AntDAS-DDA: A New Platform for Data-Dependent Acquisition Mode-Based Untargeted Metabolomic Profiling Analysis with Advantage of Recognizing Insource Fragment Ions to Improve Compound Identification.

Data-dependent acquisition (DDA) mode in ultra-high-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) can provide massive amounts of MS1 and MS/MS information of compounds in untargeted metabolomics and can thus facilitate compound identification greatly. In this work, we developed a new platform called AntDAS-DDA for the automatic processing of UHPLC-HRMS data sets acquired under the DDA mode. Several algorithms, including extracted ion chromatogram extraction, feature extraction, MS/MS spectrum construction, fragment ion identification, and MS1 spectrum construction, were developed within the platform. The performance of AntDAS-DDA was investigated comprehensively with a mixture of standard and complex plant data sets. Results suggested that features in complex sample matrices can be extracted effectively, and the constructed MS1 and MS/MS spectra can benefit in compound identification greatly. The efficiency of compound identification can be improved by about 20%. AntDAS-DDA can take full advantage of MS/MS information in multiple sample analyses and provide more MS/MS spectra than single sample analysis. A comparison with advanced data analysis tools indicated that AntDAS-DDA may be used as an alternative for routine UHPLC-HRMS-based untargeted metabolomics. AntDAS-DDA is freely available at http://www.pmdb.org.cn/antdasdda.

[Explore the mechanism of astragaloside IV-PESV on proliferation, migration, and autophagy of prostate cancer cells based on the PI3K/AKT signaling pathway].

OBJECTIVE: Explore the effects of Astragaloside IV and Scorpion Venom Peptide on the activity, migration, apoptosis, cell cycle, autophagy, and the expression of proteins related to the PI3K/AKT signaling pathway in prostate cancer cells. METHODS: The human prostate cancer cell lines LNCaP and PC-3 were randomly divided into blank control group, Astragaloside IV group, Scorpion Venom Peptide group, Astragaloside IV-Scorpion Venom Peptide group, and rapamycin (positive drug group). After corresponding drug treatments for 24 hours, logarithmic growth phase tumor cells were collected for testing. Cell proliferation was assessed using a Cell Counting Kit-8 (CCK-8) assay, Transwell assay, apoptosis assay, cell cycle assay, and immunofluorescence analysis were performed to detect the activity and migration capacity of prostate cancer cells in each group, as well as their effects on apoptosis, cell cycle, and the autophagy target LC3. Western blot analysis was employed to measure the protein expression levels of p-PI3K, p-Akt, p-mTOR, Beclin1, LC3, and P62. RESULTS: Compared to the blank control group, the Astragaloside IV-Scorpion Venom Peptide group exhibited a significant decrease in the activity of prostate cancer cells (P<0.05) and a reduction in the cell invasion ability (migration capacity) (P<0.05). The early apoptosis rate (LR), late apoptosis rate (UR), and total apoptosis rate all increased (P<0.05). The proportion of cells in the G1 phase increased (P<0.05), while the proportion in the G2+S phase decreased (P<0.05). The immunofluorescence expression of LC3 significantly increased (P<0.05). The expression of LC3Ⅱ and Beclin1 proteins in prostate cancer cells LNCaP and PC-3 was upregulated (P<0.05), while the expression of P62, p-PI3K, p-AKT, and p-mTOR proteins was downregulated (P<0.05).Astragaloside IV-Scorpion Venom Peptide is superior to the Astragaloside IV group or Scorpion Venom Peptide group alone in inhibiting the activity and migration capacity of prostate cancer cells, suppressing cell mitosis, promoting early apoptosis, upregulating the expression level of LC3, and inhibiting the PI3K/AKT pathway while promoting autophagy (P<0.05). CONCLUSION: The mechanism by which Astragaloside IV-Scorpion Venom Peptide inhibits the proliferation and migration of prostate cancer cells, suppresses cell mitosis, promotes early apoptosis, and enhances autophagy may be related to the inhibition of the PI3K/AKT pathway.

Differentiating Westlake Longjing tea from the first- and second-grade producing regions using ultra high performance liquid chromatography with quadrupole time-of-flight mass spectrometry-based untargeted metabolomics in combination with chemometrics.

There are numerous articles published for geographical discrimination of tea. However, few research works focused on the authentication and traceability of Westlake Longjing green tea from the first- and second-grade producing regions because the tea trees are planted in a limited growing zone with identical cultivate condition. In this work, a comprehensive analytical strategy was proposed by ultrahigh performance liquid chromatography-quadrupole time-of-flight mass spectrometry-based untargeted metabolomics coupled with chemometrics. The automatic untargeted data analysis strategy was introduced to screen metabolites that expressed significantly among different regions. Chromatographic features of metabolites can be automatically and efficiently extracted and registered. Meanwhile, those that were valuable for geographical origin discrimination were screened based on statistical analysis and contents in samples. Metabolite identification was performed based on high-resolution mass values and tandem mass spectra of screened peaks. Twenty metabolites were identified, based on which the two-way encoding partial least squares discrimination analysis was built for geographical origin prediction. Monte Caro simulation results indicated that prediction accuracy was up to 99%. Our strategy can be applicable for practical applications in the quality control of Westlake Longjing green tea.

Association between elevated plasma microRNA-223 content and severity of coronary heart disease.

Bioinformatics indicate that miR-223 regulates many genes associated with cholesterol metabolism, and it could also control high-density lipoprotein-cholesterol (HDL-C) uptake. As reported in previous study, miR-223 was found to be upregulated from human subjects with familial hypercholesterolaemia, however, it remains to be determined using a larger group of coronary heart disease (CHD) patients. Moreover, whether it correlates with severity of atherogenesis, has never been elucidated before. We aim to further explore the association between circulating miR-223 content and severity of CHD. Sample was collected from 300 CHD patients and 100 subjects with angiographic exclusion of CHD. MiR-223 content was detected using quantitative real-time PCR. Gensini score was used to evaluate the severity of coronary stenotic lesions. Expression of miR-223 was identified on basis of the quartiles of the Gensini score, and association between the miRNA and CHD was analyzed. Diagnostic potential of miR-223 of CHD was performed by ROC analysis. CHD patients had higher miR-223 level (13.23, 9.29-17.59 vs. 4.05, 3.06-6.11, p < .001), and the miRNA content significantly elevated following increasing Gensini score (p < .001). Gensini score was significantly associated with miR-223 expression (r= .7289, p < .001). The optimal cut-off value of miR-223 was with a sensitivity of 86.0% and specificity of 91.3%. The AUC of miR-223 was 0.933 (95%CI, 0.905-0.961). These preliminary results suggest that the expression of miR-223 may be associated with atherogenesis. The level of circulating miR-223 in predicting the severity of coronary atherosclerosis may have a relatively certain value.

AntDAS: Automatic Data Analysis Strategy for UPLC-QTOF-Based Nontargeted Metabolic Profiling Analysis.

High-quality data analysis methodology remains a bottleneck for metabolic profiling analysis based on ultraperformance liquid chromatography-quadrupole time-of-flight mass spectrometry. The present work aims to address this problem by proposing a novel data analysis strategy wherein (1) chromatographic peaks in the UPLC-QTOF data set are automatically extracted by using an advanced multiscale Gaussian smoothing-based peak extraction strategy; (2) a peak annotation stage is used to cluster fragment ions that belong to the same compound. With the aid of high-resolution mass spectrometer, (3) a time-shift correction across the samples is efficiently performed by a new peak alignment method; (4) components are registered by using a newly developed adaptive network searching algorithm; (5) statistical methods, such as analysis of variance and hierarchical cluster analysis, are then used to identify the underlying marker compounds; finally, (6) compound identification is performed by matching the extracted peak information, involving high-precision m/z and retention time, against our compound library containing more than 500 plant metabolites. A manually designed mixture of 18 compounds is used to evaluate the performance of the method, and all compounds are detected under various concentration levels. The developed method is comprehensively evaluated by an extremely complex plant data set containing more than 2000 components. Results indicate that the performance of the developed method is comparable with the XCMS. The MATLAB GUI code is available from http://software.tobaccodb.org/software/antdas .

Two new diterpenes, neocaesalpin MR and minaxin C, from Caesalpinia minax.

Two new cassane-type diterpenes, neocaesalpin MR and minaxin C, were isolated from Caesalpinia minax HANCE. The structures of these compounds were elucidated by means of spectroscopic analysis. Among these isolated compounds, neocaesalpin MR showed mild activities toward HeLa and colon cancer (HCT-8) human cancer cell lines.

A new platform for untargeted UHPLC-HRMS data analysis to address the time-shift problem.

Substantial deviations in retention times among samples pose a great challenge for the accurate screening and identifying of metabolites by ultrahigh-performance liquid chromatography high-resolution mass spectrometry (UHPLC-HRMS). In this study, a coarse-to-refined time-shift correction methodology was proposed to efficiently address this problem. Metabolites producing multiple fragment ions were automatically selected as landmarks to generate pseudo-mass spectra for a coarse time-shift correction. Refined peak alignment for extracted ion chromatograms was then performed by using a moving window-based multiple-peak alignment strategy. Based on this novel coarse-to-refined time-shift correction methodology, a new comprehensive UHPLC-HRMS data analysis platform was developed for UHPLC-HRMS-based metabolomics. Original datasets were employed as inputs to automatically extract and register features in the dataset and to distinguish fragment ions from metabolites for chemometric analysis. Its performance was further evaluated using complex datasets, and the results suggest that the new platform can satisfactorily resolve the time-shift problem and is comparable with commonly used UHPLC-HRMS data analysis tools such as XCMS Online, MS-DIAL, Mzmine2, and Progenesis QI. The new platform can be downloaded from: http://www.pmdb.org.cn/antdas2tsc.

Proteomic and metabolomic analysis of Nicotiana benthamiana under dark stress.

Exposure to extended periods of darkness is a common source of abiotic stress that significantly affects plant growth and development. To understand how Nicotiana benthamiana responds to dark stress, the proteomes and metabolomes of leaves treated with darkness were studied. In total, 5763 proteins and 165 primary metabolites were identified following dark treatment. Additionally, the expression of autophagy-related gene (ATG) proteins was transiently upregulated. Weighted gene coexpression network analysis (WGCNA) was utilized to find the protein modules associated with the response to dark stress. A total of four coexpression modules were obtained. The results indicated that heat-shock protein (HSP70), SnRK1-interacting protein 1, 2A phosphatase-associated protein of 46 kDa (Tap46), and glutamate dehydrogenase (GDH) might play crucial roles in N. benthamiana's response to dark stress. Furthermore, a protein-protein interaction (PPI) network was constructed and top-degreed proteins were predicted to identify potential key factors in the response to dark stress. These proteins include isopropylmalate isomerase (IPMI), eukaryotic elongation factor 5A (ELF5A), and ribosomal protein 5A (RPS5A). Finally, metabolic analysis suggested that some amino acids and sugars were involved in the dark-responsive pathways. Thus, these results provide a new avenue for understanding the defensive mechanism against dark stress at the protein and metabolic levels in N. benthamiana.

Proteomic and Phosphoproteomic Analysis in Tobacco Mosaic Virus-Infected Tobacco (Nicotiana tabacum).

Tobacco mosaic virus (TMV) is a common source of biological stress that significantly affects plant growth and development. It is also useful as a model in studies designed to clarify the mechanisms involved in plant viral disease. Plant responses to abiotic stress were recently reported to be regulated by complex mechanisms at the post-translational modification (PTM) level. Protein phosphorylation is one of the most widespread and major PTMs in organisms. Using immobilized metal ion affinity chromatography (IMAC) enrichment, high-pH C18 chromatography fraction, and high-accuracy mass spectrometry (MS), a set of proteins and phosphopeptides in both TMV-infected tobacco and control tobacco were identified. A total of 4905 proteins and 3998 phosphopeptides with 3063 phosphorylation sites were identified. These 3998 phosphopeptides were assigned to 1311 phosphoproteins, as some proteins carried multiple phosphorylation sites. Among them, 530 proteins and 337 phosphopeptides corresponding to 277 phosphoproteins differed between the two groups. There were 43 upregulated phosphoproteins, including phosphoglycerate kinase, pyruvate phosphate dikinase, protein phosphatase 2C, and serine/threonine protein kinase. To the best of our knowledge, this is the first phosphoproteomic analysis of leaves from a tobacco cultivar, K326. The results of this study advance our understanding of tobacco development and TMV action at the protein phosphorylation level.

Automatic data analysis workflow for ultra-high performance liquid chromatography-high resolution mass spectrometry-based metabolomics.

Data analysis for ultra-performance liquid chromatography high-resolution mass spectrometry-based metabolomics is a challenging task. The present work provides an automatic data analysis workflow (AntDAS2) by developing three novel algorithms, as follows: (i) a density-based ion clustering algorithm is designed for extracted-ion chromatogram extraction from high-resolution mass spectrometry; (ii) a new maximal value-based peak detection method is proposed with the aid of automatic baseline correction and instrumental noise estimation; and (iii) the strategy that clusters high-resolution m/z peaks to simultaneously align multiple components by a modified dynamic programing is designed to efficiently correct time-shift problem across samples. Standard compounds and complex datasets are used to study the performance of AntDAS2. AntDAS2 is better than several state-of-the-art methods, namely, XCMS Online, Mzmine2, and MS-DIAL, to identify underlying components and improve pattern recognition capability. Meanwhile, AntDAS2 is more efficient than XCMS Online and Mzmine2. A MATLAB GUI of AntDAS2 is designed for convenient analysis and is available at the following webpage: http://software.tobaccodb.org/software/antdas2.

Metabolomics Analysis Identifies Sphingolipids as Key Signaling Moieties in Appressorium Morphogenesis and Function in Magnaporthe oryzae.

The blast fungus initiates infection using a heavily melanized, dome-shaped infection structure known as the appressorium, which forcibly ruptures the cuticle to enter the rice leaf tissue. How this process takes place remains not fully understood. Here, we used untargeted metabolomics analyses to profile the metabolome of developing appressoria and identified significant changes in six key metabolic pathways, including early sphingolipid biosynthesis. Analyses employing small molecule inhibitors, gene disruption, or genetic and chemical complementation demonstrated that ceramide compounds of the sphingolipid biosynthesis pathway are essential for normal appressorial development controlled by mitosis. In addition, ceramide was found to act upstream from the protein kinase C-mediated cell wall integrity pathway during appressorium repolarization and pathogenicity in rice blast. Further discovery of the sphingolipid biosynthesis pathway revealed that glucosylceramide (GlcCer) synthesized by ceramide is the key substance affecting the pathogenicity of Magnaporthe oryzae Our results provide new insights into the chemical moieties involved in the infection-related signaling networks, thereby revealing a potential target for the development of novel control agents against the major disease of rice and other cereals.IMPORTANCE Our untargeted analysis of metabolomics throughout the course of pathogenic development gave us an unprecedented high-resolution view of major shifts in metabolism that occur in the topmost fungal pathogen that infects rice, wheat, barley, and millet. Guided by these metabolic insights, we demonstrated their practical application by using two different small-molecule inhibitors of sphingolipid biosynthesis enzymes to successfully block the pathogenicity of M. oryzae Our study thus defines the sphingolipid biosynthesis pathway as a key step and potential target that can be exploited for the development of antifungal agents. Furthermore, future investigations that exploit such important metabolic intermediates will further deepen our basic understanding of the molecular mechanisms underlying the establishment of fungal blast disease in important cereal crops.

Automatic untargeted metabolic profiling analysis coupled with Chemometrics for improving metabolite identification quality to enhance geographical origin discrimination capability.

Untargeted metabolic profiling analysis is employed to screen metabolites for specific purposes, such as geographical origin discrimination. However, the data analysis remains a challenging task. In this work, a new automatic untargeted metabolic profiling analysis coupled with a chemometric strategy was developed to improve the metabolite identification results and to enhance the geographical origin discrimination capability. Automatic untargeted metabolic profiling analysis with chemometrics (AuMPAC) was used to screen the total ion chromatographic (TIC) peaks that showed significant differences among the various geographical regions. Then, a chemometric peak resolution strategy is employed for the screened TIC peaks. The retrieved components were further analyzed using ANOVA, and those that showed significant differences were used to build a geographical origin discrimination model by using two-way encoding partial least squares. To demonstrate its performance, a geographical origin discrimination of flaxseed samples from six geographical regions in China was conducted, and 18 TIC peaks were screened. A total of 19 significant different metabolites were obtained after the peak resolution. The accuracy of the geographical origin discrimination was up to 98%. A comparison of the AuMPAC, AMDIS, and XCMS indicated that AuMPACobtained the best geographical origin discrimination results. In conclusion, AuMPAC provided another method for data analysis.

Automatic time-shift alignment method for chromatographic data analysis.

Time shift among samples remains a significant challenge in data analysis, such as quality control of natural plant extracts and metabolic profiling analysis, because this phenomenon may lead to invalid conclusions. In this work, we propose a new time shift alignment method, namely, automatic time-shift alignment (ATSA), for complicated chromatographic data analysis. This technique comprised the following alignment stages: (1) automatic baseline correction and peak detection stage for providing useful chromatographic information; (2) preliminary alignment stage through adaptive segment partition to correct alignment for the entire chromatogram; and (3) precise alignment stage based on test chromatographic peak information to accurately align time shift. In ATSA, the chromatographic peak information of both reference and test samples can be completely employed for time-shift alignment to determine segment boundaries and avoid loss of information. ATSA was used to analyze a complicated chromatographic dataset. The obtained correlation coefficients among samples and data analysis efficiency indicated that the influences of time shift can be considerably reduced by ATSA; thus accurate conclusion could be obtained.

Mass-spectra-based peak alignment for automatic nontargeted metabolic profiling analysis for biomarker screening in plant samples.

Nontargeted metabolic profiling analysis is a difficult task in a routine investigation because hundreds of chromatographic peaks are eluted within a short time, and the time shift problem is severe across samples. To address these problems, the present work developed an automatic nontargeted metabolic profiling analysis (anTMPA) method. First, peaks from the total ion chromatogram were extracted using modified multiscale Gaussian smoothing method. Then, a novel peak alignment strategy was employed based on the mass spectra and retention times of the peaks in which the maximum mass spectral correlation coefficient path was extracted using a modified dynamic programming method. Moreover, an automatic landmark peak-searching strategy was employed for self-adapting time shift modification. Missing peaks across samples were grouped and registered into the aligned peak list table for final refinement. Finally, the aligned peaks across samples were analyzed using statistical methods to identify potential biomarkers. Mass spectral information on the screened biomarkers could be directly imported into the National Institute of Standards and Technology library to select the candidate compounds. The performance of the anTMPA method was evaluated using a complicated plant gas chromatography-mass spectrometry dataset with the aim of identifying biomarkers between the growth and maturation stages of the tested plant.

Characterization of aromatic glycosides in the extracts of Trollius species by ultra high-performance liquid chromatography coupled with electrospray ionization quadrupole time-of-flight tandem mass spectrometry.

Ultra high-performance liquid chromatography coupled with electrospray ionization quadrupole time-of-flight tandem mass spectrometry (UHPLC/ESI-Q-TOF MS/MS) was used to investigate the MS fragmentation behaviors of flavone C-glycosides present in the extracts of five Trollius species. In this study, the primary MS fragmentation pathways and key diagnostic fragment ions of flavone C-glycosides were systematically investigated and summarized to distinguish different types of derivatives and to trace other analogs in Trollius species. This method was useful, rapid, efficient and sensitive and allowed the simultaneous identification of different types of flavone C-glycosides present in other medicinal plants. The features of the MS fragmentation of these compounds indicated that the product ions were primarily the result of cleavage in the saccharide moiety, followed by hydrogen rearrangement and dehydration. In this study, thirty-six components including thirty-two flavone C-glycosides, two flavone O-glycosides and two phenylethanoid glycosides, were identified in the extracts of five Trollius species. Eleven of the flavone C-glycosides were identified by comparison with reference standards, and twenty-one flavone C-glycosides were tentatively identified based on their retention times, exact mass information and fragment ions. Two potentially new flavone C-glycosides (2″-O-vanilloylorientin and 2″-O-feruloylvitexin) were successfully characterized based on the summarized fragmentation pathways, and six known flavone C-glycosides (2″-O-glucosylvitexin, 2″-O-acetylorientin, 2″-O-acetylvitexin, 3″-O-acetylorientin, 3″-O-acetylvitexin and 6″-O-acetylvitexin) were identified in these plant species for the first time. In conclusion, the fragmentation pathways proposed in this paper were helpful for the identification of different types of flavone C-glycosides when no reference standards were available.