsied solid organ tissue has long relied on visual examination using a microscope. Immunohistochemistry is critical in this process, labeling and detecting cell lineage markers and therapeutic targets. However, while the practice of immunohistochemistry has reshaped diagnostic pathology and facilitated improvements in cancer treatment, it has also been subject to pervasive challenges with respect to standardization and reproducibility. Efforts are ongoing to improve immunohistochemistry, but for some applications, the benefit of such initiatives could be impeded by its reliance on monospecific antibody-protein reagents and limited multiplexing capacity. This perspective surveys the relevant challenges facing traditional immunohistochemistry and describes how mass spectrometry, particularly liquid chromatography-tandem mass spectrometry, could help alleviate problems. In particular, targeted mass spectrometry assays could facilitate measurements of individual proteins or analyte panels, using internal standards for more robust quantification and improved interlaboratory reproducibility. Meanwhile, untargeted mass spectrometry, showcased to date clinically in the form of amyloid typing, is inherently multiplexed, facilitating the detection and crude quantification of 100s to 1000s of proteins in a single analysis. Further, data-independent acquisition has yet to be applied in clinical practice, but offers particular strengths that could appeal to clinical users. Finally, we discuss the guidance that is needed to facilitate broader utilization in clinical environments and achieve standardization.

Metabolomic and transcriptomic reveal flavonoid biosynthesis and regulation mechanism in Phlomoides rotata from different habitats.

Phlomoides rotata is a traditional medical plant at 3100-5200 m altitude in the Tibet Plateau. In this study, flavonoid metabolites were investigated in P. rotata from Henan County (HN), Guoluo County (GL), Yushu County (YS), and Chengduo County (CD) habitats in Qinghai. The level of kaempferol 3-neohesperidoside, sakuranetin, and biochanin A was high in HN. The content of limocitrin and isoquercetin was high in YS. The levels of ikarisoside A and chrysosplenol D in GL were high. Schaftoside, miquelianin, malvidin chloride, and glabrene in CD exhibited high levels. The results showed a significant correlation between 59 flavonoids and 29 DEGs. Eleven flavonoids increased with altitude. PAL2, UFGT6, COMT1, HCT2, 4CL4, and HCT3 genes were crucial in regulating flavonoid biosynthesis. Three enzymes CHS, 4CL, and UFGT, were crucial in regulating flavonoid biosynthesis. This study provided biological and chemical evidence for the different uses of various regional plants of P. rotata.

Metabolomic Characteristics of Nontuberculous Mycobacterial Pulmonary Disease.

BACKGROUND: The therapeutic challenges posed by nontuberculous mycobacterial pulmonary disease (NTM-PD) contribute to an unmet medical need. In this study, we aimed to investigate NTM-PD-specific metabolic pathways using serum metabolomics to understand disease pathogenesis. METHODS: Mass spectrometry-based untargeted metabolomic profiling of serum from patients with NTM-PD (n = 50), patients with bronchiectasis (n = 50), and healthy controls (n = 60) was performed. Selected metabolites were validated by an independent cohort and subjected to pathway analysis and classification modeling. RESULTS: Leucine, tyrosine, inosine, proline, 5-oxoproline, and hypoxanthine levels increased in the NTM-PD group compared with the healthy control group. Furthermore, levels of antioxidant metabolites (ferulic acid, α-lipoic acid, biotin, and 2,8-phenazinediamine) decreased in patients with NTM-PD. These changes were associated with arginine- and proline-related metabolism, leading to generation of reactive oxygen species. Interestingly, the observed metabolic changes in the NTM-PD group overlapped with those in the bronchiectasis group. CONCLUSIONS: In NTM-PD, 11 metabolites linked to increased oxidative stress were significantly altered from those in healthy controls. Our findings enhance a comprehensive understanding of NTM-PD pathogenesis and provide insights for novel treatment approaches.

Multiomics Profiling of Plasma Reveals Molecular Alterations Prior to a Diagnosis with Stroke Among Chinese Hypertension Patients.

We aimed to investigate the correlation between plasma proteins and metabolites and the occurrence of future strokes using mass spectrometry and bioinformatics as well as to identify other biomarkers that could predict stroke risk in hypertensive patients. In a nested case-control study, baseline plasma samples were collected from 50 hypertensive subjects who developed stroke and 50 gender-, age- and body mass index-matched controls. Plasma untargeted metabolomics and data independent acquisition-based proteomics analysis were performed in hypertensive patients, and 19 metabolites and 111 proteins were found to be differentially expressed. Integrative analyses revealed that molecular changes in plasma indicated dysregulation of protein digestion and absorption, salivary secretion, and regulation of actin cytoskeleton, along with significant metabolic suppression. C4BPA, Caprolactam, Col15A1, and HBB were identified as predictors of stroke occurrence, and the Support Vector Machines (SVM) model was determined to be the optimal predictive model by integrating six machine-learning classification models. The SVM model showed strong performance in both the internal validation set (area under the curve [AUC]: 0.977, 95% confidence interval [CI]: 0.941-1.000) and the external independent validation set (AUC: 0.973, 95% CI: 0.921-0.999).

Identification of Blood Biomarkers in Ischemic Stroke by Integrated Analysis of Metabolomics and Proteomics.

We aimed to uncover the pathological mechanism of ischemic stroke (IS) using a combined analysis of untargeted metabolomics and proteomics. The serum samples from a discovery set of 44 IS patients and 44 matched controls were analyzed using a specific detection method. The same method was then used to validate metabolites and proteins in the two validation sets: one with 30 IS patients and 30 matched controls, and the other with 50 IS patients and 50 matched controls. A total of 105 and 221 differentially expressed metabolites or proteins were identified, and the association between the two omics was determined in the discovery set. Enrichment analysis of the top 25 metabolites and 25 proteins in the two-way orthogonal partial least-squares with discriminant analysis, which was employed to identify highly correlated biomarkers, highlighted 15 pathways relevant to the pathological process. One metabolite and seven proteins exhibited differences between groups in the validation set. The binary logistic regression model, which included metabolite 2-hydroxyhippuric acid and proteins APOM_O95445, MASP2_O00187, and PRTN3_D6CHE9, achieved an area under the curve of 0.985 (95% CI: 0.966-1) in the discovery set. This study elucidated alterations and potential coregulatory influences of metabolites and proteins in the blood of IS patients.

Influence of Software Settings on the Identification Rate, Quantification Results, and Reproducibility in Profiling Post-Translational Modifications by Microflow Liquid Chromatography-Ion Mobility-Quadrupole Time-Of-Flight Analysis Using PEAKS Software.

The influence of data evaluation parameters on qualitative and quantitative results of untargeted shotgun profiling of enzymatic and nonenzymatic post-translational modifications (PTMs) was investigated in a model of bovine whey protein α-lactalbumin heated with lactose. Based on the same raw data, individual adjustments to the protein database and enzyme settings of PEAKS studio software increased the identification rate from 27 unmodified peptides to 48 and from 322 peptides in total to 535. The qualitative and quantitative reproducibility was also assessed based on 18 measurements of one sample across three batches. A total of 570 peptides were detected. While 89 peptides were identified in all measurements, the majority of peptides (161) were detected only once and mostly based on nonindicative spectra. The reproducibility of label-free quantification (LFQ) in six measurements of the same sample was similar after processing the data by either the PTM algorithm or the LFQ algorithm. In both cases, about one-third of the peptides showed a coefficient of variation of above 20%. However, the LFQ algorithm increased the number of quantified peptides from 75 to 179. Data are available at the PRIDE Archive with the data set identifier PXD050363.

Distinguish Esophageal Cancer Cells through VOCs Induced by Methionine Regulation.

Detection of exhaled volatile organic compounds (VOCs) is promising for noninvasive screening of esophageal cancer (EC). Cellular VOC analysis can be used to investigate potential biomarkers. Considering the crucial role of methionine (Met) during cancer development, exploring associated abnormal metabolic phenotypes becomes imperative. In this work, we employed headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) to investigate the volatile metabolic profiles of EC cells (KYSE150) and normal esophageal epithelial cells (HEECs) under a Met regulation strategy. Using untargeted approaches, we analyzed the metabolic VOCs of the two cell types and explored the differential VOCs between them. Subsequently, we utilized targeted approaches to analyze the differential VOCs in both cell types under gradient Met culture conditions. The results revealed that there were five/six differential VOCs between cells under Met-containing/Met-free culture conditions. And the difference in levels of two characteristic VOCs (1-butanol and ethyl 2-methylbutyrate) between the two cell types intensified with the increase of the Met concentration. Notably, this is the first report on VOC analysis of EC cells and the first to consider the effect of Met on volatile metabolic profiles. The present work indicates that EC cells can be distinguished through VOCs induced by Met regulation, which holds promise for providing novel insights into diagnostic strategies.

Applying Untargeted Lipidomics to Evaluate the Efficacy of Combined Neoadjuvant Chemotherapy and Immunotherapy for Esophageal Squamous Carcinoma Treatment.

Esophageal squamous cell carcinoma (ESCC) is an aggressive malignant tumor with a poor prognosis due to insidious symptoms that make early diagnosis difficult. Despite the combination of multiple treatment modalities, the recurrence and mortality rates of ESCC remain high. Neoadjuvant chemotherapy combined with immunotherapy is an emerging treatment modality that improves the prognosis of patients with ESCC. However, owing to the presence of hyperprogression and pseudoprogression, the currently used methods cannot accurately evaluate the efficacy of this therapy in patients, thus creating an evaluation bias and depriving these patients of the opportunity to benefit. We used untargeted lipidomics to identify the differences in lipid composition between cancer specimens and normal tissue specimens in the neoadjuvant chemotherapy combined with the immunotherapy group and the surgery-alone group of esophageal cancer patients and constructed a prediction model based on sphingomyelin 12:1;2O/30:0 and triglyceride (TG) 60:3 | TG 18:0_24:1_18 using a machine learning approach, which helps to better evaluate the neoadjuvant efficacy of combination therapy and better guide the treatment of ESCC.

How Much Storage Precision Can Be Lost: Guidance for Near-Lossless Compression of Untargeted Metabolomics Mass Spectrometry Data.

Several lossy compressors have achieved superior compression rates for mass spectrometry (MS) data at the cost of storage precision. Currently, the impacts of precision losses on MS data processing have not been thoroughly evaluated, which is critical for the future development of lossy compressors. We first evaluated different storage precision (32 bit and 64 bit) in lossless mzML files. We then applied 10 truncation transformations to generate precision-lossy files: five relative errors for intensities and five absolute errors for m/z values. MZmine3 and XCMS were used for feature detection and GNPS for compound annotation. Lastly, we compared Precision, Recall, F1 - score, and file sizes between lossy files and lossless files under different conditions. Overall, we revealed that the discrepancy between 32 and 64 bit precision was under 1%. We proposed an absolute m/z error of 10-4 and a relative intensity error of 2 × 10-2, adhering to a 5% error threshold (F1 - scores above 95%). For a stricter 1% error threshold (F1 - scores above 99%), an absolute m/z error of 2 × 10-5 and a relative intensity error of 2 × 10-3 were advised. This guidance aims to help researchers improve lossy compression algorithms and minimize the negative effects of precision losses on downstream data processing.

Offline Two-Dimensional Liquid Chromatography-Mass Spectrometry for Deep Annotation of the Fecal Metabolome Following Fecal Microbiota Transplantation.

Biological interpretation of untargeted LC-MS-based metabolomics data depends on accurate compound identification, but current techniques fall short of identifying most features that can be detected. The human fecal metabolome is complex, variable, incompletely annotated, and serves as an ideal matrix to evaluate novel compound identification methods. We devised an experimental strategy for compound annotation using multidimensional chromatography and semiautomated feature alignment and applied these methods to study the fecal metabolome in the context of fecal microbiota transplantation (FMT) for recurrent C. difficile infection. Pooled fecal samples were fractionated using semipreparative liquid chromatography and analyzed by an orthogonal LC-MS/MS method. The resulting spectra were searched against commercial, public, and local spectral libraries, and annotations were vetted using retention time alignment and prediction. Multidimensional chromatography yielded more than a 2-fold improvement in identified compounds compared to conventional LC-MS/MS and successfully identified several rare and previously unreported compounds, including novel fatty-acid conjugated bile acid species. Using an automated software-based feature alignment strategy, most metabolites identified by the new approach could be matched to features that were detected but not identified in single-dimensional LC-MS/MS data. Overall, our approach represents a powerful strategy to enhance compound identification and biological insight from untargeted metabolomics data.

Analysis of early-pregnancy metabolome in early- and late-onset gestational diabetes reveals distinct associations with maternal overweight.

AIMS/HYPOTHESIS: It is not known whether the early-pregnancy metabolome differs in patients with early- vs late-onset gestational diabetes mellitus (GDM) stratified by maternal overweight. The aims of this study were to analyse correlations between early-pregnancy metabolites and maternal glycaemic and anthropometric characteristics, and to identify early-pregnancy metabolomic alterations that characterise lean women (BMI <25 kg/m2) and women with overweight (BMI ≥25 kg/m2) with early-onset GDM (E-GDM) or late-onset GDM (L-GDM). METHODS: We performed a nested case-control study within the population-based prospective Early Diagnosis of Diabetes in Pregnancy cohort, comprising 210 participants with GDM (126 early-onset, 84 late-onset) and 209 normoglycaemic control participants matched according to maternal age, BMI class and primiparity. Maternal weight, height and waist circumference were measured at 8-14 weeks' gestation. A 2 h 75 g OGTT was performed at 12-16 weeks' gestation (OGTT1), and women with normal results underwent repeat testing at 24-28 weeks' gestation (OGTT2). Comprehensive metabolomic profiling of fasting serum samples, collected at OGTT1, was performed by untargeted ultra-HPLC-MS. Linear models were applied to study correlations between early-pregnancy metabolites and maternal glucose concentrations during OGTT1, fasting insulin, HOMA-IR, BMI and waist circumference. Early-pregnancy metabolomic features for GDM subtypes (participants stratified by maternal overweight and gestational timepoint at GDM onset) were studied using linear and multivariate models. The false discovery rate was controlled using the Benjamini-Hochberg method. RESULTS: In the total cohort (n=419), the clearest correlation patterns were observed between (1) maternal glucose concentrations and long-chain fatty acids and medium- and long-chain acylcarnitines; (2) maternal BMI and/or waist circumference and long-chain fatty acids, medium- and long-chain acylcarnitines, phospholipids, and aromatic and branched-chain amino acids; and (3) HOMA-IR and/or fasting insulin and L-tyrosine, certain long-chain fatty acids and phospholipids (q<0.001). Univariate analyses of GDM subtypes revealed significant differences (q<0.05) for seven non-glucose metabolites only in overweight women with E-GDM compared with control participants: linolenic acid, oleic acid, docosapentaenoic acid, docosatetraenoic acid and lysophosphatidylcholine 20:4/0:0 abundances were higher, whereas levels of specific phosphatidylcholines (P-16:0/18:2 and 15:0/18:2) were lower. However, multivariate analyses exploring the early-pregnancy metabolome of GDM subtypes showed differential clustering of acylcarnitines and long-chain fatty acids between normal-weight and overweight women with E- and L-GDM. CONCLUSIONS/INTERPRETATION: GDM subtypes show distinct early-pregnancy metabolomic features that correlate with maternal glycaemic and anthropometric characteristics. The patterns identified suggest early-pregnancy disturbances of maternal lipid metabolism, with most alterations observed in overweight women with E-GDM. Our findings highlight the importance of maternal adiposity as the primary target for prevention and treatment.

Role of human plasma metabolites in prediabetes and type 2 diabetes from the IMI-DIRECT study.

AIMS/HYPOTHESIS: Type 2 diabetes is a chronic condition that is caused by hyperglycaemia. Our aim was to characterise the metabolomics to find their association with the glycaemic spectrum and find a causal relationship between metabolites and type 2 diabetes. METHODS: As part of the Innovative Medicines Initiative - Diabetes Research on Patient Stratification (IMI-DIRECT) consortium, 3000 plasma samples were measured with the Biocrates AbsoluteIDQ p150 Kit and Metabolon analytics. A total of 911 metabolites (132 targeted metabolomics, 779 untargeted metabolomics) passed the quality control. Multivariable linear and logistic regression analysis estimates were calculated from the concentration/peak areas of each metabolite as an explanatory variable and the glycaemic status as a dependent variable. This analysis was adjusted for age, sex, BMI, study centre in the basic model, and additionally for alcohol, smoking, BP, fasting HDL-cholesterol and fasting triacylglycerol in the full model. Statistical significance was Bonferroni corrected throughout. Beyond associations, we investigated the mediation effect and causal effects for which causal mediation test and two-sample Mendelian randomisation (2SMR) methods were used, respectively. RESULTS: In the targeted metabolomics, we observed four (15), 34 (99) and 50 (108) metabolites (number of metabolites observed in untargeted metabolomics appear in parentheses) that were significantly different when comparing normal glucose regulation vs impaired glucose regulation/prediabetes, normal glucose regulation vs type 2 diabetes, and impaired glucose regulation vs type 2 diabetes, respectively. Significant metabolites were mainly branched-chain amino acids (BCAAs), with some derivatised BCAAs, lipids, xenobiotics and a few unknowns. Metabolites such as lysophosphatidylcholine a C17:0, sum of hexoses, amino acids from BCAA metabolism (including leucine, isoleucine, valine, N-lactoylvaline, N-lactoylleucine and formiminoglutamate) and lactate, as well as an unknown metabolite (X-24295), were associated with HbA1c progression rate and were significant mediators of type 2 diabetes from baseline to 18 and 48 months of follow-up. 2SMR was used to estimate the causal effect of an exposure on an outcome using summary statistics from UK Biobank genome-wide association studies. We found that type 2 diabetes had a causal effect on the levels of three metabolites (hexose, glutamate and caproate [fatty acid (FA) 6:0]), whereas lipids such as specific phosphatidylcholines (PCs) (namely PC aa C36:2, PC aa C36:5, PC ae C36:3 and PC ae C34:3) as well as the two n-3 fatty acids stearidonate (18:4n3) and docosapentaenoate (22:5n3) potentially had a causal role in the development of type 2 diabetes. CONCLUSIONS/INTERPRETATION: Our findings identify known BCAAs and lipids, along with novel N-lactoyl-amino acid metabolites, significantly associated with prediabetes and diabetes, that mediate the effect of diabetes from baseline to follow-up (18 and 48 months). Causal inference using genetic variants shows the role of lipid metabolism and n-3 fatty acids as being causal for metabolite-to-type 2 diabetes whereas the sum of hexoses is causal for type 2 diabetes-to-metabolite. Identified metabolite markers are useful for stratifying individuals based on their risk progression and should enable targeted interventions.

Hedan tablet ameliorated non-alcoholic steatohepatitis by moderating NF-κB and lipid metabolism-related pathways via regulating hepatic metabolites.

Non-alcoholic steatohepatitis (NASH) is a severe form of fatty liver disease. If not treated, it can lead to liver damage, cirrhosis and even liver cancer. However, advances in treatment have remained relatively slow, and there is thus an urgent need to develop appropriate treatments. Hedan tablet (HDP) is used to treat metabolic syndrome. However, scientific understanding of the therapeutic effect of HDP on NASH remains limited. We used HDP to treat a methionine/choline-deficient diet-induced model of NASH in rats to elucidate the therapeutic effects of HDP on liver injury. In addition, we used untargeted metabolomics to investigate the effects of HDP on metabolites in liver of NASH rats, and further validated its effects on inflammation and lipid metabolism following screening for potential target pathways. HDP had considerable therapeutic, anti-oxidant, and anti-inflammatory effects on NASH. HDP could also alter the hepatic metabolites changed by NASH. Moreover, HDP considerable moderated NF-κB and lipid metabolism-related pathways. The present study found that HDP had remarkable therapeutic effects in NASH rats. The therapeutic efficacy of HDP in NASH mainly associated with regulation of NF-κB and lipid metabolism-related pathways via arachidonic acid metabolism, glycine-serine-threonine metabolism, as well as steroid hormone biosynthesis.

Orientia tsutsugamushi infection reduces host gluconeogenic but not glycolytic substrates.

Orientia tsutsugamushi a causal agent of scrub typhus, is an obligate intracellular bacterium that, akin to other rickettsiae, is dependent on host cell-derived nutrients for survival and thus pathogenesis. Based on limited experimental evidence and genome-based in silico predictions, O. tsutsugamushi is hypothesized to parasitize host central carbon metabolism (CCM). Here, we (re-)evaluated O. tsutsugamushi dependency on host cell CCM as initiated by glucose and glutamine. Orientia infection had no effect on host glucose and glutamine consumption or lactate accumulation, indicating no change in overall flux through CCM. However, host cell mitochondrial activity and ATP levels were reduced during infection and correspond with lower intracellular glutamine and glutamate pools. To further probe the essentiality of host CCM in O. tsutsugamushi proliferation, we developed a minimal medium for host cell cultivation and paired it with chemical inhibitors to restrict the intermediates and processes related to glucose and glutamine metabolism. These conditions failed to negatively impact O. tsutsugamushi intracellular growth, suggesting the bacterium is adept at scavenging from host CCM. Accordingly, untargeted metabolomics was utilized to evaluate minor changes in host CCM metabolic intermediates across O. tsutsugamushi infection and revealed that pathogen proliferation corresponds with reductions in critical CCM building blocks, including amino acids and TCA cycle intermediates, as well as increases in lipid catabolism. This study directly correlates O. tsutsugamushi proliferation to alterations in host CCM and identifies metabolic intermediates that are likely critical for pathogen fitness.IMPORTANCEObligate intracellular bacterial pathogens have evolved strategies to reside and proliferate within the eukaryotic intracellular environment. At the crux of this parasitism is the balance between host and pathogen metabolic requirements. The physiological basis driving O. tsutsugamushi dependency on its mammalian host remains undefined. By evaluating alterations in host metabolism during O. tsutsugamushi proliferation, we discovered that bacterial growth is independent of the host's nutritional environment but appears dependent on host gluconeogenic substrates, including amino acids. Given that O. tsutsugamushi replication is essential for its virulence, this study provides experimental evidence for the first time in the post-genomic era of metabolic intermediates potentially parasitized by a scrub typhus agent.

An evaluation of untargeted metabolomics methods to characterize inborn errors of metabolism.

Inborn errors of metabolism (IEMs) encompass a diverse group of disorders that can be difficult to classify due to heterogenous clinical, molecular, and biochemical manifestations. Untargeted metabolomics platforms have become a popular approach to analyze IEM patient samples because of their ability to detect many metabolites at once, accelerating discovery of novel biomarkers, and metabolic mechanisms of disease. However, there are concerns about the reproducibility of untargeted metabolomics research due to the absence of uniform reporting practices, data analyses, and experimental design guidelines. Therefore, we critically evaluated published untargeted metabolomic platforms used to characterize IEMs to summarize the strengths and areas for improvement of this technology as it progresses towards the clinical laboratory. A total of 96 distinct IEMs were collectively evaluated by the included studies. However, most of these IEMs were evaluated by a single untargeted metabolomic method, in a single study, with a limited cohort size (55/96, 57%). The goals of the included studies generally fell into two, often overlapping, categories: detecting known biomarkers from many biochemically distinct IEMs using a single platform, and detecting novel metabolites or metabolic pathways. There was notable diversity in the design of the untargeted metabolomic platforms. Importantly, the majority of studies reported adherence to quality metrics, including the use of quality control samples and internal standards in their experiments, as well as confirmation of at least some of their feature annotations with commercial reference standards. Future applications of untargeted metabolomics platforms to the study of IEMs should move beyond single-subject analyses, and evaluate reproducibility using a prospective, or validation cohort.

Connection of pre-competition anxiety with gut microbiota and metabolites in wrestlers with varying sports performances based on brain-gut axis theory.

OBJECTIVE: The purpose of this study is to investigate the connection of pre-competition anxiety with gut microbiota and metabolites in wrestlers with different sports performances. METHODS: One week prior to a national competition, 12 wrestlers completed anxiety questionnaires. Faecal and urine samples were collected for the analysis of gut microbiota and metabolites through the high-throughput sequencing of the 16 S rRNA gene in conjunction with untargeted metabolomics technology. The subjects were divided into two groups, namely, achievement (CP) and no-achievement (CnP) wrestlers, on the basis of whether or not their performances placed them in the top 16 at the competition. The relationship amongst the variations in gut microbiota, metabolites, and anxiety indicators was analyzed. RESULTS: (1) The CP group exhibited significantly higher levels of "state self-confidence," "self-confidence," and "somatic state anxiety" than the CnP group. Conversely, the CP group displayed lower levels of "individual failure anxiety" and "sports competition anxiety" than the CnP group. (2) The gut microbiota in the CP group was more diverse and abundant than that in the CnP group. Pre-competition anxiety was linked to Oscillospiraceae UCG_005, Paraprevotella, Ruminococcaceae and TM7x. (3) The functions of differential metabolites in faeces and urine of the CP/CnP group were mainly enriched in caffeine metabolism, lipopolysaccharide biosynthesis and VEGF and mTOR signaling pathways. Common differential metabolites in feces and urine were significantly associated with multiple anxiety indicators. CONCLUSIONS: Wrestlers with different sports performance have different pre-competition anxiety states, gut microbiota distribution and abundance and differential metabolites in faeces and urine. A certain correlation exists between these psychological and physiological indicators.

FT-ICR-MS-based metabolomics: A deep dive into plant metabolism.

Metabolomics involves the identification and quantification of metabolites to unravel the chemical footprints behind cellular regulatory processes and to decipher metabolic networks, opening new insights to understand the correlation between genes and metabolites. In plants, it is estimated the existence of hundreds of thousands of metabolites and the majority is still unknown. Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) is a powerful analytical technique to tackle such challenges. The resolving power and sensitivity of this ultrahigh mass accuracy mass analyzer is such that a complex mixture, such as plant extracts, can be analyzed and thousands of metabolite signals can be detected simultaneously and distinguished based on the naturally abundant elemental isotopes. In this review, FT-ICR-MS-based plant metabolomics studies are described, emphasizing FT-ICR-MS increasing applications in plant science through targeted and untargeted approaches, allowing for a better understanding of plant development, responses to biotic and abiotic stresses, and the discovery of new natural nutraceutical compounds. Improved metabolite extraction protocols compatible with FT-ICR-MS, metabolite analysis methods and metabolite identification platforms are also explored as well as new in silico approaches. Most recent advances in MS imaging are also discussed.