Integrating serum pharmacochemistry, network pharmacology and untargeted metabolomics strategies to reveal the material basis and mechanism of action of Feining keli in the treatment of chronic bronchitis

ETHNOPHARMACOLOGICAL RELEVANCE

Feining keli (FNKL) is herbal preparation mainly made from Senecio cannabifolius Less., In recent years, more and more studies have found that FNKL has excellent therapeutic effects on chronic bronchitis (CB). Nevertheless, its pharmacodynamic material basis and mechanism of action are still unknown.

AIM OF THE STUDY

This study aimed to explore the pharmacodynamic material basis and mechanism of action of FNKL in treating CB.

MATERIALS AND METHODS

The CB rat model was induced using nasal drops of lipopolysaccharide (LPS) in combination with smoking. Various assessments including behavioral and body mass examination, lung index measurement, enzyme linked immunosorbent assay (ELISA), as well as histological analyses using hematoxylin and eosin (H&E) and Masson staining were conducted to validate the reliability of the CB model. The serum components of FNKL in CB rats were identified using ultra-high-performance liquid chromatography Orbitrap Exploris mass spectrometer (UHPLC-OE-MS). Network pharmacology was used to predict the network of action of the active ingredients in FNKL based on these serum components. Signaling pathways were enriched and analyzed, and molecular docking was conducted for key targets. Molecular dynamics simulations were performed using GROMACS software. The mechanism was confirmed through a series of experiments including Western blot (WB), immunofluorescence (IF), and reverse transcription (RT)-PCR. Additionally, untargeted metabolomics was employed to identify biomarkers and relevant metabolic pathways associated with the treatment of CB with FNKL.

RESULTS

In CB rats, FNKL improved body mass, lung index, and pathological damage of lung tissues. It also decreased interleukin (IL)-6, tumor necrosis factor-alpha (TNF-α), malonaldehyde (MDA) levels, and percentage of lung collagen fiber area. Furthermore, FNKL increased IL-10 and superoxide dismutase (SOD) levels, which helped alleviate bronchial inflammation in the lungs. A total of 70 FNKL chemical components were identified in CB rat serum. Through network pharmacology analysis, 5 targets, such as PI3K, AKT, NF-κB, HIF-1α, and MYD88, were identified as key targets of FNKL in the treatment of CB. Additionally, the key signaling pathways identified were PI3K/AKT pathway、NF-κB/MyD88 pathway、HIF-1α pathway. WB, IF, and RT-PCR experiments were conducted to confirm the findings. Molecular docking studies demonstrated successful docking of 16 potential active components with 5 key targets. Additionally, molecular dynamics simulations indicated the stability of quercetin-3-galactoside and HIF-1α. Metabolomics analysis revealed that FNKL primarily regulated pathways related to alpha-linolenic acid metabolism, primary bile acid biosynthesis, bile secretion, arachidonic acid metabolism, neuroactive ligand-receptor interaction, and folate biosynthesis. Furthermore, the expression levels of traumatic acid, traumatin, alpha linolenic acid, cholic acid, 2-arachidonoylglycerol, deoxycholic acid, 7,8-dihydroneopterin, and other metabolites were found to be regulated.

CONCLUSION

FNKL exhibits positive therapeutic effects on CB, with quercetin-3-galactoside identified as a key active component. The mechanism of FNKL's therapeutic action on CB involves reducing inflammatory response, oxidative stress, and regulating metabolism, and its molecular mechanism was better elucidated in a holistic manner. This study serves as a reference for understanding the pharmacodynamic material basis and mechanism of action of FNKL in treating CB, and provides avenues for exploring the effects of compounded herbal medicines on CB.

Metabolic profiles and biomarkers of Auricularia cornea based on de-oiled camphor leaf substrate

This study investigates the metabolic responses of Auricularia cornea when cultured on de-oiled leaves of Cinnamomum longepaniculatum (DeCL), an underutilized waste product. The metabolic profiles of A. cornea cultured with four different quality ratios of DeCL substrate (0 %, 14 %, 28 % and 42 %) were analyzed by UHPLC-MS/MS-based metabolomics. A total of 516 metabolites were identified and classified into 78 categories, with phenols, alkaloids and flavonoids accounting for 26.7 % of the total. In addition, 32 metabolite biomarkers associated with eight major metabolic pathways were identified. This pioneering research provides valuable insights into the utilization of DeCL, and expands our knowledge of the metabolic dynamics underlying the growth of A. cornea on alternative substrates.

Elucidating the mechanisms of Buyang Huanwu Decoction in treating chronic cerebral ischemia: A combined approach using network pharmacology, molecular docking, and in vivo validation

OBJECTIVE

This study aimed to explore the potential mechanisms of Buyang Huanwu Decoction (BHD) in regulating the AKT/TP53 pathway and reducing inflammatory responses for the treatment of chronic cerebral ischemia (CCI) using UHPLC-QE-MS combined with network pharmacology, molecular docking techniques, and animal experiment validation.

METHODS

Targets of seven herbal components in BHD, such as Astragalus membranaceus, Paeoniae Rubra Radix, and Ligusticum chuanxiong, were identified through TCMSP and HERB databases. CCI-related targets were obtained from DisGeNET and Genecards, with an intersection analysis conducted to determine shared targets between the disease and the herbal components. Functional enrichment analysis of these intersecting targets was performed. Networks of gene ontology and pathway associations with these targets were constructed and visualized. A pharmacological network involving intersecting genes and active components was delineated. A protein-protein interaction network was established for these intersecting targets and visualized using Cytoscape 3.9.1. The top five genes from the PPI network and their corresponding active components underwent molecular docking. Finally, the 2-vessel occlusion (2-VO) induced CCI rat model was treated with BHD, and the network pharmacology findings were validated using Western blot, RT-PCR, behavioral tests, laser speckle imaging, ELISA, HE staining, Nissl staining, LFB staining, and immunohistochemistry and immunofluorescence.

RESULTS

After filtration and deduplication, 150 intersecting genes were obtained, with the top five active components by Degree value identified as Quercetin, Beta-Sitosterol, Oleic Acid, Kaempferol, and Succinic Acid. KEGG pathway enrichment analysis linked key target genes significantly with Lipid and atherosclerosis, AGE-RAGE signaling pathway, IL-17 signaling pathway, and TNF signaling pathway. The PPI network highlighted ALB, IL-6, AKT1, TP53, and IL-1β as key protein targets. Molecular docking results showed the strongest binding affinity between ALB and Beta-Sitosterol. Behavioral tests using the Morris water maze indicated that both medium and high doses of BHD could enhance spatial memory in 2-VO model rats, with high-dose BHD being more effective. Laser speckle results showed that BHD at medium and high doses could facilitate CBF recovery in CCI rats, demonstrating a dose-response relationship. HE staining indicated that all doses of BHD could reduce neuronal damage in the cortex and hippocampal CA1 region to varying extents, with the highest dose being the most efficacious. Nissl staining showed that nimodipine and medium and high doses of BHD could alleviate Nissl body damage. LFB staining indicated that nimodipine and medium and high doses of BHD could reduce the pathological damage to fiber bundles and myelin sheaths in the internal capsule and corpus callosum of CCI rats. ELISA results showed that nimodipine and BHD at medium and high doses could decrease the levels of TNF-α, IL-6, IL-17, and IL-1β in the serum of CCI rats (p < 0.05). Immunohistochemistry and immunofluorescence demonstrated that BHD could activate the AKT signaling pathway and inhibit TP53 in treating CCI. Western blot and RT-PCR results indicated that nimodipine and all doses of BHD could upregulate Akt1 expression and downregulate Alb, Tp53, Il-1β, and Il-6 expression in the hippocampus of CCI rats to varying degrees (p < 0.05).

CONCLUSION

BHD exerts therapeutic effects in the treatment of CCI by regulating targets, such as AKT1, ALB, TP53, IL-1β, and IL-6, and reducing inflammatory responses.

Chemical component of differences in the endosperm of Gleditsia species seeds revealed based on comparative metabolomics

To investigate the chemical composition and interfunctional differences among the endosperm of Gleditsia species seeds (EGS), this study was conducted to determine the metabolic profiles in three EGSs based on the metabolomics approach of UPLC-ESI-MS/MS. A total of 505 metabolites were identified, of which 156 metabolites of EGS were annotated as pharmaceutical ingredients for six human diseases. A total of 110, 146, and 104 metabolites showed different accumulation patterns in the three control groups, LEGS vs. MEGS, LEGS vs. SEGS, and MEGS vs. SEGS, respectively. The metabolic profiles of EGSs differed significantly, and KEGG annotation and enrichment analyses indicated aminoacyl-tRNA biosynthesis as the key metabolic pathway of EGSs. This study enriches the understanding of the chemical composition of EGSs and provides theoretical support for the development and application of EGSs.

Automatic peak detection algorithm based on continuous wavelet transform for complex chromatograms from multi-detector micro-scale gas chromatographs

Peak detection for chromatograms, including the detection of peak retention times, peak start locations, and peak end locations, is an important processing step for extracting peak information that is used for chemical recognition. Compared to benchtop gas chromatographs, the chromatograms generated by microscale gas chromatographs (µGCs) often contain higher noise levels, peak overlap, peak asymmetry, and both positive and negative chromatographic peaks, increasing the challenges for peak detection. This paper reports an automatic peak detection algorithm based on continuous wavelet transform (CWT) for chromatograms generated by multi-detector µGCs. The relationship between chemical retention time and peak width is leveraged to differentiate chromatographic peaks from noise and baseline drift. Special features in the CWT coefficients are leveraged to detect peak overlap and asymmetry. For certain detectors that may generate positive and negative chromatographic peaks, the peaks cannot be independently detected reliably, but the peak information can be well extracted using peak information generated by other in-line single-polarity detectors. The implemented algorithm provided a true positive rate of 97.2 % and false discovery rate of 7.8 % for chromatograms generated by a µGC with three integrated detectors, two capacitive and one photoionization. The chromatograms included complex scenarios with positive and negative chromatographic peaks, up to five consecutive overlapping peaks, peak asymmetry factor up to 24, and signal-to-noise ratios spanning 9-2800.

Unraveling the mechanism of potato (Solanum tuberosum L.) tuber sprouting using transcriptome and metabolome analyses

Sprouting is an irreversible deterioration of potato quality, which leads to the production of harmful toxins and loss of the commercial value of potatoes. However, there is no report on the changes in different stages of potato sprouting through transcriptome and metabonomics. In this study, 1471 differentially expressed genes (DEGs) were found between DP and BP. In comparison with SP, a total of 6309 DEGs were detected in BP. Additionally, 6624 DEGs were identified between DP and SP. Moreover, 96 and 117 differentially accumulated metabolites (DAMs) were detected between DP and BP and between BP and SP, respectively. Furthermore, 130 DAMs were identified in total between DP and SP. In each group, a correlation analysis of DAMs and DEGs was performed to examine the regulatory network. The results indicated that the sprouting of tubers is mainly regulated by plant hormone signals, and during the sprouting of tubers, significant changes in metabolic products occur in the body. According to the combined analysis of transcriptomics and metabolomics, multiple metabolites were both positive and negative regulated by genes.

Metabolic improvements of novel microbial fermentation on black tea by Eurotium cristatum

Due to its traditional fermentation, there are obvious limits on the quality improvements in black tea. However, microbial fermentation can provide an abundance of metabolites and improve the flavor of tea. The "golden flower" fungi are widely used in the microbial fermentation of tea and has unique uses in healthcare. To further explore the improvements in black tea quality achieved via microbial fermentation, we used widely targeted metabolomics and metagenomics analyses to investigate the changes in and effects of metabolites and other microorganisms during the interaction between the "golden flower" fungi and black tea. Five key flavor metabolites were detected, the levels of catechin, epigallocatechin gallate, (-)-epicatechin gallate were decreased by different degrees after the inoculation of the "golden flower" fungus, whereas the levels of caffeine and (+)-gallocatechin increased. Botryosphaeriaceae, Botryosphaeriales, Dothideomycetes, Aspergillaceae, Trichocomaceae, and Lecanoromycetes play a positive role in the black tea fermentation process after inoculation with the "golden flower" fungi. D-Ribose can prevent hypoxia-induced apoptosis in cardiac cells, and it shows a strong correlation with Botryosphaeriaceae and Botryosphaeriales. The interaction between microorganisms and metabolites is manifested in tryptophan metabolism, starch and sucrose metabolism, and amino sugar and nucleotide sugar metabolism. In conclusion, the changes in metabolites observed during the fermentation of black tea by "golden flower" fungi are beneficial to human health. This conclusion extends the knowledge of the interaction between the "golden flower" fungi and black tea, and it provides important information for improving the quality of black tea.

Highly automatic and universal approach for pure ion chromatogram construction from liquid chromatography-mass spectrometry data using deep learning

Feature extraction is the most fundamental step when analyzing liquid chromatography-mass spectrometry (LC-MS) datasets. However, traditional methods require optimal parameter selections and re-optimization for different datasets, thus hindering efficient and objective large-scale data analysis. Pure ion chromatogram (PIC) is widely used because it avoids the peak splitting problem of the extracted ion chromatogram (EIC) and regions of interest (ROIs). Here, we developed a deep learning-based pure ion chromatogram method (DeepPIC) to find PICs using a customized U-Net from centroid mode data of LC-MS directly and automatically. A model was trained, validated, and tested on the Arabidopsis thaliana dataset with 200 input-label pairs. DeepPIC was integrated into KPIC2. The combination enables the entire processing pipeline from raw data to discriminant models for metabolomics datasets. The KPIC2 with DeepPIC was compared against other competing methods (XCMS, FeatureFinderMetabo, and peakonly) on the MM48, simulated MM48, and quantitative datasets. These comparisons showed that DeepPIC outperforms XCMS, FeatureFinderMetabo, and peakonly in recall rates and correlation with sample concentrations. Five datasets of different instruments and samples were used to evaluate the quality of PICs and the universal applicability of DeepPIC, and 95.12% of the found PICs could precisely match their manually labeled PICs. Therefore, KPIC2+DeepPIC is an automatic, practical, and off-the-shelf method to extract features from raw data directly, exceeding traditional methods with careful parameter tuning. It is publicly available at https://github.com/yuxuanliao/DeepPIC.

Insights into the Flavor Differentiation between Two Wild Edible Boletus Species through Metabolomic and Transcriptomic Analyses

Despite the popularity of wild edible mushrooms due to their delectable flavor and nutritional value, the mechanisms involved in regulating and altering their taste remain underexplored. In this study, we analyzed the metabolome and transcriptome of Boletus brunneissimus (B. brunneissimus) and Leccinum extremiorientale (L. extremiorientale), two Boletus species collected from different environments. Using UHPLC-MS, we annotated 644 peaks and identified 47 differential metabolites via OPLS-DA analysis. Eight of these were related to flavor, including L-Aspartic acid, Glycine, D-Serine, L-Serine, L-Histidine, Tryptophan, L-Isoleucine, Isoleucine, and alpha-D-Glucose. These differential metabolites were mainly concentrated in amino acid metabolism pathways. Transcriptome analysis revealed differential genes between B. brunneissimus and L. extremiorientale, which were enriched in protein processing in the endoplasmic reticulum, as well as differential genes of the same Boletus species in different environments that were enriched in the ribosome pathway. The combination of metabolome and transcriptome analyses highlighted Glycine, L-Serine, and L-Aspartic acid as the key compounds responsible for the differences between the two Boletus species. Using the O2PLS model and Pearson's coefficient, we identified key genes that modulate the differences in metabolites between the two species. These results have significant implications for the molecular breeding of flavor in edible mushrooms.

A Universal and Accurate Method for Easily Identifying Components in Raman Spectroscopy Based on Deep Learning

Raman spectroscopy has been widely used to provide the structural fingerprint for molecular identification. Due to interference from coexisting components, noise, baseline, and systematic differences between spectrometers, component identification with Raman spectra is challenging, especially for mixtures. In this study, a method entitled DeepRaman has been proposed to solve those problems by combining the comparison ability of a pseudo-Siamese neural network (pSNN) and the input-shape flexibility of spatial pyramid pooling (SPP). DeepRaman was trained, validated, and tested with 41,564 augmented Raman spectra from two databases (pharmaceutical material and S.T. Japan). It can achieve 96.29% accuracy, 98.40% true positive rate (TPR), and 94.36% true negative rate (TNR) on the test set. Another six data sets measured on different instruments were used to evaluate the performance of the proposed method from different aspects. DeepRaman can provide accurate identification results and significantly outperform the hit quality index (HQI) method and other deep learning models. In addition, it performs well in cases of different spectral complexity and low-content components. Once the model is established, it can be used directly on different data sets without retraining or transfer learning. Furthermore, it also obtains promising results for the analysis of surface-enhanced Raman spectroscopy (SERS) data sets and Raman imaging data sets. In summary, it is an accurate, universal, and ready-to-use method for component identification in various application scenarios.

Towards Accurate Identification of Antibiotic-Resistant Pathogens through the Ensemble of Multiple Preprocessing Methods Based on MALDI-TOF Spectra

Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) has been used to identify microorganisms and predict antibiotic resistance. The preprocessing method for the MS spectrum is key to extracting critical information from complicated MS spectral data. Different preprocessing methods yield different data, and the optimal approach is unclear. In this study, we adopted an ensemble of multiple preprocessing methods--FlexAnalysis, MALDIquant, and continuous wavelet transform-based methods--to detect peaks and build machine learning classifiers, including logistic regressions, naïve Bayes classifiers, random forests, and a support vector machine. The aim was to identify antibiotic resistance in Acinetobacter baumannii, Acinetobacter nosocomialis, Enterococcus faecium, and Group B Streptococci (GBS) based on MALDI-TOF MS spectra collected from two branches of a referral tertiary medical center. The ensemble method was compared with the individual methods. Random forest models built with the data preprocessed by the ensemble method outperformed individual preprocessing methods and achieved the highest accuracy, with values of 84.37% (A. baumannii), 90.96% (A. nosocomialis), 78.54% (E. faecium), and 70.12% (GBS) on independent testing datasets. Through feature selection, important peaks related to antibiotic resistance could be detected from integrated information. The prediction model can provide an opinion for clinicians. The discriminative peaks enabling better prediction performance can provide a reference for further investigation of the resistance mechanism.

Emerging Computational Methods in Mass Spectrometry Imaging

Mass spectrometry imaging (MSI) is a powerful analytical technique that generates maps of hundreds of molecules in biological samples with high sensitivity and molecular specificity. Advanced MSI platforms with capability of high-spatial resolution and high-throughput acquisition generate vast amount of data, which necessitates the development of computational tools for MSI data analysis. In addition, computation-driven MSI experiments have recently emerged as enabling technologies for further improving the MSI capabilities with little or no hardware modification. This review provides a critical summary of computational methods and resources developed for MSI data analysis and interpretation along with computational approaches for improving throughput and molecular coverage in MSI experiments. This review is focused on the recently developed artificial intelligence methods and provides an outlook for a future paradigm shift in MSI with transformative computational methods.

Combination of continuous wavelet transform and genetic algorithm-based Otsu for efficient mass spectrometry peak detection

Mass spectrometry (MS) data is susceptible to random noises and alternating baseline, posing great challenges to spectral peak detection, especially for weak peaks and overlapping peaks. Herein, an efficient peak detection algorithm combining continuous wavelet transform (CWT) and genetic algorithm-based threshold segmentation (denoted as WSTGA) for mass spectrometry was proposed. Firstly, Mexican Hat wavelet was selected as the mother wavelet by comparing the matching degree between the difference of Gaussian (DOG) and different wavelets. Subsequently, the ridges and valleys were identified from 2D wavelet coefficient matrix. Afterward, an improved threshold segmentation method, Otsu method based on genetic algorithm, was introduced to find optimal segmentation threshold and achieve better image segmentation, overcoming the deficiency of traditional Otsu method that cannot handle long-tailed unimodal histograms. Finally, the characteristic peaks were successfully identified by utilizing the ridge-valley lines in wavelet space and original spectrum. Receiver operating characteristic (ROC) curve, area under curve (AUC) and F₁ measure are used as criterions to evaluate performance of peak detection algorithms. Compared with multi-scale peak detection (MSPD) and CWT and image segmentation (CWT-IS) methods, all the results showed that WSTGA can achieve better peak detection. More importantly, the experimental results from MALDI-TOF spectra demonstrated that WSTGA can effectively detect more weak peaks and overlapping peaks while maintaining a lower false peak detection rate than MSPD and CWT-IS methods, indicating its great advantages in characteristic peak identification.

Comparative Metabolomics Analysis of Stigmas and Petals in Chinese Saffron (Crocus sativus) by Widely Targeted Metabolomics

The dried stigmas of Crocus sativus, commonly known as saffron, are consumed largely worldwide because it is highly valuable in foods and has biological activities beneficial for health. Saffron has important economic and medicinal value, and thus, its planting area and global production are increasing. Petals, which are a by-product of the stigmas, have not been fully utilized at present. We compared the metabolites between the stigmas and petals of C. sativus using a non-targeted metabolomics method. In total, over 800 metabolites were detected and categorized into 35 classes, including alkaloids, flavonoids, amino acids and derivatives, phenols and phenol esters, phenylpropanoids, fatty acyls, steroids and steroid derivatives, vitamins, and other metabolites. The metabolite composition in the petals and stigmas was basically similar. The results of the study showed that the petals contained flavonoids, alkaloids, coumarins, and other medicinal components, as well as amino acids, carbohydrates, vitamins, and other nutritional components. A principal components analysis (PCA) and an orthogonal partial least-squares discriminant analysis (OPLS-DA) were performed to screen the different metabolic components. A total of 339 differential metabolites were identified, with 55 metabolites up-regulated and 284 down-regulated. The up-regulated metabolites, including rutin, delphinidin-3-O-glucoside, isoquercitrin, syringaresinol-di-O-glucoside, dihydrorobinetin, quercetin, and gallocatechin, were detected in the petals. The down-regulated metabolites were mainly glucofrangulin B, acetovanillone, daidzein, guaiazulene, hypaphorine, indolin-2-one, and pseudouridine. KEGG annotation and enrichment analyses of the differential metabolites revealed that flavonoid biosynthesis, amino acids biosynthesis, and arginine and proline metabolism were the main differentially regulated pathways. In conclusion, the petals of C. sativus are valuable for medicine and foods and have potential utility in multiple areas such as the natural spice, cosmetic, health drink, and natural health product industries.

Integrative Analysis of the Transcriptome and Metabolome Reveals Genes Involved in Phenylpropanoid and Flavonoid Biosynthesis in the Trapa bispinosa Roxb

BACKGROUND

Trapa bispinosa Roxb. is grown worldwide as an important aquatic cash crop. Current research on Trapa bispinosa primarily focuses on the separation and identification of active ingredients, as well as the inhibitory effect on tumors; however, research on the molecular mechanism of secondary metabolite accumulation is rather limited. Consequently, an integrative analysis of transcriptome and metabolome is required to identify the key metabolic pathways, and key genes, and to explain the molecular mechanism of Trapa bispinosa.

RESULTS

The biosynthesis pathways of phenolics in Trapa bispinosa were examined through transcriptome and metabolome analyses. Transcriptome analysis yielded 42.76 million clean reads representing 81,417 unigenes with an average length of 1,752 bp. KEGG pathway analysis revealed that 1,623 unigenes, including 88 candidate unigenes related to phenolics biosynthesis, were up-regulated in Trapa bispinosa shell (FR) when compared to leaves (LF), root (RT), and stem (ST). The FR vs. LF group had the highest number of specific genes involved in phenylpropanoid, flavonoid, flavone, and flavonol biosynthesis pathways compared to all other comparison groups. In addition, RNA sequencing revealed 18,709 SSRs spanning 14,820 unigenes and 4,387 unigenes encoding transcription factors. Metabolome analysis identified 793 metabolites, including 136 flavonoids and 31 phenylpropane compounds. In the FR group compared to the LF group, there were 202 differentially accumulated metabolites (DAMs). The combined transcriptome and metabolome analyses indicated a significant correlation between 1,050 differentially expressed genes (DEGs) and 62 DAMs. This view proposes a schematic of flavonoid biosynthesis in the FR vs. LF group, providing evidence for the differences in genes and metabolites between FR and LF.

CONCLUSION

In this study, through de novo transcriptome assembly and metabolome analysis, several DEGs and DAMs were identified, which were subsequently used to build flavonoid biosynthesis pathways and a correlation network. The findings pave the way for future research into the molecular mechanisms and functional characterization of Trapa bispinosa candidate genes for phenolics biosynthesis.

Effects of Storage Temperature on Indica-Japonica Hybrid Rice Metabolites, Analyzed Using Liquid Chromatography and Mass Spectrometry

The Yongyou series of indica-japonica hybrid rice has excellent production potential and storage performance. However, little is known about the underlying mechanism of its storage resistance. In this study, Yongyou 1540 rice (Oryza sativa cv. yongyou 1540) was stored at different temperatures, and the storability was validated though measuring nutritional components and apparent change. In addition, a broad-targeted metabolomic approach coupled with liquid chromatography-mass spectrometry was applied to analyze the metabolite changes. The study found that under high temperature storage conditions (35 °C), Yongyou 1540 was not significantly worse in terms of fatty acid value, whiteness value, and changes in electron microscope profile. A total of 19 key differential metabolites were screened, and lipid metabolites related to palmitoleic acid were found to affect the aging of rice. At the same time, two substances, guanosine 3′,5′-cyclophosphate and pipecolic acid, were beneficial to enhance the resistance of rice under harsh storage conditions, thereby delaying the deterioration of its quality and maintaining its quality. Significant regulation of galactose metabolism, alanine, aspartate and glutamate metabolism, butyrate metabolism, and arginine and proline metabolism pathways were probably responsible for the good storage capacity of Yongyou 1540.

Quantitative Method for Liquid Chromatography–Mass Spectrometry Based on Multi-Sliding Window and Noise Estimation

LC-MS/MS uses information on the mass peaks and peak areas of samples to conduct quantitative analysis. However, in the detection of clinical samples, the spectrograms of the compounds are interfered with for different reasons, which makes the identification of chromatographic peaks more difficult. Therefore, to improve the chromatographic interference problem, this paper first proposes a multi-window-based signal-to-noise ratio estimation algorithm, which contains the steps of raw data denoising, peak identification, peak area calculation and curve fitting to obtain accurate quantitative analysis results of the samples. Through the chromatographic peak identification of an extracted ion chromatogram of VD2 in an 80 ng/mL standard and the spectral peak identification of data from an open-source database, the identification results show that the algorithm has a better peak detection performance. The accuracy of the quantitative analysis was verified using the LC-HTQ-2020 triple quadrupole mass spectrometer produced by our group for the application of steroid detection in human serum. The results show that the algorithm proposed in this paper can accurately identify the peak information of LC-MS/MS chromatographic peaks, which can effectively improve the accuracy and reproducibility of steroid detection results and meet the requirements of clinical testing applications such as human steroid hormone detection.

Integrated Analysis of Transcriptome and Metabolome Reveals the Mechanism of Chlorine Dioxide Repressed Potato (Solanum tuberosum L.) Tuber Sprouting

Sprouting is an irreversible deterioration of potato quality, which not only causes loss in their commercial value but also produces harmful toxins. As a popular disinfectant, ClO2 can inhibit the sprouting of potato tubers. Using transcriptomic and metabolomic approaches to understand the repressive mechanism of ClO2 in potato sprouting is yet to be reported. Sequencing the transcriptome and metabolome of potatoes treated with ClO2 in this study revealed a total of 3,119 differentially expressed genes, with 1,247 and 1,872 genes showing down- and upregulated expression, respectively. The majority of the downregulated genes were associated with plant hormone signal transduction, whereas upregulated differential genes were associated primarily with biological processes, such as phenylpropanoid biosynthesis and the mitogen-activated protein kinase (MAPK) signaling pathway. Metabonomic assays identified a total of 932 metabolites, with 33 and 52 metabolites being down- and upregulated, respectively. Downregulated metabolites were mostly alkaloids, amino acids, and their derivatives, whereas upregulated metabolites were composed mainly of flavonoids and coumarins. Integrated transcriptomic and metabolomic analyses showed that many different metabolites were regulated by several different genes, forming a complex regulatory network. These results provide new insights for understanding the mechanism of ClO2-mediated repression of potato sprouting.

Research on the Comprehensive Evaluation Method for the Automatic Recognition of Raman Spectrum under Multidimensional Constraint

Raman spectrum contains abundant substance information with fingerprint characteristics. However, due to the huge variety of substances and their complex characteristic information, it is difficult to recognize the Raman spectrum accurately. Starting from dimensions like the Raman shift, the relative peak intensity, and the overall hit ratio of characteristic peaks, we extracted and recognized the characteristics in the Raman spectrum and analyzed these characteristics from local and global perspectives and then proposed a comprehensive evaluation method for the recognition of Raman spectrum on the basis of the data fusion of the recognition results under multidimensional constraint. Based on the common spectrum database of the normal Raman and surface-enhanced Raman of thousands of substances, we analyzed the performance of the evaluation method. It shows that even for the identification of spectra from instruments of low technical specifications, the automatic recognition rate of the sample can reach 98% and above, a great improvement compared with that of the common identification algorithms, which proves the effectiveness of the comprehensive evaluation method under multidimensional constraint.

Performance Improvement of Handheld Raman Spectrometer for Mixture Components Identification Using Fuzzy Membership and Sparse Non-Negative Least Squares

Due to the advantages of low price and convenience for end-users to conduct field-based, in-situ analysis, handheld Raman spectrometers are widely used in the identification of mixture components. However, the spectra collected by handheld Raman spectrometer usually have serious peak overlapping and spectral distortion, resulting in difficulties in component identification in the mixture. A novel method for mixture components identification based on the handheld Raman spectrometer was proposed in this study. The wavelet transform and Voight curve fitting method were used to extract the feature parameters from each Raman spectral peak, including Raman shift, maximum intensity, and full width at half-maximum (FWHM), and the similarities between the mixture and each substance in the database were calculated by fuzzy membership function based on extracted feature parameters. Then, the possible substances in the mixture were preliminarily screened out as candidates according to the similarity. Finally, the Raman spectra of these candidates were used to fit the spectra of the mixture, and the fitting coefficients obtained by sparse non-negative least squares algorithm were employed to further determine the suspected substance in the mixture. The Raman spectra of 190 liquid mixture samples and 158 powder mixture samples were collected using a handheld Raman spectrometer and these spectra were used to validate the identification performance of the proposed method. The proposed method could achieve good identification accuracy for different mixture samples. It shows that the proposed method is an effective way for the component identification in mixture by using a handheld Raman spectrometer.

Fast custom wavelet analysis technique for single molecule detection and identification

Many sensors operate by detecting and identifying individual events in a time-dependent signal which is challenging if signals are weak and background noise is present. We introduce a powerful, fast, and robust signal analysis technique based on a massively parallel continuous wavelet transform (CWT) algorithm. The superiority of this approach is demonstrated with fluorescence signals from a chip-based, optofluidic single particle sensor. The technique is more accurate than simple peak-finding algorithms and several orders of magnitude faster than existing CWT methods, allowing for real-time data analysis during sensing for the first time. Performance is further increased by applying a custom wavelet to multi-peak signals as demonstrated using amplification-free detection of single bacterial DNAs. A 4x increase in detection rate, a 6x improved error rate, and the ability for extraction of experimental parameters are demonstrated. This cluster-based CWT analysis will enable high-performance, real-time sensing when signal-to-noise is hardware limited, for instance with low-cost sensors in point of care environments.

The authors introduce an accurate, fast and efficient technique to analyze sensory data. They use a continuous wavelet transform concept to look for certain patterns in noisy raw data. The superiority of this approach is demonstrated with fluorescence signals from a chip-based, optofluidic single particle sensor.

Widely targeted metabolomics analysis of enriched secondary metabolites and determination of their corresponding antioxidant activities in Elaeagnus angustifolia var. orientalis (L.)Kuntze fruit juice enhanced by Bifidobacterium animalis subsp. Lactis HN-3 fermentation

Elaeagnus angustifolia var. orientalis (L.)Kuntze fruit contains a large number of naturally occurring molecules present as glycoside, methylated, and methyl ester conjugates, which should be hydolysed or transformed to become bioactive forms. For this purpose, Bifidobacterium animalis subsp. lactis HN-3 was selected to ferment Elaeagnus angustifolia var. orientalis (L.)Kuntze fruit juice (EOJ). After fermentation, the total phenolic content (TPC) and antioxidant capacity of the EOJ increased significantly compared to the non-fermented EOJ. Using widely-targeted metabolomics analysis, polyphenolic compounds involved in the flavonoid biosynthetic pathway were determined to be up-regulated in the fermented EOJ. In addition, the metabolites generated by 8 deglycosidation, 5 demethylation, 5 hydrogenation, and 28 other reactions were detected in higher concentrations in the fermented EOJ compared to the non-fermented EOJ. Interestingly, these up-regulated metabolites have higher antioxidant and other biological activities than their metabolic precursors, which provide a theoretical basis for the development of Bifidobacterium-fermented plant products with stronger functional activities.

Recent applications of chemometrics in one- and two-dimensional chromatography

The proliferation of increasingly more sophisticated analytical separation systems, often incorporating increasingly more powerful detection techniques, such as high-resolution mass spectrometry, causes an urgent need for highly efficient data-analysis and optimization strategies. This is especially true for comprehensive two-dimensional chromatography applied to the separation of very complex samples. In this contribution, the requirement for chemometric tools is explained and the latest developments in approaches for (pre-)processing and analyzing data arising from one- and two-dimensional chromatography systems are reviewed. The final part of this review focuses on the application of chemometrics for method development and optimization.

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.

Error analysis of the spectral shift for partial least squares models in Raman spectroscopy

Raman spectroscopy paired with the partial least squares (PLS) method is commonly used for quantitative or qualitative analysis of complex samples. However, spectral shift induced by different Raman spectroscopy, different environment or different measured time will decrease the accuracy of the PLS model. In this work, the processing algorithms that improve the accuracy by removing the noise, background and varying sources of other spectral interference were first reviewed. The error induced by the spectral shift was analyzed and the formulas of the error were derived. The formulas were then used to calculate the theoretical error in the example of discriminating human and nonhuman blood. A comparison of the actual errors obtained from the mathematical method and experiment with the theoretical value demonstrated the effectiveness of the equation. The compensation for nonhuman blood according to the average error demonstrated the improvement of the accuracy. Finally, the non-uniform sampling of the Raman shift by charge-coupled device (CCD) was considered in the error equation. An accurate error equation was obtained. This work could help improve the stability of PLS models in the case of the spectral shift of the spectrometer in Raman spectroscopy.

KPIC2: An Effective Framework for Mass Spectrometry-Based Metabolomics Using Pure Ion Chromatograms

Distilling accurate quantitation information on metabolites from liquid chromatography coupled with mass spectrometry (LC-MS) data sets is crucial for further statistical analysis and biomarker identification. However, it is still challenging due to the complexity of biological systems. The concept of pure ion chromatograms (PICs) is an effective way of extracting meaningful ions, but few toolboxes provide a full processing workflow for LC-MS data sets based on PICs. In this study, an integrated framework, KPIC2, has been developed for metabolomics studies, which can detect pure ions accurately, align PICs across samples, group PICs to identify isotope and potential adducts, fill missing peaks and do multivariate pattern recognition. To evaluate its performance, MM48, metabolomics quantitation, and Soybean seeds data sets have been analyzed using KPIC2, XCMS, and MZmine2. KPIC2 can extract more true ions with fewer detecting features, have good quantification ability on a metabolomics quantitation data set, and achieve satisfactory classification on a soybean seeds data set through kernel-based OPLS-DA and random forest. It is implemented in R programming language, and the software, user guide, as well as example scripts and data sets are available as an open source package at https://github.com/hcji/KPIC2 .

A simple multi-scale Gaussian smoothing-based strategy for automatic chromatographic peak extraction

Peak detection is a critical step in chromatographic data analysis. In the present work, we developed a multi-scale Gaussian smoothing-based strategy for accurate peak extraction. The strategy consisted of three stages: background drift correction, peak detection, and peak filtration. Background drift correction was implemented using a moving window strategy. The new peak detection method is a variant of the system used by the well-known MassSpecWavelet, i.e., chromatographic peaks are found at local maximum values under various smoothing window scales. Therefore, peaks can be detected through the ridge lines of maximum values under these window scales, and signals that are monotonously increased/decreased around the peak position could be treated as part of the peak. Instrumental noise was estimated after peak elimination, and a peak filtration strategy was performed to remove peaks with signal-to-noise ratios smaller than 3. The performance of our method was evaluated using two complex datasets. These datasets include essential oil samples for quality control obtained from gas chromatography and tobacco plant samples for metabolic profiling analysis obtained from gas chromatography coupled with mass spectrometry. Results confirmed the reasonability of the developed method.

Pure ion chromatogram extraction via optimal k-means clustering

A new method called KPIC is proposed for extracting pure ion chromatogram from raw LC-MS data accurately, which is based on optimalk-means clustering. And KPIC can reduces the number of split signals and provide higher quality chromatographic peaks.

Feature extraction from resolution perspective for gas chromatography-mass spectrometry datasets

Automatic feature extraction from large-scale datasets is one of the major challenges when analyzing complex samples with gas chromatography-mass spectrometry (GC-MS).