Selenium Decipher: Trapping of Native Selenomethionine-Containing Peptides in Selenium-Enriched Milk and Unveiling the Deterioration after Ultrahigh-Temperature Treatment

Selenopeptide identification relies on databases to interpret the selenopeptide spectra. A common database search strategy is to set selenium as a variable modification instead of sulfur on peptides. However, this approach generally detects only a fraction of selenopeptides. An alternative approach, termed Selenium Decipher, is proposed in the present study. It involves identifying collision-induced dissociation-cleavable selenomethionine-containing peptides by iteratively matching the masses of seleno-amino acids in selenopeptide spectra. This approach uses variable-data-independent acquisition (vDIA) for peptide detection, providing a flexible and customizable window for secondary mass spectral fragmentation. The attention mechanism was used to capture global information on peptides and determine selenomethionine-containing peptide backbones. The core structure of selenium on selenomethionine-containing peptides generates a series of fragment ions, namely, C3H7Se+, C4H10NSe+, C5H7OSe+, C5H8NOSe+, and C7H11N2O2Se+, with known mass gaps during higher-energy collisional dissociation (HCD) fragmentation. De-selenium spectra are generated by removing selenium originating from selenium replacement and then reassigning the precursors to peptides. Selenium-enriched milk is obtained by feeding selenium-rich forage fed to cattle, which leads to the formation of native selenium through biotransformation. A novel antihypertensive selenopeptide Thr-Asp-Asp-Ile-SeMet-Cys-Val-Lys TDDI(Se)MCVK was identified from selenium-enriched milk. The selenopeptide (IC50 = 60.71 μM) is bound to four active residues of the angiotensin-converting enzyme (ACE) active pocket (Ala354, Tyr523, His353, and His513) and two active residues of zinc ligand (His387 and Glu411) and exerted a competitive inhibitory effect on the spatial blocking of active sites. The integration of vDIA and the iteratively matched seleno-amino acids was applied for Selenium Decipher, which provides high validity for selenomethionine-containing peptide identification

Cross-modal interactions caused by nonvolatile compounds derived from fermentation, distillation and aging to harmonize flavor

Chinese liquor (Baijiu), unique liquor produced in China and among the six world-renowned distilled liquors, is never a follower of others. Flavor is the essential characteristics of Baijiu which largely affect consumers’ acceptance and selection. Though the flavor of Baijiu has been widely explored, the majority of research and review mainly focused on the volatile compounds in Baijiu. The research status on detection, source and flavor contribution of nonvolatile compounds in Baijiu is clarified in the article based on available literatures and knowledge. The nonvolatile composition of Baijiu is the result of contributions of different degrees from each step involved in the production process. Gas chromatography-mass spectrometry combined with derivatization and ultra-high performance liquid chromatography coupled to mass spectrometry is the generally adopted methods for the characterization of nonvolatile compounds in Baijiu. Certain nonvolatile compounds are taste-active compounds. Cross-modal interactions caused by nonvolatile composition could affect the aroma intensity of flavor compounds in Baijiu. The work provides numerous incompletely explored but useful points for the flavor chemistry of Baijiu and lays a theoretical foundation for the better understanding of Baijiu flavor and rapid development of Baijiu industry.</p

UHPLC-Q-Orbitrap-Based Integrated Lipidomics and Proteomics Reveal Propane-1,2-diol Exposure Accelerating Degradation of Lipids <i>via</i> the Allosteric Effect and Reducing the Nutritional Value of Milk

The scandal of detecting the flavoring solvent propane-1,2-diol (PD) in milk has brought a crisis to the trust of consumers in the dairy industry, while its deposition and transformation are still indistinct. Pseudo-targeted lipidomics revealed that PD accelerated the degradation of glycerolipid (33,638.3 ± 28.9 to 104,54.2 ± 28.4 mg kg–1), phosphoglyceride (467.4 ± 8.2 to 56.6 ± 4.2 mg kg–1), and sphingolipids (11.4 ± 0.3 to 0.7 ± 0.2 mg kg–1), which extremely decreased the milk quality. Recoveries and relative standard deviations (RSDs) of the established method were 85.0–109.9 and 0.1–14.9%, respectively, indicating that the approach was credible. Protein–lipid interactions demonstrated that 10 proteins originating from fat globules were upregulated significantly and the activities of 7 enzymes related to lipid degradation were improved. Diacylglycerol cholinephosphotransferase was the only enzyme with decreased activity, and the molecular docking results indicated that PD adjusted its activity through regulating the conformation of the active center and weakening the hydrogen bond force between the enzyme and substrate. This study firstly revealed the mechanism of deposition and transformation of PD in milk, which contributed to the knowledge on the milk quality control and provided key indicators to evaluate the adverse risks of PD in dairy products

Novel Composite Proton Exchange Membrane with Connected Long-Range Ionic Nanochannels Constructed via Exfoliated Nafion–Boron Nitride Nanocomposite

Nafion–boron nitride (NBN) nanocomposites with a Nafion-functionalized periphery are prepared via a convenient and ecofriendly Nafion-assisted water-phase exfoliation method. Nafion and the boron nitride nanosheet present strong interactions in the NBN nanocomposite. Then the NBN nanocomposites were blended with Nafion to prepare NBN Nafion composite proton exchange membranes (PEMs). NBN nanocomposites show good dispersibility and have a noticeable impact on the aggregation structure of the Nafion matrix. Connected long-range ionic nanochannels containing exaggerated (−SO₃^–)_<i>n</i> ionic clusters are constructed during the membrane-forming process via the hydrophilic and H-bonding interactions between NBN nanocomposites and Nafion matrix. The addition of NBN nanocomposites with sulfonic groups also provides additional proton transportation spots and enhances the water uptake of the composite PEMs. The proton conductivity of the NBN Nafion composite PEMs is significantly increased under various conditions relative to that of recast Nafion. At 80 °C–95% relative humidity, the proton conductivity of 0.5 NBN Nafion is 0.33 S·cm^–1, 6 times that of recast Nafion under the same conditions

Novel Slightly Reduced Graphene Oxide Based Proton Exchange Membrane with Constructed Long-Range Ionic Nanochannels via Self-Assembling of Nafion

A facile method to prepare high-performance Nafion slightly reduced graphene oxide membranes (N-srGOMs) via vacuum filtration is proposed. The long-range connected ionic nanochannels in the membrane are constructed via the concentration-dependent self-assembling of the amphiphilic Nafion and the hydrophilic–hydrophobic interaction between graphene oxide (GO) and Nafion in water. The obtained N-srGOM possesses high proton conductivity, and low methanol permeability benefitted from the constructed unique interior structures. The proton conductivity of N-srGOM reaches as high as 0.58 S cm^–1 at 80 °C and 95%RH, which is near 4-fold of the commercialized Nafion 117 membrane under the same condition. The methanol permeability of N-srGOM is 2.0 × 10^–9 cm² s^–1, two-magnitude lower than that of Nafion 117. This novel membrane fabrication strategy has proved to be highly efficient in overcoming the “trade-off” effect between proton conductivity and methanol resistance and displays great potential in DMFC application

Novel Composite PEM with Long-Range Ionic Nanochannels Induced by Carbon Nanotube/Graphene Oxide Nanoribbon Composites

In the current study, carbon nanotube/graphene oxide nanoribbon (CNT/GONR) composites were obtained via a chemical “unzipping” method. Then novel CNT/GONR Nafion composite proton exchange membranes (PEMs) were prepared via a blending method. The CNT/GONR nanocomposites induce the adjustment of (SO₃^–)_<i>n</i> ionic clusters in Nafion matrix to construct long-range ionic nanochannels and keep the activity of ionic clusters at the same time. This dramatically promotes the proton transport of the CNT/GONR Nafion composite PEMs at low humidity and high temperature. The proton conductivity of the composite PEM with 0.5 wt % CNT/GONR is as high as 0.18 S·cm^–1 at 120 °C and 40%RH, nine times of recast Nafion (0.02 S·cm^–1) at the same conditions. The 1D/2D nanostructure of CNT/GONR nanocomposite also contributes to restrain the methanol permeability of CNT/GONR Nafion. The composite PEM shows a one-order-of-magnitude decrease (2.84 × 10^–09 cm²·s^–1) in methanol permeability at 40 °C. Therefore, incorporation of this 1D/2D nanocomposite into Nafion PEM is a feasible pathway to conquer the trade-off effect between proton conductivity and methanol resistance

Evaluations of different imputation methods using labeled approaches.

<p>Pearson's correlation between log-transformed p-values of student’s t-tests on FFA dataset (upper left) and BA dataset (upper right) along with different numbers of missing variables based on four imputation methods: HM (red circle), QRILC (green triangle), GSimp (blue square), and kNN-TN (purple cross). PLS-Procrustes sum of squared errors on FFA dataset (lower left) and BA dataset (lower right) along with different numbers of missing variables based on four imputation methods: HM (red circle), QRILC (green triangle), GSimp (blue square), and kNN-TN (purple cross).</p

Statin Use Is Associated with Reduced Risk of Haematological Malignancies: Evidence from a Meta-Analysis

<div><p>Background</p><p>Several observational studies have shown that statin use may modify the risk of haematological malignancies. To quantify the association between statin use and risk for haematological malignancies, we performed a detailed meta-analysis of published studies regarding this subject.</p><p>Methods</p><p>We conducted a systematic search of multiple databases including PubMed, Embase, and Cochrane Library Central database up to July 2013. Fixed-effect and random-effect models were used to estimate summary relative risks (RR) and the corresponding 95% confidence intervals (CIs). Potential sources of heterogeneity were detected by meta-regression. Subgroup analyses and sensitivity analysis were also performed.</p><p>Results</p><p>A total of 20 eligible studies (ten case-control studies, four cohort studies, and six RCTs) reporting 1,139,584 subjects and 15,297 haematological malignancies cases were included. Meta-analysis showed that statin use was associated with a statistically significant 19% reduction in haematological malignancies incidence (RR = 0.81, 95% CI [0.70, 0.92]). During subgroup analyses, statin use was associated with a significantly reduced risk of haematological malignancies among observational studies (RR = 0.79, 95% CI [0.67, 0.93]), but not among RCTs (RR = 0.92, 95% CI [0.77, 1.09]).</p><p>Conclusions</p><p>Based on this comprehensive meta-analysis, statin use may have chemopreventive effects against haematological malignancies. More studies, especially definitive, randomized chemoprevention trials are needed to confirm this association.</p></div

GSimp: A Gibbs sampler based left-censored missing value imputation approach for metabolomics studies

<div><p>Left-censored missing values commonly exist in targeted metabolomics datasets and can be considered as missing not at random (MNAR). Improper data processing procedures for missing values will cause adverse impacts on subsequent statistical analyses. However, few imputation methods have been developed and applied to the situation of MNAR in the field of metabolomics. Thus, a practical left-censored missing value imputation method is urgently needed. We developed an iterative Gibbs sampler based left-censored missing value imputation approach (GSimp). We compared GSimp with other three imputation methods on two real-world targeted metabolomics datasets and one simulation dataset using our imputation evaluation pipeline. The results show that GSimp outperforms other imputation methods in terms of imputation accuracy, observation distribution, univariate and multivariate analyses, and statistical sensitivity. Additionally, a parallel version of GSimp was developed for dealing with large scale metabolomics datasets. The R code for GSimp, evaluation pipeline, tutorial, real-world and simulated targeted metabolomics datasets are available at: <a href="https://github.com/WandeRum/GSimp" target="_blank">https://github.com/WandeRum/GSimp</a>.</p></div