Stable-Isotope N-Me Aziridination Enables Accurate Quantitative C═C Isomeric Lipidomics.

Accurate lipid quantification is essential to revealing their roles in physiological and pathological processes. However, difficulties in the structural resolution of lipid isomers hinder their further accurate quantification. To address this challenge, we developed a novel stable-isotope N-Me aziridination strategy that enables simultaneous qualification and quantification of unsaturated lipid isomers. The one-step introduction of the 1-methylaziridine structure not only serves as an activating group for the C═C bond to facilitate positional identification but also as an isotopic inserter to achieve accurate relative quantification. The high performance of this reaction for the identification of unsaturated lipids was verified by large-scale resolution of the C═C positions of 468 lipids in serum. More importantly, by using this bifunctional duplex labeling method, various unsaturated lipids such as fatty acids, phospholipids, glycerides, and cholesterol ester were accurately and individually quantified at the C═C bond isomeric level during the mouse brain ischemia. This study provides a new approach to quantitative structural lipidomics.

Mechanistic insight into glioma through spatially multidimensional proteomics.

Characterizing the tumor microenvironment at the molecular level is essential for understanding the mechanisms of tumorigenesis and evolution. However, the specificity of the blood proteome in localized region of the tumor and its linkages with other systems is difficult to investigate. Here, we propose a spatially multidimensional comparative proteomics strategy using glioma as an example. The blood proteome signature of tumor microenvironment was specifically identified by in situ collection of arterial and venous blood from the glioma region of the brain for comparison with peripheral blood. Also, by integrating with different dimensions of tissue and peripheral blood proteomics, the information on the genesis, migration, and exchange of glioma-associated proteins was revealed, which provided a powerful method for tumor mechanism research and biomarker discovery. The study recruited multidimensional clinical cohorts, allowing the proteomic results to corroborate each other, reliably revealing biological processes specific to gliomas, and identifying highly accurate biomarkers.

Nanocomposites based on nanoceria regulate the immune microenvironment for the treatment of polycystic ovary syndrome.

The immune system is closely associated with the pathogenesis of polycystic ovary syndrome (PCOS). Macrophages are one of the important immune cell types in the ovarian proinflammatory microenvironment, and ameliorate the inflammatory status mainly through M2 phenotype polarization during PCOS. Current therapeutic approaches lack efficacy and immunomodulatory capacity, and a new therapeutic method is needed to prevent inflammation and alleviate PCOS. Here, octahedral nanoceria nanoparticles with powerful antioxidative ability were bonded to the anti-inflammatory drug resveratrol (CeO2@RSV), which demonstrates a crucial strategy that involves anti-inflammatory and antioxidative efficacy, thereby facilitating the proliferation of granulosa cells during PCOS. Notably, our nanoparticles were demonstrated to possess potent therapeutic efficacy via anti-inflammatory activities and effectively alleviated endocrine dysfunction, inflammation and ovarian injury in a dehydroepiandrosterone (DHEA)-induced PCOS mouse model. Collectively, this study revealed the tremendous potential of the newly developed nanoparticles in ameliorating the proinflammatory microenvironment and promoting the function of granulosa cells, representing the first attempt to treat PCOS by using CeO2@RSV nanoparticles and providing new insights in combating clinical PCOS.

Clinical features, microbial spectrum, and antibiotic susceptibility patterns of spontaneous bacterial peritonitis in cirrhotic patients.

BACKGROUND AND AIMS: The microbial spectrum and antimicrobial resistance patterns change over time and vary across regions in patients with spontaneous bacterial peritonitis (SBP). There is an urgent need to clarify the factors associated with in-hospital mortality in these patients. METHODS: In this study, 377 patients with SBP and 794 patients with bacterascites were analyzed for the microbial spectrum, antimicrobial resistance profiles, and laboratory findings. RESULTS: The most common pathogens were Escherichia coli (96, 25.5%), Staphylococcus epidermidis (55, 14.6%), and Enterococcus faecium (42, 11.1%). Multidrug-resistant (MDR) bacteria comprised 49.7% of gram-positive bacteria (GPB) and 48.8% of gram-negative bacteria (GNB). The most sensitive antibiotics were amikacin (91.5%), meropenem (89.8%) and piperacillin/tazobactam (87.6%). Extensively drug-resistant (XDR) (OR=51.457, p < 0.001), neutrophil count (OR=1.088, p < 0.001), and the model for end-stage liver disease (MELD) score (OR=1.124, p < 0.001) were independent predictive factors of in-hospital mortality in patients with SBP. CONCLUSION: MDR represented nearly half of the bacteria isolated from patients with SBP, of which the high prevalence of extended-spectrum ß-lactamase-producing and Carbapenem-resistant bacteria is concerning. The presence of XDR, higher MELD score, and neutrophil count were independent predictive factors associated with higher in-hospital mortality in patients with SBP, indicating that intensive care should be provided to these patients.

Emergence of colistin-heteroresistant and carbapenem-resistant hypervirulent Klebsiella pneumoniae.

OBJECTIVES: To investigate the clinical emergence of colistin-heteroresistant, hypervirulent, and multidrug-resistant Klebsiella pneumoniae, and characterize the underlying molecular mechanisms. METHODS: The population analysis profiles (PAPs) method was used to detect colistin heteroresistance. The time-killing assay was used to examine the effect of colistin on carbapenem-resistant Klebsiella pneumoniae (CRKP) in vitro. Galleria mellonella larvae infection model was used to test the potential virulence. qRT-PCR assay was conducted to compare the expression levels of efflux pump genes. Next and third-generation sequencing were conducted to analyse the genomic features. RESULTS: Two colistin-heteroresistant isolates were detected from a multi-center carbapenem-resistant Enterobacterales (CRE) surveillance study in China, which exhibited similar survival rates as the K2 hypervirulent reference strain ATCC 43816 in a G. mellonella larvae model. The two isolates belonged to ST11, harbouring the iucABCD, iutA, iroBCD, and rpmA2 hypervirulent genes and pLVPK-like virulence plasmids. Colistin showed a weak effect on the heteroresistant strains in vitro. The efflux pump genes acrA, acrB, tolC, oqxA, and oqxB were upregulated in this subpopulation compared to the parental strains. CONCLUSIONS: This study showed the clinical emergence of colistin-heteroresistant, hypervirulent, and multidrug-resistant Klebsiella pneumoniae. AcrAB-TolC and OqxAB efflux overexpression were involved in mediating colistin heteroresistance.

Ultra-high-throughput mapping of the chemical space of asymmetric catalysis enables accelerated reaction discovery.

The discovery of highly enantioselective catalysts and elucidating their generality face great challenges due to the complex multidimensional chemical space of asymmetric catalysis and inefficient screening methods. Here, we develop a general strategy for ultra-high-throughput mapping of the chemical space of asymmetric catalysis by escaping the time-consuming chiral chromatography separation. The ultrafast ( ~ 1000 reactions/day) and accurate (median error < ±1%) analysis of enantiomeric excess are achieved through the ion mobility-mass spectrometry combines with the diastereoisomerization strategy. A workflow for accelerated asymmetric reaction screening is established and verified by mapping the large-scale chemical space of more than 1600 reactions of α-asymmetric alkylation of aldehyde with organocatalysis and photocatalysis. Importantly, a class of high-enantioselectivity primary amine organocatalysts of 1,2-diphenylethane-1,2-diamine-based sulfonamides is discovered by the accelerated screening, and the mechanism for high-selectivity is demonstrated by computational chemistry. This study provides a practical and robust solution for large-scale screening and discovery of asymmetric reactions.

4D-diaXLMS: Proteome-wide Four-Dimensional Data-Independent Acquisition Workflow for Cross-Linking Mass Spectrometry.

Cross-linking mass spectrometry (XL-MS) is a powerful tool for examining protein structures and interactions. Nevertheless, analysis of low-abundance cross-linked peptides is often limited in the data-dependent acquisition (DDA) mode due to its semistochastic nature. To address this issue, we introduced a workflow called 4D-diaXLMS, representing the first-ever application of four-dimensional data-independent acquisition for proteome-wide cross-linking analysis. Cross-linking studies of the HeLa cell proteome were evaluated using the classical cross-linker disuccinimidyl suberate as an example. Compared with the DDA analysis, 4D-diaXLMS exhibited marked improvement in the identification coverage of cross-linked peptides, with a total increase of 36% in single-shot analysis across all 16 SCX fractions. This advantage was further amplified when reducing the fraction number to 8 and 4, resulting in 125 and 149% improvements, respectively. Using 4D-diaXLMS, up to 83% of the cross-linked peptides were repeatedly identified in three replicates, more than twice the 38% in the DDA mode. Furthermore, 4D-diaXLMS showed good performance in the quantitative analysis of yeast cross-linked peptides even in a 15-fold excess amount of HeLa cell matrix, with a low coefficient of variation and high quantitative accuracies in all concentrations. Overall, 4D-diaXLMS was proven to have high coverage, good reproducibility, and accurate quantification for in-depth XL-MS analysis in complex samples, demonstrating its immense potential for advances in the field.

Elucidating the Underlying Reactivities of Alternating Current Electrosynthesis by Time-Resolved Mapping of Short-Lived Reactive Intermediates.

Alternating current (AC) electrolysis is an emerging field in synthetic chemistry, however its mechanistic studies are challenged by the effective characterization of the elusive intermediate processes. Herein, we develop an operando electrochemical mass spectrometry platform that allows time-resolved mapping of stepwise electrosynthetic reactive intermediates in both direct current and alternating current modes. By dissecting the key intermediate processes of electrochemical functionalization of arylamines, the unique reactivities of AC electrosynthesis, including minimizing the over-oxidation/reduction through the inverse process, and enabling effective reaction of short-lived intermediates generated by oxidation and reduction in paired electrolysis, were evidenced and verified. Notably, the controlled kinetics of reactive N-centered radical intermediates in multistep sequential AC electrosynthesis to minimize the competing reactions was discovered. Overall, this work provides direct evidence for the mechanism of AC electrolysis, and clarifies the underlying reasons for its high efficiency, which will benefit the rational design of AC electrosynthetic reactions.

Nontargeted metabolomics analysis of follicular fluid in patients with endometriosis provides a new direction for the study of oocyte quality.

Endometriosis is a common, estrogen-dependent chronic gynecological disease that endangers the reproductive system and systemic metabolism of patients. We aimed to investigate the differences in metabolic profiles in the follicular fluid between infertile patients with endometriosis and controls. A total of 25 infertile patients with endometriosis and 25 infertile controls who were similar in age, BMI, fertilization method and ovulation induction treatment were recruited in this study. Metabolomics analysis of follicular fluid was performed by two methods of high-performance liquid chromatography tandem mass spectrometry. There were 36 upregulated and 17 downregulated metabolites in the follicular fluid of patients in the endometriosis group. KEGG pathway analysis revealed that these metabolites were enriched in phenylalanine, tyrosine and tryptophan biosynthesis, aminoacyl-tRNA biosynthesis, phenylalanine metabolism and pyrimidine metabolism pathways. A biomarker panel consisting of 20 metabolites was constructed by random forest, with an accuracy of 0.946 and an AUC of 0.988. This study characterizes differences in follicular fluid metabolites and associated pathway profiles in infertile patients with endometriosis. These findings can provide a better comprehensive understanding of the disease and a new direction for the study of oocyte quality, as well as potential metabolic markers for the prognosis of endometriosis.

Effects of xanthan gum, konjac glucomannan, and arabinogalactan on the in vitro digestion and fermentation characteristics of biscuits.

Background: Diabetes and its complications have a significant economic impact on individuals and their families. A diet with a low glycemic index (GI) and high fiber content is considered to be associated with the control of blood glucose. Scope and approach: This study explored the effect of polysaccharides, i.e., xanthan gum (BXG), konjac glucomannan (BKG), and arabinogalactan (BAG), on the digestive and prebiotic characteristics of biscuits using a simulated digestion and fermentation model in vitro. Also, the rheological property and structural properties of the polysaccharides were measured to clarify their structure-activity relationships. Key findings and conclusions: During simulated gastrointestinal digestion, the results showed that three types of biscuits containing polysaccharides were low GI foods (estimated GI < 55), in which BAG had the lowest estimated GI value. During in vitro fermentation with diabetic or healthy subjects' fecal microbiota, the three types of biscuits containing polysaccharides (after digestion) decreased the fermentation pH, increased the level of short-chain fatty acids, and modulated the microbiota composition over time. Among the three types of biscuits, BAG increased the abundance of Bifidobacterium and Lactobacillus during fermentation in diabetic and healthy subjects' fecal microbiota. These results showed that the addition of a lower-viscosity polysaccharide (arabinogalactan) may be more beneficial for the blood glucose control of biscuits.

Lipidomic Characterization of Oocytes at Single-Cell Level Using Nanoflow Chromatography-Trapped Ion Mobility Spectrometry-Mass Spectrometry.

Mass spectrometry (MS)-based lipidomic has become a powerful tool for studying lipids in biological systems. However, lipidome analysis at the single-cell level remains a challenge. Here, we report a highly sensitive lipidomic workflow based on nanoflow liquid chromatography and trapped ion mobility spectrometry (TIMS)-MS. This approach enables the high-coverage identification of lipidome landscape at the single-oocyte level. By using the proposed method, comprehensive lipid changes in porcine oocytes during their maturation were revealed. The results provide valuable insights into the structural changes of lipid molecules during porcine oocyte maturation, highlighting the significance of sphingolipids and glycerophospholipids. This study offers a new approach to the single-cell lipidomic.

High-Coverage Four-Dimensional Data-Independent Acquisition Proteomics and Phosphoproteomics Enabled by Deep Learning-Driven Multidimensional Predictions.

Four-dimensional (4D) data-independent acquisition (DIA)-based proteomics is a promising technology. However, its full performance is restricted by the time-consuming building and limited coverage of a project-specific experimental library. Herein, we developed a versatile multifunctional deep learning model Deep4D based on self-attention that could predict the collisional cross section, retention time, fragment ion intensity, and charge state with high accuracies for both the unmodified and phosphorylated peptides and thus established the complete workflows for high-coverage 4D DIA proteomics and phosphoproteomics based on multidimensional predictions. A 4D predicted library containing ∼2 million peptides was established that could realize experimental library-free DIA analysis, and 33% more proteins were identified than using an experimental library of single-shot measurement in the example of HeLa cells. These results show the great values of the convenient high-coverage 4D DIA proteomics methods.

Clinical and Molecular Analysis of ST11-K47 Carbapenem-Resistant Hypervirulent Klebsiella pneumoniae: A Strain Causing Liver Abscess.

Klebsiella pneumoniae has been the predominant pathogen of liver abscess, but ST11-K47 carbapenem-resistant hypervirulent Klebsiella pneumoniae (CR-hvKP) has rarely been studied as the causative organism. We identified an ST11-K47 CR-hvKP (HvKp-su1) from the drainage fluid of a liver abscess in a Chinese man who was diagnosed with liver abscess combined with diabetes, pneumonia, pleural infection, abdominal abscess, and splenic abscess. HvKp-su1 was non-hypermucoviscous and lacked the magA and rmpA genes and pLVPK plasmid but exhibited high virulence, with a high mortality rate (90%) to wax moth larvae (G. mellonella), similar to the hypervirulent Klebsiella pneumoniae ATCC43816 (91.67%). Whole-genome sequencing and bioinformatics analysis indicated that HvKp-su1 possesses a plasmid similar to a type of pLVPK-like plasmid (JX-CR-hvKP-2-P2), which is an uncommon plasmid in CR-hvKP. HvKp-su1 carried multiple resistance genes, including blaKPC-2. blaTEM-1, blaSHV-55, and blaCTX-M-65; hypervirulence genes such as aerobactin (iutA), salmochelin (iroEN), and yersiniabactin (ybtAEPQSTUX); and the type 3 fimbriae-encoding system (mrkACDF). Moreover, v_5377 and v_5429 (cofT, CFA/III (CS8)) located on plasmid 1 were simultaneously predicted to be virulence genes. After the long-term combination use of antibiotics, the patient successfully recovered. In summary, our study clarified the clinical and molecular characteristics of a rare ST11-K47 CR-hvKP (HvKp-su1), raising great concerns about the emergence of ST11-K47 CR-hvKP with multidrug resistance and hypervirulence, and providing insights into the control and treatment of liver abscess caused by ST11-K47 CR-hvKP.

Dual-resolving of positional and geometric isomers of C=C bonds via bifunctional photocycloaddition-photoisomerization reaction system.

The biological functions of lipids largely depend on their chemical structures. The position and configuration of C=C bonds are two of the essential attributes that determine the structures of unsaturated lipids. However, simultaneous identification of both attributes remains challenging. Here, we develop a bifunctional visible-light-activated photocycloaddition-photoisomerization reaction system, which enables the dual-resolving of the positional and geometric isomerism of C=C bonds in lipids when combines with liquid chromatography-mass spectrometry. The dual-pathway reaction mechanism is demonstrated by experiments and density functional theory calculations. Based on this bifunctional reaction system, a workflow of deep structural lipidomics is established, and allows the revealing of unique patterns of cis-trans-isomers in bacteria, as well as the tracking of C=C positional isomers changes in mouse brain ischemia. This study not only offers a powerful tool for deep lipid structural biology, but also provides a paradigm for developing the multifunctional visible-light-induced reaction.

A non-destructive methodology for determination of cantaloupe sugar content using machine vision and deep learning.

BACKGROUND: To determine the maturity of cantaloupe, measuring the soluble solid content (SSC) as the indicator of sugar content based on the refractometric index is commonly practised. This method, however, is destructive and limited to a small number of samples. In this study, the coupling of a convolutional neural network (CNN) with machine vision was proposed in detecting the SSC of cantaloupe. The cantaloupe images were first acquired under controlled and uncontrolled conditions and subsequently fed to the CNN to predict the class to which each cantaloupe belonged. Four hand-crafted classical machine-learning classifiers were used to compare against the performance of the CNN. RESULTS: Experimental results showed that the CNN method significantly outperformed others, with an improvement of >100% being achieved in terms of classification accuracy, considering the data acquired under the uncontrolled environment. CONCLUSION: The results demonstrated the potential benefit to operationalize CNNs in practice for SSC determination of cantaloupe before harvesting. © 2022 Society of Chemical Industry.

Uncovering the interference from lipid fragments on the qualification and quantification of serum metabolites in matrix-assisted laser desorption/ionization time-of-flight mass spectrometric analysis.

RATIONALE: Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has exhibited great advantages in rapid analysis of metabolites. However, the influence of lipid fragments generated by in-source fragmentation (ISD) and/or post-source fragmentation (PSD) on the accurate qualification and quantification of metabolites has not been fully demonstrated. METHODS: Phospholipid standards and serum extract were analyzed by MALDI MS with both TiO2 nanoparticle (TiO2 NP) and 2,5-DHB matrices to illustrate the structures of lipid fragments and their influence on the qualitative and quantitative analysis of metabolites in biological samples. Monophasic and biphasic extraction methods were also compared for their efficiency in removing potential interferents. RESULTS: The fragment ions derived from the phosphocholine head group of phosphatidylcholines (PC) interfere with peaks of low molecular weight (LMW) metabolites at both the MS and MS2 levels. The biphasic extraction system with methanol/chloroform very efficiently removed the interference from PC fragments, and the metabolites choline and carnitine in serum were directly and accurately quantified by MALDI MS by using this biphasic extraction. CONCLUSIONS: The phospholipids could produce fragment ions through ISD and PSD in MALDI MS with both nanoparticle and organic matrices. The fragments exerted influence on the qualification and qualification of metabolites in serum. By choosing the proper extraction method, the interference from lipid fragments could be efficiently alleviated.

A protocol for investigating lipidomic dysregulation and discovering lipid biomarkers from human serums.

Lipids play important roles in various human diseases. Disease-associated lipid dysregulation and biomarkers could provide molecular clues for diagnosis, pathogenesis, and therapy. This protocol provides a step-by-step workflow to investigate lipid dysregulation and discover biomarkers in human serum samples by liquid chromatography-mass spectrometry (LC-MS)-based lipidomics and machine learning analysis. The workflow includes project design, serum collection, sample preparation, data acquisition, data processing, and machine learning analysis. For complete details on the use and execution of this profile, please refer to Hao et al. (2021).

Machine Learning of Serum Metabolic Patterns Encodes Asymptomatic SARS-CoV-2 Infection.

Asymptomatic COVID-19 has become one of the biggest challenges for controlling the spread of the SARS-CoV-2. Diagnosis of asymptomatic COVID-19 mainly depends on quantitative reverse transcription PCR (qRT-PCR), which is typically time-consuming and requires expensive reagents. The application is limited in countries that lack sufficient resources to handle large-scale assay during the COVID-19 outbreak. Here, we demonstrated a new approach to detect the asymptomatic SARS-CoV-2 infection using serum metabolic patterns combined with ensemble learning. The direct patterns of metabolites and lipids were extracted by matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) within 1 s with simple sample preparation. A new ensemble learning model was developed using stacking strategy with a new voting algorithm. This approach was validated in a large cohort of 274 samples (92 asymptomatic COVID-19 and 182 healthy control), and provided the high accuracy of 93.4%, with only 5% false negative and 7% false positive rates. We also identified a biomarker panel of ten metabolites and lipids, as well as the altered metabolic pathways during asymptomatic SARS-CoV-2 Infection. The proposed rapid and low-cost approach holds promise to apply in the large-scale asymptomatic COVID-19 screening.

High-Throughput Nano-Electrostatic-Spray Ionization/Photoreaction Mass Spectrometric Platform for the Discovery of Visible-Light-Activated Photocatalytic Reactions in the Picomole Scale.

Visible-light-activated photocatalysis has emerged as a green and powerful tool for the synthesis of various organic compounds under mild conditions. However, the expeditious discovery of novel photocatalysts and synthetic pathways remains challenging. Here, we developed a bifunctional platform that enabled the high-throughput discovery and optimization of new photochemical reactions down to the picomole scale. This platform was designed based on a contactless nano-electrostatic-spray ionization technique, which allows synchronized photoreactions and high-throughput in situ mass spectrometric analysis with a near-100% duty cycle. Using this platform, we realized the rapid screening of photocatalytic reactions in ambient conditions with a high speed of less than 1.5 min/reaction using picomolar materials. The versatility was validated by multiple visible-light-induced photocatalytic reactions, especially the discovery of aerobic C-H thiolation with low-cost organic photocatalysts without any other additives. This study provided a new paradigm for the integration of ambient ionization techniques and new insights into photocatalytic reaction screening, which will have broad applications in the development of new visible-light-promoted reactions.

Distinct lipid metabolic dysregulation in asymptomatic COVID-19.

Asymptomatic infection is a big challenge in curbing the spread of COVID-19. However, its identification and pathogenesis elucidation remain issues. Here, by performing comprehensive lipidomic characterization of serum samples from 89 asymptomatic COVID-19 patients and 178 healthy controls, we screened out a panel of 15 key lipids that could accurately identify asymptomatic patients using a new ensemble learning model based on stacking strategy with a voting algorithm. This strategy provided a high accuracy of 96.0% with only 3.6% false positive rate and 4.8% false negative rate. More importantly, the unique lipid metabolic dysregulation was revealed, especially the enhanced synthesis of membrane phospholipids, altered sphingolipids homeostasis, and differential fatty acids metabolic pattern, implicating the specific host immune, inflammatory, and antiviral responses in asymptomatic COVID-19. This study provides a potential prediagnostic method for asymptomatic COVID-19 and molecular clues for the pathogenesis and therapy of this disease.