Association of Circulating Long-Chain Free Fatty Acids and Incident Diabetes Risk Among Normoglycemic Chinese Adults: A Prospective Nested Case-Control Study.

BACKGROUND: Long-chain free fatty acids (FFAs) are associated with risk of incident diabetes. However, comprehensive assessment of the associations in normoglycemic populations is lacking. OBJECTIVE: Our study aims to comprehensively investigate the prospective associations and patterns of FFA profiles with diabetes risk among normoglycemic Chinese adults. METHODS: This is a prospective nested case-control study from the China Cardiometabolic Disease and Cancer Cohort (4C) study. We quantitatively measured 53 serum FFAs using targeted metabolomics approach in 1707 incident diabetes subjects and 1707 propensity score-matched normoglycemic controls. Conditional logistic regression models were employed to estimate odds ratios (ORs) for associations. Least Absolute Shrinkage and Selection Operator (LASSO) penalty regression and quantile g-computation (qg-comp) analyses were implemented to estimate the association between multi-FFA exposures and incident diabetes. RESULTS: The majority of odd-chain FFAs exhibited an inverse association with incident diabetes, wherein the ORs per SD increment of all 7 saturated fatty acids (SFAs), monounsaturated fatty acid (MUFA) 15:1 and polyunsaturated fatty acid (PUFA) 25:2 were ranging from 0.79 to 0.88 (95%CIs ranging between 0.71 and 0.97). Even-chain FFAs comprised 99.3% of total FFAs and displayed heterogeneity with incident diabetes. SFAs with 18 to 26 carbon atoms are inversely linked to incident diabetes, with ORs ranging from 0.81 to 0.86 (95%CIs ranging between 0.73 and 0.94). MUFAs 26:1 (OR[95%CI]: 0.85[0.76-0.94]), PUFAs 20:4 (0.84[0.75-0.94]) and 24:2 (0.87[0.78-0.97]) demonstrated significant associations. In multi-FFA exposure model, 24 FFAs were significantly associated with incident diabetes, most of which were consistent with univariate results. The mixture OR was 0.78 [0.61-0.99] (P= 0.04159). Differential correlation network analysis revealed pre-existing perturbations in intraclass and interclass FFA coregulation before diabetes onset. CONCLUSIONS: These findings underscore the variations in diabetes risk associated with FFAs across chain length and unsaturation degree, highlighting the importance of recognizing FFA subtypes in the pathogenesis of diabetes.

OsLCD3 interacts with OsSAMS1 to regulate grain size via ethylene/polyamine homeostasis control.

A fundamental question in developmental biology is how to regulate grain size to improve crop yields. Despite this, little is still known about the genetics and molecular mechanisms regulating grain size in crops. Here, we provide evidence that a putative protein kinase-like (OsLCD3) interacts with the S-adenosyl-L-methionine synthetase 1 (OsSAMS1) and determines the size and weight of grains. OsLCD3 mutation (lcd3) significantly increased grain size and weight by promoting cell expansion in spikelet hull, whereas its overexpression caused negative effects, suggesting that grain size was negatively regulated by OsLCD3. Importantly, lcd3 and OsSAMS1 overexpression (SAM1OE) led to large and heavy grains, with increased ethylene and decreased polyamines production. Based on genetic analyses, it appears that OsLCD3 and OsSAMS1 control rice grain size in part by ethylene/polyamine homeostasis. The results of this study provide a genetic and molecular understanding of how the OsLCD3-OsSAMS1 regulatory module regulates grain size, suggesting that ethylene/polyamine homeostasis is an appropriate target for improving grain size and weight.

Unlocking the power of postbiotics: A revolutionary approach to nutrition for humans and animals.

Postbiotics, which comprise inanimate microorganisms or their constituents, have recently gained significant attention for their potential health benefits. Extensive research on postbiotics has uncovered many beneficial effects on hosts, including antioxidant activity, immunomodulatory effects, gut microbiota modulation, and enhancement of epithelial barrier function. Although these features resemble those of probiotics, the stability and safety of postbiotics make them an appealing alternative. In this review, we provide a comprehensive summary of the latest research on postbiotics, emphasizing their positive impacts on both human and animal health. As our understanding of the influence of postbiotics on living organisms continues to grow, their application in clinical and nutritional settings, as well as animal husbandry, is expected to expand. Moreover, by substituting postbiotics for antibiotics, we can promote health and productivity while minimizing adverse effects. This alternative approach holds immense potential for improving health outcomes and revolutionizing the food and animal products industries.

Ru-Mg promoted reductive cross-coupling of allyl bromides and alkenes to synthesize 1,7-octadienes with an all-carbon quaternary center.

A Ru-promoted reductive cross-coupling of allyl bromides and electron-deficient alkenes to provide terminal 1,7-octadienes with magnesium as a reductant is reported herein. This approach enables the facile construction of a series of complex terminal 1,7-octadienes with an all-carbon quaternary center under mild reaction conditions, and the synthetic utility of the current method has been demonstrated by a gram scale synthesis. Preliminary mechanism investigations suggested that a radical pathway might not be involved in this transformation.

Leveraging Unidentified Metabolic Features for Key Pathway Discovery: Chemical Classification-driven Network Analysis in Untargeted Metabolomics.

Untargeted metabolomics using liquid chromatography-electrospray ionization-high-resolution tandem mass spectrometry (UPLC-ESI-MS/MS) provides comprehensive insights into the dynamic changes of metabolites in biological systems. However, numerous unidentified metabolic features limit its utilization. In this study, a novel approach, the Chemical Classification-driven Molecular Network (CCMN), was proposed to unveil key metabolic pathways by leveraging hidden information within unidentified metabolic features. The method was demonstrated by using the herbivore-induced metabolic response in corn silk as a case study. Untargeted metabolomics analysis using UPLC-MS/MS was performed on wild corn silk and two genetically modified lines (pre- and postinsect treatment). Global annotation initially identified 256 (ESI-) and 327 (ESI+) metabolites. MS/MS-based classifications predicted 1939 (ESI-) and 1985 (ESI+) metabolic features into the chemical classes. CCMNs were then constructed using metabolic features shared classes, which facilitated the structure- or class annotation for completely unknown metabolic features. Next, 844/713 significantly decreased and 1593/1378 increased metabolites in ESI-/ESI+ modes were defined in response to insect herbivory, respectively. Method validation on a spiked maize sample demonstrated an overall class prediction accuracy rate of 95.7%. Potential key pathways were prescreened by a hypergeometric test using both structure- and class-annotated differential metabolites. Subsequently, CCMN was used to deeply amend and uncover the pathway metabolites deeply. Finally, 8 key pathways were defined, including phenylpropanoid (C6-C3), flavonoid, octadecanoid, diterpenoid, lignan, steroid, amino acid/small peptide, and monoterpenoid. This study highlights the effectiveness of leveraging unidentified metabolic features. CCMN-based key pathway analysis reduced the bias in conventional pathway enrichment analysis. It provides valuable insights into complex biological processes.

Fine-Scale Characterization of Plant Diterpene Glycosides Using Energy-Resolved Untargeted LC-MS/MS Metabolomics Analysis.

Plant diterpene glycosides are essential for diverse physiological processes. Comprehensive structural characterization proved to be a challenge due to variations in glycosylation patterns, diverse aglycone structures, and the absence of comprehensive reference databases. In this study, a method for fine-scale characterization was proposed based on energy-resolved (ER) untargeted LC-MS/MS metabolomics analysis using steviol glycosides as a demonstration. Energy-dependent fragmentation patterns were unveiled by a series of model compounds. Distinct glycosylation sites were discerned by leveraging varying fragmentation energies for the precursor ions. The sugar moiety linkage at C19OOH (R1) exhibited facile and intact cleavage at low collision energies, while the sugar moiety at C13-OH (R2) demonstrated consecutive cleavage with increasing energy. Aglycone ions exhibited a higher relative intensity at NCE 50, with relative intensities ranging from 95% to 100%. Subsequently, aglycone candidates, R1 sugar composition, and R2 sugar sequence were deduced through ER-MS/MS analysis. The developed method was applied to Stevia rebaudiana leaves. A total of 91 diterpene glycosides were unambiguously identified, including 16 steviol glycosides with novel acetylglycosylation patterns. This method offers a rapid alternative for glycan analysis and the structural differentiation of isomers. The developed method enhances the understanding of diterpene glycosides in plants, providing a reliable tool for the in-depth characterization of complex metabolite profiles.

WRKY6 transcription factor modulates root potassium acquisition through promoting expression of AKT1 in Arabidopsis.

Potassium (K+ ), being an essential macronutrient in plants, plays a central role in many aspects. Root growth is highly plastic and is affected by many different abiotic stresses including nutrient deficiency. The Shaker-type K+ channel Arabidopsis (Arabidopsis thaliana) K+ Transporter 1 (AKT1) is responsible for K+ uptake under both low and high external K+ conditions. However, the upstream transcription factor of AKT1 is not clear. Here, we demonstrated that the WRKY6 transcription factor modulates root growth to low potassium (LK) stress in Arabidopsis. WRKY6 showed a quick response to LK stress and also to many other abiotic stress treatments. The two wrky6 T-DNA insertion mutants were highly sensitive to LK treatment, whose primary root lengths were much shorter, less biomass and lower K+ content in roots than those of wild-type plants, while WRKY6-overexpression lines showed opposite phenotypes. A further investigation showed that WRKY6 regulated the expression of the AKT1 gene via directly binding to the W-box elements in its promoter through EMSA and ChIP-qPCR assays. A dual luciferase reporter analysis further demonstrated that WRKY6 enhanced the transcription of AKT1. Genetic analysis further revealed that the overexpression of AKT1 greatly rescued the short root phenotype of the wrky6 mutant under LK stress, suggesting AKT1 is epistatic to WRKY6 in the control of LK response. Further transcriptome profiling suggested that WRKY6 modulates LK response through a complex regulatory network. Thus, this study unveils a transcription factor that modulates root growth under potassium deficiency conditions by affecting the potassium channel gene AKT1 expression.

The communication of artificial intelligence and deep learning in computer tomography image recognition of epidemic pulmonary infectious diseases.

The objectives are to improve the diagnostic efficiency and accuracy of epidemic pulmonary infectious diseases and to study the application of artificial intelligence (AI) in pulmonary infectious disease diagnosis and public health management. The computer tomography (CT) images of 200 patients with pulmonary infectious disease are collected and input into the AI-assisted diagnosis software based on the deep learning (DL) model, "UAI, pulmonary infectious disease intelligent auxiliary analysis system", for lesion detection. By analyzing the principles of convolutional neural networks (CNN) in deep learning (DL), the study selects the AlexNet model for the recognition and classification of pulmonary infection CT images. The software automatically detects the pneumonia lesions, marks them in batches, and calculates the lesion volume. The result shows that the CT manifestations of the patients are mainly involved in multiple lobes and density, the most common shadow is the ground-glass opacity. The detection rate of the manual method is 95.30%, the misdetection rate is 0.20% and missed diagnosis rate is 4.50%; the detection rate of the DL-based AI-assisted lesion method is 99.76%, the misdetection rate is 0.08%, and the missed diagnosis rate is 0.08%. Therefore, the proposed model can effectively identify pulmonary infectious disease lesions and provide relevant data information to objectively diagnose pulmonary infectious disease and manage public health.

Adiponectin protects against myocardial ischemia-reperfusion injury: a systematic review and meta-analysis of preclinical animal studies.

BACKGROUND: Myocardial ischemia-reperfusion injury (MIRI) is widespread in the treatment of ischemic heart disease, and its treatment options are currently limited. Adiponectin (APN) is an adipocytokine with cardioprotective properties; however, the mechanisms of APN in MIRI are unclear. Therefore, based on preclinical (animal model) evidence, the cardioprotective effects of APN and the underlying mechanisms were explored. METHODS: The literature was searched for the protective effect of APN on MIRI in six databases until 16 November 2023, and data were extracted according to selection criteria. The outcomes were the size of the myocardial necrosis area and hemodynamics. Markers of oxidation, apoptosis, and inflammation were secondary outcome indicators. The quality evaluation was performed using the animal study evaluation scale recommended by the Systematic Review Center for Laboratory animal Experimentation statement. Stata/MP 14.0 software was used for the summary analysis. RESULTS: In total, 20 papers with 426 animals were included in this study. The pooled analysis revealed that APN significantly reduced myocardial infarct size [weighted mean difference (WMD) = 16.67 (95% confidence interval (CI) = 13.18 to 20.16, P < 0.001)] and improved hemodynamics compared to the MIRI group [Left ventricular end-diastolic pressure: WMD = 5.96 (95% CI = 4.23 to 7.70, P < 0.001); + dP/dtmax: WMD = 1393.59 (95% CI = 972.57 to 1814.60, P < 0.001); -dP/dtmax: WMD = 850.06 (95% CI = 541.22 to 1158.90, P < 0.001); Left ventricular ejection fraction: WMD = 9.96 (95% CI = 7.29 to 12.63, P < 0.001)]. Apoptosis indicators [caspase-3: standardized mean difference (SMD) = 3.86 (95% CI = 2.97 to 4.76, P < 0.001); TUNEL-positive cells: WMD = 13.10 (95% CI = 8.15 to 18.05, P < 0.001)], inflammatory factor levels [TNF-α: SMD = 4.23 (95% CI = 2.48 to 5.98, P < 0.001)], oxidative stress indicators [Superoxide production: SMD = 4.53 (95% CI = 2.39 to 6.67, P < 0.001)], and lactate dehydrogenase levels [SMD = 2.82 (95% CI = 1.60 to 4.04, P < 0.001)] were significantly reduced. However, the superoxide dismutase content was significantly increased [SMD = 1.91 (95% CI = 1.17 to 2.65, P < 0.001)]. CONCLUSION: APN protects against MIRI via anti-inflammatory, antiapoptotic, and antioxidant effects, and this effect is achieved by activating different signaling pathways.

Quantified Metabolomics and Lipidomics Profiles Reveal Serum Metabolic Alterations and Distinguished Metabolites of Seven Chronic Metabolic Diseases.

The co-occurrence of multiple chronic metabolic diseases is highly prevalent, posing a huge health threat. Clarifying the metabolic associations between them, as well as identifying metabolites which allow discrimination between diseases, will provide new biological insights into their co-occurrence. Herein, we utilized targeted serum metabolomics and lipidomics covering over 700 metabolites to characterize metabolic alterations and associations related to seven chronic metabolic diseases (obesity, hypertension, hyperuricemia, hyperglycemia, hypercholesterolemia, hypertriglyceridemia, fatty liver) from 1626 participants. We identified 454 metabolites were shared among at least two chronic metabolic diseases, accounting for 73.3% of all 619 significant metabolite-disease associations. We found amino acids, lactic acid, 2-hydroxybutyric acid, triacylglycerols (TGs), and diacylglycerols (DGs) showed connectivity across multiple chronic metabolic diseases. Many carnitines were specifically associated with hyperuricemia. The hypercholesterolemia group showed obvious lipid metabolism disorder. Using logistic regression models, we further identified distinguished metabolites of seven chronic metabolic diseases, which exhibited satisfactory area under curve (AUC) values ranging from 0.848 to 1 in discovery and validation sets. Overall, quantitative metabolome and lipidome data sets revealed widespread and interconnected metabolic disorders among seven chronic metabolic diseases. The distinguished metabolites are useful for diagnosing chronic metabolic diseases and provide a reference value for further clinical intervention and management based on metabolomics strategy.

Enhancing Metabolome Annotation by Electron Impact Excitation of Ions from Organics-Molecular Networking.

Liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is widely used in untargeted metabolomics, but large-scale and high-accuracy metabolite annotation remains a challenge due to the complex nature of biological samples. Recently introduced electron impact excitation of ions from organics (EIEIO) fragmentation can generate information-rich fragment ions. However, effective utilization of EIEIO tandem mass spectrometry (MS/MS) is hindered by the lack of reference spectral databases. Molecular networking (MN) shows great promise in large-scale metabolome annotation, but enhancing the correlation between spectral and structural similarity is essential to fully exploring the benefits of MN annotation. In this study, a novel approach was proposed to enhance metabolite annotation in untargeted metabolomics using EIEIO and MN. MS/MS spectra were acquired in EIEIO and collision-induced dissociation (CID) modes for over 400 reference metabolites. The study revealed a stronger correlation between the EIEIO spectra and metabolite structure. Moreover, the EIEIO spectral network outperformed the CID spectral network in capturing structural analogues. The annotation performance of the structural similarity network for untargeted LC-MS/MS was evaluated. For the spiked NIST SRM 1950 human plasma, the annotation coverage and accuracy were 72.94 and 74.19%, respectively. A total of 2337 metabolite features were successfully annotated in NIST SRM 1950 human plasma, which was twice that of LC-CID MS/MS. Finally, the developed method was applied to investigate prostate cancer. A total of 87 significantly differential metabolites were annotated. This study combining EIEIO and MN makes a valuable contribution to improving metabolome annotation.

The effect of intercropping leguminous green manure on theanine accumulation in the tea plant: A metagenomic analysis.

Intercropping is a widely recognised technique that contributes to agricultural sustainability. While intercropping leguminous green manure offers advantages for soil health and tea plants growth, the impact on the accumulation of theanine and soil nitrogen cycle are largely unknown. The levels of theanine, epigallocatechin gallate and soluble sugar in tea leaves increased by 52.87% and 40.98%, 22.80% and 6.17%, 22.22% and 29.04% in intercropping with soybean-Chinese milk vetch rotation and soybean alone, respectively. Additionally, intercropping significantly increased soil amino acidnitrogen content, enhanced extracellular enzyme activities, particularly ß-glucosidase and N-acetyl-glucosaminidase, as well as soil multifunctionality. Metagenomics analysis revealed that intercropping positively influenced the relative abundances of several potentially beneficial microorganisms, including Burkholderia, Mycolicibacterium and Paraburkholderia. Intercropping resulted in lower expression levels of nitrification genes, reducing soil mineral nitrogen loss and N2 O emissions. The expression of nrfA/H significantly increased in intercropping with soybean-Chinese milk vetch rotation. Structural equation model analysis demonstrated that the accumulation of theanine in tea leaves was directly influenced by the number of intercropping leguminous green manure species, soil ammonium nitrogen and amino acid nitrogen. In summary, the intercropping strategy, particularly intercropping with soybean-Chinese milk vetch rotation, could be a novel way for theanine accumulation.

A New Biomarker Profiling Strategy for Gut Microbiome Research: Valid Association of Metabolites to Metabolism of Microbiota Detected by Non-Targeted Metabolomics in Human Urine.

The gut microbiome is of tremendous relevance to human health and disease, so it is a hot topic of omics-driven biomedical research. However, a valid identification of gut microbiota-associated molecules in human blood or urine is difficult to achieve. We hypothesize that bowel evacuation is an easy-to-use approach to reveal such metabolites. A non-targeted and modifying group-assisted metabolomics approach (covering 40 types of modifications) was applied to investigate urine samples collected in two independent experiments at various time points before and after laxative use. Fasting over the same time period served as the control condition. As a result, depletion of the fecal microbiome significantly affected the levels of 331 metabolite ions in urine, including 100 modified metabolites. Dominating modifications were glucuronidations, carboxylations, sulfations, adenine conjugations, butyrylations, malonylations, and acetylations. A total of 32 compounds, including common, but also unexpected fecal microbiota-associated metabolites, were annotated. The applied strategy has potential to generate a microbiome-associated metabolite map (M3) of urine from healthy humans, and presumably also other body fluids. Comparative analyses of M3 vs. disease-related metabolite profiles, or therapy-dependent changes may open promising perspectives for human gut microbiome research and diagnostics beyond analyzing feces.

Network Pharmacology-Based Approach to Investigate the Active Ingredients and Therapeutic Mechanisms of Jingu Tongxiao Pill against Osteoarthritis.

This study aimed to investigate the active ingredients and therapeutic mechanisms of Jingu Tongxiao Pill (JGTXP), a commonly used Chinese patent medicine, in treating osteoarthritis (OA) via network pharmacology analysis combined with experimental validation. First, we administered JGTXP to rat plasma and identified the candidate active compounds. Next, target prediction, protein-protein interaction, compound-target network construction, gene ontology (GO), and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were conducted for JGTXP. Lastly, the network-derived key targets and pathways were validated in vitro and in vivo. Finally, we identified 106 compounds in JGTXP and 24 absorbed compounds in the rat plasma. Network analysis revealed that JGTXP interferes with OA mainly via regulating the inflammatory response, collagen catabolic process, and osteoclast differentiation, and the nuclear factor kappa B (NF-κB) signaling pathway plays a pivotal role in these processes. Experimentally, JGTXP exerted potential protective effects on articular cartilage and inhibited expression of inflammatory mediators and collagen catabolism-related proteins, including interleukin 1 beta (IL-1ß), interleukin 6, tumor necrosis factor alpha (TNF-α), and matrix metalloproteinase (MMP) 3 and MMP13, in a papain-induced OA rat model. Consistently, mRNA expression levels of these factors and nitric oxide release were suppressed by JGTXP in an LPS-induced RAW 264.7 inflammation model. The reporter gene assay showed that JGTXP could reduce the transcriptional activity of NF-κB. Consecutive western blot analysis demonstrated that nuclear NF-κB p65, inducible nitric oxide synthase (iNOS), and cyclooxygenase 2 (COX-2) expression were inhibited while cytoplasmic NF-κB p65 was upregulated by JGTXP. Using a combination of chemical profiling, network pharmacology analysis, and experimental validation, we preliminarily clarified the active ingredients of JGTXP intervention for OA and demonstrated that JGTXP ameliorates OA, at least partially, by regulating the NF-κB signaling pathway.

Redox state and altered pyruvate metabolism contribute to a dose-dependent metformin-induced lactate production of human myotubes.

Metformin-induced glycolysis and lactate production can lead to acidosis as a life-threatening side effect, but slight increases in blood lactate levels in a physiological range were also reported in metformin-treated patients. However, how metformin increases systemic lactate concentrations is only partly understood. Because human skeletal muscle has a high capacity to produce lactate, the aim was to elucidate the dose-dependent regulation of metformin-induced lactate production and the potential contribution of skeletal muscle to blood lactate levels under metformin treatment. This was examined by using metformin treatment (16-776 µM) of primary human myotubes and by 17 days of metformin treatment in humans. As from 78 µM, metformin induced lactate production and secretion and glucose consumption. Investigating the cellular redox state by mitochondrial respirometry, we found metformin to inhibit the respiratory chain complex I (776 µM, P < 0.01) along with decreasing the [NAD+]:[NADH] ratio (776 µM, P < 0.001). RNA sequencing and phospho-immunoblot data indicate inhibition of pyruvate oxidation mediated through phosphorylation of the pyruvate dehydrogenase (PDH) complex (39 µM, P < 0.01). On the other hand, in human skeletal muscle, phosphorylation of PDH was not altered by metformin. Nonetheless, blood lactate levels were increased under metformin treatment (P < 0.05). In conclusion, the findings suggest that metformin-induced inhibition of pyruvate oxidation combined with altered cellular redox state shifts the equilibrium of the lactate dehydrogenase (LDH) reaction leading to a dose-dependent lactate production in primary human myotubes.NEW & NOTEWORTHY Metformin shifts the equilibrium of lactate dehydrogenase (LDH) reaction by low dose-induced phosphorylation of pyruvate dehydrogenase (PDH) resulting in inhibition of pyruvate oxidation and high dose-induced increase in NADH, which explains the dose-dependent lactate production of differentiated human skeletal muscle cells.

Prediction of plant secondary metabolic pathways using deep transfer learning.

BACKGROUND: Plant secondary metabolites are highly valued for their applications in pharmaceuticals, nutrition, flavors, and aesthetics. It is of great importance to elucidate plant secondary metabolic pathways due to their crucial roles in biological processes during plant growth and development. However, understanding plant biosynthesis and degradation pathways remains a challenge due to the lack of sufficient information in current databases. To address this issue, we proposed a transfer learning approach using a pre-trained hybrid deep learning architecture that combines Graph Transformer and convolutional neural network (GTC) to predict plant metabolic pathways. RESULTS: GTC provides comprehensive molecular representation by extracting both structural features from the molecular graph and textual information from the SMILES string. GTC is pre-trained on the KEGG datasets to acquire general features, followed by fine-tuning on plant-derived datasets. Four metrics were chosen for model performance evaluation. The results show that GTC outperforms six other models, including three previously reported machine learning models, on the KEGG dataset. GTC yields an accuracy of 96.75%, precision of 85.14%, recall of 83.03%, and F1_score of 84.06%. Furthermore, an ablation study confirms the indispensability of all the components of the hybrid GTC model. Transfer learning is then employed to leverage the shared knowledge acquired from the KEGG metabolic pathways. As a result, the transferred GTC exhibits outstanding accuracy in predicting plant secondary metabolic pathways with an average accuracy of 98.30% in fivefold cross-validation and 97.82% on the final test. In addition, GTC is employed to classify natural products. It achieves a perfect accuracy score of 100.00% for alkaloids, while the lowest accuracy score of 98.42% for shikimates and phenylpropanoids. CONCLUSIONS: The proposed GTC effectively captures molecular features, and achieves high performance in classifying KEGG metabolic pathways and predicting plant secondary metabolic pathways via transfer learning. Furthermore, GTC demonstrates its generalization ability by accurately classifying natural products. A user-friendly executable program has been developed, which only requires the input of the SMILES string of the query compound in a graphical interface.

Deep Characterization of Serum Metabolome Based on the Segment-Optimized Spectral-Stitching Direct-Infusion Fourier Transform Ion Cyclotron Resonance Mass Spectrometry Approach.

Direct-infusion Fourier transform ion cyclotron resonance mass spectrometry (DI-FTICR MS) shows great promise for metabolomic analysis due to ultrahigh mass accuracy and resolution. However, most of the DI-FTICR MS approaches focused on high-throughput metabolomics analysis at the expense of sensitivity and resolution and the potential for metabolome characterization has not been fully explored. Here, we proposed a novel deep characterization approach of serum metabolome using a segment-optimized spectral-stitching DI-FTICR MS method integrated with high-confidence and database-independent formula assignments. With varied acquisition parameters for each segment, a highly efficient acquisition was achieved for the whole mass range with sub-ppm mass accuracy. In a pooled human serum sample, thousands of features were assigned with unambiguous formulas and possible candidates based on highly accurate mass measurements. Furthermore, a reaction network was used to select confidently unique formulas from possible candidates, which was constructed by unambiguous formulas and possible candidates connected by the formula differences resulting from biochemical and MS transformation. Compared with full-range and conventional segment acquisition, 8- and 1.2-fold increases in observed features were achieved, respectively. Assignment accuracy was 93-94% for both a standard mixture containing 190 metabolites and a spiked serum sample with the root mean square mass error of 0.15-0.16 ppm. In total, 3534 unequivocal neutral molecular formulas were assigned in the pooled serum sample, 35% of which are contained in the HMDB. This method offers great enhancement in the deep characterization of serum metabolome by DI-FTICR MS.

A Structure-Guided Molecular Network Strategy for Global Untargeted Metabolomics Data Annotation.

Large-scale metabolite annotation is a bottleneck in untargeted metabolomics. Here, we present a structure-guided molecular network strategy (SGMNS) for deep annotation of untargeted ultra-performance liquid chromatography-high resolution mass spectrometry (MS) metabolomics data. Different from the current network-based metabolite annotation method, SGMNS is based on a global connectivity molecular network (GCMN), which was constructed by molecular fingerprint similarity of chemical structures in metabolome databases. Neighbor metabolites with similar structures in GCMN are expected to produce similar spectra. Network annotation propagation of SGMNS is performed using known metabolites as seeds. The experimental MS/MS spectra of seeds are assigned to corresponding neighbor metabolites in GCMN as their "pseudo" spectra; the propagation is done by searching predicted retention times, MS1, and "pseudo" spectra against metabolite features in untargeted metabolomics data. Then, the annotated metabolite features were used as new seeds for annotation propagation again. Performance evaluation of SGMNS showed its unique advantages for metabolome annotation. The developed method was applied to annotate six typical biological samples; a total of 701, 1557, 1147, 1095, 1237, and 2041 metabolites were annotated from the cell, feces, plasma (NIST SRM 1950), tissue, urine, and their pooled sample, respectively, and the annotation accuracy was >83% with RSD <2%. The results show that SGMNS fully exploits the chemical space of the existing metabolomes for metabolite deep annotation and overcomes the shortcoming of insufficient reference MS/MS spectra.

Full-length transcriptome profiling of Aphidius gifuensis mitochondrial genome with gene rearrangement and control region duplication.

Although mitochondrial gene rearrangement has been observed in many insect lineages, little is known about how it affects mitochondrial gene transcription. To address this question, we first constructed a quantitative transcription map for Aphidius gifuensis, a species of parasitoid wasp known to have a highly rearranged mitochondrial genome (mitogenome) and two potential control regions (CRs). Based on this transcription map, we assessed the models of the mitochondrial transcription and post-transcription cleavage. We found that the J and N strand of this mitogenome differ significantly in transcriptional regulation. On the J strand, we found two transcription initiation sites (TISs), five transcription termination sites (TTSs), and six polycistronic primary transcripts whereas only one TIS, one TTS and one polycistronic primary transcript can be found on the N strand. Most of the non-coding regions of both strands were transcribed into primary transcripts and cleaved after transcription. The proposed mode of transcription of A. gifuensis was similar to that of Drosophila, a model organism with no gene rearrangement. And two rearranged gene clusters (trnI-CR1-trnM-CR2-trnQ and trnW-trnY-trnC) seemed to have little effects on the mode of transcription. In addition, our results revealed the presence of TISs in CR1 and CR2, implying that both CRs maybe required for transcriptional regulation. Analysis of the post-transcriptional cleavage process showed that there were both "forward cleavage" and "reverse cleavage" models in A. gifuensis, and more than one way of cleavages were found in three regions. The incomplete transcripts suggested that the direction of mitochondrial RNA degradation was from 5' to 3' end and supported the view of polyadenylation-dependent RNA degradation. Our study provides insights into the transcriptional and post-transcriptional regulation processes of highly rearranged insect mitogenomes.

Nontargeted Metabolomics Revealed Novel Association Between Serum Metabolites and Incident Acute Coronary Syndrome: A Mendelian Randomization Study.

Background This study was performed to identify metabolites associated with incident acute coronary syndrome (ACS) and explore causality of the associations. Methods and Results We performed nontargeted metabolomics in a nested case-control study in the Dongfeng-Tongji cohort, including 500 incident ACS cases and 500 age- and sex-matched controls. Three metabolites, including a novel one (aspartylphenylalanine), and 1,5-anhydro-d-glucitol (1,5-AG) and tetracosanoic acid, were identified as associated with ACS risk, among which aspartylphenylalanine is a degradation product of the gut-brain peptide cholecystokinin-8 rather than angiotensin by the angiotensin-converting enzyme (odds ratio [OR] per SD increase [95% CI], 1.29 [1.13-1.48]; false discovery rate-adjusted P=0.025), 1,5-AG is a marker of short-term glycemic excursions (OR per SD increase [95% CI], 0.75 [0.64-to 0.87]; false discovery rate-adjusted P=0.025), and tetracosanoic acid is a very-long-chain saturated fatty acid (OR per SD increase [95% CI], 1.26 [1.10-1.45]; false discovery rate-adjusted P=0.091). Similar associations of 1,5-AG (OR per SD increase [95% CI], 0.77 [0.61-0.97]) and tetracosanoic acid (OR per SD increase [95% CI], 1.32 [1.06-1.67]) with coronary artery disease risk were observed in a subsample from an independent cohort (152 and 96 incident cases, respectively). Associations of aspartylphenylalanine and tetracosanoic acid were independent of traditional cardiovascular risk factors (P-trend=0.015 and 0.034, respectively). Furthermore, the association of aspartylphenylalanine was mediated by 13.92% from hypertension and 27.39% from dyslipidemia (P<0.05), supported by its causal links with hypertension (P<0.05) and hypertriglyceridemia (P=0.077) in Mendelian randomization analysis. The association of 1,5-AG with ACS risk was 37.99% mediated from fasting glucose, and genetically predicted 1,5-AG level was negatively associated with ACS risk (OR per SD increase [95% CI], 0.57 [0.33-0.96], P=0.036), yet the association was nonsignificant when further adjusting for fasting glucose. Conclusions These findings highlighted novel angiotensin-independent involvement of the angiotensin-converting enzyme in ACS cause, and the importance of glycemic excursions and very-long-chain saturated fatty acid metabolism.