Metabolic characterization of sphere-derived prostate cancer stem cells reveals aberrant urea cycle in stemness maintenance

Alteration of cell metabolism is one of the essential characteristics of tumor growth. Cancer stem cells (CSCs) are the initiating cells of tumorigenesis, proliferation, recurrence, and other processes, and play an important role in therapeutic resistance and metastasis. Thus, identification of the metabolic profiles in prostate cancer stem cells (PCSCs) is critical to understanding prostate cancer progression. Using untargeted metabolomics and lipidomics methods, we show distinct metabolic differences between prostate cancer cells and PCSCs. Urea cycle is the most significantly altered metabolic pathway in PCSCs, the key metabolites arginine and proline are evidently elevated. Proline promotes cancer stem-like characteristics via the JAK2/STAT3 signaling pathway. Meanwhile, the enzyme pyrroline-5-carboxylate reductase 1 (PYCR1), which catalyzes the conversion of pyrroline-5-carboxylic acid to proline, is highly expressed in PCSCs, and the inhibition of PYCR1 suppresses the stem-like characteristics of prostate cancer cells and tumor growth. In addition, carnitine and free fatty acid levels are significantly increased, indicating reprogramming of fatty acid metabolism in PCSCs. Reduced sphingolipid levels and increased triglyceride levels are also observed. Collectively, our data illustrate the comprehensive landscape of the metabolic reprogramming of PCSCs and provide potential therapeutic strategies for prostate cancer.

Fragmentation characteristics-based nontargeted screening method of exogenous chemical residues in animal-derived foods using reversed-phase and hydrophilic interaction liquid chromatography-high-resolution mass spectrometry

Fragmentation characteristics are crucial for nontargeted screening to discover and identify unknown exogenous chemical residues in animal-derived foods. In this study, first, fragmentation characteristics of 51 classes of exogenous chemical residues were summarized based on experimental mass spectra of standards in reversed-phase and hydrophilic interaction liquid chromatography-high-resolution mass spectrometry (MS) and mass spectra from the MassBank of North America (MoNA) library. According to the proportion of fragmentation characteristics to the total number of chemical residues in each class, four screening levels were defined to classify 51 classes of chemical residues. Then, a nontargeted screening method was developed based on the fragmentation characteristics. The evaluation results of 82 standards indicated that more than 90 % of the chemical residues with MS/MS spectra can be identified at concentrations of 100 and 500 μg/kg, and about 80 % can be identified at 10 μg/kg. Finally, the nontargeted screening method was applied to 16 meat samples and 21 egg samples as examples. As a result, eight chemical residues and transformation products (TPs) of 5 classes in the exemplary samples were found and identified, in which 3 TPs of azithromycin were identified by fragmentation characteristics-assisted structure interpretation. The results demonstrated the practicability of the nontargeted screening method for routine risk screening of food safety.

Concentric Hybrid Nanoelectrospray Ionization-Atmospheric Pressure Chemical Ionization Source for High-Coverage Mass Spectrometry Analysis of Single-Cell Metabolomics

High-coverage mass spectrometry analysis of single-cell metabolomics remains challenging due to the extremely low abundance and wide polarity of metabolites and ultra-small volume in single cells. Herein, a novel concentric hybrid ionization source, nanoelectrospray ionization-atmospheric pressure chemical ionization (nanoESI-APCI), is ingeniously designed to detect polar and nonpolar metabolites simultaneously in single cells. The source is constructed by inserting a pulled glass capillary coaxially into a glass tube that acts as a dielectric barrier layer. Benefitting from the integrated advantages of nanoESI and APCI, its limit of detection is improved by one order of magnitude to 10 pg mL-1. After the operational parameter optimization, 254 metabolites detected in nanoESI-APCI are tentatively identified from a single cell, and 82 more than those in nanoESI. The developed nanoESI-APCI is successively applied to study the metabolic heterogeneity of human hepatocellular carcinoma tissue microenvironment united with laser capture microdissection (LCM), the discrimination of cancer cell types and subtypes, the metabolic perturbations to glucose starvation in MCF7 cells and the metabolic regulation of cancer stem cells. These results demonstrated that the nanoESI-APCI not only opens a new avenue for high-coverage and high-sensitivity metabolomics analysis of single cell, but also facilitates spatially resolved metabolomics study coupled with LCM.

High-sensitivity profiling of dipeptides in sauce-flavor Baijiu Daqu by chemical derivatization and ultrahigh-performance liquid chromatography coupled with high-resolution mass spectrometry

Dipeptides in sauce-flavor Baijiu Daqu are protein degradation products during the fermentation of Daqu, which are believed to play a crucial role in the flavor and quality of Baijiu. Herein, we integrated dansyl chloride derivatization with ultrahigh-performance liquid chromatography coupled with high-resolution mass spectrometry (UHPLC-HRMS) for comprehensively profiling dipeptides in Daqu. The derivatization efficiency was higher than 99.1 % for all 17 dipeptide standards under the optimized derivatization conditions. In total, 118 dipeptides were detected in Daqu. The method was validated and the analytical characteristics including the linearity (spanned across 2-4 orders of magnitude), precision (1.2-19.9 %), limit of detection (varied from 1.1 to 53.4 pmol/mL) and the stability (3.6-15.8 %) are satisfactory. The usefulness of the method was examined by studying the distribution characteristics of dipeptides in Daqu under different production conditions. The present method provides an effective and robust strategy for comprehensively analyzing dipeptide compounds in complex biological samples.

An exposome atlas of serum reveals the risk of chronic diseases in the Chinese population

Although adverse environmental exposures are considered a major cause of chronic diseases, current studies provide limited information on real-world chemical exposures and related risks. For this study, we collected serum samples from 5696 healthy people and patients, including those with 12 chronic diseases, in China and completed serum biomonitoring including 267 chemicals via gas and liquid chromatography-tandem mass spectrometry. Seventy-four highly frequently detected exposures were used for exposure characterization and risk analysis. The results show that region is the most critical factor influencing human exposure levels, followed by age. Organochlorine pesticides and perfluoroalkyl substances are associated with multiple chronic diseases, and some of them exceed safe ranges. Multi-exposure models reveal significant risk effects of exposure on hyperlipidemia, metabolic syndrome and hyperuricemia. Overall, this study provides a comprehensive human serum exposome atlas and disease risk information, which can guide subsequent in-depth cause-and-effect studies between environmental exposures and human health.

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

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

Association between infarct location and haemorrhagic transformation of acute ischaemic stroke after intravenous thrombolysis

AIM

To evaluate the association between computed tomography (CT)-based imaging variables at the time of admission and haemorrhagic transformation (HT) after intravenous thrombolysis (IVT).

MATERIALS AND METHODS

One hundred and eight patients who were treated with IVT for acute ischaemic stroke (AIS) during January 2021 to July 2023 were analysed retrospectively. The infarct location was classified as cortical or subcortical in accordance with the Alberta Stroke Program Early CT Score (ASPECTS) system. Logistic regression and receiver operating characteristic curve analyses were performed to determine the relationship between ischaemic variables and HT.

RESULTS

Of the total, 18 (16.7%) patients had HT and seven (6.5%) had symptomatic intracerebral haemorrhage (sICH). Multivariate analysis revealed that cortical ASPECTS was independently associated with HT (odds ratio [OR], 0.197; 95% confidence interval [CI], 0.076-0.511; p=0.001) and cortical ASPECTS was independently associated with sICH (OR, 0.066; 95% CI, 0.009-0.510; p=0.009). To predict HT and sICH, cortical ASPECTS (HT area under the curve [AUC] = 0.881, sICH AUC = 0.971) provided a higher AUC compared with ASPECTS (HT AUC = 0.850, sICH AUC = 0.918).

CONCLUSION

Cortical ASPECTS seen on CT at the time of admission is associated with HT and sICH after IVT.

Resistant starch intake facilitates weight loss in humans by reshaping the gut microbiota

Emerging evidence suggests that modulation of gut microbiota by dietary fibre may offer solutions for metabolic disorders. In a randomized placebo-controlled crossover design trial (ChiCTR-TTRCC-13003333) in 37 participants with overweight or obesity, we test whether resistant starch (RS) as a dietary supplement influences obesity-related outcomes. Here, we show that RS supplementation for 8 weeks can help to achieve weight loss (mean -2.8 kg) and improve insulin resistance in individuals with excess body weight. The benefits of RS are associated with changes in gut microbiota composition. Supplementation with Bifidobacterium adolescentis, a species that is markedly associated with the alleviation of obesity in the study participants, protects male mice from diet-induced obesity. Mechanistically, the RS-induced changes in the gut microbiota alter the bile acid profile, reduce inflammation by restoring the intestinal barrier and inhibit lipid absorption. We demonstrate that RS can facilitate weight loss at least partially through B. adolescentis and that the gut microbiota is essential for the action of RS.

[Diagnosis and surgical treatment of high-risk anomalous aortic origin of coronary artery]

Objective: To analyze the diagnosis and surgical treatment of high-risk anomalous aortic origin of coronary artery (AAOCA). Methods: This is a retrospective case series study. From January 2016 to July 2023, 24 cases of high-risk AAOCA underwent surgical treatment in Department of Cardiac Surgery, Guangdong Provincial People's Hospital. There were 18 males and 6 females, operatively aged (M (IQR)) 13 (26) years (range: 0.3 to 57.0 years). They were confirmed by cardiac ultrasound and cardiac CT, all of which had anomalous coronary running between the aorta and the pulmonary artery. There were 15 cases of the right coronary artery from the left aortic sinus of Valsalva, 6 cases of left coronary artery from the right aortic sinus of Valsalva, 3 cases of the sigle coronary artery. Only 3 patients had no obvious related symptoms (2 cases were complicated with a positive exercise stress test and 1 case with other intracardiac malformations), 21 cases had a history of chest tightness, chest pain, or syncope after exercise. Three patients suffered syncope after exercise and underwent cardiopulmonary resuscitation (2 cases were treated with an extracorporeal membrane oxygenerator (ECMO)). The gap from the first symptom to the diagnosis was 4.0 (11.5) months (range: 0.2 to 84.0 months). The detection rate of coronary artery abnormalities suggested by the first cardiac ultrasound was only 37.5% (9/24). Seven patients were complicated with other cardiac diseases (4 cases with congenital heart defects, 2 cases with coronary atherosclerotic heart disease, 1 case with mitral valve disease). Results: All 24 patients underwent surgical treatment (23 cases underwent abnormal coronary artery unroofing, 1 case underwent coronary artery bypass grafting), and 5 patients underwent other intracardiac malformation correction at the same time. There were no death or surgery related complications in the hospital for 30 days after the operation. A patient with preoperative extracorporeal cardiopulmonary resuscitation was continuously assisted by ECMO after emergency AAOCA correction and had complications such as limb ischemia necrosis and renal dysfunction after the operation. During the follow-up of 2.2 (3.3) years (range: 1 month to 7.2 years), one patient who previously underwent percutaneous transluminal coronary angioplasty with a stent implant experienced significant postoperative symptomatic relief, and the other discharged patients had no related symptoms. Conclusions: The accurate rate of initial diagnosis for high-risk AAOCA is still low, but the risk of cardiovascular accidents is high. For sports-related chest pain and other symptoms, more attention should be paid to the detection of AAOCA, especially for adolescents. Exercise stress testing can be helpful in evaluating the cardiovascular risk of asymptomatic AAOCA. Instant surgical treatment can achieve satisfactory curative effects.

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

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

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

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

Strategy for Comprehensive Detection and Annotation of Gut Microbiota-Related Metabolites Based on Liquid Chromatography-High-Resolution Mass Spectrometry

Gut microbiota, widely populating the mammalian gastrointestinal tract, plays an important role in regulating diverse pathophysiological processes by producing bioactive molecules. Extensive detection of these molecules contributes to probing microbiota function but is limited by insufficient identification of existing analytical methods. In this study, a systematic strategy was proposed to detect and annotate gut microbiota-related metabolites on a large scale. A pentafluorophenyl (PFP) column-based liquid chromatography-high-resolution mass spectrometry (LC-HRMS) method was first developed for high-coverage analysis of gut microbiota-related metabolites, which was verified to be stable and robust with a wide linearity range, high sensitivity, satisfactory recovery, and repeatability. Then, an informative database integrating 968 knowledge-based microbiota-related metabolites and 282 sample-sourced ones defined by germ-free (GF)/antibiotic-treated (ABX) models was constructed and subsequently used for targeted extraction and annotation in biological samples. Using pooled feces, plasma, and urine of mice for demonstration application, 672 microbiota-related metabolites were annotated, including 21% neglected by routine nontargeted peak detection. This strategy serves as a useful tool for the comprehensive capture of the intestinal flora metabolome, contributing to our deeper understanding of microbe-host interactions.

Melatonin decreases GSDME mediated mesothelial cell pyroptosis and prevents peritoneal fibrosis and ultrafiltration failure

Peritoneal fibrosis together with increased capillaries is the primary cause of peritoneal dialysis failure. Mesothelial cell loss is an initiating event for peritoneal fibrosis. We find that the elevated glucose concentrations in peritoneal dialysate drive mesothelial cell pyroptosis in a manner dependent on caspase-3 and Gasdermin E, driving downstream inflammatory responses, including the activation of macrophages. Moreover, pyroptosis is associated with elevated vascular endothelial growth factor A and C, two key factors in vascular angiogenesis and lymphatic vessel formation. GSDME deficiency mice are protected from high glucose induced peritoneal fibrosis and ultrafiltration failure. Application of melatonin abrogates mesothelial cell pyroptosis through a MT1R-mediated action, and successfully reduces peritoneal fibrosis and angiogenesis in an animal model while preserving dialysis efficacy. Mechanistically, melatonin treatment maintains mitochondrial integrity in mesothelial cells, meanwhile activating mTOR signaling through an increase in the glycolysis product dihydroxyacetone phosphate. These effects together with quenching free radicals by melatonin help mesothelial cells maintain a relatively stable internal environment in the face of high-glucose stress. Thus, Melatonin treatment holds some promise in preserving mesothelium integrity and in decreasing angiogenesis to protect peritoneum function in patients undergoing peritoneal dialysis.

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

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

Screening and identification of unknown chemical contaminants in food based on liquid chromatography-high-resolution mass spectrometry and machine learning

Unknown or unexpected chemical contaminants and/or their transformation products in food that may be harmful to humans need to be discovered for comprehensive safety evaluation. Liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is a powerful tool for detecting chemical contaminants in food samples. However, identifying all of peaks in LC-HRMS is not possible, but if class information is known in advance, further identification will become easier. In this work, a novel MS2 spectra classification-driven screening strategy was constructed based on LC-HRMS and machine learning. First, the classification model was developed based on machine learning algorithm using class information and experimental MS2 data of chemical contaminants and other non-contaminants. By using the developed artificial neural network classification model, in total 32 classes of pesticides, veterinary drugs and mycotoxins were classified with good prediction accuracy and low false-positive rate. Based on the classification model, a screening procedure was developed in which the classes of unknown features in LC-HRMS were first predicted through the classification model, and then their structures were identified under the guidance of class information. Finally, the developed strategy was tentatively applied to the analysis of pork and aquatic products, and 8 chemical contaminants and 11 transformation products belonging to 8 classes were found. This strategy enables screening of unknown chemical contaminants and transformation products in complex food matrices.

PD-L1 overexpression induces STAT signaling and promotes the secretion of pro-angiogenic cytokines in non-small cell lung cancer (NSCLC)

BACKGROUND

Monoclonal antibodies (ICI) targeting the immune checkpoint PD-1/PD-L1 alone or in combination with chemotherapy have demonstrated relevant benefits and established new standards of care in first-line treatment for advanced non-oncogene addicted non-small cell lung cancer (NSCLC). However, a relevant percentage of NSCLC patients, even with high PD-L1 expression, did not respond to ICI, highlighting the presence of intracellular resistance mechanisms that could be dependent on high PD-L1 levels. The intracellular signaling induced by PD-L1 in tumor cells and their correlation with angiogenic signaling pathways are not yet fully elucidated.

METHODS

The intrinsic role of PD-L1 was initially checked in two PD-L1 overexpressing NSCLC cells by transcriptome profile and kinase array. The correlation of PD-L1 with VEGF, PECAM-1, and angiogenesis was evaluated in a cohort of advanced NSCLC patients. The secreted cytokines involved in tumor angiogenesis were assessed by Luminex assay and their effect on Huvec migration by a non-contact co-culture system.

RESULTS

PD-L1 overexpressing cells modulated pathways involved in tumor inflammation and JAK-STAT signaling. In NSCLC patients, PD-L1 expression was correlated with high tumor intra-vasculature. When challenged with PBMC, PD-L1 overexpressing cells produced higher levels of pro-angiogenic factors compared to parental cells, as a consequence of STAT signaling activation. This increased production of cytokines involved in tumor angiogenesis largely stimulated Huvec migration. Finally, the addition of the anti-antiangiogenic agent nintedanib significantly reduced the spread of Huvec cells when exposed to high levels of pro-angiogenic factors.

CONCLUSIONS

In this study, we reported that high PD-L1 modulates STAT signaling in the presence of PBMC and induces pro-angiogenic factor secretion. This could enforce the role of PD-L1 as a crucial regulator of the tumor microenvironment stimulating tumor progression, both as an inhibitor of T-cell activity and as a promoter of tumor angiogenesis.

Analysis of dipeptides in Chinese liquors based on dansylation combined with liquid chromatography-mass spectrometry

Dipeptides have been shown to be an important taste substance in alcoholic beverages. However, the characterization of dipeptides in Chinese liquors was poor. Here, dansylation combined with liquid chromatography - mass spectrometry was employed to analyze dipeptides in eight liquors of two flavors. Consequently, 35 dipeptides were identified from liquors and 32 of them were quantified. Dipeptide quantification showed LODs smaller than 2.5 ng/mL. The calibration curves showed concentration spans from two to three orders of magnitude with satisfactory linearity. The matrix effects in low and high concentrations were from -25.71 % to 24.19 % and -14.82 % to 20.73 %, respectively. Intra- and inter-day precision is lower than 15 % for both low and high concentrations. The dipeptide contents in sauce flavor liquors were higher than those in strong flavor liquors. Ala- and -Phe dipeptides showed their unique trends between sauce and strong flavor liquors. This study provides new clues to evaluate taste of liquors.

Z-Ligustilide Combined with Cisplatin Reduces PLPP1-Mediated Phospholipid Synthesis to Impair Cisplatin Resistance in Lung Cancer

Lung cancer is a malignant tumor with one of the highest morbidity and mortality rates in the world. Approximately 80-85% of lung cancer is diagnosed as non-small lung cancer (NSCLC), and its 5-year survival rate is only 21%. Cisplatin is a commonly used chemotherapy drug for the treatment of NSCLC. Its efficacy is often limited by the development of drug resistance after long-term treatment. Therefore, determining how to overcome cisplatin resistance, enhancing the sensitivity of cancer cells to cisplatin, and developing new therapeutic strategies are urgent clinical problems. Z-ligustilide is the main active ingredient of the Chinese medicine Angelica sinensis, and has anti-tumor activity. In the present study, we investigated the effect of the combination of Z-ligustilide and cisplatin (Z-ligustilide+cisplatin) on the resistance of cisplatin-resistant lung cancer cells and its mechanism of action. We found that Z-ligustilide+cisplatin decreased the cell viability, induced cell cycle arrest, and promoted the cell apoptosis of cisplatin-resistant lung cancer cells. Metabolomics combined with transcriptomics revealed that Z-ligustilide+cisplatin inhibited phospholipid synthesis by upregulating the expression of phospholipid phosphatase 1 (PLPP1). A further study showed that PLPP1 expression was positively correlated with good prognosis, whereas the knockdown of PLPP1 abolished the effects of Z-ligustilide+cisplatin on cell cycle and apoptosis. Specifically, Z-ligustilide+cisplatin inhibited the activation of protein kinase B (AKT) by reducing the levels of phosphatidylinositol 3,4,5-trisphosphate (PIP3). Z-ligustilide+cisplatin induced cell cycle arrest and promoted the cell apoptosis of cisplatin-resistant lung cancer cells by inhibiting PLPP1-mediated phospholipid synthesis. Our findings demonstrate that the combination of Z-Ligustilide and cisplatin is a promising approach to the chemotherapy of malignant tumors that are resistant to cisplatin.

Suspect and nontarget screening of mycotoxins and their modified forms in wheat products based on ultrahigh-performance liquid chromatography-high resolution mass spectrometry

Various forms of mycotoxins commonly exist in food and pose a significant risk to human health. Here a comprehensive suspect and nontarget screening strategy for both parent and modified mycotoxins was developed using ultrahigh-performance liquid chromatography-high resolution mass spectrometry (UHPLCHRMS). We constructed an in-house MS/MS database containing 82 mycotoxins in 8 categories. Then fragmentation characteristics of different classes of mycotoxins were rapidly extracted by a Python program "Fragmentation pattern screener (FPScreener)" and nontarget screening rules were determined by analyzing the frequencies and average intensities of fragmentation characteristics. Using the suspect and nontarget screening strategy, we successfully identified six parent mycotoxins and eight modified mycotoxins with different confidence levels in contaminated wheat and flour samples. This strategy enables screening of unknown parents and modified mycotoxins in food matrices with corresponding fragmentation characteristics.

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

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

CT-based radiomics nomogram for the preoperative prediction of microsatellite instability and clinical outcomes in colorectal cancer: a multicentre study

AIM

To develop and validate a computed tomography (CT)-based radiomics nomogram for preoperative prediction of microsatellite instability (MSI) status and clinical outcomes in colorectal cancer (CRC) patients.

MATERIALS AND METHODS

This retrospective study enrolled 497 CRC patients from three centres. Least absolute shrinkage and selection operator regression was utilised for feature selection and constructing the radiomics signature. Univariate and multivariate logistic regression analyses were employed to identify significant clinical variables. The radiomics nomogram was constructed by integrating the radiomics signature and the identified clinical variables. The performance of the nomogram was evaluated through receiver operating characteristic curves, calibration curves, and decision curve analysis. Kaplan-Meier analysis was performed to investigate the prognostic value of the nomogram.

RESULTS

The radiomics signature comprised 10 radiomics features associated with MSI status. The nomogram, integrating the radiomics signature and independent predictors (age, location, and thickness), demonstrated favourable calibration and discrimination, achieving areas under the receiver operating characteristic (ROC) curves (AUCs) of 0.89 (95% confidence interval [CI]: 0.83-0.95), 0.87 (95% CI: 0.79-0.95), 0.88 (95% CI: 0.81-0.96), and 0.86 (95% CI: 0.78-0.93) in the training cohort, internal validation cohort, and two external validation cohorts, respectively. The nomogram exhibited superior performance compared to the clinical model (p<0.05). Additionally, survival analysis demonstrated that the nomogram successfully stratified stage II CRC patients based on prognosis (hazard ratio [HR]: 0.357, p=0.022).

CONCLUSION

The radiomics nomogram demonstrated promising performance in predicting MSI status and stratifying the prognosis of patients with CRC.

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

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

Can SGRT be a Substitute for Plan Verification Procedure?

PURPOSE/OBJECTIVE(S)

Verification of plan (VP) has been part of our pre-treatment workflow for treatment isocenter verification. Currently, our center uses simulator for VP procedure for all our patients before the treatment. We would like to investigate if SGRT could be a good substitute for VP procedure to simplify our pre-treatment workflow.

MATERIALS/METHODS

In Group A (A-c, A-t, A-a), 20 patients of each treatment site (cranial, thorax and abdomen) were selected randomly. Patients did not go through VP procedure. During the first fraction of treatment, the therapists were guided by SGRT system (Vision RT, UK) and aligned the patient to 3mm and 1°using a standard region of interest (ROI). First CBCT was taken as a reference to customize the ROI for better suitability. Next, the patient was re-aligned to 1mm and 1°using the new ROI. Second CBCT was acquired, and 6 degrees of freedoms shifts were recorded. In Group B (B-c, B-t, B-a), 20 patients of each treatment site (cranial, thorax and abdominal) that were assigned for VP over the same period as Group A patients. Group B patients were aligned based on the skin markings drawn during VP procedure. CBCTs were taken at the first fraction of treatment and shifts were recorded.

RESULTS

A total of 60 CBCT images were analyzed for each group of patients. The absolute mean and standard deviations were shown in Table 1. The results indicated that Group A is superior, if not comparable, to Group B. Table 1: The absolute mean and standard deviations of first fraction of CBCT positioning errors for Group A and B patients.

CONCLUSION

With appropriate ROI, SGRT is a good or superior substitute for plan verification procedure. Localization verification can be done during day one of treatment which ease the pre-treatment workflow to both patients and clinical team. Analysis of customized ROI will be further studied in the future.

Prediction of plant secondary metabolic pathways using deep transfer learning

BACKGROUND

Plant secondary metabolites are highly valued for their applications in pharmaceuticals, nutrition, flavors, and aesthetics. It is of great importance to elucidate plant secondary metabolic pathways due to their crucial roles in biological processes during plant growth and development. However, understanding plant biosynthesis and degradation pathways remains a challenge due to the lack of sufficient information in current databases. To address this issue, we proposed a transfer learning approach using a pre-trained hybrid deep learning architecture that combines Graph Transformer and convolutional neural network (GTC) to predict plant metabolic pathways.

RESULTS

GTC provides comprehensive molecular representation by extracting both structural features from the molecular graph and textual information from the SMILES string. GTC is pre-trained on the KEGG datasets to acquire general features, followed by fine-tuning on plant-derived datasets. Four metrics were chosen for model performance evaluation. The results show that GTC outperforms six other models, including three previously reported machine learning models, on the KEGG dataset. GTC yields an accuracy of 96.75%, precision of 85.14%, recall of 83.03%, and F1_score of 84.06%. Furthermore, an ablation study confirms the indispensability of all the components of the hybrid GTC model. Transfer learning is then employed to leverage the shared knowledge acquired from the KEGG metabolic pathways. As a result, the transferred GTC exhibits outstanding accuracy in predicting plant secondary metabolic pathways with an average accuracy of 98.30% in fivefold cross-validation and 97.82% on the final test. In addition, GTC is employed to classify natural products. It achieves a perfect accuracy score of 100.00% for alkaloids, while the lowest accuracy score of 98.42% for shikimates and phenylpropanoids.

CONCLUSIONS

The proposed GTC effectively captures molecular features, and achieves high performance in classifying KEGG metabolic pathways and predicting plant secondary metabolic pathways via transfer learning. Furthermore, GTC demonstrates its generalization ability by accurately classifying natural products. A user-friendly executable program has been developed, which only requires the input of the SMILES string of the query compound in a graphical interface.

A metabolome atlas of mouse brain on the global metabolic signature dynamics following short-term fasting

Calorie restriction (CR) or a fasting regimen is considered one of the most potent non-pharmacological interventions to prevent chronic metabolic disorders, ameliorate autoimmune diseases, and attenuate aging. Despite efforts, the mechanisms by which CR improves health, particularly brain health, are still not fully understood. Metabolic homeostasis is vital for brain function, and a detailed metabolome atlas of the brain is essential for understanding the networks connecting different brain regions. Herein, we applied gas chromatography-mass spectrometry-based metabolomics and lipidomics, covering 797 structurally annotated metabolites, to investigate the metabolome of seven brain regions in fasted (3, 6, 12, and 24 h) and ad libitum fed mice. Using multivariate and univariate statistical techniques, we generated a metabolome atlas of mouse brain on the global metabolic signature dynamics across multiple brain regions following short-term fasting (STF). Significant metabolic differences across brain regions along with STF-triggered region-dependent metabolic remodeling were identified. We found that STF elicited triacylglycerol degradation and lipolysis to compensate for energy demand under fasting conditions. Besides, changes in amino acid profiles were observed, which may play crucial roles in the regulation of energy metabolism, neurotransmitter signaling, and anti-inflammatory and antioxidant in response to STF. Additionally, this study reported, for the first time, that STF triggers a significant elevation of N-acylethanolamines, a class of neuroprotective lipids, in the brain and liver. These findings provide novel insights into the molecular basis and mechanisms of CR and offer a comprehensive resource for further investigation.

Resistant starch decreases intrahepatic triglycerides in patients with NAFLD via gut microbiome alterations

Non-alcoholic fatty liver disease (NAFLD) is a hepatic manifestation of metabolic dysfunction for which effective interventions are lacking. To investigate the effects of resistant starch (RS) as a microbiota-directed dietary supplement for NAFLD treatment, we coupled a 4-month randomized placebo-controlled clinical trial in individuals with NAFLD (ChiCTR-IOR-15007519) with metagenomics and metabolomics analysis. Relative to the control (n = 97), the RS intervention (n = 99) resulted in a 9.08% absolute reduction of intrahepatic triglyceride content (IHTC), which was 5.89% after adjusting for weight loss. Serum branched-chain amino acids (BCAAs) and gut microbial species, in particular Bacteroides stercoris, significantly correlated with IHTC and liver enzymes and were reduced by RS. Multi-omics integrative analyses revealed the interplay among gut microbiota changes, BCAA availability, and hepatic steatosis, with causality supported by fecal microbiota transplantation and monocolonization in mice. Thus, RS dietary supplementation might be a strategy for managing NAFLD by altering gut microbiota composition and functionality.

[Effects of advanced platelet-rich fibrin on deep partial-thickness burn wounds in nude mice]

Objective: To explore the effects of advanced platelet-rich fibrin (A-PRF) on deep partial-thickness burn wounds in nude mice and its mechanism. Methods: The experimental study method was adopted. Forty healthy volunteers in Subei People's Hospital were recruited, including 32 females and 8 males, aged 60 to 72 years. Leukocyte platelet-rich fibrin (L-PRF) and A-PRF membranes were prepared after venous blood was extracted from them. The microstructure of two kinds of platelet-rich fibrin (PRF) membranes was observed by field emission scanning electron microscope. The number of samples was 3 in the following experiments. The L-PRF and A-PRF membranes were divided into L-PRF group and A-PRF group and cultured, and then the release concentrations of platelet-derived growth factor-AB (PDGF-AB) and vascular endothelial growth factor (VEGF) in culture supernatant were determined by enzyme-linked immunosorbent assay on culture day 1, 3, 7, and 14. Mice L929 fibroblasts (Fbs) were divided into L-PRF group and A-PRF group, and cultured with L-PRF or A-PRF conditioned medium, respectively. On culture day 1, 3, and 7, the cell proliferation activity was detected by thiazole blue method. The cell migration rate was detected and calculated at 24 h after scratching by scratch test. Thirty-six male BALB/c nude mice aged 6-8 weeks were selected to make a deep partial-thickness burn wound on one hind leg, and then divided into normal saline group, L-PRF group, and A-PRF group, according to the random number table, with 12 mice in each group. The wounds of nude mice in normal saline group were only washed by normal saline, while the wounds of nude mice in L-PRF group and A-PRF group were covered with the corresponding membranes in addition. The wounds of nude mice in the 3 groups were all bandaged and fixed with dressings. On treatment day 4, 7, and 14, the wound healing was observed and the wound healing rate was calculated. Masson staining was used to observe the new collagen in wound tissue, and immunohistochemical staining was used to detect the percentage of CD31 positive cells in the wound. Data were statistically analyzed with independent sample t test, analysis of variance for repeated measurement, analysis of variance for factorial design, one-way analysis of variance, and least significant difference test. Results: L-PRF membrane's dense network structure was composed of coarse fibrin bundles, with scattered white blood cells and platelets with complete morphology. A-PRF membrane's loose network structure was composed of fine fibrin bundles, with scattered small amount of deformed white blood cells and platelets. On culture day 1, the release concentration of PDGF-AB in PRF culture supernatant in A-PRF group was significantly higher than that in L-PRF group (t=5.73, P<0.05), while the release concentrations of VEGF in PRF culture supernatant in the two groups were similar (P>0.05). On culture day 3, 7, and 14, the release concentrations of PDGF-AB and VEGF in PRF culture supernatant in A-PRF group were significantly higher than those in L-PRF group (with t values of 6.93, 7.45, 5.49, 6.97, 8.97, and 13.64, respectively, P<0.05). On culture day 3, 7, and 14, the release concentrations of PDGF-AB and VEGF in PRF culture supernatant in the two groups were all significantly higher than those in the previous time points within the group (P<0.05). On culture day 1, 3, and 7, the proliferation activity of mice Fbs in A-PRF group was 0.293±0.034, 0.582±0.054, and 0.775±0.040, respectively, which were significantly stronger than 0.117±0.013, 0.390±0.036, and 0.581±0.037 in L-PRF group (with t values of 8.38, 5.14, and 6.16, respectively, P<0.05). At 24 h after scratching, the migration rate of mice Fbs in A-PRF group was (60.9±2.2)%, which was significantly higher than (39.1±2.3)% in L-PRF group (t=11.74, P<0.05). On treatment day 4, the wound exudates of nude mice in L-PRF group and A-PRF group were less with no obvious signs of infection, while the wounds of nude mice in normal saline group showed more exudation. On treatment day 7, the wounds of nude mice in L-PRF group and A-PRF group were dry and crusted, while there was still a small amount of exudate in the wounds of nude mice in normal saline group. On treatment day 14, the wounds of nude mice in A-PRF group tended to heal; a small portion of wounds remained in nude mice in L-PRF group; the wound of nude mice was still covered with eschar in normal saline group. On treatment day 4, 7, and 14, the wound healing rate and percentage of CD31 positive cells of nude mice in L-PRF group were all significantly higher than those in normal saline group (P<0.05); compared with those in normal saline group and L-PRF group, the wound healing rate of nude mice in A-PRF group was significantly increased (P<0.05), the newborn collagen was orderly and evenly distributed, with no excessive deposition, and the percentage of CD31 positive cells was significantly increased (P<0.05). Conclusions: The stable fibrin network structure of A-PRF can maintain the sustained release of growth factors, accelerate cell proliferation, and promote cell migration, so as to shorten the healing time and improve the healing quality of deep partial-thickness burn wounds in nude mice.

Carbon Quantum Dots Derived from Herbal Medicine as Therapeutic Nanoagents for Rheumatoid Arthritis with Ultrahigh Lubrication and Anti-inflammation

As a typical chronic inflammatory joint disease with swelling and pain syndromes, rheumatoid arthritis (RA) is closely related to articular lubrication deficiency and excessive proinflammatory cytokines in its progression and pathogenesis. Herein, inspired by the dual effects of joint lubrication improvement and anti-inflammation to treat RA, two novel potential therapeutic nanoagents have been developed rationally by employing herbal medicine-derived carbon quantum dots (CQDs), i.e., safflower (Carthamus tinctorius L.) CQDs and Angelica sinensis CQDs, yielding ultrahigh lubrication and anti-inflammation bioefficacy. In vitro experimental results show that the two nanoagents display excellent friction reduction due to their good water solubility and spherical structure. Using RA rat models, it is indicated that the nanoagents significantly relieved swelling symptoms and inhibited the expression of related inflammatory cytokines, including IL-1, IL-6, and TNF-α, indicating their extraordinary anti-inflammation bioefficacy. Thus, combining the lubricating and anti-inflammation bioefficacy of CQDs derived from herbal medicine is an attractive strategy to develop new nanoagents for RA treatment.

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

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

Metabolomics acts as a powerful tool for comprehensively evaluating vaccines approved under emergency: a CoronaVac retrospective study

Introduction

To control the COVID-19 pandemic, great efforts have been made to realize herd immunity by vaccination since 2020. Unfortunately, most of the vaccines against COVID-19 were approved in emergency without a full-cycle and comprehensive evaluation process as recommended to the previous vaccines. Metabolome has a close tie with the phenotype and can sensitively reflect the responses to stimuli, rendering metabolomic analysis have the potential to appraise and monitor vaccine effects authentically.

Methods

In this study, a retrospective study was carried out for 330 Chinese volunteers receiving recommended two-dose CoronaVac, a vaccine approved in emergency in 2020. Venous blood was sampled before and after vaccination at 5 separate time points for all the recipients. Routine clinical laboratory analysis, metabolomic and lipidomic analysis data were collected.

Results and discussion

It was found that the serum antibody-positive rate of this population was around 81.82%. Most of the laboratory parameters were slightly perturbated within the relevant reference intervals after vaccination. The metabolomic and lipidomic analyses showed that the metabolic shift after inoculation was mainly in the glycolysis, tricarboxylic acid cycle, amino acid metabolism, urea cycle, as well as microbe-related metabolism (bile acid metabolism, tryptophan metabolism and phenylalanine metabolism). Time-course metabolome changes were found in parallel with the progress of immunity establishment and peripheral immune cell counting fluctuation, proving metabolomics analysis was an applicable solution to evaluate immune effects complementary to traditional antibody detection. Taurocholic acid, lysophosphatidylcholine 16:0 sn-1, glutamic acid, and phenylalanine were defined as valuable metabolite markers to indicate the establishment of immunity after vaccination. Integrated with the traditional laboratory analysis, this study provided a feasible metabolomics-based solution to relatively comprehensively evaluate vaccines approved under emergency.

Crosstalk between Breast Milk N-Acetylneuraminic Acid and Infant Growth in a Gut Microbiota-Dependent Manner

The healthy growth of infants during early life is associated with lifelong consequences. Breastfeeding has positive impacts on reducing obesity risk, which is likely due to the varied components of breast milk, such as N-acetylneuraminic acid (Neu5Ac). However, the effect of breast milk Neu5Ac on infant growth has not been well studied. In this study, targeted metabolomic and metagenomic analyses were performed to illustrate the association between breast milk Neu5Ac and infant growth. Results demonstrated that Neu5Ac was significantly abundant in breast milk from infants with low obesity risk in two independent Chinese cohorts. Neu5Ac from breast milk altered infant gut microbiota and bile acid metabolism, resulting in a distinct fecal bile acid profile in the high-Neu5Ac group, which was characterized by reduced levels of primary bile acids and elevated levels of secondary bile acids. Taurodeoxycholic acid 3-sulfate and taurochenodeoxycholic acid 3-sulfate were correlated with high breast milk Neu5Ac and low obesity risk in infants, and their associations with healthy growth were reproduced in mice colonized with infant-derived microbiota. Parabacteroides might be linked to bile acid metabolism and act as a mediator between Neu5Ac and infant growth. These results showed the gut microbiota-dependent crosstalk between breast milk Neu5Ac and infant growth.

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

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

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

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

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

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

Screening strategy for 1210 exogenous chemicals in serum by two-dimensional liquid chromatography-mass spectrometry

Humans are at risk of exogenous exposure to exogenous chemicals. Challenges exist for the comprehensive monitoring of residues with different physical and chemical properties in serum. Here, an on-line two-dimensional liquid chromatography (2D-LC) - high resolution mass spectrometry system (HRMS) was developed, expanding the range of the partition coefficient in octanol/water of the residue analysis from -8 to 12. A high-coverage serum residue screening strategy was further designed by integrating 2D-LC system with HRMS full MS/data independent acquisition and automatic spectral library searching. This strategy enables to simultaneously screen 1210 pesticides, veterinary/human drugs, other chemical pollutants and their metabolites in serum with a single analysis. Method validation showed 92% and 81% of 1022 residues spiked in serum could be detected at 50 ng/mL and 5 ng/mL, respectively. The developed method was applied to the analysis of 24 separately pooled serum samples, 58 suspect residues were found, some of them were detected at high frequencies over than 50%. Among them, 4,6-Dinitro-O-cresol and probable carcinogenic folpet are highly toxic, and cimaterol is banned in China. Collectively, this study developed a 2D-LC-HRMS -based screening strategy for screening pesticides, veterinary/human drugs, and other chemical pollutants in serum, it is helpful for studying the effect of exogenous exposures on human health.

Profiling and annotation of carbonyl compounds in Baijiu Daqu by chlorine isotope labeling-assisted ultrahigh-performance liquid chromatography-high resolution mass spectrometry

Carbonyl compounds are among the most important flavor substances that affect the taste of Baijiu. However, high coverage analysis of carbonyl compounds is obstructed due to the poor ionization efficiency of these compounds. Here we report a chlorine isotope labeling-assisted ultrahigh-performance liquid chromatography-high resolution mass spectrometry-based method (CIL-UHPLCHRMS) for profiling and annotation of carbonyl compounds in sauce flavored-Baijiu Daqu. 4-Chloro-2-hydrazinylpyridine was demonstrated to be a good labeling reagent that could achieve highly sensitive profiling and high-coverage screening of carbonyl compounds in the absence of heavy isotope labeling reagents. In the analysis of eight carbonyl standards representing different carbonyl categories, l-(-)-fucose, 2-carboxybenzaldehyde, 2-hydroxyacetophenone and heptan-2-one could be ionized only after labeling and MS signals were significantly increased for other 4 standards with an enhancement factor ranging from 181-fold for 3-methoxysalicylaldehyde to 3141-fold for tridecan-2-one. The annotation was achieved based on multidimensional information including MS1, predicted t_R, in silico MS/MS and manually annotated fragments. In total, 487 carbonyl compounds were detected in Baijiu Daqu, among which, 314 (64.5%) of them were positively or putatively identified. The outcome of the linearity (with a linear range of 2, 3 orders of magnitude), precision (less than 10%), and limit of detection (varied from 0.07 to 0.10 nM) indicated that the method was adequate for profiling carbonyl compounds in complex biological samples. The established method was successfully applied to study carbonyl compounds in Baijiu Daqu with different colors and different seasons. Taken collectively, the present work provides an effective, simple and economic strategy for comprehensive analysis of carbonyl compounds in complex matrix samples.

[Association between vitamin D level and blood eosinophil count in healthy population and patients with chronic obstructive pulmonary disease]

OBJECTIVE

To investigate the prevalence of vitamin D deficiency and its association with blood eosinophil count in healthy population and patients with chronic obstructive pulmonary disease (COPD).

METHODS

We analyzed the data of a total 6163 healthy individuals undergoing routine physical examination in our hospital between October, 2017 and December, 2021, who were divided according to their serum 25(OH)D level into severe vitamin D deficiency group (< 10 ng/mL), deficiency group (< 20 ng/mL), insufficient group (< 30 ng/mL) and normal group (≥30 ng/mL). We also retrospectively collected the data of 67 COPD patients admitted in our department from April and June, 2021, with 67 healthy individuals undergoing physical examination in the same period as the control group. Routine blood test results, body mass index (BMI) and other parameters were obtained from all the subjects, and logistic regression models were used to investigate the association between 25(OH)D levels and eosinophil count.

RESULTS

The overall abnormal rate of 25(OH)D level (< 30 ng/mL) in the healthy individuals was 85.31%, and the rate was significantly higher in women (89.29%) than in men. Serum 25(OH)D levels in June, July, and August were significantly higher than those in December, January, and February. In the healthy individuals, blood eosinophil counts were the lowest in severe 25(OH)D deficiency group, followed by the deficiency group and insufficient group, and were the highest in the normal group (P < 0.05). Multivariable regression analysis showed that an older age, a higher BMI, and elevated vitamin D levels were all risk factors for elevated blood eosinophils in the healthy individuals. The patients with COPD had lower serum 25(OH)D levels than the healthy individuals (19.66±7.87 vs 26.39±9.28 ng/mL) and a significantly higher abnormal rate of serum 25(OH)D (91% vs 71%; P < 0.05). A reduced serum 25(OH)D level was a risk factor for COPD. Blood eosinophils, sex and BMI were not significantly correlated with serum 25(OH)D level in patients with COPD.

CONCLUSION

Vitamin D deficiency is common in both healthy individuals and COPD patients, and the correlations of vitamin D level with sex, BMI and blood eosinophils differ obviously between healthy individuals and COPD patients.

Annotation of Dipeptides and Tripeptides Derivatized via Dansylation Based on Liquid Chromatography-Mass Spectrometry and Iterative Quantitative Structure Retention Relationship

Small peptides such as dipeptides and tripeptides show various biological activities in organisms. However, methods for identifying dipeptides/tripeptides from complex biological samples are lacking. Here, an annotation strategy involving the derivatization of dipeptides and tripeptides via dansylation was suggested based on liquid chromatography-mass spectrometry (LC-MS) and iterative quantitative structure retention relationship (QSRR) to choose dipeptides/tripeptides by using a small number of standards. First, the LC-autoMS/MS method and initial QSRR model were built based on 25 selected grid-dipeptides and 18 test-dipeptides. To achieve high-coverage detection, dipeptide/tripeptide pools containing abundant dipeptides/tripeptides were then obtained from four dansylated biological samples including serum, tissue, feces, and soybean paste by using the parameter-optimized LC-autoMS/MS method. The QSRR model was further optimized through an iterative train-by-pick strategy. Based on the specific fragments and t_R tolerances, 198 dipeptides and 149 tripeptides were annotated. The dipeptides at lower annotation levels were verified by using authentic standards and grid-correlation analysis. Finally, variation in serum dipeptides/tripeptides of three different liver diseases including hepatitis B infection, liver cirrhosis, and hepatocellular carcinoma was characterized. Dipeptides with N-prolinyl, C-proline, N-glutamyl, and N-valinyl generally increased with disease severity. In conclusion, this study provides an efficient strategy for annotating dipeptides/tripeptides from complex samples.

Quantitatively biomechanical response analysis of posterior musculature reconstruction in cervical single-door laminoplasty

BACKGROUND AND OBJECTIVE

The current trend of laminoplasty is developing toward the goal of muscle preservation and minimum tissue damage. Given this, muscle-preserving techniques in cervical single-door laminoplasty have been modified with protecting the spinous processes at the sites of C2 and/or C7 muscle attachment and reconstruct the posterior musculature in recent years. To date, no study has reported the effect of preserving the posterior musculature during the reconstruction. The purpose of this study is to quantitatively evaluate the biomechanical effect of multiple modified single-door laminoplasty procedures for restoring stability and reducing response level on the cervical spine.

METHODS

Different cervical laminoplasty models were established for evaluating kinematics and response simulations based on a detailed finite element (FE) head-neck active model (HNAM), including ① C3 - C7 laminoplasty (LP_C37), ② C3 - C6 laminoplasty with C7 spinous process preservation (LP_C36), ③ C3 laminectomy hybrid decompression with C4 - C6 laminoplasty (LT_C3 + LP_C46) and ④ C3 - C7 laminoplasty with unilateral musculature preservation (LP_C37 + UMP). The laminoplasty model was validated by the global range of motion (ROM) and percentage changes relative to the intact state. The C2 - T1 ROM, axial muscle tensile force, and stress/strain levels of functional spinal units were compared among the different laminoplasty groups. The obtained effects were further analysed by comparison with a review of clinical data on cervical laminoplasty scenarios.

RESULTS

Analysis of the locations of concentration of muscle load showed that the C2 muscle attachment sustained more tensile loading than the C7 muscle attachment, primarily in flexion-extension (FE) and in lateral bending (LB) and axial rotation (AR), respectively. Simulated results further quantified that LP_C36 primarily produced 10% decreases in LB and AR modes relative to LP_C37. Compared with LP_C36, LT_C3 + LP_C46 resulted in approximately 30% decreases in FE motion; LP C37 + UMP also showed a similar trend. Additionally, when compared to LP_C37, LT_C3 + LP_C46 and LP C37 + UMP reduced the peak stress level at the intervertebral disc by at most 2-fold as well as the peak strain level of the facet joint capsule by 2-3-fold. All these findings were well correlated with the result of clinical studies comparing modified laminoplasty and classic laminoplasty.

CONCLUSIONS

Modified muscle-preserving laminoplasty is superior to classic laminoplasty due to the biomechanical effect of the posterior musculature reconstruction, with a retained postoperative ROM and loading response levels of the functional spinal units. More motion-sparing is beneficial for increasing cervical stability, which probably accelerates the recovery of postoperative neck movement and reduces the risk of the complication for eventual kyphosis and axial pain. Surgeons are encouraged to make every effort to preserve the attachment of the C2 whenever feasible in laminoplasty.

PFO5DoDA disrupts hepatic homeostasis primarily through glucocorticoid signaling inhibition

Legacy per- and polyfluoroalkyl substances (PFASs) are a worldwide health concern due to their potential bioaccumulation and toxicity in humans. A variety of perfluoroether carboxylic acids (PFECAs) have been developed as next-generation replacements of legacy PFASs. However, information regarding their possible environmental and human health risks is limited. In the present study, we explored the effects of PFECAs on mice based on long-term exposure to environmentally relevant doses of perfluoro-3,5,7,9,11-pentaoxadodecanoic acid (PFO5DoDA). Results showed that PFECAs exposure suppressed many cellular stress signals and resulted in hepatomegaly. PFO5DoDA acted as an agonist of the peroxisome proliferator-activated receptor (PPAR) in vitro and modulated PPAR-dependent gene expression in the liver. Importantly, PFECAs had an inhibitory effect on the glucocorticoid receptor (GR), which may contribute to the extensive suppression of stress signals. Of note, the GR suppression induced by PFECAs was not reported by legacy perfluorooctanoic acid (PFOA). PFO5DoDA-induced changes in both GR and PPAR signals remodeled hepatic metabolic profiles, including decreased fatty acids and amino acids and increased β-oxidation. Mechanistically, PFO5DoDA inhibited GR transactivation by degradation of GR proteins. Our results emphasize the potential risk of PFECAs to human health, which were introduced to ease concerns regarding legacy PFASs.

Development of a fast and robust liquid chromatography-mass spectrometry-based metabolomics analysis method for neonatal dried blood spots

Dried blood spot (DBS) samples have been widely used in many fields including newborn screening, with the advantages in transportation, storage and non-invasiveness. The DBS metabolomics research of neonatal congenital diseases will greatly expand the understanding of the disease. In this study, we developed a liquid chromatography-mass spectrometry-based method for neonatal metabolomics analysis of DBS. The influences of blood volume and chromatographic effects on the filter paper on metabolite levels were studied. The levels of 11.11 % metabolites were different between 75 μL and 35 μL of blood volumes used for DBS preparation. Chromatographic effects on the filter paper occurred in DBS prepared with 75 μL whole blood and 6.67 % metabolites had different MS responses when central disks were compared with outer disks. The DBS storage stability study showed that compared with - 80 °C storage, storing at 4 °C for 1 year had obvious influences on more than half metabolites. Storing at 4 °C and - 20 °C for short term (< 14 days) and - 20 °C for longer term (1 year) had less influences on amino acids, acyl-carnitines and sphingomyelins, but greater influences on partial phospholipids. Method validation showed that this method has a good repeatability, intra-day and inter-day precision and linearity. Finally, this method was applied to investigate metabolic disruptions of congenital hypothyroidism (CH), metabolic changes of CH newborns were mainly involved in amino acid metabolism and lipid metabolism.

A Strategy for Uncovering the Serum Metabolome by Direct-Infusion High-Resolution Mass Spectrometry

Direct infusion nanoelectrospray high-resolution mass spectrometry (DI-nESI-HRMS) is a promising tool for high-throughput metabolomics analysis. However, metabolite assignment is limited by the inadequate mass accuracy and chemical space of the metabolome database. Here, a serum metabolome characterization method was proposed to make full use of the potential of DI-nESI-HRMS. Different from the widely used database search approach, unambiguous formula assignments were achieved by a reaction network combined with mass accuracy and isotopic patterns filter. To provide enough initial known nodes, an initial network was directly constructed by known metabolite formulas. Then experimental formula candidates were screened by the predefined reaction with the network. The effects of sources and scales of networks on assignment performance were investigated. Further, a scoring rule for filtering unambiguous formula candidates was proposed. The developed approach was validated by a pooled serum sample spiked with reference standards. The coverage and accuracy rates for the spiked standards were 98.9% and 93.6%, respectively. A total of 1958 monoisotopic features were assigned with unique formula candidates for the pooled serum, which is twice more than the database search. Finally, a case study of serum metabolomics in diabetes was carried out using the developed method.

Steroid Profiling for the Diagnosis of Congenital Adrenal Hyperplasia by Microbore Ultra-performance Liquid Chromatography-Tandem Mass Spectrometry

BACKGROUND

A rapid and accurate measurement approach for 17α-hydroxyprogesterone (17-OHP) and related steroids in amount/volume-limited clinic samples is of importance for precise newborn diagnosis of congenital adrenal hyperplasia (CAH) and its subtypes in clinic.

METHODS

Sixteen steroids (17-OHP, androstenedione, cortisol, tetrahydro-11-deoxycortisol, pregnenolone, progesterone, 11-deoxycorticosterone, corticosterone, 21-deoxycortisol, 11-deoxycortisol, dehydroepiandrosterone, testosterone, aldosterone, 17α-hydroxypregnenolone, dihydrotestosterone and 18-hydroxycorticosterone) were included in the panel of high-throughput microbore ultra-performance liquid chromatography-tandem mass spectrometry. Samples were collected from 126 normal subjects and 65 patients including different subtypes of CAH.

RESULTS

The method was validated with satisfactory analytical performance in linearity, repeatability, recovery and limit of detection. Reference intervals for 16 steroids were established by quantifying the level of steroids detected in normal infants. The applicability of the method was tested by differentiating steroid metabolic characteristics between normal infants and infants with CAH, as well as between infants with different CAH subtypes. The relevance of 17-OHP, 21-deoxycortisol, and 17-OHP/11-deoxycortisol for 21-hydroxylase deficiency screening was demonstrated. The level of 11-deoxycorticosterone, 11-deoxycortisol, progesterone and androstenedione can be used for the diagnosis of different rare subtypes of CAH.

CONCLUSION

This study provides a strategy for highly efficient steroid analysis of amount/volume-limited clinic samples and holds great potential for clinical diagnosis of CAH.

[DTX2 overexpression promotes migration and invasion of colorectal cancer cells through the Notch2/Akt axis]

OBJECTIVE

To investigate the effect of changes in DTX2 expression level on migration and invasion of colorectal cancer (CRC) cells and explore the mechanism.

METHODS

Two CRC cell lines SW620 and LoVo were transfected with a specific shRNA targeting DTX2 (DTX2-shRNA) or a DTX2-overexpressing plasmid (pcDNA-DTX2), and the transfection efficiency was evaluated with RT-qPCR and Western blotting. Scratch and Transwell assays were used to assess the changes in migration and invasion ability of the transfected cells, and the cellular expression levels of Notch2, NICD, AKT, p-Akt and MMP-2/9 proteins were detected with Western blotting. The CRC cells were co-transfected with pcDNA-DTX2 and Notch2 siRNA to assess the effect of Notch2 knockdown on DTX2 overexpression-induced enhancement of cell migration and invasion.

RESULTS

The expression levels of DTX2 at both the mRNA and protein levels were significantly decreased in CRC cells transfected with DTX2- shRNA (P < 0.01) and increased in cells transfected with pcDNA-DTX2 (P < 0.01). Scratch and Transwell assays showed that the migration and invasion abilities of CRC cells were significantly lowered following DTX2 knockdown (P < 0.01) and were enhanced in cells with DTX2 overexpression (P < 0.01). The expression levels of Notch2, NICD, p-Akt and MMP-2 proteins decreased significantly in CRC cells with DTX2 knockdown (P < 0.05) and increased obviously in DTX2-overexpressing cells (P < 0.05). In both of the two CRC cell lines, transfection with Notch2 siRNA obviously reversed the effect of DTX2 overexpression in promoting cell migration and invasion (P < 0.01) and expressions of the related proteins.

CONCLUSION

DTX2 overexpression promotes migration and invasion of CRC cells through the Notch2/Akt axis, suggesting the potential of DTX2 as a new biological indicator of CRC.

Integrative analysis of metabolomics and proteomics unravels purine metabolism disorder in the SOD1G93A mouse model of amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with progressive paralysis of limbs and bulb in patients, the cause of which remains unclear. Accumulating studies suggest that motor neuron degeneration is associated with systemic metabolic impairment in ALS. However, the metabolic reprogramming and underlying mechanism in the longitudinal progression of the disease remain poorly understood. In this study, we aimed to investigate the molecular changes at both metabolic and proteomic levels during disease progression to identify the most critical metabolic pathways and underlying mechanisms involved in ALS pathophysiological changes. Utilizing liquid chromatography-mass spectrometry-based metabolomics, we analyzed the metabolites' levels of plasma, lumbar spinal cord, and motor cortex from SOD1^G93A mice and wildtype (WT) littermates at different stages. To elucidate the regulatory network underlying metabolic changes, we further analyzed the proteomics profile in the spinal cords of SOD1^G93A and WT mice. A group of metabolites implicated in purine metabolism, methionine cycle, and glycolysis were found differentially expressed in ALS mice, and abnormal expressions of enzymes involved in these metabolic pathways were also confirmed. Notably, we first demonstrated that dysregulation of purine metabolism might contribute to the pathogenesis and disease progression of ALS. Furthermore, we discovered that fatty acid metabolism, TCA cycle, arginine and proline metabolism, and folate-mediated one‑carbon metabolism were also significantly altered in this disease. The identified differential metabolites and proteins in our study could complement existing data on metabolic reprogramming in ALS, which might provide new insight into the pathological mechanisms and novel therapeutic targets of ALS.

[Bushen Huoxue Fang improves recurrent miscarriage in mice by down-regulating the JAK2/STAT3 pathway]

OBJECTIVE

To investigate the efficacy of Bushen Huoxue Fang (BSHXF, a traditional Chinese medicine formula) for improving recurrent spontaneous abortion (RSA) in mice and the role of tyrosine kinase (JAK2) and transcriptional activator (STAT3) signaling pathway in its therapeutic mechanism.

METHODS

Female CBA/J mice were caged with male DBA/2 mice to establish RSA mouse models, which were randomly divided into model group, dydrogesterone group and BSHXF group, with the female mice caged with male BALB/c mice as the control group (n=6). From the first day of pregnancy, the mice were subjected to daily intragastric administration of BSHXF, dydrogesterone, or distilled water (in control and model groups) for 12 days. After the treatments, serum levels of antithrombin III (AT-III), activated protein C (APC), tissue plasminogen activator (t-PA), progesterone, human chorionic gonadotropin (HCG), and estradiol (E2) were detected in each group using ELISA. HE staining was used to observe the morphological changes of the endometrium of the mice. Western blotting was performed to determine the expressions of p-JAK2, p-Stat3 and Bcl-2 in the placenta of the mice.

RESULTS

Compared with the control mice, the mouse models of RSA showed a significantly increased embryo loss rate with decreased serum levels of AT-III, T-PA, progesterone, APC and HCG, increased placental expressions of p-JAK2, p-STAT3 and Bax, and decreased expression of Bcl-2 (P < 0.05). Treatments with BSHXF and dydrogesterone both increased serum levels of AT-III, t-PA and HCG in the mouse models; Serum APC level was significantly reduced in BSHXF group and serum progesterone level was significantly increased in dydrogesterone group (P < 0.05).

CONCLUSION

BSHXF can improve the prethrombotic state and inhibit cell apoptosis by downregulating the JAK2/STAT3 pathway to increase the pregnancy rate in mouse models of RSA.

Retention time prediction for small samples based on integrating molecular representations and adaptive network

Retention time (RT) can provide orthogonal information different from that of mass spectrometry and contribute to identifying compounds. Many machine learning methods have been developed and applied to RT prediction. In application, the training data size is usually small in most chromatography systems. To enhance the performance of RT prediction, this study proposes a RT prediction method based on multi-data combinations and adaptive neural network (MDC-ANN). MDC-ANN establishes the RT prediction model for the target chromatographic system through transfer learning and a base deep learning model trained on a big dataset. It selects the optimal molecular representation combination from the multiple input candidates and automatically determines the neural network structure according to the determined input combination. MDC-ANN was compared with two new efficient deep learning methods, three transferring methods and four popular machine learning methods on 14 small datasets and showed advantages in MAE, MedAE, MRE and R² in most cases. The experiment results illustrated that integrating multiple molecular representations can provide more information, improve the performance of RT prediction and contribute to compound annotation, different chromatographic systems may use different molecular representation combinations to obtain good RT prediction performance. Hence, MDC-ANN which automatically determines the best combination of molecular representations for a specific system is promising for predicting RTs accurately in real applications.