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.

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.

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.

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.

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.

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.

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.

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.

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.

Ex vivo instability of lipids in whole blood: preanalytical recommendations for clinical lipidomics studies.

Reliability, robustness, and interlaboratory comparability of quantitative measurements is critical for clinical lipidomics studies. Lipids' different ex vivo stability in blood bears the risk of misinterpretation of data. Clear recommendations for the process of blood sample collection are required. We studied by UHPLC-high resolution mass spectrometry, as part of the "Preanalytics interest group" of the International Lipidomics Society, the stability of 417 lipid species in EDTA whole blood after exposure to either 4°C, 21°C, or 30°C at six different time points (0.5 h-24 h) to cover common daily routine conditions in clinical settings. In total, >800 samples were analyzed. 325 and 288 robust lipid species resisted 24 h exposure of EDTA whole blood to 21°C or 30°C, respectively. Most significant instabilities were detected for FA, LPE, and LPC. Based on our data, we recommend cooling whole blood at once and permanent. Plasma should be separated within 4 h, unless the focus is solely on robust lipids. Lists are provided to check the ex vivo (in)stability of distinct lipids and potential biomarkers of interest in whole blood. To conclude, our results contribute to the international efforts towards reliable and comparable clinical lipidomics data paving the way to the proper diagnostic application of distinct lipid patterns or lipid profiles in the future.

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.

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.

AtMCM10 promotes DNA replication-coupled nucleosome assembly in Arabidopsis.

Minichromosome Maintenance protein 10 (MCM10) is essential for DNA replication initiation and DNA elongation in yeasts and animals. Although the functions of MCM10 in DNA replication and repair have been well documented, the detailed mechanisms for MCM10 in these processes are not well known. Here, we identified AtMCM10 gene through a forward genetic screening for releasing a silenced marker gene. Although plant MCM10 possesses a similar crystal structure as animal MCM10, AtMCM10 is not essential for plant growth or development in Arabidopsis. AtMCM10 can directly bind to histone H3-H4 and promotes nucleosome assembly in vitro. The nucleosome density is decreased in Atmcm10, and most of the nucleosome density decreased regions in Atmcm10 are also regulated by newly synthesized histone chaperone Chromatin Assembly Factor-1 (CAF-1). Loss of both AtMCM10 and CAF-1 is embryo lethal, indicating that AtMCM10 and CAF-1 are indispensable for replication-coupled nucleosome assembly. AtMCM10 interacts with both new and parental histones. Atmcm10 mutants have lower H3.1 abundance and reduced H3K27me1/3 levels with releasing some silenced transposons. We propose that AtMCM10 deposits new and parental histones during nucleosome assembly, maintaining proper epigenetic modifications and genome stability during DNA replication.

SKF96365 impedes spinal glutamatergic transmission-mediated neuropathic allodynia.

Spinal nerve injury causes mechanical allodynia and structural imbalance of neurotransmission, which were typically associated with calcium overload. Store-operated calcium entry (SOCE) is considered crucial elements-mediating intracellular calcium homeostasis, ion channel activity, and synaptic plasticity. However, the underlying mechanism of SOCE in mediating neuronal transmitter release and synaptic transmission remains ambiguous in neuropathic pain. Neuropathic rats were operated by spinal nerve ligations. Neurotransmissions were assessed by whole-cell recording in substantia gelatinosa. Immunofluorescence staining of STIM1 with neuronal and glial biomarkers in the spinal dorsal horn. The endoplasmic reticulum stress level was estimated from qRT-PCR. Intrathecal injection of SOCE antagonist SKF96365 dose-dependently alleviated mechanical allodynia in ipsilateral hind paws of neuropathic rats with ED50 of 18 µg. Immunofluorescence staining demonstrated that STIM1 was specifically and significantly expressed in neurons but not astrocytes and microglia in the spinal dorsal horn. Bath application of SKF96365 inhibited enhanced miniature excitatory postsynaptic currents in a dosage-dependent manner without affecting miniature inhibitory postsynaptic currents. Mal-adaption of SOCE was commonly related to endoplasmic reticulum (ER) stress in the central nervous system. SKF96365 markedly suppressed ER stress levels by alleviating mRNA expression of C/EBP homologous protein and heat shock protein 70 in neuropathic rats. Our findings suggested that nerve injury might promote SOCE-mediated calcium levels, resulting in long-term imbalance of spinal synaptic transmission and behavioral sensitization, SKF96365 produces antinociception by alleviating glutamatergic transmission and ER stress. This work demonstrated the involvement of SOCE in neuropathic pain, implying that SOCE might be a potential target for pain management.

MetEx: A Targeted Extraction Strategy for Improving the Coverage and Accuracy of Metabolite Annotation in Liquid Chromatography-High-Resolution Mass Spectrometry Data.

Liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is the most popular platform for untargeted metabolomics studies, but compound annotation is a challenge. In this work, we developed a new LC-HRMS data-targeted extraction method called MetEx for metabolite annotation. MetEx contains the retention time (tR), MS1, and MS2 information of 30 620 metabolites from freely available spectral databases, including MoNA and KEGG. The tR values of 95.4% of the compounds in our database were calculated by the GNN-RT model. The MS2 spectra of 39.4% compounds were also predicted using CFM-ID. MetEx was initially examined on a mixture of 634 standards, considering chemical coverage and accurate metabolite assignment, and later applied to human plasma (NIST SRM 1950), human urine, HepG2 cells, mouse liver tissue, and mouse feces. MetEx correctly assigned 252 out of 253 standards detected in our instruments. The platform also provided 8.0-44.2% more compounds in the biological samples compared to XCMS, MS-DIAL, and MZmine 2. MetEx is implemented and visualized in R and freely available at http://www.metaboex.cn/MetEx.

Novel Method for Comprehensive Annotation of Plant Glycosides Based on Untargeted LC-HRMS/MS Metabolomics.

Glycosides are a large family of secondary metabolites in plants, which play a critical role in plant growth and development. Due to the complexity and diversity in structures and the limited availability of authentic standards, comprehensive annotation of the glycosides remains a great challenge. In this study, using maize as an example, a deep annotation method of glycosides was proposed based on untargeted liquid chromatography-high-resolution tandem mass spectrometry metabolomics analysis. First, knowledge-based in silico aglycone and glycosyl/acyl-glycosyl libraries were built. A total of 1240 known and potential aglycones from databases and literature were recorded. Next, the MS parameters beneficial to aglycone ion-rich MS/MS were explored using 1782 high-resolution MS/MS spectra of glycosides from the MassBank of North America (MoNA) and confirmed by 52 authentic glycoside standards. Then, screening rules for aglycon ions in MS/MS were recommended. Glycoside candidates were further filtered by MS/MS-based chemical classification and MS/MS similarity of aglycon-glycoside pairs. Finally, the glycosylation sites of flavonoid mono-O-glycosides were recommended by characteristic fragmentation patterns. The developed method was validated using glycosides and nonglycosides from the MoNA library. The annotation accuracy rates were 96.8, 94.9, and 98.0% in negative ion mode (ESI-), positive ion mode (ESI+), and the combined ESI- & ESI+, respectively. The annotation specificity was 99.6% (ESI-), 99.6% (ESI+), and 99.2% (ESI- & ESI+). A total of 274 glycosides (including 34 acyl-glycosides) were tentatively annotated in maize by the developed method. The method enables effective and reliable annotation for plant glycosides.

Femtosecond laser nanostructuring on a 4H-SiC surface by tailoring the induced self-assembled nanogratings.

Ultrafast laser micromachining of crystalline silicon carbide (SiC) has great perspectives in aerospace industry and integrated circuit technique. In this report, we present a study of femtosecond laser nanostructuring on the surface of an n-type 4H-SiC single crystal. Except for uniform nanogratings, new types of large-area periodic structures including nanoparticle array and nanoparticle-nanograting hybrid structures were induced on the surface of 4H-SiC by scanning irradiation. The effects of pulse energy, scan speed, and the polarization direction on the morphology and periodicity of nanogratings were systematically explored. The proper parameter window for nanograting formation in pulse energy-scan speed landscape is depicted. Both the uniformity and the periodicity of the induced nanogratings are polarization dependent. A planar light attenuator for linear polarized light was demonstrated by aligning the nanogratings. The transition between different large-area periodic structures is achieved by simultaneous control of pulse energy and scan interval using a cross scan strategy. These results are expected to open up an avenue to create and manipulate periodic nanostructures on SiC crystals for photonic applications.

Metabolome-Wide Association Study of Multiple Plasma Metals with Serum Metabolomic Profile among Middle-to-Older-Aged Chinese Adults.

Metal exposure has been associated with risk of various cardio-metabolic disorders, and investigation on the association between exposure to multiple metals and metabolic responses may reveal novel clues to the underlying mechanisms. Based on a metabolome-wide association study of 17 plasma metals with untargeted metabolomic profiling of 189 serum metabolites among 1992 participants within the Dongfeng-Tongji cohort, we replicated two metal-associated pathways, linoleic acid metabolism and aminoacyl-tRNA biosynthesis, with novel metal associations (false discovery rate, FDR < 0.05), and we also identified two novel pathways, including biosynthesis of unsaturated fatty acids and alpha-linolenic acid metabolism, as associated with metal exposure (FDR < 0.05). Moreover, two-way orthogonal partial least-squares analysis showed that five metabolites, including aspartylphenylalanine, free fatty acid 14:1, uridine, carnitine C14:2, and LPC 18:2, contributed most to the joint covariation between the two data matrices (12.3%, 8.3%, 8.0%, 7.4%, and 7.3%, respectively). Further BKMR analysis showed significant positive joint associations of plasma Al, As, Ba, and Zn with aspartylphenylalanine and of plasma Ba, Co, Mn, and Pb with carnitine C14:2, when all the metals were at the 55th percentiles or above, compared with the median. We also found significant interactions between As and Ba in the association with aspartylphenylalanine (P for interaction = 0.048) and between Ba and Pb in the association with carnitine C14:2 (P for interaction < 0.001). Together, these findings may provide new insights into the mechanisms underlying the adverse health effects induced by metal exposure.

Rapid Purification of Fucoxanthin from Phaeodactylum tricornutum.

Fucoxanthin is a natural marine xanthophyll and exhibits a broad range of biological activities. In the present study, a simple and efficient two-step method was used to purify fucoxanthin from the diatom, Phaeodactylum tricornutum. The crude pigment extract of fucoxanthin was separated by silica gel column chromatography (SGCC). Then, the fucoxanthin-rich fraction was purified using a hydrophile-lipophile balance (HLB) solid-phase extraction column. The identification and quantification of fucoxanthin were determined by high-performance liquid chromatography (HPLC) and electrospray ionization mass spectrometry (ESI-MS). This two-step method can obtain 92.03% pure fucoxanthin and a 76.67% recovery rate. In addition, 1H and 13C NMR spectrums were adopted to confirm the identity of fucoxanthin. Finally, the purified fucoxanthin exhibited strong antioxidant properties in vitro with the effective concentration for 50% of maximal scavenging (EC50) of 1,1-Dihpenyl-2-picrylhydrazyl (DPPH) and 2,2'-Azinobis-(3-ethylbenzthiazoline-6-sulphonate) (ABTS) free radicals being 0.14 mg·mL-1 and 0.05 mg·mL-1, respectively.