162 research outputs found
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 ÎĽM) 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
3D-MPEA: A Graph Attention Model-Guided Computational Approach for Annotating Unknown Metabolites in Interactomics via Mass Spectrometry-Focused Multilayer Molecular Networking
The
spectral matching strategy of MS2 fragment spectrograms serves
as a ubiquitous method for compound characterization within the matrix.
Nevertheless, challenges arise due to the deficiency of distinctions
in spectra across instruments caused by coelution peak-derived fragments
and incompleteness of the current spectral reference database, leading
to dilemma of multidimensional omics annotation. The graph attention
model embedded with long short-term memory was proposed as an optimized
approach involving integrating similar MS2 spectra into molecular
networks according to the isotopic ion peak cluster spacing features
to collapse diverse ion species and expand the spectral reference
library, which efficiently evaluated the substance capture capacity
to 123.1% than classic substance perception tactics. The versatility
and utility of the established annotation procedure were showcased
in a study on the stimulation of pork mediated by 2,2-bis(4-hydroxyphenyl)propane
and enabled the global metabolite annotation from knowns to unknowns
at metabolite-lipid-protein level. On the spectra for which in silico
extended spectral library search provided a group truth, 83.5–117.1%
accuracy surpassed 1.2–14.3% precision after manual validation.
β-Ala-His dipeptidase was first evidenced as the critical node
related to the transformation of α-helical (36.57 to 35.74%)
to random coil (41.53 to 42.36%) mediated by 2,2-bis(4-hydroxyphenyl)propane,
ultimately triggering an augment of catalytic performance, inducing
a series of oxidative stress, and further intervening in the availability
of animal-derived substrates. The integration of ionic fragment feature
networks and long short-term memory models allows the effective annotation
of recurrent unknowns in organisms and the deciphering of unacquainted
matter in multiomics
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 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<sup>–1</sup> 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<sup>–9</sup> cm<sup>2</sup> s<sup>–1</sup>, 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 (SO<sub>3</sub><sup>–</sup>)<sub><i>n</i></sub> 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<sup>–1</sup> at 120 °C and 40%RH, nine times of recast Nafion
(0.02 S·cm<sup>–1</sup>) 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<sup>–09</sup> cm<sup>2</sup>·s<sup>–1</sup>) 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
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