TNIP3 protects against pathological cardiac hypertrophy by stabilizing STAT1.

Pathological cardiac hypertrophy is one of the major risk factors of heart failure and other cardiovascular diseases. However, the mechanisms underlying pathological cardiac hypertrophy remain largely unknown. Here, we identified the first evidence that TNFAIP3 interacting protein 3 (TNIP3) was a negative regulator of pathological cardiac hypertrophy. We observed a significant upregulation of TNIP3 in mouse hearts subjected to transverse aortic constriction (TAC) surgery and in primary neonatal rat cardiomyocytes stimulated by phenylephrine (PE). In Tnip3-deficient mice, cardiac hypertrophy was aggravated after TAC surgery. Conversely, cardiac-specific Tnip3 transgenic (TG) mice showed a notable reversal of the same phenotype. Accordingly, TNIP3 alleviated PE-induced cardiomyocyte enlargement in vitro. Mechanistically, RNA-sequencing and interactome analysis were combined to identify the signal transducer and activator of transcription 1 (STAT1) as a potential target to clarify the molecular mechanism of TNIP3 in pathological cardiac hypertrophy. Via immunoprecipitation and Glutathione S-transferase assay, we found that TNIP3 could interact with STAT1 directly and suppress its degradation by suppressing K48-type ubiquitination in response to hypertrophic stimulation. Remarkably, preservation effect of TNIP3 on cardiac hypertrophy was blocked by STAT1 inhibitor Fludaradbine or STAT1 knockdown. Our study found that TNIP3 serves as a novel suppressor of pathological cardiac hypertrophy by promoting STAT1 stability, which suggests that TNIP3 could be a promising therapeutic target of pathological cardiac hypertrophy and heart failure.

The Key Role of Plant Hormone Signaling Transduction and Flavonoid Biosynthesis Pathways in the Response of Chinese Pine (Pinus tabuliformis) to Feeding Stimulation by Pine Caterpillar (Dendrolimus tabulaeformis).

In nature, plants have developed a series of resistance mechanisms to face various external stresses. As understanding of the molecular mechanisms underlying plant resistance continues to deepen, exploring endogenous resistance in plants has become a hot topic in this field. Despite the multitude of studies on plant-induced resistance, how plants respond to stress under natural conditions remains relatively unclear. To address this gap, we investigated Chinese pine (Pinus tabuliformis) using pine caterpillar (Dendrolimus tabulaeformis) under natural conditions. Healthy Chinese pine trees, approximately 10 years old, were selected for studying induced resistance in Huangtuliangzi Forestry, Pingquan City, Chengde City, Hebei Province, China. Pine needles were collected at 2 h and 8 h after feeding stimulation (FS) via 10 pine caterpillars and leaf clipping control (LCC), to simulate mechanical damage caused by insect chewing for the quantification of plant hormones and transcriptome and metabolome assays. The results show that the different modes of treatments significantly influence the contents of JA and SA in time following treatment. Three types of differentially accumulated metabolites (DAMs) were found to be involved in the initial response, namely phenolic acids, lipids, and flavonoids. Weighted gene co-expression network analysis indicated that 722 differentially expressed genes (DEGs) are positively related to feeding stimulation and the specific enriched pathways are plant hormone signal transduction and flavonoid biosynthesis, among others. Two TIFY transcription factors (PtTIFY54 and PtTIFY22) and a MYB transcription factor (PtMYB26) were found to be involved in the interaction between plant hormones, mainly in the context of JA signal transduction and flavonoid biosynthesis. The results of this study provide an insight into how JA activates, serving as a reference for understanding the molecular mechanisms of resistance formation in conifers responding to mandibulate insects.

Self-Powered and Self-Recovered Mechanoluminescent Electronic Skins for Detecting and Distinguishing Diverse External Stimuli.

The concept of simulating external mechanical stimuli to generate luminescence has been a long-standing aspiration in real-time dynamic visualization. However, creating self-power and self-restoring mechanoluminescent electronic skins for artificial sensors poses significant challenges. In this study, we introduce a cutting-edge triboelectric-mechanoluminescent electronic skin (TMES) that exhibits a remarkable response to multiple external stimuli. This advancement is achieved by integrating a mechanoluminescent intermediate layer within a triboelectric nanogenerator (TENG). When pressure is applied to TMES, the maximum detection voltage can reach hundreds of volts and the maximum correlation sensitivity is 11.76 V/N. Moreover, we incorporate luminescence materials into mechanoluminescence layer, and the maximum absolute sensitivity SR can reach 1.41%. The device can not only distinguish between external stimuli such as pressing and bending but also continuously track external mechanical stimuli. A 4 × 4 matrix and motion prediction of 8 different postures were established to further demonstrate the significant advantages of the developed device in spatial detection. The versatility and performance of the TMES hint at its vast potential in areas such as human-computer interaction and wearable electronics, paving the way for more intuitive and dynamic technological interfaces.

AntDAS-GCMS: A New Comprehensive Data Analysis Platform for GC-MS-Based Untargeted Metabolomics with the Advantage of Addressing the Time Shift Problem.

Over the years, a number of state-of-the-art data analysis tools have been developed to provide a comprehensive analysis of data collected from gas chromatography-mass spectrometry (GC-MS). Unfortunately, the time shift problem remains unsolved in these tools. Here, we developed a novel comprehensive data analysis strategy for GC-MS-based untargeted metabolomics (AntDAS-GCMS) to perform total ion chromatogram peak detection, peak resolution, time shift correction, component registration, statistical analysis, and compound identification. Time shift correction was specifically optimized in this work. The information on mass spectra and elution profiles of compounds was used to search for inherent landmarks within analyzed samples to resolve the time shift problem across samples efficiently and accurately. The performance of our AntDAS-GCMS was comprehensively investigated by using four complex GC-MS data sets with various types of time shift problems. Meanwhile, AntDAS-GCMS was compared with advanced GC-MS data analysis tools and classic time shift correction methods. Results indicated that AntDAS-GCMS could achieve the best performance compared to the other methods.

Method for multi-task learning fusion network traffic classification to address small sample labels.

In the context of the proliferated evolution of network service types and the expeditious augmentation of network resource deployment, the requisition for copious labeled datasets to facilitate superior performance in traffic classification methods, particularly those hinging on deep learning, is imperative. Nonetheless, the procurement and annotation of such extensive datasets necessitate considerable temporal and human resource investments. In response to this predicament, this work introduces a methodology, termed MTEFU, leveraging a deep learning model-based multi-task learning algorithm, strategically designed to mitigate the reliance on substantial labeled training samples. Multiple classification tasks, encompassing duration, bandwidth size, and business traffic category, are incorporated, with a shared parameter strategy implemented amongst tasks to assure the transference of information across disparate tasks. Employing CNN, SAE, GRU, and LSTM as multi-task learning classification models, training validation and experimental testing were conducted on the QUIC dataset. A comparative analysis with single-task and ensemble learning methods reveals that, in the context of predicting network traffic types, the accuracy derived from the multi-task learning strategy, even with a mere 150 labeled samples, can emulate the 94.67% accuracy achieved through single-task learning with a fully labeled dataset of 6139 samples.

A novel comprehensive strategy for high-thoroughly studying released compounds during the combustion process of herbs. A case study for Artemisia argyi Levl. et Vant.

The comprehensive study of compound variations in released smoke during the combustion process is a great challenge in many scientific fields related to analytical chemistry like traditional Chinese medicine, environment analysis, food analysis, etc. In this work, we propose a new comprehensive strategy for efficiently and high-thoroughly characterizing compounds in the online released complex smokes: (i) A smoke capture device was designed for efficiently collecting chemical constituents to perform gas chromatography-mass spectrometry (GC-MS) based untargeted analysis. (ii) An advanced data analysis tool, AntDAS-GCMS, was used for automatically extracting compounds in the original acquired GC-MS data files. Additionally, a GC-MS data analysis guided instrumental parameter optimizing strategy was proposed for the optimization of parameters in the smoke capture device. The developed strategy was demonstrated by the study of compound variations in the smoke of traditional Chinese medicine, Artemisia argyi Levl. et Vant. The results indicated that more than 590 components showed significant differences among released smokes of various moxa velvet ratios. Finally, about 88 compounds were identified, of which phenolic compounds were the most abundant, followed by aromatics, alkenes, alcohols and furans. In conclusion, we may provide a novel approach to the studies of compounds in online released smoke.

Artificial Soil-Like Material Enhances CO2 Bio-Valorization into Chemicals in Gas Fermentation.

Gas fermentation offers a carbon-neutral route for producing industrial feedstocks using autotrophic microbes to convert carbon dioxide (CO2) in waste gases, such as industrial emissions and biogas, into valuable chemicals or biofuels. However, slow microbial metabolism owing to low gaseous solubility causes significant challenges in gas fermentation. Although chemical or genetic manipulations have been explored to improve gas fermentation, they are either nonsustainable or complex. Herein, an artificial soil-like material (SLM) inspired by natural soil was fabricated to improve the growth and metabolism ofCupriavidus necatorfor enhanced poly-ß-hydroxybutyrate (PHB) biosynthesis from CO2 and hydrogen (H2). Porous SLM comprises low-cost nanoclay, boehmite, and starch and serves as a biocarrier to facilitate the colonization of bacteria and delivery of CO2 to bacteria. With 3.0 g/L SLM addition, the solubility of CO2 in water increased by ∼4 times and biomass and PHB production boosted by 29 and 102%, respectively, in the 24 h culture. In addition, a positive modulation was observed in the metabolism of PHB biosynthesis. PHB biosynthesis-associated gene expression was found to be enhanced in response to the SLM addition. The concentrations of intermediates in the metabolic pathway of PHB biosynthesis, such as pyruvate and acetyl-CoA, as well as reducing energy (ATP and NADPH) significantly increased with SLM addition. SLM also demonstrated the merits of easy fabrication, high stability, recyclability, and plasticity, thereby indicating its considerable potential for large-scale application in gas fermentation.

Geographical discrimination of Flos Trollii by GC-MS and UHPLC-HRMS-based untargeted metabolomics combined with chemometrics.

For centuries, Flos Trollii has been consumed as functional tea and a folk medicine in China's north and northwest zones. The quality of Flos Trollii highly depends on the producing zones. Unfortunately, few studies have been reported on the geographical discrimination of Flos Trollii. This work comprehensively investigated Flos Trollii compounds with an integration strategy combining gas chromatography-mass spectrometry (GC-MS) and ultrahigh-performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS) with chemometrics to explore the differences between Flos Trollii obtained from various origins of China. About 71 volatile and 22 involatile markers were identified with GC-MS and UHPLC-HRMS, respectively. Geographical discrimination models were synthetically investigated based on the identified markers. The results indicated that the UHPLC-HRMS coupled with the fisher discrimination model provided the best prediction capability (>97%). This study provides a new solution for Flos Trollii discrimination.

Using UHPLC-HRMS-based comprehensive strategy to efficiently and accurately screen and identify illegal additives in health-care foods.

Accurately and high-thoroughly screening illegal additives in health-care foods continues to be a challenging task in routine analysis for the ultrahigh performance liquid chromatography-high resolution mass spectrometry based techniques. In this work, we proposed a new strategy to identify additives in complex food matrices, which consists of both experimental design and advanced chemometric data analysis. At first, reliable features in the analyzed samples were screened based on a simple but efficient sample weighting design, and those related to illegal additives were screened with robust statistical analysis. After the MS1 in-source fragment ion identification, both MS1 and MS/MS spectra were constructed for each underlying compound, based on which illegal additives can be precisely identified. The performance of the developed strategy was demonstrated by using mixture and synthetic sample datasets, indicating an improvement of data analysis efficiency up to 70.3 %. Finally, the developed strategy was applied for the screening of unknown additives in 21 batches of commercially available health-care foods. Results indicated that at least 80 % of false-positive results can be reduced and 4 additives were screened and confirmed.

Identification of Oak-Barrel and Stainless Steel Tanks with Oak Chips Aged Wines in Ningxia Based on Three-Dimensional Fluorescence Spectroscopy Combined with Chemometrics.

With the increased incidence of wine fraud, a fast and reliable method for wine certification has become a necessary prerequisite for the vigorous development of the global wine industry. In this study, a classification strategy based on three-dimensional fluorescence spectroscopy combined with chemometrics was proposed for oak-barrel and stainless steel tanks with oak chips aged wines. Principal component analysis (PCA), partial least squares analysis (PLS-DA), and Fisher discriminant analysis (FDA) were used to distinguish and evaluate the data matrix of the three-dimensional fluorescence spectra of wines. The results showed that FDA was superior to PCA and PLS-DA in classifying oak-barrel and stainless steel tanks with oak chips aged wines. As a general conclusion, three-dimensional fluorescence spectroscopy can provide valuable fingerprint information for the identification of oak-barrel and stainless steel tanks with oak chips aged wines, while the study will provide some theoretical references and standards for the quality control and quality assessment of oak-barrel aged wines.

Orientin regulates the proliferation and migration of hepatocellular carcinoma cells.

Orientin is a flavone isolated from medicinal plants used in traditional Chinese medicine (TCM) that suppresses the growth of cancer cells in vitro. The effects of orientin in hepatoma carcinoma cells remain unknown. The aim of this paper is to investigate the effects of orientin on the viability, proliferation, and migration of hepatocellular carcinoma cells in vitro. In this study, we found that orientin could inhibit the proliferation, migration, and the activation of NF-κB signaling pathway in hepatocellular carcinoma cells. An activator of NF-κB signaling pathway, PMA, could abolish the inhibitory effect of orientin on NF-κB signaling pathway and proliferation and migration of Huh7 cells. These findings raise the possibility that orientin can be used in the treatment of hepatocellular carcinoma.

Flexible multi-color electroluminescent devices with a high transmission conducting hydrogel and an organic dielectric.

Flexible electroluminescent devices have sparked widespread interest due to their tremendous applications in bioinspired electronics, smart wearables, and human-machine interfaces. In these applications, it is important to reduce the operating electrical frequency and realize color modulation. Herein, flexible electroluminescent devices have been fabricated with phosphor layers by a solution method. Using polyvinylidene difluoride as a dielectric layer and ionic hydrogels as electrodes, the devices can be effectively driven even when the operating frequency is 0.1 kHz. More importantly, the devices can exhibit multi-color emission, including blue, green, red and white. The results show that the developed devices are promising for flexible optoelectronics.

A comparison of feature extraction capabilities of advanced UHPLC-HRMS data analysis tools in plant metabolomics.

Data analysis of ultrahigh performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS) is an essential and time-consuming step in plant metabolomics and feature extraction is the fundamental step for current tools. Various methods lead to different feature extraction results in practical applications, which may puzzle users for selecting adequate data analysis tools to deal with collected data. In this work, we provide a comprehensive method evaluation for some advanced UHPLC-HRMS data analysis tools in plant metabolomics, including MS-DIAL, XCMS, MZmine, AntDAS, Progenesis QI, and Compound Discoverer. Both mixtures of standards and various complex plant matrices were specifically designed for evaluating the performances of the involved method in analyzing both targeted and untargeted metabolomics. Results indicated that AntDAS provide the most acceptable feature extraction, compound identification, and quantification results in targeted compound analysis. Concerning the complex plant dataset, both MS-DIAL and AntDAS can provide more reliable results than the others. The method comparison is maybe useful for the selection of suitable data analysis tools for users.

The impact of Ag nanoparticles on methane emission in two typical paddy soils.

Numerous applications of Ag nanoparticles (AgNPs) have increased the likelihood of their release and accumulation in agroecosystem. Thus far, few studies have evaluated the impacts of AgNPs to soil methane emissions and the microbial dynamics. In this study, microcosmic experiments were conducted to investigate the responses of methanogenic processes from two paddy soils (Cambisols and Ultisols) subjected to four AgNPs doses (0.1, 1, 10 and 50 mg/kg). The results showed that 0.1 and 1 mg/kg AgNPs had no significant effects on CH4 emissions, but 50 mg/kg AgNPs increased soil CH4 emissions in both paddy soils. The aggravation effect of AgNPs on CH4 emissions was more apparent in Ultisols compared to Cambisols paddy soils. Real-time PCR suggested that 50 mg/kg AgNPs significantly increased the ratio of methanogenic to bacterial gene for both paddy soils. Amplicon sequencing indicated that methanogenic community was clustered into a separate group after 50 mg/kg AgNPs exposure. Structural equation model illustrated that Methanosarcinales was both significantly responded to AgNPs in Cambisols and Ultisols soils; however, Methanocellales significantly responded to AgNPs only in Cambisols soils. Subsequently, uncontrolled use of AgNPs may account as an environmental risk due to the potentially increased soil CH4 emissions in paddy ecosystems.

Carnosol inhibits cerebral ischemia-reperfusion injury by promoting AMPK activation.

BACKGROUND: Carnosol is a phytopolyphenol (diterpene) found and extracted from plants of Mediterranean diet, which has anti-tumor, anti-inflammatory and antioxidant effects. However, its role in ischemic stroke has not been elucidated. METHODS: Primary neurons subjected to oxygen-glucose deprivation (OGD) was used to investigate the effect of carnosol in vitro. A mouse MCAO model was used to evaluate the effect of carnosol on ischemic stroke in vivo. The mRNA level of inflammatory and apoptosis-related genes was determined by RT-PCR. The protein level of total and phosphorylated AMPK was determined by WB. H&E and Immunofluorescent assay was used to investigate the necrosis, inflammation and apoptosis in brain tissue. RESULTS: Carnosol protected the activity of primary neurons subjected to oxygen-glucose deprivation (OGD) in vitro, as well as inhibited inflammation and apoptosis. Furthermore, carnosol could significantly reduce the infarct and edema volume and protect against neurological deficit in vivo, and had a significant inhibitory effect on brain neuroinflammation and apoptosis. Mechanically, carnosol could activate AMPK, and the effect of carnosol on cerebral ischemia-reperfusion injury cell model could be abolished by AMPK phosphorylation inhibitor. CONCLUSION: Carnosol has a protective effect on ischemic stroke, and this effect is achieved through AMPK activation. Our study demonstrates the protective effect of carnosol on cerebral ischemia-reperfusion injury and provides a new perspective for the clinical treatment of ischemic stroke.

A study of flavor variations during the flaxseed roasting procedure by developed real-time SPME GC-MS coupled with chemometrics.

Volatile compound variations during the roasting procedure play an essential role in the flaxseed-related product. In this work, we proposed a new strategy to high-throughput characterize the dynamic variations of flavors in flaxseed. Volatile compounds released at various roasting times were comprehensively investigated by a newly developed real-time solid-phase microextraction coupled with gas chromatography-mass spectrometry (GC-MS). Raw data files were analyzed by our advanced GC-MS data analysis software AntDAS-GCMS. Chemometric methods such as principal component analysis and partial least squares-discrimination analysis have realized the differences of samples with various roasting times. Finally, a total of 51 compounds from 11 aromas were accurately identified and confirmed with standards, and their variations as a function of roasting time were studied. In conclusion, we provided a new solution for the online monitoring of volatile compounds during the industrial roasting process.

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.

Six-Transmembrane Epithelial Antigen of Prostate 3 Promotes Hepatic Insulin Resistance and Steatosis.

Nonalcoholic fatty liver disease (NAFLD) is a clinicopathological syndrome characterized by excessive deposition of fatty acids in the liver. Further deterioration leads to nonalcoholic steatohepatitis, cirrhosis, and hepatocellular carcinoma, creating a heavy burden on human health and the social economy. Currently, there are no effective and specific drugs for the treatment of NAFLD. Therefore, it is important to further investigate the pathogenesis of NAFLD and explore effective therapeutic targets for the prevention and treatment of the disease. Six-transmembrane epithelial antigen of prostate 3 (STEAP3), a STEAP family protein, is a metalloreductase. Studies have shown that it can participate in the regulation of liver ischemia-reperfusion injury, hepatocellular carcinoma, myocardial hypertrophy, and other diseases. In this study, we found that the expression of STEAP3 is upregulated in NAFLD. Deletion of STEAP3 inhibits the development of NAFLD in vivo and in vitro, whereas its overexpression promotes palmitic acid/oleic acid stimulation-induced lipid deposition in hepatocytes. Mechanistically, it interacts with transforming growth factor beta-activated kinase 1 (TAK1) to regulate the progression of NAFLD by promoting TAK1 phosphorylation and activating the TAK1-c-Jun N-terminal kinase/p38 signaling pathway. Taken together, our results provide further insight into the involvement of STEAP3 in liver pathology.

Pyrroloquinoline quinone ameliorates renal fibrosis in diabetic nephropathy by inhibiting the pyroptosis pathway in C57BL/6 mice and human kidney 2 cells.

Diabetic nephropathy (DN), which is characterized by renal fibrosis, is a major complication of diabetes, a disease that afflicted more than 460 million people worldwide in 2019. Pyroptosis is an essential signaling pathway in DN-related injuries, such as renal fibrosis. Pyrroloquinoline quinone (PQQ) is a naturally occurring bioactive compound that protects human kidney 2 (HK-2) cells from oxidative stress-induced damage caused by high glucose concentrations. However, the nature and underlying mechanism of the effect of PQQ on DN-related renal fibrosis remains unclear. In this study, we evaluated whether PQQ has potential protective effects against renal fibrosis due to DN by establishing type 1 diabetes in mice via streptozotocin treatment and then inhibiting their pyroptosis signaling pathway. We found that compared to control mice, the area of renal fibrosis and injury were significantly increased in diabetic mice, and this was accompanied by increased levels of expression of collagen Ⅰ and transforming growth factor-ß1; increased concentrations of the inflammatory cytokines, interleukin (IL)-1ß, IL-6, and tumor necrosis factor-α; and activation of the pyroptosis pathway components nucleotide-binding oligomerization domain-like receptor family pyrin domain-containing 3 (NLRP3), caspase-1, IL-1ß, and IL-18. All of these changes were reversed by PQQ treatment. Analogously, we treated cultured HK-2 cells with a high concentration of glucose (35 mmol/L), which caused these cells to exhibit significantly increased concentrations of reactive oxygen species (ROS), phosphorylated (p)-nuclear factor kappa B (NF-κB), p-IkappaB, NLRP3, caspase-1, IL-1ß, and IL-18, and the loss of mitochondrial transmembrane potential. However, PQQ treatment significantly blunted these effects. In conclusion, in this study we demonstrated that PQQ attenuates renal fibrosis by alleviating mitochondrial dysfunction, reducing ROS production, and inhibiting the activation of the NF-κB/pyroptosis pathway under conditions of DN and hyperglycemia.

A chemometric strategy to automatically screen selected ion monitoring ions for gas chromatography-mass spectrometry-based pseudotargeted metabolomics.

The pseudotargeted metabolomics based on gas chromatography-mass spectrometry (GC-MS) has the advantage of filtering out artifacts originating from sample treatment and accurately quantifying underlying compounds in the analyzed samples. However, this technique faces the problem of selecting high-quality selective ions for performing selected ion monitoring (SIM) on instruments. In this work, we proposed AntDAS-SIMOpt, an automatic untargeted strategy for SIM ion optimization that was accomplished on the basis of an experimental design combined with advanced chemometric algorithms. First, a group of diluted quality control samples was used to screen underlying compounds in samples automatically. Ions in each of the resolved mass spectrum were then evaluated by using the developed algorithms to identify the SIM ion. A Matlab graphical user interface (GUI) was designed to facilitate routine analysis, which can be obtained from http://www.pmdb.org.cn/antdassimopt. The performance of the developed strategy was comprehensively investigated by using standard and complex plant datasets. Results indicated that AntDAS-SIMOpt may be useful for GC-MS-based metabolomics.